SELF-PRESENTATION ON SOCIAL MEDIA – WHEN SELF-ENHANCEMENT CONFRONTS SELF-VERIFICATION IN A VIRTUAL PUBLIC SPACE

BY

ANLAN ZHENG

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Communications and Media

in the Graduate College of the University of Illinois at Urbana-Champaign, 2019

Urbana, Illinois Doctoral Committee: Associate Professor Brittany Duff, Chair Professor Patrick Vargas Associate Professor Mike Yao Professor Ron Faber

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ABSTRACT

Social media has become an important part of people’s daily life. Every day people

frequently switch between the physical world and the digital world created by social media.

Recent research has examined the impact of social media on people’s self-evaluation (e.g.

Valkenburg, Peter & Schouten, 2006; Vogel, Rose, Roberts & Eckles, 2014; Gonzales &

Hancock, 2009). However, there has not been any research that examined this issue from the

perspective of psychological motives – that is, what are the underlying motives that drive people

to behave in such patterns? Understanding such psychological motives underlying people’s

social media use is important because they affect how people evaluate themselves, which further

drives their behavior on social media.

Three studies reported here suggest that the public nature of social media influences self-

enhancement and self-verification, which are two major psychological motives of people’s self-

evaluation process. Specifically, it was found that when self-disclosing on social media, people

tend to only self-enhance but not self-verify. In comparison, in the offline world, they both self-

enhance and self-verify. Moreover, it was also found that on social media, people tend to only

self-enhance but not self-verify with distant friends. However, they would like to both self-

enhance and self-verify with close friends. In addition, implications of the findings in advertising

industry are discussed.

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ACKNOWLEDGEMENTS

Many people offered help during my journey of completing a PHD. I would like to take a

few paragraphs to express my appreciation to them.

Firstly, I would like to thank my advisor – Dr. Brittany Duff. You have always been such

a caring academic mother, who taught me how to walk since the first time I stepped in the

wonderland of research, and helped me find my way out when I was lost. You have also been

encouraging me to seek where my real interests are and stick to them. Even beyond academic life,

the way you think about life and everything has such a huge impact on me, that I would always

take a step back before I move and consider “what will Brittany think and do about it?” when I

am stuck in a tough situation. You have been such a professor, a mentor, a family member and a

friend in my life. I would not have been able to obtain any of my achievements without your

selfless help.

I would also like to thank members of my dissertation committee. Dr. Patrick Vargas

opened my eyes to research methods when I was a master student, and has been generously

offering valuable insights for my work, not only for my dissertation, but also for many of my

research work. Dr. Mike Yao also graciously spent his time helping me sort out questions in my

research and guide me to figure my way out. Dr. Ron Faber, my academic grandfather, provided

valuable advice to my dissertation to help me see the big picture. I also have gratitude for his

encouragement and mental support in the process of completion of my dissertation. Finally, I

appreciate Dr. Amanda Mabry-Flynn’s help in my prelim.

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Except for the wonderful professors, a group of great friends and graduate student

colleagues also generously offered help in this journey. I would like to specially thank Shili, who

has been a sweet cohort, friend and roommate who would unhesitatingly offer her help when I

was beaten by all the “bothering little things” in my life. Xiaohan and Jing have also been such

nice friends who patiently helped me figure out all the “ANOVAs” in my experiment design,

listened to my complaints, and together nudged our way out of the “miserable life” of graduate

college. I miss our time together in Café Bene. I would also like to thank the folks in the “MAD

Lab”: Coco, Giang, Jason, Joe, Lulu, Mia, Regina and Zoe.

Finally, I would like to express my gratitude to my mother, who inspired me at the

startpoint of this whole journey, and offered both financial and mental support. You encouraged

me when I wanted to give up, and let me know that there is always a harbor for me regardless of

whether I would obtain a doctor degree or not. You deserve half of the honor that I achieved.

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To my grandfather

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TABLE OF CONTENTS

CHAPTER 1: LITERATURE REVIEW ............................................................................ 1

CHAPTER 2: STUDY 1 ................................................................................................... 25

CHAPTER 3: STUDY 2 ................................................................................................... 53

CHAPTER 4: STUDY 3 ................................................................................................... 86

CHAPTER 5: GENERAL DISCUSSION ...................................................................... 122

REFERENCE .................................................................................................................. 138

APPENDIX A: SELF-ATTRIBUTE QUESTIONNAIRE ............................................. 152

APPENDIX B: WITHIN-ATTRIBUTE FEEDBACK-SEEKING QUESTIONS ......... 153

APPENDIX C: BETWEEN-ATTRIBUTE FEEDBACK-SEEKING QUESTIONS ..... 155

APPENDIX D: SHORTENED TEXAS SOCIAL BEHAVIOR INVENTORY ............ 156

APPENDIX E: DEBRIEF ............................................................................................... 157

APPENDIX F: FILLER QUESTIONS (IN THE PERSONALITY TEST) ................... 158

APPENDIX G: RELATIONSHIP CLOSENESS INVENTORY .................................. 163

APPENDIX H: TIE-STRENGTH MANIPULATION BY WILCOX AND STEPHEN 167

APPENDIX I: TIE STRENGTH MANIPULATION FOR STUDY 3 .......................... 168

APPENDIX J: THE UNIDIMENSIONAL RELATIONSHIP CLOSENESS SCALE .. 169

APPENDIX K: CONSENT FORM ................................................................................ 170

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CHAPTER 1: LITERATURE REVIEW

The reciprocal-influence between self-concept and social media use has been the focus of

many recent media studies. A great number of studies examined how individual differences in

the “self” impact people’s motivation and behaviors on social media. For example, Kramer and

Winter (2008) found that self-efficacy is strongly related to the number of social media friends,

the level of details in the online profile, and the style of the photos posted on social media;

Seidman (2012) found that the Big Five could predict belonging and self-presentational needs of

Facebook use; in their national survey, Correa, Hinsley & De Zuinga (2010) found that while

people’s personalities may predict their social media consumption, gender and age also serve as

moderators in this relationship.

Moreover, there is also research that looks at how social media use, in turn, can influence

people’s self-concept, especially their self-evaluation. For instance, Valkenburg, Peter &

Schouten (2006) found that receiving positive feedback on adolescents’ social media profile

would enhance their self-esteem and well-being, while receiving negative feedback would cause

a reverse effect; Vogel, Rose, Roberts & Eckles (2014) found that exposure to upward

comparison information on social media would cause lower self-esteem; Gonzales & Hancock

(2009) argued that viewing and updating people’s Facebook profile would lead to higher self-

esteem. Despite these studies in the impact of social media use on self-evaluation, there has been

little research that examines the underlying psychological mechanism of this relationship

between social media use and self-evaluation.

In social psychology, there are two major motives considered in the self-evaluation

process: self-enhancement and self-verification (Sedikides, 1993). Self-enhancement colors

people’s self-perception with positivity, and motivates people to present a positive self-image

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and to seek favorable self-relevant information. Nevertheless, this desire for adulation could be

washed out by a competing motive - self-verification, which drives people to maintain stability

of their preexisting self-views. Previous research has examined how the two motives together

accomplish self-evaluation (Sedikides, 1993). There is also some research that investigated how

the two motives might compete with each other (Shrauger, 1975; Sedikides, 1993), yet co-exist

in some conditions (Swann et al., 1989). Such theories in the psychological motives of self-

evaluation process could be employed to explain how social media use influences people’s self-

evaluation. However, social media is a context different from the lab settings of the previously

mentioned research. A factor that distinguishes social media from such settings is its public

nature.

Being in public has been found to cause people’s behavior to deviate from what they are

in private. For example, in public, people self-present with more modesty than they do in private

in order to avoid interpersonal antipathy (Powers & Zuroff, 1988). In addition, there is also

research that found a higher tendency to conform to the majority’s opinion in public, which

suggests a larger self-enhancement effect (Schlenker, 1975; Brown & Gallagher, 1992).

Unlike the lab settings, social media is a “publicly accessible” virtual space where users

could disclose self-relevant information to the whole Internet and read other users’ information

(Bateman, Pike & Butler, 2011). The public nature of social media thus may cause differences

when applying findings from previous lab-setting research into the context of social media.

Hence, despite a variety of features of social media (e.g. editable posts, interactive, etc.), this

dissertation only looks at the public nature of social media – that is, this dissertation examines

how the public nature of social media use impacts people’s self-evaluation process from the

perspective of self-enhancement and self-verification theory.

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The remainder of the chapter discusses the literature that provides the theoretical

foundation for the dissertation. Firstly, self-enhancement theory and self-verification theory will

be introduced. Next, the effect of publicness and self-presentational concern on people’s

behavior will be discussed.

1.1 SELF-ENHANCEMENT THEORY

Self-enhancement has been conceptualized in many ways. For example, John and Robins

(1994) argued that self-enhancement happens when self-perceptions are more positive than a

credible criterion; Kitayama et al. (1997) defined self-enhancement as a tendency to maintain

and enhance self-esteem; Alicke and Sedikides (2009) defined self-enhancement as “interests

that individuals have in advancing one or more self-domains or defending against negative self-

views”. In general, self-enhancement is commonly defined as a striving to increase the positivity

and reduce the negativity of one’s self-views (Sedikides, 1993; Leary, 2007). This definition

makes sense, since despite the variety of the definition of self-enhancement (e.g. enhancing self-

esteem, defending against negative self-views, etc.), all the definitions reflect the fact that people

have a need to view themselves positively (Heine et al., 1999). Moreover, literature says that,

there are two facets of the self-enhancement motive – self-promotion, which indicates a pursuit

of a positive self-view; and self-protection, which refers to the avoidance in the harm to one’s

self-view (Sedikides & Strube, 1997). Hence, either increasing the positivity or decreasing the

negativity of one’s self-views could help achieve an overall positive self-view. Therefore, this

dissertation employs the definition of self-enhancement as “a striving to increase the positivity

and reduce the negativity of one’s self-views”. The notion that people seek self-enhancement has

received ample evidence (e.g. Alicke, 1985; Svenson, 1981), and has become the foundation of

many theories in psychology, such as the third-person effect (Davison, 1983) and the above-

average effect (Svenson, 1981).

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1.1.1 Evidence of self-enhancement

1.1.1.1 The positive illusions of the self

In Taylor and Brown’s (1988) comprehensive literature review in self-enhancement, they

concluded a self-enhancing triad: unrealistically positive views of the self, exaggerated

perception of personal control, and unrealistic optimism.

By and large, evidence suggests that most people hold a very positive view of the self

(Greenwald, 1980; Taylor & Brown, 1988). First of all, there exists a tendency to see one’s self

better than the average, which is called “the above-average effect”. In Alicke’s (1985) study,

people were asked to rate how accurately positive and negative personality traits described

themselves. It turned out that people tended to regard positive personality traits to be more

descriptive of themselves than negative personality traits. They also perceived positive

personality traits to be more characteristic of themselves than of the average person, and negative

personality traits to be less characteristic of themselves than of the average person. This effect

has been found among a variety of traits. For example, the third-person effect (Davison, 1983)

asserts that people routinely perceive themselves as less susceptible to persuasive messages than

the average person. Also it was reported that individuals overwhelmingly believed that their

driving ability was above the average (Svenson, 1981). Most university students rated

themselves as better than the fifty percent of their peers in attributes such as athletic ability and

leadership (Dunning et al., 1989). Finally, in Cross’s survey (1977), more than 90% college

professors said that their teaching ability was above average. Ironically, in Pronin et al.’s (2002)

study, even when told of the fact that people’s self-perception is usually distorted by such self-

serving bias, people reported that their peer’s self-evaluation was influenced by such bias to

more extent than their own self-evaluation was.

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Moreover, beyond one’s judgment about the self, this “above-average effect” also affects

people’s perceptions toward their intimate friends. Research found that people tend to see their

close friends and relatives as better than the average (Brown, 1986; Hall & Taylor, 1976). In

Brown’s (1986) series of studies, participants were given a list of adjectives and were asked to

rate the adjectives based on how well each adjective could describe themselves. Compared to

their friends, results showed that people used better terms to describe themselves. Meanwhile,

their evaluations of their friends were found to be significantly more positive than their

evaluations of average people, which suggested that anything related to the self may be

enveloped in the glow of this above-average effect (Hogg & Cooper, 2003).

1.1.1.2 Illusions of personal control

Another aspect of self-enhancement concerns people’s exaggerated perception of their

personal control. Research found that people sometimes overestimate their personal control over

outcomes (Taylor & Brown, 1988; Hogg & Cooper, 2003). In a series of studies conducted by

Langer and her colleagues (Langer, 1975; Langer & Roth, 1975), people were found to believe

that they have control over situations which are determined by chance, such as lotteries and dice-

throwing, especially when personal skills are introduced into such situations. For example,

people tended to wager more money in a card game when the confederate (their competitor in the

game) behaved as a confident person compared to an unconfident person (Langer, 1975); they

also believed that they had more control over the outcome if they throw the dice by themselves

than if someone did it for them (Fleming & Darley, 1986).

Even though the illusion of personal control has been found to be popular, one group of

people has been shown to be immune to such an illusion – depressed people. Compared to non-

depressed people, depressed people were found to be able to provide more accurate perception of

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personal control over the chance-related tasks (Alloy & Abramson, 1979). Alloy and Abramson

(1979) presented both depressed and non-depressed participants with a series of tasks varying in

the degree of contingency. After finishing the tasks, participants were asked to estimate the

contingency between their task performance and the outcome. It turned out that the depressed

participants were more accurate than the non-depressed throughout the series of studies.

However, the non-depressed participants tended to overestimate the contingency between their

task performance and the non-contingent outcome when the outcome was desired, while

underestimating the contingency when the contingent outcome was undesired. Moreover, even

among non-depressed people, those in a negative mood were also found to have more realistic

perception of personal control than those in a positive mood (Alloy, Abramson & Viscusi, 1981).

This suggests that mood could be a boundary of such self-enhancement effect.

1.1.1.3 Unrealistic optimism

Unrealistic optimism refers to the phenomenon that people believe that the present is

better than the past, and that the future will be better than the present (Taylor & Brown, 1988).

Specifically, they believe that, compared to their peers, they will experience more pleasant

events (such as getting a nice first job and having a gifted child; Weinstein, 1980), and less

unfortunate events (such as having a car accident and being unable to get a job; Robertson, 1977;

Weinstein, 1980).

Such effect also extends to one’s perception about their close friends – that is, people

believe that their close friends would have a better future than the average, but not as good as

their own future (Regan, Snyder & Kassin, 1995). In short, such unrealistic optimism drives

people to think that the future, particularly their own, will be great. However, since fate will not

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smile on everyone, the optimism that the participants in the past research shows should be

illusory (Taylor & Brown, 1988).

1.1.1.4 Self-serving bias in attribution

Except judgment about the self, self-enhancement also affects how people explain events

related to them. Self-serving bias refers to the phenomenon that people attribute positive

outcomes internally to themselves, while negative outcomes externally to the environmental

factors (Hogg & Cooper, 2003). This way, they are able to take credit for personal success, but

leave the responsibility of failure on external factors.

The procedure of such research is usually as follows. Participants are firstly asked to

perform a task that measures a personal trait such as intelligence and sociability. Afterwards, the

participants are given either success or failure feedback at random. Finally, participants answer

questions to make attributions for the task outcome (Campbell & Sedikides, 1999; Arkin,

Appleman & Burger, 1980; Brown & Rogers, 1991). Such questions usually ask to what extent

participants believe that the task outcome is caused by internal factors, such as personal ability,

and by external factors, such as luck and task difficulty. The self-serving bias would be verified

if participants attribute success to internal factors but attribute failure to external factors.

1.1.1.5 Selective attention and self-enhancement

Another phenomenon that has been widely attributed to self-enhancement is people’s

selective attention to their strength over weakness. It was found that people would neglect the

information that questions their positive self-concept (Sedikides & Strube, 1997; Sedikides &

Green, 2000). Moreover, the more challenging the information is, the more motivated people are

to neglect it (Greenwald, 1981).

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Sedikides and Green (2000) found that people recalled more positive feedback than

negative feedback when the feedback was self-relevant and central to self-concept. In their study,

participants were firstly asked to finish a fake personality test on four personal attributes (two

central and two peripheral to self-concept), and then randomly received either positive or

negative feedback on each attribute. Some participants received feedback on themselves, while

some received feedback on other people. Afterwards, participants were given a surprise recall

task, in which they were asked to recall as much feedback as possible. It turned out that only

when the feedback was self-relevant and central to self-concept would people recall more

positive feedback than negative feedback. In their follow-up study, they found that the

underlying mechanism of such effect was the fact that people allocated less processing time to

central negative self-relevant feedback than central positive self-relevant feedback. In fact,

previous research had already found that people would selectively expose themselves to the

information that corresponds to previous decisions (Festinger, 1962). By neglecting negative

self-relevant information, people are able to keep their self-concept consistent and stable

(Greenwald, 1980).

1.1.2 Boundaries of self-enhancement

Even though self-enhancement has been regarded as one of the major motives of self-

evaluation process, it would be arbitrary to say that self-enhancement always controls people’s

mental life. In fact, previous research found that there are several factors that may moderate the

self-enhancement effect, creating conditions in which self-enhancement might be constrained.

An important boundary is social context.

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Social context here refers to the people with whom an individual interact. Specifically,

there are two factors of social context that might moderate self-enhancement: publicness and tie

strength.

First of all, being observed in public might curtail the self-enhancement effect. It is

argued that people would behave with modesty in the public because public self-aggrandization

may cause interpersonal antipathy (Powers & Zuroff, 1988; Schlenker & Leary, 1982). For

example, Brown and Gallagher (1992) examined the impact of publicness on self-serving bias. In

their study, they operationalized self-serving bias as a perceived favorable comparison between

self and others. Participants were given a test and randomly assigned either success or failure

results. After they received the results, the participants finished a self-reference task, in which

they were asked to use either positive or negative adjectives to describe themselves and others.

The more positive adjectives were used, the more favorable the description was. Their results

showed that, in private, people rated themselves as superior to others in order to cope with the

ego threat caused by failure results. However, in public, failure led to more egalitarian

evaluations of self and others. Brown and Gallagher (1992) argued that the results could be

explained by the fact that people are afraid that public boasting might give rise to interpersonal

antipathy, as well as the fact that public behavior leads to more commitment than private beliefs,

so that people are more cautious with their public announcement. To summarize, publicness

could suppress self-enhancement strivings due to self-presentational concerns in public.

However, the fact that such “self-enhancement strivings” might be harmed by publicness

does not mean that “self-enhancement motive” could be weakened by publicness. As Sedikides

and Strube (1997) argued, the differences in the self-enhancement phenomenon might actually

originate from whether people candidly or strategically act on self-enhancement motive. For

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example, in Brown and Gallagher’s (1992) study, the public modesty that the participants

showed should rather be regarded as a self-presentational strategy. Since public boasting is likely

to result in antipathy, people masked the self-serving bias with modesty. Therefore, since what

drives people to adopt this strategy is their desire to make people think favorably of them and to

avoid possible criticism, this kind of public modesty could be considered as a form of self-

enhancement. It was the motive that made the difference.

Following publicness, another factor of social context that could moderate self-

enhancement is tie-strength. Previous research found that interaction with close others would

weaken the self-enhancement motive (Hogg & Cooper, 2003; Sedikides et al., 2002; Clark &

Mills, 1979). Since close friends would be aware of one’s prior performance, and will have

future interactions with the person, people would employ less self-boasting and more public

modesty in their self-presentation to these friends (Schlenker, 1975; Baumeister, 1982). For

example, Schlenker and Leary (1982) found that an audience rated a person more negatively if

the person presents exaggerated modesty with a success outcome or unrealistic boasting with

failure. The audience gave more favorable feedback to people who exhibited self-evaluation in

accordance with their actual ability. Therefore, it is reasonable to argue that strong tie strength

with an audience could curtail the self-presenter’s self-enhancement motive.

1.1.3 Restraints of self-enhancement theory

Despite the ample evidence of self-enhancement motive in the past research, there is

voice of reservation about self-enhancement theory. Specifically, it is argued that almost all

studies that provide evidence for self-enhancement are potentially confounded by self-

verification motive (Fiske, Gilbert & Linzey, 2010; Taylor & Brown, 1988).

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Self-verification refers to the tendency to confirm that the self is the type of the person

that an individual thought he/she already is (Swann & Read, 1981; Sedikides, 1993). In the past

research, self-enhancement was regarded as supported if participants exhibited a tendency to

embrace positive self-relevant information. However, the past research neglected a fact that most

people usually have positive self-views. According to Diener and Diener (2009), more than 70%

of people across 31 nations maintain a high level of self-esteem and positive self-views. That is

to say, the findings of the past research that people prefer positive self-relevant information

might actually reflect a self-verification rather than a self-enhancement tendency – the positively

self-viewing people might juts have confirmed their chronically positive self-view, rather than

pursuing a positive self-view. For example, Swann et al. (1987) conducted a study in which they

recruited both positively and negatively self-viewing participants. In the study, participants

finished a task and were randomly given either favorable or unfavorable feedback. Researchers

then assessed participants’ reaction to the feedback as a measure of self-enhancement, such as

the perceived accuracy of the feedback, participants’ attribution of feedback, etc. It turned out

that those with positive self-views manifested the self-enhancing pattern, but those with negative

self-views displayed an opposite pattern – they believed that the unfavorable feedback was more

accurate than the favorable feedback, and attributed the unfavorable feedback to themselves

rather than the evaluator. The result suggests a tendency to verify, rather than enhance one’s

existing self-views.

Similarly, when intentionally recruiting both positively and negatively self-viewing

participants, the widely-accepted evidence of self-enhancement – selective recall of positive

feedback – also displays a divergent pattern. In line with the past research, those who possessed

positive self-views recalled more positive feedback. However, the negatively self-viewing people

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were found to be able to recall more negative feedback than positive feedback (Swann & Read,

1981; Story, 1998).

Overall, this evidence suggests that the self-enhancement theory is problematic – the

theory says that self-enhancement is a major and basic psychological motive for human beings,

but it could not account for the opposite psychological pattern of people who possess negative

self-views. As a challenge to self-enhancement, or rather as a supplement of self-enhancement,

self-verification theory was proposed. The comparison between the two motives has been the

subject of debate over decades. To set the stage for an analysis of the comparison between the

two motives, I will briefly characterize the self-verification theory.

1.2 SELF-VERIFICATION THEORY

1.2.1 Evidence of self-verification theory

Self-verification refers to the process in which people create a social reality to ensure the

stability of their self-conceptions (Swann & Read, 1981). Stated differently, people are

motivated to verify their preexisting self-conceptions (Sedikides, 1993). The theory originates

from the idea that people have an inborn preference for familiar and predictable things because

they enable people to survive by predicting and controlling the nature of the environment (Swann

et al., 1989). An example is mere exposure effect (Zajonc, 1968), which suggests that people

would grow to love things gradually familiar to them. Analogous to this preference to

confirmatory instances, when it comes to self-conceptions, it is reasonable to assume that people

would also tend to maintain stable and consistent self-views. That is to say, people would be

more likely to seek verification for their certain self-conceptions compared to their uncertain

self-conceptions. Evidence shows that the desire to self-verify could influence people’s behavior.

For example, people may selectively attend to self-confirmatory feedback (Swann & Read,

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1981), use strategies to elicit such self-confirmatory feedback (Curtis & Miller, 1986), choose

partners who are more likely to support their self-views (Swann et al., 1989) and interpret the

feedback in the way that promotes the survival of their preexisting self-conceptions (Swann et

al., 1987). In short, self-verification is a tendency for people to seek stable, pre-existing self-

conceptions.

The central assumption of self-verification theory is that people would prefer self-

confirmatory feedback regardless of its valence. Different from self-enhancement theory, which

asserts that people would prefer favorable evaluations and avoid unfavorable evaluations, self-

verification theory predicts that people would be inclined to favorable feedback about their

positively self-viewing attributes and unfavorable feedback about their negatively self-viewing

attributes.

Note that, self-view here is not equal to self-esteem. Different from self-esteem, which

refers to people’s overall evaluation of the self (Rosenberg, 1965), self-view is people’s

evaluation of a specific personal trait. Since self-esteem and self-view work on different scales,

they are not necessarily consistent with each other. For example, one could have high self-

esteem, but a negative self-view in his driving skill. Reversely, one could see himself as a good

driver, but have a low overall evaluation on himself. Swann et al. (1989) tested how level of self-

esteem and positivity of self-view separately impact the self-evaluation process. They found that

it was the positivity of specific self-views rather than the level of self-esteem that potentially

determined whether people self-enhance or self-verify. Since then, research in the field began to

emphasize on the effect on self-verification of positivity of self-view on a specific personal

attribute, rather than that of global self-esteem (e.g. Swann et al., 1994; Swann et al., 2003;

Kwang & Swann, 2010) .

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In summary, both self-enhancement theory and self-verification theory predict that

people with positive self-views would be motivated to maintain them, in spite of different

reasons. However, the two theories make different predictions about people with negative self-

views. That is, self-enhancement theory assumes that people with negative self-views prefer

positive evaluations because they want to enhance their self-images in others’ mind; on the

contrary, self-verification theory predicts that such people prefer negative feedback because it is

consistent with their preexisting self-views.

Simply put, as discussed in the previous section, since most people tend to have positive

views of their personal traits, they might be inclined to receive favorable feedback due to either

self-enhancement or self-verification, or even both. Therefore, it is not feasible to discriminate

self-verification from self-enhancement by examining positively self-viewing people’s

preference on feedback. A better test for self-verification is whether people will prefer

unfavorable feedback when such feedback is consistent with negative self-views (Hogg &

Cooper, 2003).

A great deal of research seems to pass the test for self-verification proposed by Hogg and

Cooper (2003). For example, in the third study of Swann et al. (1989), participants were asked to

choose an evaluator to interact among three evaluators who gave them either favorable, or

unfavorable, or both favorable and unfavorable feedback on some of their personal attributes.

The results showed that the participants tended to interact with those who gave them unfavorable

feedback in their negatively self-viewing attributes, which suggested a self-verification tendency.

In addition, spouses have been widely employed as participants for self-verification

research, for the reason that for most people, spouses are the “truly significant others” who

validate their identities (Swann, De La Ronde & Hixon, 1994). By confirming each other’s self-

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views, a couple offers one another a conviction that their self-concept is stable and that their life

is coherent (Lecky, 1945; Swann, De La Ronde & Hixon, 1994). De La Ronde and Swann

(1998) tested such self-verification tendency between couples. In the study, they had one spouse

rate the other one (the target) on several personal attributes, and then randomly gave either

favorable or unfavorable bogus feedback to the target (they told the target that the feedback came

from the spouse). It was found that those who received feedback that disconfirmed their self-

views would struggle to refute or justify the feedback in the following interaction with their

spouse, regardless of the valence of the feedback.

1.2.2 Self-verification versus self-enhancement

As discussed in the previous sections, self-enhancement and self-verification make the

same prediction of people with positive self-views. That is, those with positive self-views would

prefer favorable feedback. However, the two motives contradict with each other in responses of

people with negative self-views. Self-enhancement theory predicts that people with negative self-

views would still prefer positive feedback due to the self-serving bias. On the contrary, self-

verification theory asserts that those people would prefer negative feedbacks due to the need of

the stability of their self-conceptions.

In terms of the conflicting prediction of the two theories, it seems that one theory could

be overthrown by merely examining the relevant responses of people who possess negative self-

views in certain attributes. However, a great deal of research has done this but generated mixed

results. Some studies found that enhancement effects override verification effects; some found

the reverse. For example, Sedikides (1993) compared diagnosticity questions with the strength of

self-enhancement, self-verification and self-assessment in the process of self-evaluation. She

found self-enhancement as the most powerful motive in the self-evaluation process, followed by

16

self-verification. However, Swann et al. (1994) argued people would lean toward self-verifying

evaluations when they believe that their relationship with the evaluator is secure and strong.

Scholars proposed several explanations to the mixed results. Shrauger (1975) suggested that it

was the different dependent variables used in the previous research that produced mixed results.

That is, some dependent variables are more likely to bolster self-enhancement, while some

dependent variables support self-verification. For example, in their meta-analysis of the critical

tests of the two theories, Kwang and Swann (2010) found that desire for self-verification is

greater than self-enhancement when evaluator choice (i.e. the preference for interacting with

evaluators who are more likely to give favorable versus unfavorable feedback) is examined as

dependent variable. However, when feedback preference (i.e. the tendency to solicit feedback

pertaining to positive versus negative attributes) is measured as dependent variable, self-

enhancement becomes more powerful than self-verification. In summary, based on Kwang and

Swann (2010)’s meta-analysis, behavioral reactions and feedback-seeking as dependent variables

are more likely to support self-verification, while affective response tends to produce self-

enhancement effects.

In line with Kwang and Swann’s (2010) findings, some scholars argued that self-

enhancement could only exert impact on affective but not cognitive reactions. Self-verification

motive, however, mainly drives cognitive reactions (Shrauger, 1975; Kwang & Swann, 2010).

The underlying reason is that the cognitive and affective systems are independent from each

other and are designed to perform different tasks (Zajonc, 1980; Swann et al., 1987). According

to these theories (e.g. Shrauger, 1975; Swann et al., 1987), the cognitive system enables the

organism to classify stimuli and analyze logical issues relevant to the stimuli. It takes a relatively

long time to process information and make comparisons. The affective system, however, is a

17

rather primitive system, which drives people to quickly respond to objects that pose threats to

them. Since reaction speed is a priority for survival when facing threats, affective system may

sacrifice accuracy and respond to a stimulus merely based on whether is it favorable or

unfavorable (Zajonc, 1980; Swann et al., 1987). Thus, when it comes to self-relevant feedback,

people with negative self-views may have rather ambivalent responses to unfavorable

evaluations – they may cognitively prefer such feedback due to its consistency with existing self-

knowledge, but affectively feel it annoying (Swann et al., 1987). In another word, even though

people would prefer favorable feedback, they are still not able to reject the accuracy of the

feedback consistent with their self-views. This suggests that the different self-motives are not

necessarily opposed to each other – they may co-exist, but work in different systems and exert

influence on self-evaluation in different levels.

Most of the research discussed above was conducted in the neutral context (e.g.

Sedikides, 1993; Swann et al., 1987; Kwang & Swann, 2010). Nonetheless, another line of

research was conducted in the situation –specific context, and concluded a series of factors that

may strengthen one self-motive while suppressing another. For example, the more control an

individual takes over outcome, the less likely he or she would be to self-enhance (Hogg &

Cooper, 2003). Gollwitzer and Kinney (1989) found that the timing of decision-making would

determine whether people adopt a self-enhancing or self-verifying view. Specifically, when

people are trying to make a decision, they would prefer self-verifying information for a realistic

view of action- outcome expectancies, in order to make the right decision. However, once

decision is made, people no longer have control over the outcome. Thus, when they are trying to

implement a decision, people usually display a strong unrealistic optimism, suggesting self-

enhancement tendency.

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Likewise, the influence of the degree of personal control also extends to one’s self-view

on a particular personal attribute. Similar to the timing of decision-making, when an attribute is

unmodifiable to a person, he or she would prefer self-enhancing feedback to that attribute,

because he or she is not able to exert any control over the attribute (Dauenheimer et al., 2002).

However, when people believe that the attribute is malleable, he or she would be more open and

tolerant to negative feedback, suggesting a self-assessment and self-verification tendency

(Dunning, 1995).

In addition to the factors mentioned above, there is a factor, which could also potentially

influence the strength of each self-motive, but has rarely been studied in the literature. This

factor is publicness. Since publicness is a significant feature of social media, the effect of

publicness on the strength of self-enhancement and self-verification basically determines how

social media changes people’s self-evaluation process, compared to a traditional social network.

In the following section, literature of the moderating effect of publicness on self-enhancement

and self-verification will be discussed.

1.3 PUBLICNESS, SELF-ENHANCEMENT AND SELF-VERIFICATION

In the previous research testing the conflict between self-enhancement and self-

verification theory, participants were usually asked to perform a task, then receive feedback

based on their performance of the task, and finally finish a series of questions to measure self-

enhancement and self-verification motives (e.g. Schlenker, 1975; Swann et al., 1989). The tasks

range from intelligence tests (Schlenker, 1975), reading a speech (Swann et al., 1987) to

personality tests (Swann et al., 1989; Sedikides, 1993). Only a few studies used public self-

presentation as a task in their studies (e.g. Brown & Gallagher, 1992; Schlenker, 1975).

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However, none of the past research directly looked at how publicness influences the strength of

self-enhancement and self-verification.

Nevertheless, it is important to examine the impact of publicness on self-motives, if one

wants to learn more about the psychological and behavioral influence of social media use. The

reason is simple: different from an offline environment, social media is a public area where

everything people post could be visible to other users. All the feedback and comments under

one’s posts would be visible to everyone, hence constituting a part of one’s impression in others’

mind. Therefore, by investigating how publicness influences the strength of self-enhancement

and self-verification, researchers could learn social media’s impact on people’s self-evaluation,

and its further impact on human behavior. Hence, the present research intends to examine how

the publicness factor of social media influences the strength of self-enhancement and self-

verification in the self-evaluation process.

1.3.1 Self-enhancement and publicness

As discussed in the previous section, there are not many studies that directly look at the

impact of publicness on self-enhancement and self-verification. However, literature in publicness

indirectly supports the assertion that publicness would strengthen self-enhancement, leading to

the dominance of self-enhancement over self-verification. This section will firstly discuss why

publicness would strengthen self-enhancement from the perspective of self-presentational

concern, then talk about two streams of theory that bolster the assertion: self-awareness theory

and the pressure of future social interaction on self-presentation.

1.3.1.1 Self-presentational concern in public

When it comes to publicness in the context of social media, it is inevitable to mention

self-presentation. According to Kramer and Winter’s survey (2008), self-presentation is one of

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the major goals of social media use. Self-presentation is defined as people’s use of social

behavior as a means of communicating information about themselves to others. Such self-

presentation is aimed at establishing, maintaining or refining an image of the individual in the

minds of others (Goffman, 1959). That is to say, the definition of self-presentation bears the

nature of publicness – a person has to present their self-image to somebody, and the “somebody”

is the public, regardless of whether they are presenting to a group audience, or just one person.

Since a person’s public image presented to others influences the quality and quantity of rewards

and social approval that one receives from social interaction (Schlenker, 1975), people strive to

present a positive self-image to the public, despite the valence of their own self-views (e.g.

Goffman, 1955; Schlenker, 1975).

Moreover, according to Baumeister (1982), there are two main motives underlying self-

presentation: one is to please audiences; the other is public self-image construction. Both motives

are closely related to self-enhancement because each motive requires a positive public self-image

to be achieved. Therefore, it is reasonable to assert that publicness of self-presentation would

drive self-enhancement as the major motive because the goal of self-presentation is to make the

audience think of the presenter in a positive direction. However, since self-verification aims to

sustain one’s self-conception, rather than to present the self-conception to the public, it is

possible that self-verification would not be as powerful as self-enhancement when people self-

present on social media. Consequently, in public, people’s behavior would be mainly driven by

self-enhancement; while in a private condition, self-verification would play a more important

role because of people’s need for stability.

1.3.1.2 Self-awareness theory

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A stream of research that supports the dominant role of self-enhancement in public is

self-awareness theory. Duval and Wicklund (1972) proposed two types of self-awareness:

objective and subjective self-awareness. They asserted that an individual’s attention could be

directed either inward toward one’s inner self or outward toward external environments.

Subjective self-awareness occurs when one’s attention is directed away from their self to

external objects in the environment – that is, they are the “subject” of their consciousness (Duval

& Wicklund, 1972). On the other hand, objective self-awareness refers to a state in which one’s

attention is focused inward on themselves. He thus becomes the object of his own

consciousness. Research found that objective self-awareness could make people more conscious

of the evaluative aspects of their actions – they tend to conform to social standards (Ickes,

Wicklund & Ferris, 1973), are concerned about their appearances to audiences (Duval &

Wicklund, 1972), and behave socially strategically (Arkin, Gabrenya, Appelman & Cochrane,

1979).

Feingstein et al. (1975) further categorized “self” into private self and public self. Private

self stands for the inner thoughts and feelings hidden from the others, while public self is the part

of the self that is constructed and displayed to the public. It is argued that the effects of focusing

on the public aspects of the self are different from the effects of focusing on the private aspects

of the self (Feingstein, 1987). Heightened private self-awareness makes people more likely to

express their inner feelings and personal beliefs (Ickes, Layden & Barnes, 1978). Public self-

awareness, however, drives people to act according to what they think others think they should

do, and conform to the majority’s opinions (Ickes, Layden & Barnes, 1978; Duval & Wicklund,

1972; Sheier & Carver, 1980). For example, Insko et al. (1973) used a camera to induce public

self-awareness in their study. They assigned participants to either “camera condition” or “no-

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camera condition”, and asked them to write an essay to support LSD, which was

counterattitudinal to their pretested beliefs. Afterwards, they assessed participants’ attitude

toward the freedom of using LSD. It was found that a cognitive dissonance only happened in the

“camera condition” because participants were more aware of the inconsistency between their

argument and the majority’s opinions. However, those in the “no-camera condition” would stick

to their essay argument, showing no cognitive dissonance.

The underlying rationales of this stream of studies are basically the same – camera as a

reminder of the presence of other observers, introduces public self-awareness, which makes

people aware of the impression-management nature of their actions, thus catering to social

standards. However, the reason why public self-awareness would cause conformity has never

been explicitly examined in literature. As Schlenker (1975) said, “it is not yet clear precisely

why a state of objective self-awareness produces the effects it does”. Relating the self-awareness

theory to self-enhancement theory, it is possible that the nature of the effects caused by public

self-awareness is self-enhancement motive. As self-enhancement theory proposes, people have

an inherent need to make others think favorably of themselves. They may achieve a favorable

public image by either presenting a positive self or avoiding the risk of being disliked

(Schlenker, 1975). Therefore, when people are in a state of public self-awareness, being aware of

the presence of the audience, they would try to avoid negative self-presentation by agreeing with

the majority, hence showing a self-enhancement tendency. On the contrary, in private, they

would be more likely to express their inner thinking, which corresponds to the self-verification

tendency. Thus, this stream of research in self-awareness theory could be considered as evidence

for the dominance of self-enhancement motive in the public situation.

1.3.1.3 Pressure of future interaction

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Another stream of research looking at publicness and self-enhancement mainly focused

on the pressure of future interaction exerted on current public self-presentation. It is argued that

people would behave with modesty in the public because public self-aggrandization may cause

interpersonal antipathy (Powers & Zuroff, 1988; Schlenker & Leary, 1982). Schlenker (1975)

once investigated the relationship between “publicness” and the conflict between self-

consistency and self-enhancement. In his study, he operationalized self-enhancement as a

favorable public self-presentation, while self-verification as a self-consistency presentation.

“Publicness” was manipulated as individual performance either anonymous or known to the

group. It was found that people would show self-consistency rather than self-enhancement

tendency when self-presenting in a public condition compared to an anonymous condition

because future public events would invalidate an aggrandizing self-presentation. Brown and

Gallagher (1992) also found that self-consistency would override self-enhancement when

confronting public failure for the same reason. They operationalized self-enhancement as a

perceived favorable comparison between self and others, and self-verification as an egalitarian

view of the self-other comparison. The findings of the past research are of value in predicting

people’s self-presentation in the public. However, the “self-consistency” effects found in these

studies cannot be equalized to “self-verification”. First of all, with expectation of future

interaction as a moderator, the “self-consistency” in these studies refers to the consistency

between self-presentation and other’s perceived expectations of that person, rather than the

consistency between self-presentation and one’s self-view. Second, as discussed the previous

sections, the “self-consistency” effect found in previous research is rather a self-presentation

strategy than a psychological motive. What drives people to adopt this strategy is their desire to

make people think favorably of them and to avoid possible criticism, which could be considered

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as a form of self-enhancement. Therefore, what the previous research found was possibly the

effects of self-enhancement, rather than effects of self-verification.

In conclusion, previous research has provided ample evidence in support of the

strengthening effect of publicness on self-enhancement. However, rarely is there evidence

showing that publicness would influence the magnitude of self-verification motive. Thus, there

are even fewer studies looking at how publicness influences the conflict between self-

enhancement and self-verification. Given the fact that self-verification motivates people to stick

to who they think they are, which contradicts with the self-enhancing nature of public self-

presentation, it is possible that publicness would make self-enhancement override self-

verification. Self-verification, on the other hand, would exert more impact in a private situation.

Therefore, it is expected that self-enhancement will have stronger effect in the public condition

than in the private condition. On the contrary, self-verification will have stronger effect in the

private condition than in the public condition.

In order to fill the gap in literature, the present research conducted three experiments to

help understand the effect of publicness on the strength of self-enhancement and self-verification

in the context of social media. First of all, to set a basis for the following research, Study 1

replicates Swann et al. (1989), a study that found both self-enhancement and self-verification, in

order to test the existence of both self-enhancement and self-verification effect. Based on Study

1, Study 2 applies the theories tested in Study 1 in the context of social media. Specifically,

Study 2 looked at how publicness influences self-enhancement and self-verification in the private

condition (feedback-seeking) versus public condition (feedback-disclosure). Finally, Study 3

examines how tie-strength could moderate the self-enhancement effect on social media.

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CHAPTER 2: STUDY 1

Despite the great number of studies that provided evidence for self-enhancement and self-

verification, some of the studies potentially suffer from a small sample size problem. For

example, in Study 1 of Swann et al. (1989), only 42 participants were recruited; Langer (1975)

only tested 36 female participants in her study about illusion of personal control; Dunning (1989)

only had 27 participants in his Study 1 of a series of studies about self-serving bias. Small

sample size in experimental design could be problematic because it is associated with low

statistical power (Button et al., 2013), inflated false discovery rate (Colquhoun, 2014), inaccurate

effect size estimation (Albers & Lakens, 2017), etc. In order to set a grounded basis for the

following research, the present research conducted a replication of the Study 1 of Swann et al.

(1989) with a relatively large sample size. The aim of Study 1 is to replicate Study 1 in Swann et

al. (1989) in order to test the existence of both a self-enhancement and self-verification effect.

2.1 SWANN ET AL. (1989) STUDY 1

In their paper, Swann et al. (1989) argued that people are motivated to self-enhance and

self-verify simultaneously, regardless of their self-esteem level. Despite the fact that their Study

1 was aimed at testing the moderating effect of self-esteem level on self-enhancement and self-

verification, rather than purely examining the existence of the two self-motives, the present

research was conducted as a replication of this study because: 1) the study verified both self-

enhancement and self-verification effect; 2) The paper is influential in this field – it was cited

589 times by other works, and some future studies used exactly the same scales and measures for

self-verification (e.g. Swann et al., 1992; Giesler et al., 1996); 3) the researchers only measured

self-esteem rather than manipulating self-esteem, which made the study cleaner to replicate,

compared to the studies that induced and manipulated moderators.

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2.1.1 Measures

In Swann et al.’s (1989) Study 1, self-enhancement was operationalized as whether

people are more interested in receiving feedback pertaining to their positive attributes than

negative attributes. A between-attribute measure of feedback seeking was used to measure self-

enhancement effect (see APPENDIX C). Self-verification was operationalized as people’s

tendency to seek more favorable feedback than unfavorable feedback about their best attributes,

and more unfavorable than favorable feedback about their worst attributes. A within-attribute

measure of feedback seeking was used to measure self-verification (see Appendix B). The

Rosenberg self-esteem scale (1965) was used to measure self-esteem.

2.1.2 Procedure

In the original study (Swann et al., 1989), participants went to the lab and were told that

they were going to have a personality test with a computer program (it was 1989!). Participants

firstly finished a Self-Attributes Questionnaire (Pelham & Swann, 1989; see Appendix A) in

which they rated themselves on five attributes (intellectual capability, skill at sports, physical

attractiveness, competency in art and music, and social skills). In this questionnaire, participants

were asked to rate themselves on the five attributes compared to other college students their own

age on a graduated-interval scale ranging from 1 (bottom 5%) to 10 (top 5%). They also rated

how certain they were of each of the ratings and how important each attribute is to them. They

then completed the Rosenberg self-esteem scale (1965) and some filler questions.

Afterwards, an experimenter input the results into the computer. In order to bolster the

credibility of the cover story, they had the computer pause for 17 seconds and make noise,

pretending that the computer was running and analyzing the data. Then the computer generated

more filler questions saying that it needed more information to analyze the participants’

27

personality. After participants answered the questions, the computer audibly “analyzed” the

information again. All these manipulations were used to convince the participants that they are

really doing a personality test. In reality, the computer was not analyzing the data, and all the

filler questions were prepared.

Finally, the experimenter told the participants that they could get some feedback from the

computer, but not the whole report due to time limit. And then they asked participants to rank the

five attributes based on the extent to which they would prefer to receive feedback (between-

attributes measure for self- enhancement). In addition, the experimenter presented participants

with a feedback-seeking questionnaire (within-attributes measure for self-verification). In the

questionnaire, there were six questions for each of the five attributes. Among the six questions,

there were three questions that may solicit favorable feedback (e.g. “what is this person’s

greatest intellectual strength?”), and three that may solicit negative feedback (e.g. “what about

this person makes you think he/she would have problems in academia?”). Participants were

instructed to choose two questions from each set of six questions to receive answers.

2.1.3 Swann et al. (1989) analysis and results

Between-attributes feedback seeking (self-enhancement). Swann et al. (1989) concluded

within-subjects (best vs. worst attribute) ANOVA and found that people were more likely to

solicit feedback about their best attributes than worst attributes, F (1,19) = 37.17, p<.001. The

results indicate a self-enhancement effect. There was no significant interaction of attributes-

rating and self-esteem. Swann et al. (1989) noted in their paper that they analyzed data with

ANOVA even though the dependent variable – rank, was categorical because literature showed

that ANOVA was a useful way to analyze this kind of categorical data, and that alternative

statistical approaches (such as chi-square) corroborated the results of ANOVA.

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Within-attributes feedback seeking (self-verification). Swann et al. (1989) firstly analyzed

the data from participants who had one very good attribute (rated 6 or above) and one very bad

attribute (rated 4 or below), which limited the number of participants to N = 20. A 2 (best vs.

worst attribute) by 2 (unfavorable vs. favorable feedback seeking) within-subjects ANOVA

revealed a significant interaction between attribute-rating and feedback seeking, F (1,19) = 6.78,

p=.017. Participants were more interested in seeking more favorable feedback than unfavorable

feedback about their best attributes, F (1, 19) = 4.17, p=.055. On the other hand, they were more

likely to seek more unfavorable feedback than favorable feedback about their negative attributes,

F (1,19) = 4.13, p = .056. The results indicate a self-verification effect.

However, when they included all participants in the sample, they found the effect weaker.

Specifically, they found that even though people still wanted more favorable feedback than

unfavorable feedback about their best attribute, they did not show a difference in sampling

favorable and unfavorable feedback about their worst attribute.

2.2 THE PRESENT STUDY

Since the Swann et al. (1989) study was conducted almost thirty years ago, some

elements of the study could be considered outdated today (e.g. the “humming computer” in the

cover story). Therefore, in order to make the study fit the modern context, four modifications

were made in this replication.

First, the cover story was changed. The story about “a computer that can calculate one’s

personality” was removed. Instead, participants were informed that the researchers were

collecting data about people’s personality traits. In the study, they were going to finish a

personality test. As a side benefit of participating in the study, they could receive partial analysis

of their answer to the questions. So they would have to rank the five attributes based on the

29

extent to which they wanted to receive the analysis in each attribute (between-attribute feedback-

seeking measure). Besides, they would also have to choose from the feedback-seeking

questionnaire (Pelham & Swann, 1989) the questions to which they mostly wanted to receive

answers for each attribute.

Second, rather than coming to the lab, participants finished the whole procedure online

on Amazon Mturk. Since the computer element in the cover story was eliminated in the present

study, there is no need for participants to come to the lab.

Third, in order to strengthen self-verification effect, Swann et al. (1989) pre- selected

participants who scored at two extremes on the Texas Social Behavior Inventory (TSBI, a

measure of social self-esteem; Helmreich & Stapp, 1974). However, if self-verification is a vital

human need, as Swann (1992) said, it should exist among the whole population, regardless of

social self-esteem. Moreover, in the later research about self-verification, in which participants

were not pre-selected by their TSBI score (e.g. Kraus & Chen, 2009; Pettit & Joiner, 2001), a

self- verification effect was still found. Therefore, in the present study, participants were not pre-

selected. However, even though Swann et al. (1989) did not find a significant effect of TSBI

score on self-verification in their data analysis, the pre- selection may still have exerted influence

on the results of the study, because participants who had extreme scores on TSBI in the pretest

may be more likely to select extreme options in the main studies than average people. Thus,

since participants were not pre-selected in the present study, a larger sample size was recruited to

ensure effect size. Participants were still asked to complete the TSBI in the study, but they were

not screened based on the TSBI score.

Fourth, Swann et al. (1989) only recruited female participants in their study because of

their greater availability in the subject pool. In the present study, both male and female

30

participants were recruited. Data retrieved from male and female participants will be compared

to examine whether there is a systematic difference between male and female participants.

In summary, the present study mainly modified the cover story, location and sample size

of the study. However, the manipulation and measures of the present study was the same as the

ones used in study 1 in Swann et al. (1989).

In addition, same as the Study 1 of Swann et al. (1989), self-enhancement was measured

with a within-subjects study design (best vs. worst attribute). Self-enhancement would be

verified if people display a preference to solicit feedback pertaining to their best versus worst

attributes. On the other hand, self-verification was measured with a 2 (best vs. worst attribute) by

2 (unfavorable vs. favorable feedback seeking) within-subjects experimental design. Self-

verification would be manifested if there were a significant interaction between attribute-rating

and feedback seeking, such that people prefer favorable feedback versus unfavorable feedback

about their best attribute, while they prefer unfavorable feedback versus favorable feedback

about their worst attribute. See table 1 for a summary of operationalization and measures.

Table 1. Summary of operationalization and measures for Study 1   Operationalization   Measures   Data  analysis  Self-­‐enhancement  

Whether  people  would  prefer  to  seek  feedback  about  their  best  attribute  versus  worst  attribute  

Between-­‐attribute  questionnaire:  rank  each  of  the  five  attributes  based  on  the  extent  to  which  participants  want  to  hear  about  the  attribute.  Self-­‐enhancement  will  be  indicated  if  the  best  attribute  versus  worst  attribute  receives  higher  rank  in  interest  in  receiving  feedback  

Two  rounds  of  data  analysis  for  both  self-­‐enhancement  and  self-­‐verification.  1st  round:  full  data  set  2nd  round:  only  participants  were  analyzed  who  had  a  best  (score  6  or  higher)  and  a  worst  (score  4  or  lower)    

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Table 1. Summary of operationalization and measures for Study 1 (cont.) Self-­‐verification  

Whether  people  would  prefer  unfavorable  feedback  versus  favorable  feedback  for  their  worst  attribute  

Within-­‐attribute  questionnaire:  for  each  attribute,  choose  two  questions  out  of  a  list  of  six  questions  which  consists  of  three  favorable-­‐feedback-­‐soliciting  questions  and  three  unfavorable-­‐feedback-­‐soliciting  questions.  Self-­‐verification  will  be  found  if  people  choose  more  unfavorable-­‐feedback-­‐soliciting  questions  versus  favorable-­‐feedback-­‐soliciting  questions  for  worst  attribute    

Note:  if  a  participant  has  two  “best  attribute”  (e.g.  two  attributes  score  10),  then  the  number  of  favorable/unfavorable  feedback-­‐soliciting  questions  will  be  averaged  (as  dependent  variable)  

2.2.1 Hypotheses

As a replication study, since the measures of the present study are exactly the same as the

Study 1 of Swann et al. (1989), the hypotheses are also the same. Swann et al. (1989) suggested

that people are simultaneously motivated to self-enhance and self-verify. That is, as a result of

self-enhancement effect, people would like to hear about feedback in the attributes in which they

have positive self- views. However, when it comes to what kind of feedback about which they

would like to hear, they would self-verify by displaying preference for unfavorable feedback for

their negatively self-viewing attributes.

Hypothesis 1: People will show a self-enhancement effect, such that they will be more

interested in receiving feedback about their best attribute versus worst attribute.

Hypothesis 2: People will show a self-verification effect, such that they will seek more

favorable feedback than unfavorable feedback in their best attribute; however, they will seek

more unfavorable feedback than favorable feedback in their worst attribute.

2.2.2 Participants

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In the meta-analysis of Kwang and Swann (2010), the average effect size of the previous

papers for self-enhancement effect was r = 0.41. Using G*Power, finding a simple effect in a

one-way ANOVA with 80% power requires N=48. On the other hand, the average effect size of

the papers for self-verification effect was r = 0.19. Finding a simple effect in a between-subject

one-way ANOVA with 80% power requires N=212. However, the present study uses a within-

subject design. According to G*Power, a between-within interaction requires N=56 to find a

simple effect with 80% power. Since simple correlations do not stabilize until approximately N =

150 (Schonbrodt & Perugini, 2013), the present study requires at least 150 participants.

159 participants were recruited from Amazon Mturk and finished the questionnaire.

Among the 159 participants, 62.2% were female (N=99), 36.5% were male (N=58). One

participant was transgender, and one participant did not want to indicate gender information. In

terms of ethnicity, 69.2% participants were white (N=110), 13.8% were African American

(N=22), 6.9% were Asian (N=11), 1.89% were American Indian or Alaska native (N=3), 1.3%

were native Hawaiian or Pacific Islander (N=2). 10 participants indicated that they belong to

other races, and 1 participant left the question blank. As for education, 41.5% participants

(N=66) had a bachelor degree, 20.7% participants (N=33) had some college education but they

did not earn any degree, 18.9% participants (N=30) had an associate degree in college, 14.5%

participants (N=23) had a high school degree, 3.8% participants had a master’s degree (N=6). A

participant did not want to disclose education information.

7 participants’ data were dropped because they assigned the same number for all (or

almost all) SAQ attributes. It is possible that they did not pay much attention to the test and gave

every attribute the same number. Therefore, their data were dropped.

In Swann et al. (1989) Study 1, only female college students were recruited. In order to

33

get closer to Swann et al.’s research, the premium classification function of Amazon Mturk was

used – only workers born between 1992 and 1999 (age 18-25) were qualified to participate in the

study.

Amazon Mechanical Turk (MTurk) is a widely-used online platform for researchers to

recruit participants and conduct research. A great deal of research evaluated the quality of data

obtained from MTurk (e.g. Buhrmester, Kwang & Gosling, 2011; Woo, Keith & Thornton, 2015;

Paolacci & Chandler, 2014), and found that the data are free from the factors which are

commonly considered to be threats to research validity, such as participants’ motivation (Woo,

Keith & Thornton, 2015), participation repetition (Holden, Dennie & Hicks, 2013),

compensation (Buhrmester, Kwang & Gosling, 2011), and selection bias (Woo, Keith and

Thornton, 2015).

However, a bigger concern of obtaining data online is poor data quality (Kraut et al.,

2004; Chandler, Mueller & Paolacci, 2014). In a less-controlled online environment, participants

may be less attentive. They may skip instruction and misunderstand instruction without the

guidance of an experimenter (Birnbaum, 2004). This is problematic especially for the

experimental designs that have complex procedures and instructions. For example, studies in

visual processing may measure participants’ reaction time by instructing them to press a key on

the keyboard to indicate the characteristics of a target (e.g. color, direction, ect.) among several

lures (e.g. Buetti et al., 2016). In this case, it is important for participants to understand and

follow the instruction to provide valid data. However, if participants are confused about which

key should be pressed for which characteristics, the reaction time would be elongated, but

researchers would have no way to determine whether it is caused by human performance or

confusion about instruction. Dandurand, Shultz and Onishi’s (2008) research could confirm this

34

concern. In their study, they replicated on the Internet a long, cognitively demanding and lab-

based problem-solving task. They found that online participants were less accurate and took a

longer time than lab participants.

However, the data quality concern seems to mainly impact tasks that either require

precise timing or long and complex tasks rather than relatively short and simple tasks. The

reason is that the short and simple tasks do not require much attention resource than the complex

ones. Researchers compared the results from lab and online experiments for questionnaires

widely-used in psychology experiments (e.g. the Boredom Proneness Scale, Farmer & Sundberg,

1986; the Index of Sexual Satisfaction, Hudson, Harrison & Crosscup, 1981). The questionnaires

are relatively simple, and the instruction is straightforward. It was found that the results obtained

in the two settings were generally the same (Gosling, Vazire, Srivastava, & John, 2004;

Meyerson & Tryon, 2003). In the present study, since the questionnaires are short and simple,

chances are low that participants would misunderstand the tasks. The data retrieved from Mturk

thus should be free from the data quality concern.

2.2.3 Procedure

At the beginning of the online experiment, after agreeing with the consent form,

participants were informed that the aim of the study was to collect data of people’s personality

traits. In this study, they were going to finish a series of questionnaires that measure different

aspects of their personality. The first questionnaire was the self-attributes questionnaire (Pelham

& Swann, 1989), in which participants rate themselves on five attributes (intellectual capability,

skill at sports, physical attractiveness, competency in art and music, and social skills) on a

graduated-interval scale ranging from 1 (bottom 5%) to 10 (top 5%) , compared to their peers at

their age. They also rate how certain they are in each rating, as well as the importance of each

35

attribute to them.

Afterwards, they answered the “personality test”. The purpose of having the “personality

test” here was to make participants believe that the test was able to predict their personality

traits. The “personality test” consisted of two parts. The first part was 16 questions from the

shortened “TSBI” (Helmreich & Stapp, 1974; see ;), which measures social self-esteem. The

shortened “TSBI” asked participants to rate (on a five-point scale) the extent to which they

believe a self-descriptive statement is characteristic of themselves (e.g. “I’m not likely to speak

to people until they speak to me”; “I would describe myself as self-confident”). In order to

increase the credibility of the cover story, the second part was made of several filler questions

rephrased from the Buzzfeed quizzes about personality, such as “which color do you prefer?”; “if

you were an animal, what would you be?”; “please write 3-5 sentences describing what you

usually look like in your friends’ eyes”. Buzzfeed quizzes are personality quizzes that people

could share on social media. The quizzes ask random questions such as “what kind of pizza

slices are you?”. People usually take and share such quizzes for fun. The average quiz gets

shared 1900 times on social media, according to BuzzSumo data. In the present study, the

personality test questions are excerpted from Buzzfeed quizzes to simulate such experience.

Questions from Buzzfeed quizzes were chosen as filler questions in the present study because

people answer and share these kinds of questions in real life. Hence, using these questions in the

study would make people more engaged and believe the cover story.

After they finished the “personality test”, they were told that they would receive a partial

analysis of their answers, and that they would have to rank the five attributes (a.k.a intelligence,

athletics, social, artistic ability and personal attractiveness) based on the extent to which they

want to receive answers about the attributes. In addition, they were also presented with the

36

feedback-seeking questionnaire (see APPENDIX C), and were asked to choose two questions

from each set of six questions to receive answers (e.g. “what is some evidence that this person

has good social skills?”; “what is some evidence that this person doesn’t have very good social

skills?”). Finally, participants were debriefed of the real intent of the study and the fact that they

were not going to receive any analysis from the researchers (see debrief in Appendix E). They

then received $1 from the researcher via Amazon Mturk.

2.2.4 Results

2.2.4.1 First round of data analysis (full data set)

As a replication of the Study 1 of Swann et al. (1989), the analysis strategy and method

of the present study are the same to Swann et al.’s. Specifically, to test hypothesis 1, a within-

subjects ANOVA was conducted with attribute (best vs. worst) as the repeated measure factor

and the rank of each attribute as dependent variable. To test hypothesis 2, a two-way within-

subjects ANOVA was conducted with attribute (best vs. worst) and feedback (favorable vs.

unfavorable) as repeated measure factors, as well as the number of favorable-feedback-soliciting

questions and the number of unfavorable-feedback-soliciting questions as dependent variables.

2.2.4.2 Between-attribute feedback seeking

Hypothesis 1: People will show a self-enhancement effect, such that they will be more

interested in receiving feedback about their best attribute than worst attribute.

The between-attribute feedback-seeking questionnaire measures whether people would

prefer feedback pertaining to their positively self-viewing characteristics when given an

opportunity to sample feedback about any of their attributes. Identical to what Swann et al.

(1989) did, the attribute that received the highest rating on the Self-Attribute Questionnaire (see

Appendix A) was labeled as the “best attribute”, while the attribute that received the lowest

37

rating is labeled as the “worst attribute”. The average ranks of the best and worst attribute were

compared. Thus, which attribute among the five personal attributes is the best or the worst

attribute does not matter. What matters is the fact that the participant has a best and a worst

attribute, as well as the corresponding ranks of the attributes.

In the first place, in order to find out whether gender would make a systematic difference,

a 2 (attribute: best vs. worst) by 2 (gender: female vs. male) mixed ANOVA was conducted. The

results showed that there was no interaction between gender and attribute, which means that

gender does not make any systematic differences in this effect, F (3, 149) = .751, p = .523 > .05.

Same as Swann et al. (1989) Study 1, a within-subjects (best vs. worst attribute) analysis

of variance (ANOVA) of the average ranks was conducted. The results supported hypothesis 1

that people are more interested in receiving feedback about their best attribute than their worst

attribute, F (1, 151) = 26.454, p < .000, partial η 2 = .15. The means displayed in Table 2

corroborate this conclusion – the average rank of best attribute (M=2.55) is significantly higher

than the rank of worst attribute (M=3.57; 1 means participants most want to hear about the area

while 5 means they least want to hear about the area).

In order to replicate the Study 1 in Swann et al. (1989), the self-esteem score (TSBI) was

added to the previous ANOVA analysis, even though the present research has no intention in

examining the moderating effect of self-esteem. Same as Swann et al.’s conclusion, a repeated

measure ANCOVA shows that self-esteem (the TSBI score) had no influence on feedback

seeking, F (2, 150) = .743, p = .39 > .05.

38

Figure 1. Rank for best attribute and worst attribute

Table 2. Rank for best attribute and worst attribute

95% Confidence Interval

Attribute Mean SE Lower Upper

Best 2.58 0.111 2.36 2.80 Worst 3.61 0.111 3.39 3.82

2.2.4.3 Within-attribute feedback seeking

Hypothesis 2: People will show a self-verification effect, such that they will seek

more favorable feedback than unfavorable feedback about their best attribute; however,

they will seek more unfavorable feedback than favorable feedback about their worst

attribute.

The within-attribute feedback-seeking questionnaire measures what kind of feedback

people would solicit pertaining to their strengths and weakness. The number of questions (0-2)

chosen by participants that may solicit favorable (and unfavorable) feedback for a certain

attribute was used to measure whether people would like to solicit favorable or unfavorable

feedback about that attribute. Take as an example a participant whose best attribute is “social

skills”. The participant was asked to choose the two questions out of six questions about social

skills on which they most wanted to receive feedback. If the participant chose two questions that

both solicit favorable feedback (e.g. “what is some evidence that this person has good social

0  

1  

2  

3  

4  

5  

Best   Worst  

39

skills?”; “what about this person shows s/he would be confident in social situations), then for this

participant, the favorable feedback would be 2, while the unfavorable feedback would be 0. If a

participant chooses a question that solicit favorable feedback (e.g. “what is some evidence that

this person has good social skills?”) and a question that could solicit unfavorable feedback (e.g.

“what is some evidence that this person doesn’t have very good social skills?”), then for this

participants, the favorable feedback would be 1, while the unfavorable feedback would also be 1.

Therefore, the dependent variable (number of favorable- and unfavorable-feedback soliciting

questions) ranges from 0 to 2. The number of favorable-feedback-soliciting questions and the

number of unfavorable-feedback-soliciting question would add up to 2.

Firstly, in order to examine whether gender makes a systematic difference, a 2 (best vs.

worst SAQ attribute) by 2 (unfavorable vs. favorable feedback seeking) by 2 (female vs. male

gender) mixed ANOVA was conducted and did not find a three-way interaction, F (2, 150) =

0.411, p = .664 > .05. Therefore, gender does not make any systematic difference here. A

repeated measure ANCOVA shows that social self-esteem (the TSBI score) does not impact

what kind of feedback that participants seek for their best versus worst attributes, F (2, 150) =

.013, p = .909.

A 2 (best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback seeking)

within-subjects ANOVA revealed an interaction between attribute and the type of feedback, F (1,

151) = 53.5, p < .000, η 2 = .35. Similar to Swann et al.’s findings, simple-effect tests revealed

that people solicited more favorable (M=1.49) than unfavorable feedback (M=0.51) about their

best attributes, t (1, 151) = 9.81, p < .000. However, they are equally inclined to solicit

unfavorable (M=0.92) and favorable (M=1.08) feedback about their worst attributes, t (1, 151) =

1.59, p = .38. The simple-effect test also showed that people wanted more favorable feedback for

40

their best attribute (M = 1.49) versus worst attribute (M = .92), t (1, 151) = 7.31, p < .000.

However, they wanted more unfavorable feedback for their worst attribute (M = 1.08) versus

best attribute (M = .51), t (1, 151) = -7.31, p < .000.

Figure 2. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (first round)

Table 3. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (first round)

95% Confidence Interval

Attribute Feedback Mean SE Lower Upper

best favorable 1.487 0.0496 1.389 1.585 unfavorable 0.513 0.0496 0.415 0.611 worst favorable 0.921 0.0496 0.823 1.019 unfavorable 1.079 0.0496 0.981 1.177

2.2.4.4 Second round of data analysis (moderately self-viewing excluded)

Same as Swann et al. (1989), in the second run, I only included those who believed that

they had one very good attribute (rated 6 or above in SAQ) and one very bad attribute (rated 4 or

below in SAQ). Since the Self-Attribute Questionnaire is a 10-point scale, if a participant’s

highest rating on an attribute is below 6, his best attribute is likely to be considered as negative

by himself. It is the same if one’s lowest rating on an attribute is above 4. Thus, the participant’s

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

41

self-view could not match his feedback-seeking tendency. Therefore, getting rid of those

participants who do not have very good or very bad attribute could make self-verification more

salient. In this run of data analysis, 41 participants were eliminated, which left N = 111.

Hypothesis 1: People will show a self-enhancement effect, such that they will be more

interested in receiving feedback about their best attribute than worst attribute.

Even though Swann et al. (1989) did not test hypothesis 1 (about self-enhancement) in

the second wave of data analysis, the present study still ran an analysis with self-enhancement to

examine whether the exclusion of participants with moderate self-views would influence self-

enhancement effect.

A repeated-measure ANOVA showed that people are more interested in hearing about

their best attribute (M = 2.48) versus worst attribute (M = 3.57), F (1, 110) = 21.9, p < .000,

partial η 2 = .17. Similar to the analysis with the full data set, the results indicate a self-

enhancement effect.

Figure 3. Rank for best attribute and worst attribute (second round)

Table 4. Rank for best attribute and worst attribute (second round)

95% Confidence Interval

Attribute Mean SE Lower Upper

best 2.48 0.135 2.21 2.74 worst 3.57 0.135 3.30 3.83

0  1  2  3  4  5  

Best   Worst  

42

Hypothesis 2: People will show a self-verification effect, such that they will seek

more favorable feedback than unfavorable feedback about their best attribute; however,

they will seek more unfavorable feedback than favorable feedback about their worst

attribute.

A repeated-measure ANOVA shows an overall reliable interaction between attribute and

feedback, F (1, 110) = 60.26, p < .000, partial η 2 = .35. Simple effect tests revealed that people

tended to solicit more favorable (M = 1.6) than unfavorable feedback (M = .4) about their best

attributes, t (1, 110) = 10.24, p < .000. However, they were still equally inclined for unfavorable

feedback (M=1.13) and favorable feedback (M = .87) about their worst attribute, t (1,110) = -

2.14, p = .14. It also showed that people wanted more favorable feedback for best attribute (M =

1.6) versus favorable feedback for worst attribute (M = .87), t (1, 110) = 7.76, p < .000. They

also wanted more unfavorable feedback for worst attribute (M = 1.13) versus unfavorable

feedback for best attribute (M = .4), t (1, 110) = 6.72, p < .000.

Figure 4. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (second round)

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

43

Table 5. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (second round)

95% Confidence Interval

Attribute Feedback Mean SE Lower Upper

best favorable 1.604 0.0589 1.487 1.720 unfavorable 0.396 0.0589 0.280 0.513 worst favorable 0.874 0.0589 0.758 0.990 unfavorable 1.126 0.0589 1.010 1.242

In summary, the results of Study 1 partially replicated the findings of the Study 1 of

Swann et al. (1989). Specifically, Study 1 supports self-enhancement theory in that people prefer

to hear about their best attribute versus worst attribute (Hypothesis 1). However, Study 1 failed

to replicate the self-verification effect found in Swann et al. (1989; Hypothesis 2).

2.2.4.5 Data analysis after “pre-selecting” participants

As discussed in the literature review section, Swann et al. (1989) pre-selected participants

who scored at two extremes on the Texas Social Behavior Inventory (Helmreich & Stapp, 1974).

Their rationale is that people with extreme self-esteem level may have extreme self-views, thus

making self-verification effect more salient. Even though it has been argued in the previous

section that self-verification effect should exist among the whole population, as a replication

study, the present study tried to simulate the “pre-selection procedure” of Swann et al. (1989) in

data analysis, in order to see whether the “extreme participants” would make self-verification

effect more salient. Specifically, in this round of data analysis, the present study excluded the

data of participants whose TSBI score was in the middle third of the sample (N = 50), and only

analyzed the responses from those whose TSBI score was in the upper and lower third of the

sample (N = 102).

44

2.2.4.5.1 Between-attribute feedback-seeking

A within-subjects analysis of variance (ANOVA) of the average rank of each attribute

was conducted. The results supported hypothesis 1 that people are more interested in hearing

about feedback about their best attribute (M = 2.62) than their worst attribute (M = 3.72), F (1,

101) = 20.328, p < .000.

Figure 5. Rank for best attribute and worst attribute (pre-selected)

Table 6. Rank for best attribute and worst attribute (pre-selected)

95% Confidence Interval

Attribute Mean SE Lower Upper

Best 2.62 0.111 2.36 2.80 Worst 3.72 0.111 3.39 3.82

2.2.4.5.2 Within-attribute feedback-seeking

Same as previously, to test self-verification effect, two rounds of data analysis were

conducted. In the first round of data analysis, the whole 102 participants were included. In the

second round, only those who had a best attribute that scored higer than 6 and a worst attribute

that scored lower than 4 were included (N = 73).

In the first round, a 2 (best vs. worst attribute) by 2 (favorable vs. unfavorable feedback)

within-subject ANOVA showed an interaction between attribute and feedback, F (1, 101) =

33.806, p < .000, partial η 2 = .25. Simple-effect tests revealed that people tended to solicit more

0  

1  

2  

3  

4  

5  

Best   Worst  

45

favorable feedback (M = 1.48) than unfavorable feedback (M = .52) about their best attribute, t

(1, 101) = 8.004, p < .000. However, they didn’t show a tendency to solicit more unfavorable

feedback (M = 1.05) than favorable feedback (M = .95) about their worst attribute, t (1, 101) = -

.817, p = .846.

Figure 6. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (pre-selected; first

round)

Table 7. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (pre-selected; first

round)

95% Confidence Interval

Attribute Feedback Mean SE Lower Upper

best favorable 1.480 0.0600 1.362 1.599 unfavorable 0.520 0.0600 0.401 0.638 worst favorable 0.951 0.0600 0.833 1.069 unfavorable 1.049 0.0600 0.931 1.167

In the second round, only 73 participants were included who had relatively extreme self-

views (best attribute scored higher than 6, and worst attribute scored lower than 4). There was an

overall interaction between attribute and feedback, F (1, 72) = 42.36, p < .000, partial η 2 = .37.

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

46

Simple-effect tests showed that people preferred more favorable feedback (M = 1.59) than

unfavorable feedback (M = .41) about their best attribute, t (1, 72) = 8.25, p < .000. It also

showed that people wanted more favorable feedback for best attribute (M = 1.59) versus worst

attribute (M = .88), t (1, 72) = 6.51, p < .000. Compared to their worst attribute (M = 1.12), they

wanted more unfavorable feedback for their best attribute (M = .4), t (1, 72) = 5.08, p < .000.

However, they did not show a preference for unfavorable feedback (M = 1.12) than favorable

feedback (M = .88) about their worst attribute, t (1, 72) = -1.73, p = .314. Thus, hypothesis 2 was

not supported.

Figure 7. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (pre-selected; second

round)

Table 8. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute (pre-selected; second

round)

95% Confidence Interval

Attribute Feedback Mean SE Lower Upper

best favorable 1.589 0.0714 1.448 1.730 unfavorable 0.411 0.0714 0.270 0.552 worst favorable 0.877 0.0714 0.736 1.018 unfavorable 1.123 0.0714 0.982 1.264

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

47

In summary, after excluding the data of participants who had moderate level of self-

esteem, the present study still failed to replicate Swann et al. (1989) Study 1 in terms of a self-

verification effect. There are two possible reasons that might explain such a failure to replicate

the results. First, since a third of the sample was eliminated, the power was reduced, indicating

that the probability of detecting self-verification effect was likely reduced. As discussed

previously, since simple correlations do not stabilize until the sample size reaches N = 150

(Schonbrodt & Perugini, 2013), the present study needs at least 150 participants. However, after

eliminating the 50 participants with moderate self-esteem, there were only 102 participants left.

Therefore, the lack of power might be reason that the present study failed to find self-verification

effect in this run of data analysis. Second, the “extreme” participants in the present study may

not be as extreme as those in Swann et al. (1989). Swan et al. (1989) did not publish the range of

participants’ self-esteem score, but they did tell readers that they pre-selected 42 participants

from a pool of 486 people. However, in the present study, 102 participants were selected out of

152 participants. It is possible that the range of the possible self-esteem scores is much smaller

than Swann et al. (1989), indicating less extremity of self-esteem. Moreover, considering the

exact TSBI score, the range of participants’ TSBI score in the present study (18-51) sits at the

upper middle of the whole range of TSBI score (0-64), which also indicates a low level of

extremity of self-esteem.

2.3 DISCUSSION

The goal of Study 1 is to replicate a classic study in the field to verify the existence of

self-enhancement and self-verification, in order to set up a basis for the following studies. Since

the classic studies usually used relatively small sample size in their experiments, the present

study recruited larger number of participants for greater confidence and power.

48

As discussed in the Results section, the results of Study 1 partially confirmed the findings

of the Study 1 of Swann et al. (1989). Study 1 verified people’s self-enhancement tendency in

that participants displayed more interest in receiving feedback pertaining to their best attributes

than their worst attributes.

However, the results did not show a self-verification effect even after the exclusion of

participants who had moderate self-views. There are two possible reasons. Firstly, as mentioned

previously, Swann et al. (1989) pre-selected both high-self-esteem and low-self-esteem people as

their participants. Admittedly, previous research found that depressed people tend to search for

negative feedback because this kind of feedback is familiar and predictable to them (Swann et

al., 1992; Swann, Wenzlaff and Tafarodi, 1992; Giesler et al., 1996). However, the present study

did not pre-select participants for the reason that if the self-verification effect is a vital need of

human-being, it should not exist only in a certain group of people. The failure to replicate the

results of Swann et al. (1989) could be regarded as a challenge to the notion that self-verification

is a basic human need.

However, it would be arbitrary to conclude whether an effect really exists merely based

on a replication study. It is conceivable that a study cannot be successfully replicated every time.

An Open Science Collaboration led by Nosek (2015) tried to replicate 100 published

psychological studies using high-powered designs and the original materials. Compared to the

original studies, 97% of which had significant results, only 36% of replication studies had

significant results. Moreover, replication effects were only half of the magnitude of original

effects. They argued that this large decline from the original studies to the replication studies

resulted from low-power designs with publication bias. In addition, some unanticipated factors in

the sample, setting and procedure may also alter the results. Therefore, in the present study, even

49

if similar materials and procedure were employed, the self-verification effect may be still

undetectable.

Nevertheless, the results of simple-effect tests for the interaction between attribute (best

vs. worst) and feedback-type (favorable vs. unfavorable) offer an alternative operationalization

of self-verification effect. Swann et al. (1989) operationalized self-verification as a within-

attribute difference – a preference for unfavorable feedback versus favorable feedback for worst

attribute. Even though the present study did not find a within-attribute difference, it did find a

within-subject difference instead. According to the simple-effect tests for the interaction between

attribute and feedback type in the second round of data analysis, people wanted significantly

more favorable feedback for best attribute compared to worst attribute. Meanwhile, they also

wanted more unfavorable feedback for worst attribute compared to best attribute. That is to say,

the same person showed different patterns when seeking feedback for best attribute versus worst

attribute. When facing the best attribute, the person apparently wanted a great deal of praise (M =

1.49). However, when facing the worst attribute, the preference for favorable feedback declined

(M = .92). Similarly, for the best attribute, the person did not want to hear about much

unfavorable feedback (M = 0.51). However, when it came to the worst attribute, the preference

for unfavorable feedback significantly increased (M = 1.08).

In literature, self-verification was defined as people’s strivings to ensure the stability of

their self-conceptions (Swann & Read, 1981). It is usually evidenced by the consistency between

one’s pre-existing self-views and feedback seeking – in the present study, it was operationalized

as the interaction between attribute (best vs. worst) and feedback type (favorable vs.

unfavorable). Swann et al. (1989) looked at the interaction from the perspective of attribute –

that is, whether people want more unfavorable feedback versus favorable feedback for their

50

negatively self-viewing attributes. However, if we take person rather than attribute as unit of

analysis (so look at the interaction from the perspective of feedback type), the question turns into

whether a person would want more unfavorable feedback for worst attribute versus best attribute.

The preference for unfavorable feedback versus favorable feedback for one’s negatively self-

viewing attributes could also be regarded as an evidence for self-verification – it shows the

consistency between one’s negatively self-viewing attribute and a preference for a negative

feedback.

Therefore, even though Study 1 did not show a self-verification tendency from a within-

attribute perspective, it did show a self-verification effect from a within-person perspective.

Additionally, compared to Swann at el.’s (1989) “within-attribute” operationalization

used in the pre-selected low-self-esteem participants, the “within-person” operationalization of

self-verification might be more appropriate in the general population. The underlying reason is

that people tend to respond consistently to survey questions – it would be hard to them to choose

more favorable feedback for their positively self-viewing attributes, and then choose more

unfavorable feedback for the negatively self-viewing attribute in a survey. In social psychology,

this pressure is called “a desire or preference for consistency” (Cialdini, 1984). Previous research

found that tempting participants to make a biased statement in the first step would result in a

systematic difference in the answers to the following questions. For example, Falk and

Zimmermann (2013) found that making participants agree that everybody deserves a second

chance significantly reduced the chance that they agreed that murderer should be imprisoned for

the rest of his life. Since the depressed people (and those with low self-esteem) are already

familiar with negative feedback, they may choose more unfavorable feedback for both positively

and negatively self-viewing attributes (Swann et al., 1992). Thus, if we operationalize self-

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verification as whether people seek more unfavorable feedback for the worst attribute, it would

be easy to find such effect among the depressed people. However, for the average person who

feels positive about themselves, they might tend to seek favorable feedback for both best and

worst attributes, and may feel inconsistent if seeking positive feedback for some attributes, while

seeking negative feedback for the others.

In the present study, even though participants did not show an absolute preference for

unfavorable feedback for the worst attribute (which might be caused by a preference for

consistency), they did show a comparative decline in choosing favorable feedback for the worst

attribute versus the best attribute. Since participants could only choose 2 questions for each

attribute, a difference of 0.5 in the mean could be considered as a large difference. Therefore, for

the average population, the “within-person” conceptualization of self-verification might be a

better one compared to the “within-attribute” conceptualization.

In summary, there are two implications of Study 1. First of all, Study 1 found a self-

enhancement effect when people are deciding which attribute to seek feedback about (hypothesis

1), which provides a theoretical base for further research. Secondly, Study 1 offered an

alternative conceptualization of self-verification effect. Different from Swann et al.’s (1989)

“within-attribute” operationalization, the present study conceptualized self-verification from a

“within-person” perspective. More specifically, self-verification is operationalized as “a

tendency to want more unfavorable feedback for negatively self-viewing attribute versus

positively self-viewing attribute”. This operationalization is based on how a person differently

seeks feedback for positively self-viewing attributes and negatively self-viewing attributes.

Compared to the “within-attribute” operationalization, which measures the how unfavorable

feedback was chosen between two options (for best attribute versus worst attribute), the “within-

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person” operationalization measures how many times (out of two times) unfavorable feedback

was chosen for the worst attribute. Hence, it is true that Study 1 did not find a self-verification

effect as operationalized as a “within-attribute” effect by Swann et al. (1989). However, based on

the “within-person” operationalization of self-verification effect, Study 1 did find a self-

verification effect.

Built on the evidence in the existence of self-enhancement and self-verification provided

by Study 1, Study 2 explores how self-enhancement and self-verification motive change in the

context of social media. Specifically, Study 2 looks at how publicness could moderate self-

enhancement and self-verification motives. In Study 1, participants were given the chance to

decide what kind of self-relevant feedback they would like to seek from the personality test.

However, when posting on social media, rather than receiving self-relevant feedback from

others, people are usually the disclosers of such self-relevant information – they decide what

information to be shared, and what information to be withheld (Kramer & Winter, 2008). The

role of information receiver in the experimental setting is thus potentially different from the role

of information discloser on social media. This difference may moderate self-enhancement and

self-verification effects because self-disclosure involves self-presentational concerns (Kramer &

Winter, 2008). Study 2 thus aims at finding out how this moderating effect of publicness impacts

self-enhancement and self-verification in the context of social media.

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CHAPTER 3: STUDY 2

Study 2 aims at applying the theory tested in Study 1 into the context of self-presentation

on social media. Specifically, Study 2 looks at the moderating effect of publicness on self-

enhancement and self-verification. As discussed at the end of chapter 2, publicness may

moderate self-enhancement and self-verification because self-disclosure to the public involves

self-presentational concerns.

According to the literature reviewed in Chapter 1, self-presentational concern may

strengthen self-enhancement and weaken self-verification. Since a person’s public image

presented to others influences the quality and quantity of rewards and social approval that he

receives from social interaction (Schlenker, 1975), people strive to present a positive self-image

to the public, despite the valence of their self-views (e.g. Goffman, 1955; Schlenker, 1975).

Therefore, when people are self-disclosing to an audience, self-enhancement effect could be

stronger than it is in the feedback-seeking condition; while self-verification could be weaker in a

self-disclosure condition than in a feedback-seeking condition. Study 2 intends to experimentally

test this prediction.

3.1 LITERATURE REVIEW

3.1.1 Manipulation of publicness

The idea of publiness is inherited from Goffman’s (1955) metaphor of people’s public

presentation as similar to a performance on a stage for an audience. Goffman (1955) stated that

people would behave differently when they know that others are watching or aware of them

(front-stage behavior), compared to when they think no one is watching (back-stage behavior).

The idea of “knowing others are watching or aware of them” is the original definition of

publicness. In the context of social media, Slevin (2000) argued that, different from

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Goffman(1955)’s definition, nowadays publicness does not require the self-presenter and

audience to physically stay in the same place. Slevin (2000) defined publicness in social media

as “using communication media to make information and their points view visible and available

to others”. By his definition, on social media, the audience does not always have unrestricted

access to the self-presenter’s performance and information. Meanwhile, the self-presenter may

also have different level of perceived publicness on social media. Slevin (2000) defined

publicness on social media as an active behavior. However, publicness could also be static –

being aware that using social media implies that the information posted on social media is visible

to others. Since the present research looks at how this awareness influences the way people

disclose information on social media, in this dissertation, publicness is defined as “being aware

that one’s information posted on social media could be visible and available to others”.

In literature, publicness has been manipulated in a variety of ways. For example, Brown

and Gallagher (1992) manipulated publicness as whether participants were observed by an

experimenter or not. In their study, they asked participants to answer a series of questions on a

computer. In the public condition, the experimenter moved her chair behind the participants and

observed the whole process of how the participants answered the questions. In contrast, in the

private condition, the experimenter sat at the other corner of the room, thus leaving the

participants alone. Their manipulation check showed that participants in the public condition

believed that the experimenter was more aware of their performance than did those in the private

condition. On the other hand, some research used anonymity to manipulate publicness. For

example, in Schlenker’s (1975) study, participants were sorted into groups and were asked to

complete some analogy problems with their group members. They were told that they were

going to share answers in the group and finally vote for a best solution. Those assigned to the

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public condition were told that their own answers would be shared with their group members.

Therefore, all the group members would be knowledgeable of their individual performance. On

the other hand, participants in the private condition were told that their answers would be

distributed anonymously. Thus, the group would read everybody’s answers without knowing

who actually contributed to the answers. The findings of the research showed that in the

anonymous condition, participants showed more favorable self-presentations than those in the

public condition. Their self-presentation was also less affected by the expectation of their actual

performance than those in the public condition.

In recent research in computer-mediated communication, publicness has been

manipulated by the functions of social-networking sites. For example, Johnson and Van Der

Heide (2015) investigated how sharing media content on social media affects people’s

subsequent media preference. In their study, they asked participants to post their opinions about a

photo either on a public blog, or to a private automated analytic software. They then asked

participants to rate their liking on the photo on which they made comments a week later to see if

there is an attitude change as a result of the opinion-sharing behavior.

3.1.2 Feedback-seeking versus feedback-disclosure on social media

Even though some of previous studies manipulated publicness by either disclosing or

keeping private participants’ self-relevant information (e.g. Brown & Gallagher, 1992;

Schlenker, 1975; Johnson & Van Der Heide, 2015), these studies never explicitly acknowledged

the conditions as “information-disclosure” or “information-seeking”. In the present study, since

the self-relevant information would be feedback about participants’ self-attributes, the private

condition will be labeled as “feedback-seeking” while the public condition will be labeled as

“feedback-disclosure”. The difference between the two conditions is discussed as follows.

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First of all, feedback-disclosure is a subset of self-disclosure. Self-disclosure is defined as

a process of revealing personal information to others (Jourard, 1971; Greene, Derlega &

Mathews, 2006). It is typically an intentional act communicated via the verbal descriptions of a

person, his or her experiences and feelings (Chelune, 1975). Self-disclosure meets the vital

human needs for social connectedness and social rewards (Tamir & Mitchell, 2012), while

carrying the risk of giving up privacy to others (Jourard, 1971). Since self-disclosure involves a

trade-off between achieving disclosure goals and decreasing personal risks (Petronio, 2012),

people usually carefully select the content of self-disclosure depending on the audience

(Bazarova and Choi, 2014).

Compared to the traditional offline self-disclosure, self-disclosure on social media differs

both in audience and disclosure goals. Even though self-disclosure on social media could be

shared dyadically via private messages and groupings, there are a number of messages sent out

publicly to the entire network (Gilbert & Karahalios, 2009). With such public self-disclosure, a

single visit to one’s post wall may enable a stranger to acquire more information than years of

acquaintance (Bazarova & Choi, 2014). Such public nature of online self-disclosure thus leads to

different self-disclosure goals.

According to the functional theory of self-disclosure (Derlega & Grzelak, 1979), people’s

self-disclosure goals fall into five categories: social validation, self-expression, relational

development, identity clarification and social control. Social validation refers to the process of

validating one’s self-concept and self-value. Self-expression could be regarded as an outlet of

negative emotions in order for stress relief. Relational development seeks to increase the

closeness to other people. Identity clarification indicates one’s identity and social position.

Finally, social control means gaining social resources by strategically disclosing personal

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information. The self-disclosure goals may be activated by personality characteristics and

situational cues (Omarzu, 2000). While relational development is the most salient goal in dyadic

self-disclosure, impression management (self-presentation) plays a role as the major goal of

public self-disclosure (Miller & Read, 1987; Bazarova & Choi, 2014). As discussed in the

Literature review section, since impression management (self-presentation) concerns one’s social

rewards and approval, people usually strive to present a positive image to the public (Schlenker,

1975), which may bolster self-enhancement effect. Thus, self-disclosure should be closely

related to self-enhancement. People would show stronger self-enhancement effect in disclosure

condition versus seeking condition.

Hypothesis 3a: People will display a self-enhancement effect such that the best

attribute versus the worst attribute will receive higher rank in interest in receiving

feedback.

Hypothesis 3b: This effect will be moderated by whether they are seeking feedback

or disclosing feedback to an audience.

Compared to feedback-disclosure (self-disclosure), feedback-seeking involves different

motives. Ashford and Cummings (1983) identified two major motives of active feedback-

seeking. Firstly, people could obtain useful information by feedback-seeking to acquire new

skills and evaluate their abilities. The second motive is about impression management. Since

both the act of seeking feedback and the content of feedback received would influence one’s

public image (Morrison & Bies, 1991), people would take self-presentational concern into

consideration when they are trying to solicit feedback from others. The motive to obtain useful

information could motivate self-verification strivings because getting unfavorable feedback

consistent with negative self-views may help with self-improvement. The motive for impression

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management would bolster self-enhancement, as discussed previously. However, in the present

study, participants in the feedback seeking condition will only receive private, automated

feedback from a personality test, which does not involve any audience for impression-

management. Therefore, in the present study, those in the feedback-seeking condition would be

mainly concerned with obtaining information about the self. Thus, compared to the feedback-

disclosure condition, participants in feedback-seeking condition should display more self-

verification and less self-enhancement tendency.

Hypothesis 4a: People in the feedback-seeking condition will show a self-verification

effect, such that they will seek more unfavorable feedback for their worst attribute versus

best attribute.

Hypothesis 4b: people in the disclosure condition will not show a self-verification

effect, such that the difference between the number of unfavorable feedback that they want

to disclose to the audience for their worst attribute and the best attribute will equal 0.

All in all, in the past research, the manipulation of publicness centers around whether an

individual’s performance is identifiable to the individual and whether the performance is known

by the audience. Following this rationale, the present study manipulates private versus public as

feedback-seeking versus feedback-disclosure. Specifically, feedback-seeking condition refers to

the condition in which participants have the option to seek certain feedback from a personality

test. They do not have to share the feedback with any other people, thus keeping the feedback

private. This condition reveals which part of the self that the participants would like to learn by

themselves. On the other hand, participants in the feedback-disclosure condition would receive

feedback from the personality test, and have the option to disclose certain feedback to their social

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media friends publicly. This condition would reveal which part of the self that the participants

would like their friends to learn.

3.1.3 Measures

In Study 1, self-enhancement was operationalized as whether people are more interested

in seeking feedback pertaining to their best attribute than worst attribute. It was measured with

the between-attributes questionnaire (see APPENDIX C), in which participants were asked to

rank the personal attributes based on the extent to which they would like to receive feedback

about each attribute. Self-verification was operationalized as a tendency to seek more

unfavorable than favorable feedback about their worst attribute. It was measured with the within-

attributes questionnaire (see Appendix B).

In the literature, there is a variety of ways that self-enhancement and self-verification

have been measured. For example, to measure self-enhancement, Alicke (1985) gave participants

a list of positive and negative personality traits, and asked them to rate how accurately each trait

was descriptive of them. In this case, self-enhancement was operationalized as higher rating of

the positive traits than the negative traits, which indicated that people tended to think positively

of themselves. Sedikides and Green (2000) gave participants either favorable or unfavorable

feedback, and then used a surprise recall task to see how much feedback that the participants

could remember. Self-enhancement was operationalized as more favorable feedback recalled by

the participants than unfavorable feedback. With that being said, the nature of such measures for

self-enhancement is the test on whether people would be more willing to acknowledge, accept,

and present the positive statement of the self than the negative statement of the self. Similarly,

the between-attribute questionnaire that Swann et al. (1989) used is in line with this notion –

self-enhancement would be manifested if participants show a preference in hearing about their

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positive attributes versus negative attributes. Therefore, the present study employed the between-

attribute questionnaire to measure self-enhancement for the feedback-seeking group. An adjusted

between-attribute questionnaire (see APPENDIX C) was used for the feedback-disclosure group,

in which self-enhancement was operationalized as people’s interests in disclosing feedback to

their Facebook friends. All references to “feedback-seeking” in the instructions were changed

into “feedback-disclosure”.

Similar to self-enhancement, self-verification was also measured in different ways in

literature. For example, Swann et al. (1992) pre-selected negatively self-viewing depressed

people as participants, and gave participants three sorts of bogus feedback about their personality

traits: one favorable, one neutral and one unfavorable. They told participants that the three sets of

feedback came from three different evaluators, and asked participants to rate how much they

would prefer to meet and interact with each evaluator. Self-verification was operationalized as

the negatively self-viewing participants’ preference for the evaluator who gave unfavorable

feedback over other evaluators. In Swann and Read (1981, study 2), they measured self-

verification based on whether people would solicit reactions from their interaction partners that

confirmed their existing self-conception. The basic question that all these measures ask is

whether negatively self-viewing people would prefer negative feedback to positive feedback. In

these measures, this preference for negative feedback was operationalized as evaluator choice,

feedback-soliciting behavior, feedback recall, etc.

Swann et al.’s (1989) within-attribute questionnaire used feedback-soliciting behavior to

measure self-verification: they asked participants to choose two questions from six questions for

each attribute. Among the six questions, three solicited favorable feedback (e.g. what is the

evidence that this person has good social skills?) and three solicited unfavorable feedback (e.g.

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what is the evidence that this person does not have good social skills?). Self-verification would

be manifested if participants choose more questions that could solicit favorable feedback for

their positively self-viewing attributes, and more questions that could solicit unfavorable

feedback for their negatively self-viewing attributes.

Compared to other measures, a merit of Swann et al.’s (1989) within-attribute

questionnaire is the fact that it does not involve any interaction between the participants and

(fake) evaluators. In some measures that involve the interaction between the participants and

evaluators (e.g. Swann et al., 1992), social interaction concern may confound with self-

verification effect – participants may embrace the unfavorable feedback, but they might not be

likely to interact with the evaluator who gave the unfavorable feedback due to the fear of

embarrassment. After all the “evaluators” are the ones who point out their drawbacks. The

participants may agree with the feedback but consider the evaluator as mean and hard to get

along with. In contrast, Swann et al.’s (1989) measure does not involve any social interaction or

self-disclosure to other people, thus avoiding such confounds. In addition, the present study

intends to introduce “publicness” as an independent variable. Publicness would aggrandize such

confounding effect because being criticized with one’s own drawback by others in the public

would be even more daunting than it is in the private condition – people may not want to interact

with the person who point out their drawbacks in the public, even though the feedback is

accurate. Therefore, the present study also used the within-attribute questionnaire from Swann et

al. (1989; see Appendix B).

There is a problem of the measures discussed above - not every participant finds a “best”

and a “worst” attribute with the Self-Attribute Questionnaire. Study 1 of Swann et al. (1989) and

Study 1 of the present research confirmed this notion – almost a half of participants in Swann et

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al. (1989) and more than a third of participants in Study 1 of the present research were excluded

in the second round of data analysis when only those were considered who had a best (score 6

and higher) and a worst (score 4 and lower) attribute. However, such problem is inevitable –

different people have different best and worst attributes, and it is impossible to initiate a list of

attributes in which every participant could find a best and a worst attribute. A possible harm of

the problem is that after excluding at least a third of participants from the pool, the sample of the

study may not be able to reach the desired size, thus jeopardizing the credibility of the study.

However, increasing the number of participants could easily solve such problem. Hence, the

present study still used the measures for self-enhancement and self-verification adapted from

Swann et al. (1989).

Study 1 offered an alternative conceptualization of self-verification – that is, “a tendency

that a person wants more unfavorable feedback for negatively self-viewing attribute versus

positively self-viewing attribute”. As discussed in Study 1, this “within-person”

operationalization of self-verification could be more appropriate for the general population

compared to Swann et al.’s (1989) because this operationalization is less likely to be influenced

by the “pressure for consistency”. Therefore, for self-verification, with the same measures as

Swann et al. (1989), Study 2 will come up with hypotheses with the “within-person”

operationalization, and come up with research questions with the “within-attribute”

operationalization.

Research Question1: Will people show a self-verification effect as Swann et al.

(1989) operationalized, such that they will want more unfavorable feedback versus

favorable feedback for their worst attribute?

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Since self-verification is operationalized as people’s desire for unfavorable feedback for

worst attribute versus best attribute, if people in the seeking condition have stronger self-

verification effect, they should want more unfavorable feedback for the worst attribute compared

to those in the disclosure condition. Research Question 2 explores this issue.

Research Question 2: Will people in the seeking condition want more unfavorable

feedback for the worst attribute compared to those in the disclosure condition?

Table 9. Summary of operationalization and measures for Study 2   Operationalization   Measures   Data  

analyses  Self-­‐enhancement  

Whether  people  would  prefer  to  seek  (or  disclose)  feedback  about  their  best  attribute  versus  worst  attribute  

Between-­‐attribute  questionnaire:  rank  each  of  the  five  attributes  based  on  the  extent  to  which  participants  want  to  hear  about  the  attribute.  Self-­‐enhancement  will  be  indicated  if  best  attribute  versus  worst  attribute  receives  higher  rank    

Two  rounds  of  data  analysis  for  both  self-­‐enhancement  and  self-­‐verification.  1st  round:  full  data  set  2nd  round:  only  participants  were  analyzed  who  had  a  best  (score  6  or  higher)  and  a  worst  (score  4  or  lower)  attribute    

Self-­‐verification  

Swann  et  al.  (1989)’s  (within-­‐attribute  operationalization)  

The  present  study  (within-­‐person  operationalization)  

Within-­‐attribute  questionnaire:  for  each  attribute,  choose  two  questions  out  of  a  list  of  six  questions  which  consists  of  three  favorable-­‐feedback-­‐soliciting  questions  and  three  unfavorable-­‐feedback-­‐soliciting  questions.  Self-­‐verification  will  be  indicated  if  people  choose  more  unfavorable-­‐feedback-­‐soliciting  questions  for  worst  attribute  versus  best  attribute  

Whether  people  would  seek  (or  disclose)  more  unfavorable  versus  favorable  feedback  for  their  worst  attribute  

Whether  people  would  seek  (or  disclose)  more  unfavorable  feedback  for  their  worst  attribute  versus  best  attribute  

Note:  if  a  participant  has  two  “best  attribute”  (e.g.  two  attributes  score  10),  then  the  number  of  favorable/unfavorable  feedback-­‐soliciting  questions  will  be  averaged  (as  dependent  variable)  

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3.1.4 Hypotheses

3.1.4.1 The between-attribute questionnaire

Hypothesis 3a: The best attribute will receive higher rank in interest in receiving

feedback versus the worst attribute.

Hypothesis 3b: This effect will be moderated by whether people are seeking feedback

or disclosing feedback. Specifically, the effect will be stronger in the disclosure condition

versus seeking condition.

3.1.4.2 The within-attribute questionnaire

Hypothesis 4a: People in the seeking condition will show a self-verification effect,

such that they will seek more unfavorable feedback for their worst attribute versus best

attribute.

Hypothesis 4b: People in the disclosure condition will not show a self-verification

effect, such that the difference between the number of unfavorable feedback that they want

to disclose to the audience for their worst attribute and the best attribute will equal 0.

Research Question1: Will people show a self-verification effect as Swann et al.

operationalized, such that they will want more unfavorable feedback versus favorable

feedback for their worst attribute?

Research Question 2: Will people in the seeking condition want more unfavorable

feedback for the worst attribute compared to those in the disclosure condition?

3.2 PROCEDURE

Two hundred and twenty participants were recruited via Amazon Mturk. Similar to Study

1, at the beginning, participants were told that the aim of the study was to collect data of people’s

personality traits, and that they would see two sets of questionnaires that measure their

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personality from different perspectives. They then finished the first questionnaire – the self-

attributes questionnaire (see Appendix A), which asked them to self-rate the five personal

attributes compared to their peers at their age. They also rated how certain they were in each

rating, as well as the importance of each attribute to them.

Afterwards, they finished the same “personality test” as the one used in Study 1, which

consisted of the ‘TSBI” questions (see Appendix D) and the filler questions (see Appendix E).

After they finish the “personality test”, participants in the “feedback-seeking condition” were

told that they would be able to receive partial analysis of their answers, and that they would have

to rank the five attributes based on the extent to which they want to receive answers about the

attributes. In addition, they were also presented with the feedback-seeking questionnaire (see

Appendix B), and were asked to choose two questions from each set of six questions to receive

answers.

On the other hand, participants in the “feedback-disclosure condition” were told that they

would receive the complete analysis in each of their five attributes, and that they would have the

option to repost the analysis in each of the five attributes on their social media. Then they were

asked to finish the two adapted feedback-seeking measures (see Appendix B and 3). With the

between-attributes measure, participants ranked the five attributes based on the extent to which

they wanted to disclose the analysis of each attribute to the audience. With the within-attributes

measure, participants were presented with the five sets of questions for each personal attributes

from the feedback-seeking questionnaire, and were asked to choose two questions from each set

to share with the audience. Finally, participants were debriefed and received $1 from the

researcher via Amazon Mturk.

3.3 PARTICIPANTS

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220 participants were recruited from Amazon Mturk. Study 2 was set as invisible to

workers who participated in Study 1, so they were not able to take Study 2. Unlike Study 1,

Study 2 did not restrict participants’ age to 18-25, because 1) the self-enhancement and self-

verification effect should be able to be found in all population, rather than only young people; 2)

people in different life stages may have different social media use habit, but they should be

driven by similar psychological motive when using social media. Hence, Study 2 recruited

participants across different ages.

Among the 220 participants, 11 participants’ data were eliminated because they assigned

equal number for all of (or almost all of) the five SAQ attributes. Chances are high that they did

not pay attention to the instruction during the task and just selected the same number for all

items. Therefore, they were excluded from the data set. One participant was excluded because he

only gave a word “no” in a filler question, which asked participants to use 3-5 sentences to

describe their images in their friends’ eyes. Since most participants were able to provide the

description, the participant who only gave a “no” word for that question was suspected as not

paying attention to the test. Therefore, the participant was excluded.

Among the 208 participants, 116 (55.8%) were male while 92 (44.2%) were female; 148

(71.2%) were white, 21 (10.1%) were African American, 21 (10.1%) were Asian, 9 (4.3%) were

American Indian or Pacific Islander, and 9 (4.3%) were others. 29 (13.9%) participants’ age lied

in the range from 18-24; 111 (53.4%) participants aged 25-34; 40 (19.2%) participants were 35-

44; 12 (5.8%) were 45-54; 8 (3.8%) were 55-64 and 3 (1.4%) were 65-74. 21 (10.1%)

participants’ highest education level was high school; 35 (16.8%) of them attended college but

did not earn a degree; 86 (41.3%) participants earned a bachelor degree in college; 17 (8.2%) of

them had an associate degree in college; 46 (22.1%) had a master’s degree and 3 (1.4%) had a

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doctoral degree.

3.4 RESULTS

To test hypothesis 3a and 3b, data of the within-attribute questionnaire was run as a

repeated measure ANOVA with condition (seeking vs. disclosure) as a between-subjects

independent variable and the rank of each of the five SAQ attribute as dependent variable.

Hypothesis 4 and the research questions were tested with data from the between-attribute

questionnaire. Data was run with a three-way repeated-measure ANOVA with attribute (best vs.

worst) and feedback (favorable vs. unfavorable) as within-subjects independent variables, as well

as condition (seeking vs. disclosure) as a between-subjects independent variable. The number of

favorable-feedback-soliciting questions and the number of unfavorable-feedback-soliciting

questions were taken as dependent variable.

In addition, when testing hypothesis 4a and 4b and the research questions, data from

“seeking condition” and “disclosure condition” was analyzed separately. A 2 (best vs. worst

attribute) by 2 (unfavorable vs. favorable feedback) within-subjects ANOVA was conducted for

each condition to see whether there is an overall interaction effect between attribute and the type

of feedback. Post hoc tests were used for both conditions to examine whether participants tended

to solicit more favorable feedback versus unfavorable feedback about their best attribute, and

whether they tended to solicit more unfavorable feedback versus favorable feedback about their

worst attribute. Note that, for the disclosure condition, since it is statistically impossible to test

that an effect does not exist, such that “people in the disclosure condition will not want to

disclose more unfavorable feedback for their worst attribute versus best attribute”, the difference

between the number of unfavorable feedback chosen for worst attribute and best attribute was

calculated and was compared to 0, in order to examine whether there is a significant difference

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between the worst attribute and best attribute in receiving unfavorable feedback.

As discussed in Study 1, the fact that not every participant could find a best and a worst

attribute in the five SAQ attributes could make self-verification effect less salient. Therefore,

similar to Study 1, a second round of data analysis was conducted, in which only participants

who had extreme self-views were left in order for a more salient self-verification effect.

3.4.1 Data analysis round 1 (full dataset)

3.4.1.1 Between-attribute feedback seeking

Hypothesis 3a: The best attribute versus worst attribute will receive higher rank in

interests in receiving feedback.

A repeated-measure ANOVA showed that people are more interested in receiving

feedback about their best attribute versus their worst attribute, F (1, 207) = 43.656, p < .000,

partial η 2 = 0.175. The average rank of best attribute (M=2.45) is significantly higher than the

rank of worst attribute (M=3.57; 1 means participants most want to hear about the area while 5

means they least want to hear about the area).

Hypothesis 3b: This effect will be moderated by whether they are seeking feedback

or disclosing feedback. Specifically, the effect will be stronger in the disclosure condition

versus seeking condition.

There was no significant interaction between attribute (best vs. worst) and conditions

(feedback-seeking vs. feedback-disclosure), F (1, 207) = .219, p = .64 > .05. That is to say,

participants in the feedback-disclosure condition did not show a larger self-enhancement effect

compared to those in the feedback-seeking condition, despite the fact that they both displayed a

strong self-enhancement effect. Therefore, hypothesis 3 was partially supported.

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Figure 8. Rank for best attribute and worst attribute in the seeking condition versus disclosure

condition (1 means participants want to hear or disclose about the attribute most, while 5 means

they want to hear or disclose about the attribute worst)

Table 10. Rank for best attribute and worst attribute in seeking condition versus disclosure

condition

95% Confidence Interval

Seeking(1)or Disclosure(2) Attribute Mean SE Lower Upper

1 best 2.48 0.139 2.21 2.75 worst 3.52 0.139 3.25 3.79 2 best 2.42 0.141 2.14 2.70 worst 3.62 0.141 3.34 3.89

3.4.1.2 Within-attribute feedback seeking

Data was run with a 2 (best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable

feedback) within-subjects ANOVA with condition (seeking vs. disclosure) as a between-subjects

independent variable. Results revealed a main effect and two interaction effects. Firstly, there is

a main effect of feedback type, F (1, 207) = 36.83, p < .000, partial η 2 = 0.152, indicating that

people sought more favorable feedback (M=1.21) than unfavorable feedback (M=0.79).

There is an interaction between conditions (seeking vs. disclosure) and feedback type

(favorable vs. unfavorable), F (1, 207) = 12.136, p = .001, partial η 2 = 0.056. Specifically, there

0  0.5  1  

1.5  2  

2.5  3  

3.5  4  

4.5  5  

Best   Worst  

seeking  

disclosure  

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is a greater discrepancy between the number of favorable and unfavorable feedback-soliciting

questions for disclosure condition, compared to seeking condition, which means that the main

effect of feedback type was attenuated by conditions (seeking vs. disclosure). Simple-effect tests

showed that people wanted more favorable feedback (M = 1.31) versus unfavorable feedback (M

= .69) in the disclosure condition, t (1, 206) = 6.69, p < .000. However, they showed equal

tendency for favorable (M = 1.09) and unfavorable feedback (M = 0.91) in the seeking condition,

t (1, 206) = 1.85, p = .255.

Simple-effect tests also showed that people in the disclosure condition wanted more

favorable feedback (M = 1.31) compared to those in the seeking condition (M = 1.08), t (1, 206)

= -3.48, p = .003. In parallel, people in the seeking condition wanted more unfavorable feedback

(M = .91) compared to those in the disclosure condition (M = .69), t (1, 206) = -3.48, p = .003.

Finally, there is an interaction effect between attribute (best vs. worst) and feedback

(favorable vs. unfavorable), F (1, 207) = 31.49, p < .000, partial η 2 = 0.133. Simple-effect tests

showed that people wanted more favorable feedback (M = 1.37) versus unfavorable feedback (M

= 0.63) for their best attribute, t (2, 206) = 8.11, p < .000. However, they were equally inclined

for favorable (M = 1.03) and unfavorable feedback (M = 0.97) for their worst attribute, t (1, 206)

= .581, p = .94.

It was also found that people wanted more favorable feedback for their best attribute (M

= 1.37) versus worst attribute (M = 1.03), t (1, 206) = 5.61, p < .000, but they wanted more

unfavorable feedback for their worst attribute (M = .97) versus best attribute (M = .63), t (1, 206)

= -5.61, p < .000.

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Hypothesis 4a: People in the seeking condition will show a self-verification effect,

such that they will seek more unfavorable feedback for their worst attribute versus best

attribute.

To test hypothesis 4a, only data from the seeking condition was examined. A 2 (best vs.

worst attribute) by 2 (unfavorable vs. favorable feedback) within-subjects ANOVA revealed a

main effect of feedback type, F (1, 105) = 4.17, p = .044, partial η 2 = 0.038, indicating that

people overall wanted more favorable feedback (M = 1.09) than unfavorable feedback (M = .91).

In addition, there is an interaction between attribute and the type of feedback, F (1, 105)

= 14.63, p < .000, partial η 2 = 0.122. Simple-effect tests revealed that people wanted to solicit

more favorable feedback for best attribute (M = 1.255) versus worst attribute (M = .915), t (1,

105) = 3.82, p = .001. Meanwhile, they wanted more unfavorable feedback for worst attribute (M

= 1.085) versus best attribute (M = .745), t (1, 105) = 3.82, p = .001. They also wanted more

favorable (M=1.255) versus unfavorable feedback (M=0.745) about their best attributes, t (1,

105) = 4.19, p < .000. However, they are equally inclined to solicit unfavorable (M=1.085) and

favorable (M= .915) feedback about their worst attributes, t (2, 105) = -1.4, p = .985 > .05.

Hypothesis 4a was supported.

Figure 9. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition

0  0.5  1  

1.5  2  

Best   Worst  

Favorable  

Unfavorable  

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Table 11. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition

Hypothesis 4b: People in the disclosure condition will not show a self-verification

effect, such that the difference between the number of unfavorable feedback that they want

to disclose to the audience for their worst attribute versus the best attribute will be equal to

0.

To test hypothesis 4b, only data from participants assigned to the feedback-disclosure

condition was analyzed. The difference between the number of unfavorable feedback for worst

attribute versus best attribute was calculated by subtracting the number of unfavorable feedback

for the best attribute from that for the worst attribute. A one-sample t-test found that the

difference between the number of unfavorable feedback between worst and best attribute was

different from 0, t (1, 101) = 4.13, p < .000, mean difference = .353, Cohen’s d = .409, indicating

that people wanted more unfavorable feedback for their worst attribute (M = .86) versus best

attribute (M = .51). Therefore, there was a self-verification effect found in the disclosure

condition. Hypothesis 4b was not supported.

To examine the interaction between attribute and feedback in the disclosure condition, a 2

(best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback) within-subjects

ANOVA showed a main effect of feedback type, F (1, 101) = 37.6, p < .000, partial η 2 = 0.271,

95% Confidence Interval

Feedback Attribute Mean SE Lower Upper

favorable best 1.255 0.0608 1.135 1.375 worst 0.915 0.0608 0.795 1.035 unfavorable best 0.745 0.0608 0.625 0.865 worst 1.085 0.0608 0.965 1.205

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indicating that overall people wanted more favorable feedback (M = 1.31) than unfavorable

feedback (M = 0.69).

There is an overall reliable interaction between attribute and the type of feedback, F (1,

101) = 17.04, p < .000, partial η 2 = 0.144. Simple-effect tests revealed that people solicited more

favorable feedback for best attribute (M = 1.49) versus worst attribute (M = 1.137), t (1, 101) =

4.13, p < .000. Meanwhile, they wanted more unfavorable feedback for worst attribute (M =

0.863) versus best attribute (M = .51), t (1, 101) = 4.13, p < .000, which could also be regarded

as an evidence of self-verification effect. It was also found that they wanted more favorable

(M=1.49) versus unfavorable feedback (M=0.51) about their best attributes, t (2, 101) = 7.35, p <

.000. However, there is no significant difference between the extent to which they wanted to

solicit unfavorable feedback (M=1.137) and favorable feedback (M= .863) about their worst

attributes, t (1, 101) = 2.06, p = .245 > .05.

Figure 10. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the disclosure

condition

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

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Table 12. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the disclosure

condition

95% Confidence Interval

Feedback Attribute Mean SE Lower Upper

favorable best 1.490 0.0667 1.359 1.622 worst 1.137 0.0667 1.006 1.269 unfavorable best 0.510 0.0667 0.378 0.641 worst 0.863 0.0667 0.731 0.994

Research Question1: Will people in the seeking condition show a self-verification

effect as Swann et al. (1989) operationalized, such that they will want more unfavorable

feedback versus favorable feedback for their worst attribute?

The research question 1 can be answered by the results of hypothesis 4a. Results showed

that in the seeking condition, people are equally inclined to solicit unfavorable (M=1.085) and

favorable (M= .915) feedback about their worst attributes, t (2, 105) = -1.4, p = .985. Therefore,

self-verification effect as operationalized by Swann et al. (1989) was not found in the present

study.

Research Question 2: Will people in the seeking condition want more unfavorable

feedback for the worst attribute compared to those in the disclosure condition?

A repeated-measure ANOVA was conducted with the number of unfavorable-feedback-

soliciting questions as the dependent variable, attribute (best vs. worst) as the repeated-measure

factor and condition (seeking vs. disclosure) as the between-subject factor. Results showed a

main effect of attribute, F (1, 207) = 31.50, p < .000, partial η 2 = .13. Specifically, they wanted

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to disclose more unfavorable feedback for their worst attribute (M = .98) versus the best attribute

(M = .63).

However, there is no interaction effect between attribute (best vs. worst) and condition

(seeking vs. disclosure), F (1, 207) = .011, p = .91. That is to say, people’s preference for

unfavorable feedback for worst attribute versus best attribute was not moderated by condition

(seeking vs. disclosure).

In summary, in the first round of data analysis, it was found that people showed a self-

enhancement in both seeking and disclosure condition. However, publicness (seeking vs.

condition) was not found to moderate the self-enhancement effect. On the other hand,

participants in both the seeking and disclosure condition showed a self-verification effect.

However, publicness (seeking vs. disclosure) was not found to moderate it.

3.4.2 Data analysis round 2 (moderately self-viewing participants excluded)

The first round of data analysis showed a self-verification effect as the within-person

difference in both seeking and disclosure conditions. However, it did not reveal significant

within-attribute differences that would indicate a self-verification effect. Moreover, publicness

was not found to impact either self-enhancement or self-verification. As discussed in Study 1, a

possible reason is that some participants did not have what they consider a very good and very

bad attribute in the SAQ, which made the within-attribute difference weak. Therefore, similar to

Study 1, in the second run of data analysis, only those who had a best attribute (score 6 or

higher) and a worst attribute (score 4 or lower) were selected (N=106). The data set was divided

into two sets (feedback-seeking and feedback-disclosure) in order to separately test hypothesis 4a

and hypothesis 4b. in order to examine whether the exclusion of moderate self-viewing people

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would influence self-enhancement, hypothesis 3a and 3b were also tested in this round of data

analysis.

3.4.2.1 Between-attribute questionnaire

Hypothesis 3a: The best attribute will receive higher rank in interest in receiving

feedback versus the worst attribute.

A repeated-measure ANOVA showed that people are more interested in receiving

feedback about their best attribute versus their worst attribute, F (1, 105) = 18.56, p < .000,

partial η 2 = 0.151. The average rank of best attribute (M=2.39) is significantly higher than the

rank of worst attribute (M=3.50; 1 means participants most want to hear about the area while 5

means they least want to hear about the area).

Hypothesis 3b: This effect will be moderated by whether people are seeking feedback

or disclosing feedback. Specifically, the effect will be stronger in the disclosure condition

versus seeking condition.

However, there is no significant interaction between attribute (best vs. worst) and

conditions (feedback-seeking vs. feedback-disclosure), F (1, 105) = .001, p = .97 Similar to the

first round of data analysis, participants in the feedback-disclosure condition did not show larger

self-enhancement effect compared to those in the feedback-seeking condition. Hypothesis 3 was

partially supported.

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Figure 11. Rank for best attribute and worst attribute in the seeking condition versus disclosure

condition

Table 13. Rank for best attribute and worst attribute in seeking condition versus disclosure condition

95% Confidence

Interval

Seeking(1)or Disclosure(2) Attribute Mean SE Lower Upper

1 best 2.39 0.198 1.99 2.78 worst 3.49 0.198 3.10 3.88 2 best 2.39 0.211 1.97 2.80 worst 3.51 0.211 3.09 3.93

3.4.2.2 Within-attribute questionnaire

Hypothesis 4a: People in the seeking condition will show a self-verification effect,

such that they will seek more unfavorable feedback for their worst attribute versus best

attribute.

To test hypothesis 4a, only data from participants who had clear positive and negative

SAQ attributes and were in the feedback-seeking condition were analyzed, which left N = 57. A

2 (best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback) within-subjects

ANOVA did not show any main effect. However, there was an interaction between attribute and

0  0.5  1  

1.5  2  

2.5  3  

3.5  4  

4.5  5  

Best   Worst  

seeking  

disclosure  

78

the type of feedback, F (1, 56) =15.091, p < .000, partial η 2 = 0.212. Simple effect tests revealed

that people wanted more favorable feedback for their best attribute (M = 1.23) versus worst

attribute (M = .84), t (1, 56) = 3.88, p = .002, but more unfavorable feedback for their worst

attribute (M = 1.16) versus unfavorable feedback for best attribute (M = .77), t (1, 56) = 3.88, p =

.002. Hypothesis 4a was supported.

It also showed that people tended to solicit more favorable (M=1.228) than unfavorable

feedback (M=0.772) about their best attributes, t (1, 56) = 2.825, p = .034. However, the results

did not show a tendency that people tended to solicit more unfavorable feedback (M=1.158)

versus favorable feedback (M= .842) for their worst attribute, t (1, 56) = -1.956, p = .319 > .05.

Hypothesis 5a was supported.

Figure 12. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition (second round)

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

79

Table 14. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition (second round)

95% Confidence Interval

Feedback Attribute Mean SE Lower Upper

favorable best 1.583 0.0931 1.398 1.768 worst 1.396 0.0931 1.211 1.581 unfavorable best 0.417 0.0931 0.232 0.602 worst 0.604 0.0931 0.419 0.789

Hypothesis 4b: People in the disclosure condition will not show a self-verification

effect, such that the difference between the amount of unfavorable feedback that they want

to disclose to the audience for their worst attribute versus the best attribute will be equal to

0.

To test hypothesis 4b, only data from participants assigned to the feedback-disclosure

condition was analyzed. A one-sample t-test found that the difference between the number of

unfavorable feedback between worst and best attribute was not different from 0, t (1, 47) = 1.7, p

= .095, which means that there was no self-verification effect in the disclosure condition.

Hypothesis 4b was supported.

To examine the interaction between attribute and feedback type in the disclosure

condition, a two-way repeated-measure ANOVA found a main effect of feedback type, F (1, 48)

= 42.43, p < .000, partial η 2 = 0.365, indicating that people wanted more favorable feedback (M

= 1.489) than unfavorable feedback (M = 0.511).

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However, the results did not show any significant interaction between feedback and type

of feedback, F (1, 48) = 2.904, p = .095 > .05, which means that there is no self-verification

effect found.

Figure 13. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

disclosure condition (second round with participants with moderate self-views)

Table 15. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

disclosure condition (second round)

95% Confidence Interval

Feedback Attribute Mean SE Lower Upper

favorable best 1.583 0.0931 1.398 1.768 worst 1.396 0.0931 1.211 1.581 unfavorable best 0.417 0.0931 0.232 0.602 worst 0.604 0.0931 0.419 0.789

Research Question1: Will people in the seeking condition show a self-verification

effect as Swann et al. (1989) operationalized, such that they will want more unfavorable

feedback versus favorable feedback for their worst attribute?

0  

0.5  

1  

1.5  

2  

Best   Worst  

favorable  

unfavorable  

81

Results showed that in the seeking condition, people are equally inclined to solicit

unfavorable feedback (M=1.158) and favorable feedback (M= .842) for their worst attributes, t

(1, 56) = -1.956, p = .319 > .05. That is to say, the self-verification effect as operationalized by

Swann et al. (1989) was still not found in the second round of data analysis.

Research Question 2: Will people in the disclosure condition want more favorable

feedback for the worst attribute compared to those in the seeking condition?

A repeated-measure ANOVA was conducted with the number of unfavorable-feedback-

soliciting questions as dependent variable, attribute (best vs. worst) as repeated-measure factor

and condition (seeking vs. disclosure) as between-subject factor. Results showed a main effect of

attribute, F (1, 105) = 16.46, p < .000, partial η 2 = .14. Specifically, they wanted to disclose

more unfavorable feedback for their worst attribute (M = .91) versus the best attribute (M = .61).

However, there is no interaction effect between attribute (best vs. worst) and condition

(seeking vs. disclosure), F (1, 105) = 1.15, p = .29. That is to say, people’s preference for

unfavorable feedback for worst attribute versus best attribute was not moderated by condition

(seeking vs. disclosure).

In summary, similar to the first round of data analysis, the second round found that

people showed a self-enhancement effect in both seeking and disclosure condition. However,

publicness (seeking vs. condition) was not found to moderate the self-enhancement effect.

However, different from the first round of data analysis, in the second round, participants in the

feedback-seeking condition showed a self-verification effect, while those in the disclosure

condition did not display any self-verification tendency. This indicates that publicness did

moderate self-verification for people with a positively self-viewing and a negatively self-viewing

self-attribute.

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3.5 DISCUSSION

Study 2 examined the effect of perceived publicness on self-enhancement and self-

verification effect. Specifically, Study 2 looks at whether self-enhancement would be

strengthened while self-verification would be weakened when people are self-disclosing to

others, compared to a more private condition when they are seeking feedback about themselves.

The results show that people display a self-enhancement effect both in seeking condition

and disclosure condition. Hypothesis 3 was only partially supported because there was not a

significant difference between the seeking condition and disclosure condition despite the fact that

both conditions showed a strong self-enhancement effect. A possible reason is that the self-

enhancement effect is so strong in both conditions that there is no significant difference between

them. The effect size for the self-enhancement effect in the feedback-seeking condition is partial

η 2 = 0.165; in the feedback-disclosure condition the effect size is partial η 2 = 0.184, both of

which indicate a moderate to strong effect size.

Another possible reason involves ceiling effects. Since self-boasting might cause social

antipathy (Leary & Schlenker, 1992), even if people are driven by a strong self-enhancement

motive, they may not present a corresponding self-enhancing behavior in public. Hence, it would

be hard to find the difference in self-enhancement between the seeking condition and disclosure

condition by merely observing people’s behavior.

Moreover, in the second round of data analysis, participants in both conditions showed a

strong preference for favorable feedback for their best attribute. As for their worst attribute,

participants in the feedback-disclosure condition showed that they wanted more favorable

feedback than unfavorable feedback. However, participants in the seeking group did not show

any preference for either favorable or unfavorable feedback. Even though Swann et al. (1989)

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did not conceptualize the preference for favorable feedback for worst attribute as a measure for

self-enhancement effect, this preference actually can be regarded as evidence for self-

enhancement effect if considering the definition of self-enhancement - a striving to increase the

positivity and reduce the negativity of one’s self-views (Sedikides, 1993; Leary, 2007). Based on

this definition, if a person wants to disclose to an audience more favorable self-relevant feedback

versus unfavorable self-relevant feedback, the person is trying to increase the positivity and

reduce the negativity of his public image. Therefore, the preference for favorable feedback over

unfavorable feedback for worst attribute in the feedback-disclosure condition can be regarded as

evidence that people tend to self-enhance when self-disclosing to an audience.

It is noteworthy that in the first round of data analysis, people in both seeking and

disclosure conditions showed an equal tendency to solicit favorable and unfavorable feedback for

worst attribute. After participants were excluded who had moderate self-views on the SAQ

attributes (which were rated 4-6 in SAQ questionnaire), the results showed that participants in

the disclosure condition wanted to disclose significantly more favorable feedback (M= 1.396)

than unfavorable feedback (M=. 604, p= .001). This finding might be explained by the fact that

people with moderate self-views on the SAQ attributes (which were rated 4-6 in SAQ

questionnaire) do not care which feedback is disclosed to others because these self-attributes are

equally mediocre in their minds. In contrast, for those who do have a very good and a very bad

attribute, disclosure of feedback in these attributes, especially the bad attributes, may exert big

influence on their public images.

The two rounds of data analysis found inconsistent results for self-verification effect. In

the first round, self-verification effect was found in both seeking and disclosure conditions.

However, in the second round, self-verification was only found in the seeking condition – there

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was no self-verification found in the disclosure condition. The only difference between the two

rounds of data analysis is the fact that participants with moderate self-views (those who do not

have a best attribute that scored 6 or higher and a worst attribute that scored 4 or lower) were

excluded in the second round. Therefore, a possible explanation for the inconsistent results is that

people with extremely negative attributes would not self-verify with these attributes because

disclosure of feedback about these attributes would harm their public image. However, for those

with moderate self-views, disclosure about the mediocre attributes to the public would not exert

too much influence on their public image. Therefore, they would show larger self-verification

effect in the disclosure condition compared to those with extreme self-views.

However, Study 2 did not find any within-attribute difference for self-verification effect.

In both rounds of data analysis, participants were equally inclined for favorable and unfavorable

feedback for their worst attribute in both conditions. This means that similar to Study 1, Study 2

still failed to replicate the Study 1 findings of Swann et al. (1989). The possible reasons have

already been discussed in Study 1.

Regardless, there are three contributions of Study 2. Firstly, Study 2 replicated Study 1 in

terms of the “within-person” operationalization of self-verification effect. Secondly, Study 2

found that publicness could moderate self-verification effect – that is, publicness could weaken

self-verification effect.

Most importantly, Study 2 found that people are driven by a strong self-enhancement

effect when self-disclosing to an audience. Some of previous studies that claimed that using

social media could boost people’s self-esteem (such as Kramer & Winter, 2008; Wilcox &

Stephen, 2012) based their claim on the premise that people’s online self-presentation behaviors

are driven by self-enhancement. However, this premise has never been empirically found. For

85

example, Wilcox and Stephen (2012) found that merely browsing one’s own social media profile

page could temporarily increase his self-esteem because people usually present an ideal self-

image on social media. Therefore, merely looking at the ideal self-image presented on social

media would make people feel positive about themselves, thus having a higher level of self-

esteem. However, whether people really tend to present a more ideal self on social media than

the real-life situation awaits confirmation. The present study provides a theoretical base for such

studies by finding that people’s online self-disclosure behaviors are mainly driven by self-

enhancement motive, thus making people more likely to present ideal than actual self-image on

social media.

Together, the findings of Study 2 suggest that people tend to self-enhance but not self-

verify when self-disclosing on social media. Based on this knowledge of self-discloser’s

psychological motives, Study 3 looks at how self-enhancement influences specific self-

disclosure behaviors on social media. Specifically, Study 3 investigates how people differently

self-disclose to close audiences and distant audiences on social media under the impact of self-

enhancement and self-verification effect.

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CHAPTER 4: STUDY 3

Study 2 found that self-enhancement is the dominant psychological motive when people

self-disclose in public. Based on findings of Study 2, Study 3 looks at the potential moderating

effect of tie-strength on self-enhancement and self-verification in the context of social media.

Despite the fact that at most times, people share information with the whole audience on social

media, some social media enables users to set the posts only visible to certain groups or friend

circles (e.g. Facebook users can make a post visible to the public, only friends, or specific

friends). Even when people post messages visible to all social media friends, they may be

thinking of only a subset of that audience. A factor to which this type of functions is related is tie

strength, as social media users may group social media friends based on how close they are to

various audience on social media.

Past research has found that strong tie strength between the self-presenter and audience

would result in less self-enhancing behavior of the self-presenter in public. For example, the self-

presenters may describe themselves less favorably with the presence of acquaintances than

strangers (Tice et al., 1995), and describe themselves with modesty and self-deprecation if the

audience already has favorable background information about them (Baumeister and Jones,

1978). As discussed in the previous sections, such modesty is a self-presentation strategy rather

than a psychological motive. The seemingly modest, or even self-deprecating behaviors, as a

means to avoid an unfavorable impression in audience’s mind, might be actually driven by a self-

enhancement motive (e.g. Brown & Gallagher, 1992). Study 1 and Study 2 have already

examined the impact of publicness on self-enhancement and self-verification as psychological

motives, and found that self-enhancement would be strengthened while self-verification would

87

be weakened by publicness. On the basis of Study 1 and Study 2, Study 3 looks at specific self-

enhancing behavior when people self-disclose to the public on social media. Thus, the present

study is not confined in the domain of psychological motives, but rather extending to the area of

self-enhancing behaviors in the context of social media and the nature of the audience.

4.1 LITERATURE REVIEW

This section will firstly introduce the definition and manipulation of tie strength.

Afterwards, the literature will be discussed in the relationship between tie strength and self-

enhancing behavior.

4.1.1 Definition and manipulation of tie strength

Granovetter (1973) defined tie strength as “a combination of the amount of time, the

emotional intensity, the intimacy (mutual confiding), and the reciprocal services which

characterize the tie”. Interestingly, even if Granovetter (1973) did not give a precise definition of

tie strength, the subsequent research in this field failed to come up with a better one. Even

though some researchers tried to come up with a definition of tie strength from different

perspectives (e.g. Petroczi, Nepusz and Bazso defined tie strength as “a quantifiable property that

characterizes the link between two nodes”, 2007), most of the definitions are only descriptions of

certain characteristics of tie strength, rather than a definition of tie strength. They only expanded

the list of indicators of tie strength based on Granovetter (1973). For example, Nan Lin et al.

(1981) found that demographic factors such as social distance, education level and race and

gender may predict tie strength; Wellman and Wortley (1990) showed that emotional support

indicates a strong tie. However, there is not a conceptual definition so far that reveals the nature

of tie strength.

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For the reason that the classic definition of tie strength is a loose collection of some

indicators of tie strength, many of the measures of tie strength are built on a single one of or a

combination of these indicators. For example, Lin et al. (1978) used frequency of contact to

measure “the amount of time” dimension of tie strength. They assumed that strong tie would be

related to high level of contact frequency; Friedkin (1980) measured the “mutual confiding”

dimension of tie strength with mutual acknowledgement of contact. It was concluded in his

research that a strong tie is indicated by mutual acknowledgment from both sides. With the

prevalence of social media, in more recent research, scholars in social media analytics proposed

more quantitative measures to calculate tie strength with social media data. Thanks to the ability

to quickly extract massive social media data with advanced computational techniques, such

research was able to measure tie strength with a combination of multiple indicators of tie

strength. For example, Gilbert and Karahalios (2009) built a model to predict friendship strength,

based on a dataset of over 2,000 social media ties. In their research, they identified 74 Facebook

variables as potential predictors of tie strength. The 74 Facebook variables actually came from 7

major dimensions of tie strength proposed by the theories of tie strength.

Among all the indicators and predictors of tie strength mentioned above, “closeness”, as a

measure of intensity dimension of a relationship, has been the most common tactic to measure tie

strength in literature (Marsden & Campbell, 1984; Murray et al., 1981). A possible reason is that

“closeness” corresponds to Granovetter’s differentiation between strong versus weak ties. Even

though Granovetter left the precise definition of tie strength to future research, he characterized

tie strength as strong versus weak ties. Strong ties are the “close friends” (as Granovetter

defined) whose social circle overlap with you (Gilbert & Karahalios, 2009), and with whom you

share personal connection (Ryu & Feick, 2007). On the contrary, weak ties are merely

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acquaintances with whom you have distant relationships (Ryu & Feick, 2007; Wilcox &

Stephen, 2012). Strong ties are closer social relations than weak ties (Wilcox & Stephen, 2012).

Moreover, Marsden and Campbell (1984) found that closeness was the best indicator of tie

strength among other indicators, such as frequency of contact and breadth of topics discussed

with the friends, because closeness was the only indicator of tie strength uncorrelated with other

indicators. In line with Granovetter’s theory, in the subsequent research in this field, “strong tie”

was usually operationalized as “close friends”, while “weak tie” was operationalized as

“acquaintances or friends of friends” (e.g. Marsden & Campbell, 1984; Wilcox & Stephen,

2012).

However, a problem with such measures is that they used “closeness” to measure tie

strength, but they left the concept of “closeness” vague. For example, Murray et al. (1981) asked

participants whether a friend “knew your work well” and whether he “knew you well personally”

to measure closeness between two people; Marsden and Campbell (1984) measured closeness by

asking participants to name some friends and then indicate whether each friend named was an

acquaintance, or a good friend, or a very close friend; Wilcox and Stephen (2012) directly asked

participants to name five close friends and five distant friends, and then asked participants how

much each friend’s opinion matters to them. Despite the fact that different people have different

standards in measuring relationship closeness, a more precise standard should be added in such

measures to make sure that the participants reach the same standard in regarding a friend as

“close friend”.

There are existent scales to measure relationship closeness, such as the Relationship

Closeness Inventory (Berscheid, Snyder & Omoto, 1989) and the Unidimensional Relationship

Closeness Scale (Dibble, Levine & Park, 2012). However, the present study will not try to

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improve measures for tie strength because the present study only has to manipulate, rather than

measure tie strength. Instead, the present study will utilize measures for relationship closeness in

the manipulation task to instruct participants to identify close versus distant friends. Details will

be discussed in the measure and manipulation section.

To manipulate the tie strength between participants and their friends, the present study

will use a priming task to make participants focus on either close friends or distant friends in

their minds before they decide which type of feedback they want to disclose on social media.

Moreover, questions measuring relationship closeness will be asked as a manipulation check at

the end of the study. Further details about tie-strength manipulations will be discussed in the

measure and manipulation section.

4.1.2 Tie strength and self-motives

Past research found that strong tie strength would lead to less self-boasting and more self-

verifying behavior in public situation (e.g. Tice et al., 1995; Schlenker & Leary, 1982). The first

reason is, compared to distant friends, close friends could be more aware of one’s prior

performance, which would make self-boasting unnecessary and annoying (Schlenker & Leary,

1982).

Tice et al. (1995) pointed out that people tend to behave with more modesty with friends

than strangers, because friends already know one’s prior performance. It is not necessary to

repeat the good qualities to the friends. Moreover, reiterating one’s positive characteristics too

often would be regarded as being arrogant, conceited, self-centered and boastful, which may

annoy the audience (Schlenker & Leary, 1982). In addition, since friends already know one’s

past failure and success, it is easy for them to realize the overly positive claims about one’s self

(Tice et al., 1995). On the contrary, when facing strangers, it is appropriate to emphasize one’s

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positive attributes because otherwise the strangers would have no way knowing one’s merits and

achievements, thus not having any positive impression about the person. Furthermore, the risk is

low of having positive claims about the self, because the strangers do not have any background

information about the person. Consistent with the theory, Tice et al. (1995) found that people

would describe themselves more favorably in an interview with the presence of a stranger than a

friend. Similar to the friend-versus-stranger situation, people may present a more self-enhancing

image to distant friends than to close friends, because distant friends have less prior knowledge

about the person. Therefore, from the perspective of self-enhancement, in order to avoid

antipathy from close friends and to gain admiration from distant friends, it is reasonable to

hypothesize that people would display less self-enhancing behavior for close friends, while

displaying more self-enhancing behavior for distant friends.

Hypothesis 5a: People will display a self-enhancement effect such that the best

attribute will receive higher rank in interest in receiving feedback versus the worst

attribute.

Hypothesis 5b: This effect will be moderated by whether they self-disclose to close

friends or distant friends.

From the perspective of self-verification, past research found that people strive to self-

verify with significant others because the costs of not being verified by the closest people in

one’s life is high – if even one’s significant others do not know or understand the person, who

else would? Thus, when a significant-other representation is activated, people usually think and

behave in the way that they typically do with their significant others (Chen, Boucher & Tapias,

2006). Research in self-verification found that spouses or couples in a long-term romantic

relationship feel most intimate when they are verified by each other, because receiving verifying

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feedback, even if negative feedback, affirms that their partners really know and understand them

(Swann et al., 1994; Campbell, Lackenbauer & Muise, 2006). Krause and Chen (2009) asked

participants to rate some self-attributes, and rate how they would like others to perceive

themselves on the attributes. They found that for those participants primed with a significant

other, the difference between the self-rating and the desire-rating almost diminished, while the

no-priming condition still saw the difference between the self-rating and the desire-rating. The

results indicated a strong self-verification effect. In summary, literature in self-enhancement and

self-verification both supports the notion that strong tie strength would cause more self-verifying

behavior while less self-enhancing behavior.

Hypothesis 6a: People will display a self-verification effect, such that they will want

to disclose more unfavorable feedback for their worst attribute versus best attribute.

Hypothesis 6b: This effect will be moderated by whether they self-disclose to close

friends or distant friends. Specifically, the effect will be stronger in the “close-friend”

condition versus “distant-friend” condition.

4.1.3 Measures and manipulation

4.1.3.1 Measures for main test

There are two differences between Study 2 and Study 3 in terms of experiment design: 1)

there is no feedback-seeking condition in Study 3 – all participants will be told that they have the

chance to disclose self-relevant feedback on social media; 2) in Study 3, participants will be

primed with either close friends or distant friends before they decide which feedback to disclose

on social media. The differences would not interfere with the measures for self-enhancement and

self-verification. Therefore, measures for self-enhancement and self-verification effect will be

the same as the ones used in Study 2. That is, self-enhancement effect will be measured with the

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adapted between-attributes questionnaire (see APPENDIX C), in which participants will be

asked to rank the personal attributes based on the extent to which they would like to disclose

feedback about each attribute to their social media friends. Higher rank in the interest in

receiving feedback about the best attribute versus the worst attribute would indicate self-

enhancement effect. On the other hand, self-verification will be operationalized as a tendency to

disclose more unfavorable feedback for one’s worst attribute versus best attribute. It will be

measured with the adapted within-attributes questionnaire (see Appendix B). The rationale for

using the measures has already been demonstrated in Study 2 – the measures involve less

confounds (such as unnecessary interaction with evaluators) compared to other measures.

In terms of manipulation of tie strength, as mentioned in the literature review section,

closeness will be used to manipulate tie strength. Specifically, a priming task will be employed

to make participants focus on either their close friends or distant friends when they are

considering disclosing feedback to their social media friends.

In literature, there are two ways to prime participants: conceptual priming and mind-set

priming. Conceptual priming involves the activation of specific representation in people’s

memory (Higgins, 1996). Once a concept is activated, the related concepts will be triggered in

the subsequent information processing (Neely, 1977). For example, in order to prime participants

with either a high or low sense of power, Galinsky and Magee (2003) had participants write an

essay about a situation in which either the participants had control over others, or others had

control over the participants. They then had two coders rate the level of power reported by

participants in the essay as a manipulation check. Another way to prime participants is called

mind-set priming, which activates procedural knowledge. Different from conceptual priming that

activates memory about a specific concept, mind-set priming involves priming participants with

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a way of thinking (Galinsky & Magee, 2003). Since the present study only needs to prime

participants with the concept of close or distant friends, only conceptual priming will be used.

There are a couple of ways to conceptually prime participants with tie strength in

literature. One way is to ask participants to write an essay about a close friend or a distant friend

or an inanimate object (such as a tree), in order to activate the concept of “close or distant friend”

in participants’ memory (Kraus & Chen, 2009). Such priming tasks usually only ask participants

to describe one specific friend. However, since the context of the present study is to ask

participants to disclose feedback to a specific group of their social media audience, only asking

them to describe one specific friend may not be the best option. Another way to prime

participants is a name-listing task (Wilcox & Stephen, 2012). In Wilcox and Stephen study

(2012), participants were asked to name either five close friends or five distant friends, and

indicate how important each friend’s opinion is to them. At the end of the study, they were asked

about questions such as “during the task, I thought about my close friends” as a manipulation

check. Admittedly, it is difficult to say how many friends should be named in order to invoke the

concept of “a group of people”. However, “five friends” could be at least a better option than

“one friend” in terms of balancing time limit and the sense of “a group of people”. Hence, the

present study will employ a name-listing task similar to what Wilcox and Stephen (2012) did in

their study (See Appendix H).

While the task in Wilcox and Stephen (2012) appears to be the most appropriate, there

are several problems with their original priming task. First, friendship quality may be

confounded with friendship closeness. That is, a close friend might be a friend with whom you

have a good relationship, while a distant friend might be a friend with whom you have a so-so or

even bad relationship. Therefore, in order to rule out this confound, participants will be asked to

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name either five close friends who they have a good relationship with, or five distant friends who

they also have a good relationship with.

The second problem involves task difficulty. Wilcox and Stephen (2012) asked

participants to name either five close Facebook friends or five distant Facebook friends.

Compared to close friends, it might be more difficult to name five distant Facebook friends

because people less frequently think of distant friends, which could lead to other effects in terms

of searching memory, association, etc. In order to reduce task difficulty for the “distant-friend”

condition, the present study will ask participants to name three instead of five Facebook friends

in both conditions. Participants will be asked to rate task difficulty at the end of the task as a

manipulation check.

The third problem is that they did not provide a standard for participants about what kind

of friends should be considered as “close friends” or “distant friends”. As mentioned in the

literature review section, measures for closeness will be used here for participants to identify

close versus distant friends. Specifically, wordings adapted from the Relationship Closeness

Inventory (Berscheid, Snyder & Omoto, 1989) will be used in the instruction of the manipulation

task. Berscheid, Snyder and Omoto (1989) asked participants to identify a person with whom

they had the “closet, deepest, most involved, and most intimate relationship”. Since the context

of the present study is social media rather than interpersonal communication, merely priming

participants with close friends in the real life is not enough – people may not interact with them

on social media. Therefore, in the present study, participants will be told to name three friends

who not only have the good relationship that bears high (or low) level of closeness, depth,

involvement, and intimacy, but also follow and interact with them on social media. In order to

make sure the manipulation works, manipulation-check questions will be asked at the end of the

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study, such as “during the task, I thought about my close friends”; “during the task, I thought

about friends whose opinions matter”, etc. (see Appendix H).

In summary, the manipulation of tie strength in the present study is as follows: in the first

step, for participants in “strong-tie” condition, they will be asked to name three close friends who

they share good relationship that bears high level of closeness, depth, involvement, and intimacy,

and also follow and interact with the participants on social media. For those in “weak-tie”

condition, participants will be asked to name three distant friends who they share good but low

level of closeness, depth, involvement and intimacy relationship, and also follow and interact

with them on social media. Then they’ll be asked to indicate how much each friend’s opinion

matters to them. After participants finish the whole procedure, as a manipulation check, they will

be asked to indicate the extent to which they agree with the statements, such as “during the task,

I thought about my close friends”; “during the task, I thought about friends whose opinions

matter”, etc.

4.1.3.2 Pretest measures

The Relationship Closeness Inventory (RCI; Berscheid Snyder & Omoto, 1989) is a

widely-used measure for relationship closeness. Based on the conceptualization of closeness as

interdependence between people by Kelley et al. (1983). It was designed to measure relationship

closeness from three perspectives: frequency, diversity and strength. However, the RCI has some

limitations that make it unsuitable for the present study.

First of all, the RCI was developed in 1989 when many of the Internet applications (such

as social media, smart phones) had yet to be invented. If used nowadays, the RCI would ignore a

great number of virtual interpersonal interactions. For example, to measure diversity, Berscheid

Snyder and Omoto (1989) gave participants a list of activities such as “went to a bar”, “ate a

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meal” and “planned a social event”. They then asked participants to check what they did with

their friends in the past week. However, for modern people, playing video games, interacting on

social media, chatting on the phone are also activities that they usually do with friends. Since

people nowadays have a larger variety of activities to do with friends compared to 1989, the list

of activities cannot really represent the “diversity” dimension of a relationship. Moreover, since

the present study mainly focuses on the importance of the audience’s opinion to the self-

presenter, the diversity dimension of a relationship is not quite relevant to the present study.

Besides, the RCI items mainly focus on the behavioral indicators of closeness, while

ignoring affective and emotional indicators. For example, to measure the strength dimension,

participants will be presented with diverse life domains, and will be asked to rate on a 7-point

likert scale about the extent to which their friends influence this life domain. The items range

from mundane life events (e.g. what one watches on TV) to important life decisions (e.g. career

plans). To measure frequency, the inventory asks participants how much time they spend with

their friends in the past week in the morning, afternoon and evening. However, compared to the

behavioral aspects of a relationship, affective and emotional impacts of the audience might be

even more relevant to the context of social media because the interaction on social media could

happen without any behavioral interaction in the real life. For example, a person may have a

close friend who lives in a different city. Even though they do not spend much time together,

they may still be emotionally bonded and keep eyes on each other’s life on social media. Hence,

what the present study needs is a measure that assesses the overall psychological impact that a

friend has on the participants.

Based on the RCI, Dibble, Levine and Park (2012) developed the Unidimensional

Relationship Closeness Scale (URCS; see Appendix A0), which remained consistent with

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Berscheid et al.’s (1989) conceptualization of closeness, but overcome some limitations of RCI.

The URCS consists of 12 items built on the three dimensions: frequency (e.g. When I have free

time I choose to spend it with my ___”), diversity (e.g. “my ___ and I spend a lot of time

together”) and strength (e.g. “my ___ is a priority in my life”). The scale measures both aspects

of “behaving close” (e.g. “my ___ and I disclose important personal things to each other”) and

“feeling close” (e.g. I think about my ___ a lot”). The scale asks respondents to rate the extent to

which they agree with the 12 items on a 7-point Likert scale, and then average the score into a

single overall closeness score. Since the URCS is unidimensional and short, includes both

behavior and affective indicators of closeness, while being built on the same conceptualization of

closeness as RCI, the present study employs the URCS instead of the RCI as the measure for

closeness.

Even though the URCS includes items about the impact of the named friends on the

respondents (e.g. “I consider my ___ when making important decisions”), there is not any item

that directly asks about the importance of the friends’ opinion on the respondents. Therefore, in

the pretest, besides the items in URCS, participants will also be asked to indicate the importance

of the named friends’ opinion to them.

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Table 16. Summary of operationalization and measures for Study 3   Operationalization   Measures   Data  

analyses  Self-­‐enhancement  

Whether  people  would  prefer  to  seek  (or  disclose)  feedback  about  their  best  attribute  versus  worst  attribute  

Between-­‐attribute  questionnaire:  rank  each  of  the  five  attributes  based  on  the  extent  to  which  participants  want  to  hear  about  the  attribute.  Self-­‐enhancement  will  be  indicated  if  best  attribute  versus  worst  attribute  receives  higher  rank    

Two  rounds  of  data  analysis  for  both  self-­‐enhancement  and  self-­‐verification.  1st  round:  full  data  set  2nd  round:  only  participants  were  analyzed  who  had  a  best  (score  6  or  higher)  and  a  worst  (score  4  or  lower)  attribute    

Self-­‐verification  

Swann  et  al.  (1989)’s  (within-­‐attribute  operationalization)  

The  present  study  (within-­‐person  operationalization)  

Within-­‐attribute  questionnaire:  for  each  attribute,  choose  two  questions  out  of  a  list  of  six  questions  which  consists  of  three  favorable-­‐feedback-­‐soliciting  questions  and  three  unfavorable-­‐feedback-­‐soliciting  questions.  Self-­‐verification  will  be  indicated  if  people  choose  more  unfavorable-­‐feedback-­‐soliciting  questions  for  worst  attribute  versus  best  attribute  

Whether  people  would  seek  (or  disclose)  more  unfavorable  versus  favorable  feedback  for  their  worst  attribute  

Whether  people  would  seek  (or  disclose)  more  unfavorable  feedback  for  their  worst  attribute  versus  best  attribute  

Note:  if  a  participant  has  two  “best  attribute”  (e.g.  two  attributes  score  10),  then  the  number  of  favorable/unfavorable  feedback-­‐soliciting  questions  will  be  averaged  (as  dependent  variable)  

4.2 HYPOTHESES

4.2.1 The between-attribute questionnaire

Hypothesis 5a: People will display a self-enhancement effect, such that the best

attribute will receive a higher rank in interest in receiving feedback versus the worst

attribute.

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Hypothesis 5b: This effect will be moderated by whether people self-disclose to close

friends or distant friends. Specifically, the effect will be stronger in the “distant-friend”

condition versus “close-friend” condition.

4.2.2 The within-attribute questionnaire

Hypothesis 6a: People will display a self-verification effect, such that they will want

to disclose more unfavorable feedback for their worst attribute versus best attribute.

Hypothesis 6b: This effect will be moderated by whether they are self-disclosing to

close friends or distant friends, such that the discrepancy between worst and best attribute

in receiving unfavorable feedback will be larger in the “close friend” condition versus

“distant friend” condition.

Research Question 3: Will people show a self-verification effect as Swann et al.

(1989) operationalized, such that they will want to disclose more unfavorable feedback

versus favorable feedback for the worst attribute?

4.3 PARTICIPANTS

In order to overcome the problem of small sample size in the previous studies, 277

participants were recruited from Amazon Mturk. The study was set as invisible to workers who

participated in Study 1 and Study 2, so they were not able to take Study 3.

Among the 277 participants, one participant’s data was removed because he indicated

that he did not use Facebook. Three people were removed because they wrote “N/A” in the

name-listing task. A participant was removed because he randomly typed some words in the

name-listing task. Additionally, 28 participants’ data was removed because they assigned equal

(or almost equal) number for all of the five SAQ attributes. It is possible that they did not pay

attention to the instruction during the task and just selected the same number for all items. The

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number of participants eliminated in this study is much more than the number in Study 1 (N = 7

out of 159) and Study 2 (N = 12 out of 220). A possible reason is that there are more tasks (e.g.

the name-listing task) in Study 3 compared to the previous two studies, which made participants

less patient. Another possible reason involves the privacy issue of Facebook. The instruction of

the task told participants that they would have the option to disclose the self-relevant feedback

specifically on Facebook. However, some of participants may have had antipathy to Facebook

and such quizzes related to personal attributes due to the Facebook data privacy scandal exposed

in 2018. Therefore, they may not have been willing to disclose their real thoughts about

themselves, thus giving random answers to the questions.

Among the 244 participants, 152 (62.3%) were male, 91 (37.3%) were female and 1

(0.4%)was transgender; 184 (75.4%) were white, 34 (13.9%) were African American, 17 (6.9%)

were Asian, 1 (0.1%) were American Indian or Pacific Islander, and 8 (3.3%) were others. 22

(9.0%) participants’ age lied in the range from 18-24; 114 (46.7%) participants aged 25-34; 62

(25.4%) participants were 35-44; 31 (12.7%) were 45-54; 10 (4.1%) were 55-64, 3 (1.2%) were

65-74; 2 (0.8%) were 75-84. 26 (10.6%) participants’ highest education level was high school;

40 (16.4%) of them attended college but did not earn a degree; 110 (45.1%) participants earned a

bachelor degree in college; 25 (10.2%) of them had an associate degree in college; 40 (16.4%)

had a master’s degree and 2 (0.8%) had a doctoral degree (see appendix for the table).

4.4 PROCEDURE

4.4.1 Pretest

A pretest was conducted to ensure that the manipulation of tie strength was strong

enough. 40 Amazon Mturk workers participated in the pretest. In the pretest, participants were

firstly instructed to complete the manipulation task - for participants in the “close” condition,

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they were asked to name a close friend with whom they shared a good relationship that bears a

high level of closeness, depth, involvement, and intimacy, and also followed and interacted on

social media. For those in the “distant” condition, participants were asked to name a distant

friend with whom they shared a good relationship that bears a low level of closeness, depth,

involvement and intimacy, but followed and sometimes interacted with them on social media.

After they completed the task, they were asked to indicate the importance of the name’s friend’s

opinion to them. Then they were asked to finish the Unidimensional Relationship Closeness

Scale (Dibble et al., 2012; see Appendix J) to check the closeness between them and their

friends. At the end, they were asked about their social media use behavior, such as “what social

media did you use most frequently in the past month?”, “what percent of your social media

friends could be considered as your close friends?”. After they finished answering the questions,

they received $0.2 via Amazon Mturk.

4.4.2 Main test

Identical to study 1 and Study 2, at the beginning, participants were told that the aim of

the study was to collect data of people’s personality traits. Then they were asked to finish the

Self-Attributes Questionnaire and the “personality test”. The questions in the Self-Attribute

Questionnaire and the “personality test” were exactly the same to the ones used in Study 1 and

Study 2.

Afterwards, participants were asked to finish the name-listing task as a manipulation of

closeness. In the “close” condition, participants were asked to name three Facebook friends with

whom they had a good relationship that bears a high level of closeness, depth, involvement and

intimacy, and follow and interact on Facebook. They then were asked to indicate how much each

friend’s opinion mattered to them on a 7-point Likert scale. In the “distant” condition,

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participants listed names of three Facebook friends with whom they had a good relationship that

bears a low level of closeness, depth, involvement and intimacy, but follow and interact with

them on Facebook. They then indicated the importance of each friend’s opinion to them. After

finishing the name-listing task, all the participants were told that they would receive analysis for

each personality attribute and that they would have a chance to disclose the results on Facebook.

They then ranked the five attributes based on the extent to which they wanted to share the

analysis of the attributes to the three friends on Facebook (the adapted between-attribute

feedback-seeking questionnaire, see APPENDIX C). As participants in Study 2 did, they also

chose questions from the adapted feedback-seeking questionnaire (see Appendix B) as an

indicator of whether they would like to disclose favorable or unfavorable feedback on Facebook.

Finally, participants completed a series of questions as a manipulation check for tie strength (e.g.

“during the task, I thought about my close friends”, “I thought about friends whose opinions

matter,” “I thought about friends who are influential to me”; see Appendix H). Finally,

participants were debriefed and received $1 from the researcher via Amazon Mturk.

Unlike Study 2 in which participants were told that they had the chance to disclose

feedback to any social media, participants in Study 3 were told that they had the chance to

disclose feedback specifically on Facebook. In the manipulation task, they were also told to

name a Facebook friend. The reason underlying the difference is simple: since people may post

different content on different social media according to the social media’s features (e.g.

Instagram features pictures; Snapchat focuses on videos, etc.), restricting the social media to a

specific one would reduce possible confusions – e.g. if a participant thinks of Snapchat when

told that he has chance to post the textual personality results to social media, the context would

become very artificial and confusing. Moreover, compared to other social media whose users

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(audience) are usually a specific group of people (e.g. users of Snapchat are usually young

people), Facebook has wider range of users, thus leaving larger room for participants to indicate

friends close to them both on social media and in the real life. Moreover, in the pretest,

participants were asked about what social media they used most frequently in the past month.

Results showed that 90% of participants included Facebook in their answer, followed by

Instagram (30%), indicating that Facebook is the most widely-used social media among the

participants.

4.5 RESULTS

4.5.1 Manipulation check

The manipulation check questions asked about whether participants thought of their close

friends, whether they thought of friends whose opinion matters, and whether they thought of

friends who are influential to them. A one-way ANOVA examined how people focused on their

close friends during the task with condition (close vs. distant friend) as an independent variable.

Results showed that, compared to those in the “distant-friend” condition (M = 4.39), participants

in the “close-friend” condition (M = 5.61) were more focused on close friends during the task, F

(1, 243) = 38.3, p < .000. Compared to the “distant-friend” condition (M = 4.67), those in the

“close-friend” condition (M = 5.67) also thought more about Facebook friends whose opinion

matters to them, F (1, 243) = 24.8, p < .000. Participants in the “close-friend” condition also

thought about more people who are influential to them, F (1, 243) = 27.3, p < .000. There was no

difference between the two conditions in terms of the perceived difficulty of the name-listing

task, F (1, 243) = 7.72, p = .978.

4.5.2 Main test

To test hypothesis 5, data of the between-attribute questionnaire was run as a repeated

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measure ANOVA with attribute (best vs. worst) as a repeated-measure factor, condition (close

vs. distant friend) as a between-subjects independent variable and the rank of each of the five

SAQ attributes as dependent variables. Hypothesis 6 and the Research Question 3 were tested

with data from the within-attribute questionnaire. Data was run with a three-way repeated-

measure ANOVA with attribute (best vs. worst) and feedback (favorable vs. unfavorable) as

within-subjects independent variables, as well as condition (close vs. distant friend) as a

between-subjects independent variable. The number of favorable-feedback-soliciting questions

and the number of unfavorable-feedback-soliciting questions were taken as dependent variable.

Post-hoc tests were performed to examine how people treated their best attribute versus the worst

attribute. Similar to Study 1 and 2, a second round of data analysis was conducted in which only

participants who had extreme self-views were analyzed in order for a more salient self-

verification effect.

4.5.2.1 Data analysis round 1 (full dataset)

4.5.2.1.1 Between-attribute questionnaire

Hypothesis 5a: People will display a self-enhancement effect, such that the best

attribute will receive higher rank in interest in receiving feedback versus the worst

attribute.

A repeated-measure ANOVA showed that people are more interested in receiving

feedback about their best attribute versus their worst attribute, F (1, 243) = 29.05, p < .000,

partial η 2 = 0.107. The average rank of best attribute (M = 2.5) is significantly higher than the

rank of worst attribute (M = 3.38; 1 means participants most want to hear about the area while 5

means they least want to hear about the area). Hypothesis 5a was supported.

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Hypothesis 5b: This effect will be moderated by whether people are self-disclosing to

their close friends or distant friends. Specifically, the effect will be stronger in the “distant-

friend” condition versus “close-friend” condition.

There is no significant interaction between attribute (best vs. worst) and condition (close

vs. distant friend), F (1, 243) = .92, p = .34 > .05. That is to say, participants in the “distant-

friend” condition (M = 2.99) did not show a larger self-enhancement effect compared to those in

the “close-friend” condition (M = 2.9). Therefore, hypothesis 5b was not supported.

Figure 14. Rank for best attribute and worst attribute in the seeking condition versus disclosure

condition (first round)

Table 17. Rank for best attribute and worst attribute in seeking condition versus disclosure

condition (first round)

95% Confidence Interval

close(1) or distant(2) attribute Mean SE Lower Upper

1 best 2.48 0.135 2.22 2.75 worst 3.32 0.135 3.05 3.58 2 best 2.53 0.131 2.27 2.78 worst 3.45 0.131 3.20 3.71

4.5.2.1.2 Within-attribute questionnaire

Data was run with a 2 (best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable

0  0.5  1  

1.5  2  

2.5  3  

3.5  4  

4.5  5  

Best   Worst  

close  

distant  

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feedback) within-subjects ANOVA with condition (close vs. distant friend) as a between-

subjects independent variable. Results revealed a main effect and two interaction effects. Firstly,

there is a main effect of feedback type, F (1, 243) = 118.6, p < .000, partial η 2 = 0.329,

indicating that people generally wanted to disclose more favorable feedback (M=1.36) than

unfavorable feedback (M=0.64).

Moreover, there is an interaction between condition (close vs. distant) and feedback type

(favorable vs. unfavorable), F (1, 243) = 4.43, p = .036, partial η 2 = 0.018. Specifically, there is

a greater discrepancy between the number of favorable and unfavorable feedback-soliciting

questions for the “distant-friend” condition, compared to the “close-friend” condition, which

means that the effect of feedback type was stronger in the “distant-friend” condition. Simple-

effect tests showed that people wanted to disclose more favorable feedback (M = 1.29) versus

unfavorable feedback (M = .71) in the “close-friend” condition, t (1, 243) = 6.11, p < .000. They

also wanted to disclose more favorable (M = 1.43) versus unfavorable feedback (M = 0.57) in

the “distant-friend” condition, t (1, 206) = 9.34, p < .000.

Hypothesis 6a: People will display a self-verification effect, such that they will want

to disclose more unfavorable feedback for their worst attribute versus best attribute.

There is an interaction between attribute (best vs. worst) and feedback (favorable vs.

unfavorable), F (1, 243) = 11.06, p = .001, partial η 2 = 0.04. Simple-effect tests showed that

people wanted more favorable feedback for their best attribute (M = 1.45) versus worst attribute

(M = 1.27), t (1, 243) = 3.33, p = .006. However, they wanted more unfavorable feedback for

their worst attribute (M = .73) versus best attribute (M = .55), t (1, 206) = -3.33, p = .006.

Hypothesis 6a was supported.

It was also found that people wanted to disclose more favorable feedback (M = 1.45)

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versus unfavorable feedback (M = 0.55) for their best attribute, t (1, 243) = 10.51, p < .000.

Meanwhile, they also wanted to disclose more favorable (M = 1.27) versus unfavorable feedback

(M = 0.73) for their worst attribute, t (1, 243) = 6.28, p < .000.

Figure 15. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition

Table 18. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition

Hypothesis 6b: This effect will be moderated by whether they are self-disclosing to

close friends or distant friends, such that the discrepancy between worst and best attribute

in receiving unfavorable feedback will be larger in the “close friend” condition versus

“distant friend” condition.

0  

0.5  

1  

1.5  

2  

Best   Worst  

Favorable  

Unfavorable  

95% Confidence Interval

feedback attribute Mean SE Lower Upper

favorable best 1.454 0.0432 1.369 1.539 worst 1.271 0.0432 1.186 1.356 unfavorable best 0.546 0.0432 0.461 0.631 worst 0.729 0.0432 0.644 0.814

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A repeated-measures ANOVA was conducted with the number of unfavorable-feedback-

soliciting questions as the dependent variable, attribute (best vs. worst) as the repeated-measure

factor and condition (close vs. distant friend) as the between-subject factor. Results showed a

main effect of attribute, F (1, 243) = 11.06, p = .001, partial η 2 = .044. Specifically, they wanted

to disclose more unfavorable feedback for their worst attribute (M = .73) versus the best attribute

(M = .54).

However, there is no interaction effect between attribute (best vs. worst) and condition

(close vs. distant friend), F (1, 243) = 2.1, p = .148. That is to say, people’s preference for

unfavorable feedback for worst attribute versus best attribute in the “close condition” (M = 1.29)

was not different from preference for unfavorable feedback for worst versus best attribute in the

“distant condition” (M = 1.43). Hypothesis 6b was not supported.

Figure 16. Number of unfavorable-feedback-soliciting questions chosen for best attribute and

worst attribute (first round)

0  

0.5  

1  

1.5  

2  

Best   Worst  

close  

distant  

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Table 19. Number of unfavorable-feedback-soliciting questions chosen for best attribute and

worst attribute (first round)

Research Question 3: Will people show a self-verification effect as Swann et al.

(1989) operationalized, such that they will want to disclose more unfavorable feedback

versus favorable feedback for the worst attribute?

People wanted to disclose more favorable feedback (M = 1.45) versus unfavorable

feedback (M = 0.55) for their best attribute, t (1, 243) = 10.51, p < .000. Meanwhile, they wanted

to disclose more favorable (M = 1.27) versus unfavorable feedback (M = 0.73) for their worst

attribute, t (1, 243) = 6.28, p < .000. Therefore, people did not show a self-verification effect as

Swann et al. (1989) operationalized.

4.5.2.1.3 Self-verification in the “close-friend” and “distant-friend” condition

In order to have a better understanding of how self-verification worked separately in the

“close-friend” and “distant-friend” condition, data was divided into two sets (“close-friend” and

“distant-friend”). The two sets of data were analyzed separately.

Self-verification in “close-friend” condition

Only data of participants in the “close-friend” condition was analyzed, which left N =

119. A 2 (best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback) within-

subjects ANOVA found a main effect of feedback type, F (1, 118) =36.1, p < .000, partial η 2 =

95% Confidence Interval

feedback attribute Mean SE Lower Upper

favorable best 1.454 0.0432 1.369 1.539 worst 1.271 0.0432 1.186 1.356 unfavorable best 0.546 0.0432 0.461 0.631 worst 0.729 0.0432 0.644 0.814

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0.236. Specifically, people wanted to disclose more favorable feedback (M = 1.29) versus

unfavorable feedback (M = .71).

In addition, there was an interaction between attribute and the type of feedback, F (1,

119) =10.0, p = .002, partial η 2 = 0.079. Simple effect tests revealed that people wanted more

favorable feedback for best attribute (M = 1.42) versus worst attribute (M = 1.16), t (1, 119) =

3.17, p = .01, but more unfavorable feedback for worst attribute (M = 0.84) versus their best

attribute (M = .58), t (1, 119) = -3.17, p = .01. That is to say, there is a self-verification effect

found in the “close-friend” condition.

Self-verification in “distant-friend” condition

Only data of participants in the “distant-friend” condition was analyzed (N = 127. A 2

(best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback) within-subjects

ANOVA found a main effect of feedback type, F (1, 126) = 90.18, p < .000, partial η 2 = 0.419.

Specifically, people wanted to disclose more favorable feedback (M = 1.43) versus unfavorable

feedback (M = .57). However, there is no interaction effect found between attribute and

feedback, F (1, 126) = 2.0, p = .159. Therefore, there is no self-verification effect found in the

“distant-friend” condition.

4.5.2.2 Data analysis round 2 (moderately-self-viewing excluded)

Similar to Study 1 and Study 2, the fact that not every participant was able to indicate a

best and a worst attribute in the Self-Attribute Questionnaire may have made self-verification

effect weak. In the second round of data analysis, only those who had a best attribute (score 6 or

higher) and a worst attribute (score 4 or lower) were selected (N=171).

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Hypothesis 5a: People will display a self-enhancement effect, such that the best

attribute will receive higher rank in interest in receiving feedback versus the worst

attribute.

A repeated-measure ANOVA showed that people are more interested in receiving

feedback about their best attribute versus their worst attribute, F (1, 170) = 15.40, p < .000,

partial η 2 = 0.084. The average rank of best attribute (M = 2.50) is significantly higher than the

rank of worst attribute (M = 3.31; 1 means participants most want to hear about the area while 5

means they least want to hear about the area). Hypothesis 5a was supported.

Hypothesis 5b: This effect will be moderated by whether people are self-disclosing to

their close friends or distant friends. Specifically, the effect will be stronger in the “distant-

friend” condition versus “close-friend” condition.

There is no significant interaction between attribute (best vs. worst) and condition (close

vs. distant friend), F (1, 170) = .88, p = .35 > .05. That is to say, participants in the “distant-

friend” condition (M = 2.92) did not show a larger self-enhancement effect compared to those in

the “close-friend” condition (M = 2.89). Therefore, hypothesis 5b was not supported.

To test self-verification, data was run with a 2 (best vs. worst SAQ attribute) by 2

(unfavorable vs. favorable feedback) within-subjects ANOVA with condition (close vs. distant

friend) as a between-subjects independent variable. Results revealed a three-way interaction

effect, a main effect and a two-way interaction effect. Firstly, there is a three-way interaction

effect, F (1, 170) = 4.86, p = .029, partial η 2 = .028. The three-way interaction will be broken

down by conditions (close vs. distant friends) in the subsequent analyses.

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Secondly, there is a main effect of feedback type, F (1, 170) = 85.63, p < .000, partial η 2

= 0.336, indicating that people sought more favorable feedback (M=1.38) than unfavorable

feedback (M=0.62).

Figure 17. Rank for best attribute and worst attribute in the seeking condition versus disclosure

condition (second round)

Table 20. Rank for best attribute and worst attribute in seeking condition versus disclosure

condition (second round)

95% Confidence Interval

Close(1) or Distant(2) Attribute Mean SE Lower Upper

1 best 2.58 0.164 2.26 2.90 worst 3.19 0.164 2.87 3.52 2 best 2.42 0.160 2.11 2.74 worst 3.42 0.160 3.11 3.74

Hypothesis 6a: People will display a self-verification effect, such that they will want

to disclose more unfavorable feedback for their worst attribute versus best attribute.

There is an interaction effect between attribute (best vs. worst) and feedback (favorable

vs. unfavorable), F (1, 170) = 13.12, p < .000, partial η 2 = 0.072. Simple-effect tests showed that

people wanted more favorable feedback for their best attribute (M = 1.45) versus worst attribute

(M = 1.27), t (1, 170) = 3.62, p = .002, but they wanted more unfavorable feedback for their

0  0.5  1  

1.5  2  

2.5  3  

3.5  4  

4.5  5  

Best   Worst  

close  

distant  

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worst attribute (M = .73) versus best attribute (M = .55), t (1, 170) = -3.62, p = .002. Hypothesis

6a was supported.

It was also found that people wanted to disclose more favorable feedback (M = 1.50)

versus unfavorable feedback (M = 0.50) for their best attribute, t (1, 170) = 9.53, p < .000. They

also wanted to disclose more favorable (M = 1.27) versus unfavorable feedback (M = 0.73) for

their worst attribute, t (1, 170) = 5.08, p < .000.

Figure 18. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition (second round)

Table 21. Number of favorable-feedback-soliciting questions and number of unfavorable-

feedback-soliciting questions chosen for best attribute and worst attribute in the feedback-

seeking condition (second round)

95% Confidence Interval

Attribute Feedback Mean SE Lower Upper

best favorable 1.497 0.0522 1.395 1.600 unfavorable 0.503 0.0522 0.400 0.605 worst favorable 1.265 0.0522 1.162 1.368 unfavorable 0.735 0.0522 0.632 0.838

0  

0.5  

1  

1.5  

2  

Best   Worst  

Favorable  

Unfavorable  

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Hypothesis 6b: This effect will be moderated by whether they are self-disclosing to

close friends or distant friends, such that the discrepancy between worst and best attribute

in receiving unfavorable feedback will be larger in the “close friend” condition versus

“distant friend” condition.

A repeated-measure ANOVA was conducted with the number of unfavorable-feedback-

soliciting questions as dependent variable, attribute (best vs. worst) as the repeated-measure

factor and condition (close vs. distant friend) as the between-subject factor found a main effect of

attribute, F (1, 170) = 13.12, p < .000, partial η 2 = .072. Specifically, they wanted to disclose

more unfavorable feedback for their worst attribute (M = .73) versus the best attribute (M = .05).

There is an interaction effect between attribute (best vs. worst) and condition (close vs.

distant friend), F (1, 170) = 5.86, p = .017, partial η 2 = .034. Simple-effect tests showed that

people in the “close-friend” condition wanted to disclose more unfavorable feedback for their

worst attribute (M = .87) versus best attribute (M = .49), t (1,170) = -4.06, p < .000. However,

those in the “distant-friend” condition did not show any significant difference in disclosing

unfavorable feedback for their best (M = .51) or worst attribute (M = .60), t (1,170) = -1.02, p =

.739. That is to say, there is a self-verification effect in the “close-friend” condition, but no such

effect in the “distant-friend” condition. Hypothesis 6b was supported.

Figure 19. Number of unfavorable-feedback-soliciting questions chosen for best attribute and

worst attribute (second round)

0  

0.5  

1  

1.5  

2  

Best   Worst  

close  

distant  

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Table 22. Number of unfavorable-feedback-soliciting questions chosen for best attribute and

worst attribute (second round)

95% Confidence Interval

Unfav Feedback Close(1) or Distant(2) Mean SE Lower Upper

best 1 0.492 0.0742 0.346 0.638 2 0.510 0.0734 0.365 0.654 worst 1 0.866 0.0742 0.720 1.012 2 0.600 0.0734 0.456 0.745

Research Question 3: Will people show a self-verification effect as Swann et al.

(1989) operationalized, such that they will want to disclose more unfavorable feedback

versus favorable feedback for the worst attribute?

According to the results of hypothesis 6a, people wanted to disclose more favorable (M =

1.27) versus unfavorable feedback (M = 0.73) for their worst attribute, t (1, 170) = 5.08, p <

.000. Therefore, there was no self-verification effect found as Swann et al. (1989)

operationalized.

4.5.2.2.1 Self-verification in the “close-friend” and “distant-friend” condition

Similar to the first round of data analysis, to examine self-verification separately in the

“close-friend” and “distant-friend” condition, data was divided into two sets (“close-friend” and

“distant-friend”) and was analyzed separately.

Self-verification in “close-friend” condition

Only data of participants in the “close-friend” condition was analyzed, which left N = 84.

A 2 (best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback) within-subjects

ANOVA found a main effect of feedback type, F (1, 83) =29.2, p < .000, partial η 2 = 0.262.

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Specifically, people wanted to disclose more favorable feedback (M = 1.32) versus unfavorable

feedback (M = .68).

In addition, there was an interaction between attribute and the type of feedback, F (1, 83)

=16.5, p < .001, partial η 2 = 0.168. Simple effect tests revealed that people wanted more

favorable feedback for best attribute (M = 1.51) versus worst attribute (M = 1.13), t (1, 83) =

4.07, p < .000, but more unfavorable feedback for worst attribute (M = 0.87) versus unfavorable

feedback for best attribute (M = .49), t (1, 119) = -4.07, p < .000. Therefore, there is a self-

verification effect found in the “close-friend” condition.

Self-verification in “distant-friend” condition

Only data of participants in the “distant-friend” condition was analyzed (N = 89). A 2

(best vs. worst SAQ attribute) by 2 (unfavorable vs. favorable feedback) within-subjects

ANOVA found a main effect of feedback type, F (1, 88) = 59.63, p < .000, partial η 2 = 0.407.

Specifically, people wanted to disclose more favorable feedback (M = 1.44) versus unfavorable

feedback (M = .56). However, there is no interaction effect found between attribute and

feedback, F (1, 88) = 1.03, p = .312. Therefore, there is no self-verification effect found in the

“distant-friend” condition.

4.6 DISCUSSION

Study 3 examines how tie strength moderates the self-enhancement and self-verification

effects when people consider self-disclosing on social media. Results found self-enhancement

and self-verification effect in both “close-friend” and “distant-friend” conditions. It was also

found that tie strength could moderate the self-verification effect. Specifically, self-verification

only appeared in the “close-friend” condition; there was no self-verification found in the

“distant-friend” condition. However, the results did not show that tie strength moderated self-

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enhancement. The findings were consistent in both rounds of data analysis.

The “ceiling effect” of self-enhancement might be a possible reason for why there was no

moderation effect of tie strength found for self-enhancement. As discussed in literature review,

self-boasting is likely to cause social antipathy (Schlenker & Leary, 1982), especially when one

is self-presenting to someone who already knew them. Therefore, even when people are driven

by a strong self-enhancement motive and want the audience to adore them, they might be careful

with disclosing such ambition due to the fear of social antipathy. Thus, when self-disclosing to

the “distant friends”, even though they might be driven by a strong self-enhancement motive,

they would probably not display such strong self-enhancing tendency. The increase in self-

enhancement is thus no longer observable.

The finding that there was no self-verification found in the “distant-friend” condition

speaks to the finding of Study 2 that people do not self-verify when self-disclosing on social

media. Different from Study 2, which informed participants that they had a chance to disclose

feedback to the general social media audience, Study 3 primed participants with either close

friends or distant friends. By this way, Study 3 actually restricted the audience as either

participants’ close friends or distant friends on social media. It is reasonable that people tend to

self-verify with close friends because they need to affirm that their close friends know and

understand who they are. However, people do not self-verify with either “distant friends” on

social media or the general social media audience. It would be interesting to consider the concept

of the “general social media friends”, which include “close friends”, “distant friends”,

colleagues, strangers, etc. What do people think of when they consider general social media

friends or contacts? What is the perceived relationship closeness between them and the general

social media friends? The questions await future research.

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Another issue involves how to find the real motive underlying a behavior. As discussed

in the literature review, self-enhancing (and self-verifying) behavior and motive do not always

match. A self-verifying behavior (e.g. making fun of one’s own drawback) could absolutely be

driven by a self-verification motive. However, the behavior also may be driven by a self-

enhancement motive – the person might be striving to create a humorous self-image to the

audience, or want others to believe that they are modest. In the present study, people were found

to only self-verify with close friends. A possible reason is that people need close friends’ opinion

to stabilize their self-concept. However, another possible reason is that such modesty is a

strategy to gain close friends’ affection and avoid their antipathy. Because close friends are more

important than distant friends, the cost of self-boasting is much higher with close friends than

distant friends. Therefore, people would self-verify more with close friends. If that’s the case, the

decrease in self-verification could be regarded as a synonym of self-enhancement. The present

study only looked at how people present self-enhancing and self-verifying behavior to an

audience, but did not delve into how self-enhancement (and/or self-verification) as a

psychological motive and self-enhancement (and /or self-verification) as a behavior interact with

each other. This issue leaves potential for future research.

An important contribution of Study 3 is that it found that tie-strength would moderate the

self-verification effect on social media. That is to say, people wanted to both self-enhance and

self-verify with close friends on social media. However, they only wanted to self-enhance with

distant friends. This finding has important implications for social media and the advertising

industry. For the advertising industry, the fact that people want to both self-enhance and self-

verify with close friends means that they would like to present both actual self and ideal self to

their “close friends” group on social media. However, with distant friends, people only want to

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self-enhance, which means that they are more likely to present an ideal self on social media with

these friends.

Therefore, in social media users’ “close-friend” group, they might be likely to make

associations with brands, or share and comment on advertisements congruent with both their

actual self and ideal self. In contrast, when they set the posts visible to their distant friends or the

whole Internet, they might be more likely to make associations with brands, or share and

comment on advertisements which could express their ideal self. Advertisers thus have the

potential to send the advertisement to specific social media users based on their habits in

restricting the visibility of posts as well as other data.

Table 23: Summary of findings of Study 1, 2 and 3 (full data)   Self-­‐enhancement   Self-­‐verification  

Within-­‐attribute  (Swann  et  al.,  1989)  

Within-­‐person  

Study  1   Found   Not  found   Found  

Study  2   Found   Not  found   Found  with  both  “seeking”  and  “disclosure”  condition  

Study  3   Found   Not  found   Found  only  with  the  “close  friend”  condition  

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Table 24: Summary of findings of Study 1, 2 and 3 (moderately-self-viewed excluded)   Self-­‐enhancement   Self-­‐verification  

Within-­‐attribute  (Swann  et  al.,  1989)  

Within-­‐person  

Study  1   Found   Not  found   Found  

Study  2   Found   Not  found   Found  only  with  the  “seeking”  condition  

Study  3   Found   Found  only  with  the  “close  friend”  condition  

Found  only  with  the  “close  friend”  condition  

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CHAPTER 5: GENERAL DISCUSSION

The impact of social media on people’s psychology and behavior has recently aroused

both researchers’ and advertisers’ interests because social media is becoming an inseparable part

of people’s daily life. According to the report from Pew Research (Smith & Anderson, 2018),

73% American adults said that they were Youtube users, and 68% reported that they were

Facebook users. How this recently-emerging everyday activity changes people’s behavior pattern

thus becomes an intriguing topic. For advertisers, knowledge in this area could help them target

their audience precisely, especially for online advertising; for media researchers, social media

use may challenge some previous findings in media psychology, because it fundamentally

changes the way people communicate with each other.

This dissertation examined how social media use (public presentation with varied tie-

strength) and self-evaluation reciprocally influence with each other. On the one hand, using

social media may alter people’s self-evaluation process because people have stronger self-

presentational concerns on social media, compared to everyday offline situations. On the other

hand, this self-evaluation process may in turn change the way people self-present on social

media. Since self-enhancement and self-verification are considered as two major motives of the

self-evaluation process in social psychology (Sedikides, 1993), this dissertation tried to examine

how people self-present on social media, and how the mass and public audience on social media

influences their self-presentation, from the perspective of self-enhancement and self-verification.

The three studies of the dissertation together constitute the parts of the larger issue of

self-presentation in a mass audience age. Study 1 replicated a classic study to examine how self-

enhancement and self-verification work in a private interpersonal situation. Study 2 investigated

how these two psychological motives work differently in a private situation versus a public

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situation (social media). Finally, Study 3 looked at how a specific social media feature – the

grouping function may influence the two psychological motives when people are self-disclosing

on social media. The findings of the three studies are summarized as follows.

Study 1 replicated a classic study in the area (Swann et al., 1989) to corroborate the

existence of self-enhancement and self-verification. Based on Swann et al.’s (1989)

operationalization of self-enhancement and self-verification, Study 1 found a strong self-

enhancement effect. However, unlike Swann et al., no self-verification effect was found.

Despite the fact that there was no self-verification effect found with the within-attribute

measure by Swann et al. (1989), the results of Study 1 suggest an alternative operationalization

of self-verification effect – that is, the within-person measure. According to the results of Study

1, participants wanted more unfavorable feedback for their worst attribute versus best attribute,

while they wanted more favorable feedback for their best attribute versus worst attribute. In other

words, from best attribute to worst attribute, people’s preference for unfavorable feedback

significantly increased, while their preference for favorable feedback significantly decreased.

That is to say, a person shows different patterns when seeking feedback for best attribute versus

worst attribute. Based on the definition of self-verification – a human need for stability of their

self-conceptions (Swann & Read, 1981), a stronger desire for unfavorable feedback for negative

self-viewed attributes compared to positively self-viewed attributes could be operationalized as

self-verification because it shows a consistency between pre-existing self-views and feedback

seeking. In summary, with the within-person measure, self-verification is operationalized as a

stronger desire for unfavorable feedback for worst attribute versus best attribute.

Study 2 looked at how social media context would influence people’s self-enhancement

and self-verification motives. Specifically, Study 2 examined the moderating effect of publicness

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on self-enhancement and self-verification. The results are concluded as three points. Firstly,

Study 2 replicated the findings of Study 1 in that there is both a self-enhancement and self-

verification effect found in the “feedback-seeking condition” of Study 2. Secondly, there is no

moderating effect of publicness on the self-enhancement effect– participants in both “seeking”

and “disclosure” condition showed a strong self-enhancement effect. Thirdly, publicness was

found to moderate the self-verification effect with extremely self-viewing people – with the

sample of participants who had extreme self-views, self-verification was found with the

“seeking” group, but not with the “disclosure” group. In summary, the results of Study 2 indicate

that publicness would weaken self-verification effect. However, publicness does not exert any

influence on the self-enhancement effect. In short, in a private situation, people want to both self-

enhance and self-verify; while in a public situation, they only want to self-enhance.

Built on Study 1 and Study 2, Study 3 examined how specific social media features

would influence self-enhancement and self-verification when people are self-disclosing on social

media. Specifically, Study 3 investigated the moderating effect of tie strength between audience

and self-discloser on self-enhancement and self-verification. Results showed that when self-

disclosing on social media, people tend to self-verify with close friends but not with distant

friends. However, tie strength was not found to moderate self-enhancement. In conclusion, even

though Study 2 found that people want to self-enhance but not self-verify on social media, when

people are self-disclosing to their close friends, they still tend to both self-enhance and self-

verify with close friends. However, when they think about self-disclosing to distant friends only,

they only want to self-enhance, but not self-verify.

5.1 THEORETICAL IMPLICATION

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There are four theoretical implications of this dissertation. First of all, Study 1 partially

replicated the results of Swann et al. (1989) that people have a self-enhancement need. However,

Study 1 failed to find self-verification effect as Swann et al. (1989) did. A possible reason is the

fact that Swann et al. (1989) pre-selected participants who had extremely high or low self-

esteem. The inclusion of people with extremely low self-esteem could enlarge the self-

verification effect because people with low self-esteem tend to seek unfavorable feedback for

both positively and negatively self-viewed attributes. However, the current study did not pre-

select participants. Since self-verification is operationalized by Swann et al. (1989) as a

preference for unfavorable feedback versus favorable feedback for one’s negatively self-viewed

attributes, the inclusion of the extremely low-self-esteem participants would enlarge the self-

verification effect. If the inclusion of the extreme self-view participants is the main factor that

caused the failure of the replication study, then it is reasonable to speculate that self-verification

is not an effect that exists among general population. Whether this effect only exists among

extremely self-viewed people leaves open to future research.

Secondly, the dissertation offers an alternative operationalization of self-verification.

Swann et al. (1989) operationalized self-verification as a within-attribute difference – a

preference for unfavorable feedback versus favorable feedback for one’s negatively self-viewed

attributes. However, this dissertation suggests that the within-person difference could be a better

measure. The within-person measure operationalizes self-verification as a stronger desire for

unfavorable feedback for negatively self-viewed attribute versus positively self-viewed attribute.

This measure could be a better measure among general population; while Swann et al.’s (1989)

within-attribute measure may only work with the depressed or low-self-esteem population. The

underlying reason could be due to consistency pressure. It was found that people tend to be

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consistent when responding to a survey (Cialdini, 1984). It would be hard for them to completely

change their choice of feedback from the best attribute to the worst attribute. Since in general

people usually have relatively positive self-views, they may show preference for favorable

feedback versus unfavorable feedback for both positively and negatively self-viewed attributes.

However, the situation is different with depressed people. Literature in self-verification

found that people with depression tend to seek negative feedback because this kind of feedback

is familiar and predictable to them (Swann et al., 1992; Swann, Wenzlaff and Tafarodi, 1992;

Giesler et al., 1996). Even though Swann et al. (1989) did not particularly include the depressed

people as participants, it is possible that the pre-selected low self-esteem participants also

preferred negative feedback for both best and worst attributes because of their familiarity with

such feedback. Therefore, it would be easier to find people’s preference for unfavorable

feedback versus favorable feedback for their worst attribute because these people may prefer

unfavorable feedback for both best and worst attributes. However, for the general population

who usually has positive self-views, they would feel inconsistent if they choose more favorable

feedback for the positively self-viewed attributes, while choosing more unfavorable feedback for

the negatively self-viewed attributes. Thus, it would be hard to find a self-verification effect with

the within-attribute measure in the general population. Even though self-esteem was not found to

exert any influence on the self-verification effect in Study 1, it should be noted that participants

in Study 1 were not pre-selected. The low self-esteem score of the participants does not mean

that they had extremely negative self-views. It is possible that the within-attribute questionnaire

may only work for participants with extremely low self-esteem.

In contrast, the within-person measure compares how a single person changes his/her

choice of feedback between positively- and negatively-self-viewed attributes. As the results of

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Study 1 indicated, people showed an increase in desire for unfavorable feedback from positively

self-viewed attributes to negatively self-viewed attributes. Even though the results did not show

that they preferred unfavorable feedback versus favorable feedback for the negatively self-

viewed attributes in total, this change would be enough to show a self-verification tendency.

The third implication involves the relationship between self-enhancement and self-

verification. Do self-enhancement and self-verification necessarily contradict to each other?

Does the decrease in self-verification imply the increase in self-enhancement, and vice versa? As

Study 1 shows, self-enhancement and self-verification as psychological motives do not

necessarily contradict to each other, and could happen at the same time – they could even be

measured simultaneously with different measures. However, how do people self-enhance and

self-verify at the same time? What kind of behavior would this interaction between self-

enhancement and self-verification finally lead to?

In Swann et al. (1989) and this dissertation, self-enhancement and self-verification were

measured separately – self-enhancement was measured with the between-attribute questionnaire,

which examined whether people would prefer feedback about their strengths versus weaknesses;

while self-verification was measured with the within-attribute questionnaire, which looked at

what kind of feedback people would seek for their strengths and weaknesses. There seems to be

an underlying hierarchy behind the two measures: in the first step, when people have the freedom

to choose what personal attribute to seek (or disclose) feedback about, they tend to choose the

positively self-viewed attributes, which is consistent with self-enhancement. In the second step,

when people are forced to seek (or disclose) feedback about their weaknesses, they seek (or

disclose) unfavorable feedback about the weaknesses, which indicates a self-verification effect.

With this hierarchy, it seems that self-enhancement is usually the dominant motive that

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influences people’s behavior in a natural setting, because people would always go with their

strengths and avoid weaknesses in the first step, thus would not go to the second step. However,

in the real life, this hierarchy does not always occur in this way. Self-enhancement and self-

verification could be manifested in a variety of ways except for feedback seeking. For example, a

person could talk about their weakness with positive words, such as “I am not good at math, but I

am trying to learn it and have already made progress”. Put in other words, there are millions of

ways that people could self-enhance and self-verify at the same time.

The discussion above implies that self-enhancement and self-verification could co-exist

and work with different routes – they do not necessarily compete with each other. Therefore, a

person can self-enhance and self-verify at the same time; the decrease in self-enhancement does

not mean an increase in self-verification. Self-enhancement or self-verification alone could not

adequately explain people’s behavior.

The fourth implication of the dissertation is the fact that it examined self-enhancement

and self-verification effect in the context of publicness. As discussed in the literature review

chapter, it is important to learn the impact of publicness on the self-motives if we want to

understand how social media use changes the way people self-evaluate and self-present, for the

reason that social media is a public area where there is an audience watching the users. However,

most previous research in self-motives was conducted in a relatively private or interpersonal

situation. It is possible that publicness could change the way people self-enhance and self-verify,

thus changing their self-evaluation and further behavior.

Study 2 found that people would self-enhance in both “feedback-seeking” and “feedback-

disclosure” condition. The self-enhancement effect was not moderated by publicness. In terms of

self-verification effect, it was found that people would self-verify in the “feedback-seeking”

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condition. However, no self-verification was found in the “feedback-disclosure” condition. This

means people only self-enhance but don’t self-verify in the public situation. This finding

confirms the notion that publicness would weaken self-verification effect.

Interestingly, despite the fact that Study 2 did not find any self-verification in the

disclosure condition, Study 3 found that people would self-verify when self-disclosing to close

friends. In Study 3, all participants were told that they would have the chance to disclose the

feedback to their Facebook friends. The moderator here was tie strength – some participants

were primed with close friends, while others were primed with distant friends. Therefore, all

participants were in the “feedback-disclosure” condition. Based on the results of Study 2, there

should be no self-verification effect found in the “feedback-disclosure” condition. However,

there was self-verification effect found in the “close-friend” condition in Study 3. It is reasonable

that people would want to self-verify with close friends because they need to affirm that their

close friends know and understand who they are. This finding implies that, when people set their

social media posts only visible to close friends, they would both self-enhance and self-verify.

However, when they do not set any restriction, they only self-enhance. Moreover, a more

interesting question is: when people think of the general social media friends, would they think

of their close friends? What is the tie strength between people and their general social media

friends?

5.2 PRACTICAL IMPLICATION

The major practical implication of the dissertation is based on the finding that tie strength

moderates self-verification effect in the public situation. Specifically, Study 3 found that people

would both self-enhance and self-verify with close friends. However, they only self-enhance

with distant friends. In the context of social media, this finding means that, when they use the

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grouping function of a social media to restrict the visibility of their posts to audience, people

would tend to post both self-enhancing and self-verifying self-relevant content to the “close-

friend group”, but only post self-enhancing content to the “distant-friend group” as well as the

general social media friends.

The application of the finding in advertising industry may be useful in the context of self-

congruence theory in marketing. The self-congruence theory says that people use products to

define and express their self-concept (Aaker, 1999; Belk, 1988). There are two kinds of self-

concept – ideal self and actual self. Ideal  self  refers  to  the  hopes  that  people  hold  for  

themselves  or  that  they  believe  others  hold  for  them.  Actual  self,  on  the  other  hand,  is  

defined  as  the  attributes  that  people  believe  they  actually  possess  (Moretti  and  Higgins,  

1990).      

According to the self-congruence theory, consumers could achieve self-congruence by

purchasing a product either similar to their ideal self or to their actual self (Ekinsi and Riley,

2003). An actually self-congruent brand reflects who the consumers perceive themselves to be,

while an ideally self-congruent brand reflects who the consumers desire themselves to be (Ekinsi

and Riley, 2003). Relating self-congruence theory to self-enhancement theory versus self-

verification theory, it is reasonable to predict that self-enhancement motive would drive people

to prefer ideally self-congruent brands, because self-enhancement motive boosts ideal self

(Moretti and Higgins, 1990), thus leading to preference to ideally self-congruent brands. Self-

verification, on the other hand, could make people prefer actually self-congruent products

because self-verification motive would drive people to seek the consistency between their actual

self and the brands.

Based on the results of Study 2, when social media users post self-relevant content to the

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whole network, they tend to self-enhance but hardly self-verify. Therefore, they may be likely to

associate themselves with ideally self-congruent brands by commenting, reposting or sharing

information about these brands. It would be hard for them to make association with actually self-

congruent brands. On the other hand, according to Study 3, when people restrict the visibility of

their social media contents to certain groups, they may also share information about actually self-

congruent brands because they tend to self-verify with friends, especially close friends. Hence,

with social media data, advertisers could mainly target social media users whose actual self

matches the brand’s image and frequently restrict their posts visible to certain groups, for the

reason that they are likely to share the ads on social media. On the other hand, for those who

usually do not restrict the visibility of the posts, advertisers could target those whose ideal self

match the brand’s image, because they usually self-enhance but rarely self-verify on social

media.

5.3  LIMITATIONS  AND  FUTURE  RESEARCH  

There are several questions remain unresolved in the dissertation. The first question

involves the measure for self-verification. In this dissertation, to measure self-verification,

participants were asked to choose two questions out of a list of six questions. With this measure,

self-verification was actually measured with a three-point scale, on which 0 and 2 means a strong

preference for either favorable or unfavorable feedback, while 1 means equal preference for

favorable and unfavorable feedback. Hence, 1 is a midpoint that allows people to self-enhance

and self-verify at the same time. However, if the measure asks participants to choose three

questions out of the six questions, the scale would become a four-point scale, which does not

allow participants to be neutral. If participants were forced to choose between preference for

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favorable feedback and preference for unfavorable feedback, would self-verification be found to

be stronger? This question leaves open to future research.

The second issue involves the relationship between the ratings of attributes and the

importance of each attribute to each participant. In the Self-Attribute Questionnaire (see

Appendix A), participants were asked to rate each of the five attributes (intelligence, social

skills, athletic ability, artistic ability and physical attractiveness) on their certainty and the

importance of each attribute. However, Swann et al. (1989) never explained how they used this

part of data in their study. Similarly, in the present research, participants indicated the certainty

and the importance of each attribute. However, the data was not used to answer any of the

research questions either.

In their paper exploring the impact of self-views on self-esteem, Pelham and Swann

(1989) noted that the certainty and importance of an attribute to a person influence the meaning

of the attribute to the person. That is, people not only ask “am I good at it?”, but also ask “what it

means to be good or bad at this attribute?” (Pelham and Swann, 1989). They found that high

levels of certainty and importance of a positive attribute could lead to high self-esteem. It is easy

to understand why certainty and importance matter – for example, a person might be very good

at cooking, but they have been dreaming of becoming a scientist since their childhood. Thus,

their talent in cooking may not lead to any increase in his self-esteem because cooking is not

important to them. In contrast, the people’s achievements in science could largely boost their

self-esteem because it is the most important domain in their life. As for certainty, it was found

that people usually weigh attributes with high certainty more than attributes with low certainty in

self-assessment (Pelham and Swann, 1989).

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It is also possible that people’s ability in an attribute may influence the importance of the

attribute. That is, people may rate as “unimportant” the attributes at which they are not good,

while rate as “important” the attributes at which they are good. For one thing, people may devote

more resources to improve the attributes that they believe as important. Therefore, the

“important” attributes would become the “best” attributes. For another, people may persuade

themselves that the “worst” attributes are less important as a protection of self-esteem (Pelham

and Swann, 1989).

The data from Study 1 was used to explore the relationship between importance and

attribute ratings. A repeated-measure ANOVA was conducted with attribute (best vs. worst) as

the independent variable and importance of the attribute as the dependent variable. Results

showed that there was a significant difference between best attribute (M = 8.18) and worst

attribute (M = 4.32) in importance, F (1, 151) = 244, p < .001, indicating that the positively self-

viewing attributes are regarded as more important than the negatively self-viewing attributes.

The results imply that since the best and worst attributes bear different levels of importance,

people may be motivated to look at the best and worst attributes differently. For example, they

may be more likely to solicit feedback about the best attributes because they are more important

than the worst attributes. Hence, importance could be an alternative explanation to the results of

the present dissertation.

The relationship between certainty and attribute ratings was also examined. A repeated-

measure ANOVA was conducted with attribute (best vs. worst) as independent variable and

certainty of each attribute as dependent variable. However, results did not show any significant

differences between attribute (best vs. worst) and certainty, F (1,151) = 2.83, p = .095. This

means that certainty does not influence the way people perceive best versus worst attributes.

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Another issue concerns how to find the real motive underlying a behavior. As discussed

in the literature review chapter and the Study 3 chapter, it is difficult to tell whether a seemingly

self-verifying behavior really reflects a self-verification motive. For example, a person might

make fun of his drawbacks on social media, which seems to be a self-verifying behavior.

However, the real intent of this behavior might be the fact that he wants others to consider him as

a humorous person, or the kind of person who has an accurate self-perception, which indicates a

self-enhancement motive. The results of Study 3 pretest shows that people care more about close

friends’ opinion. However, they are not more interested in presenting a more ideal self-image to

their close friends versus the distant friends. It is possibly because the reduction in self-appraisal

is a protection from antipathy of close friends. Since close friends are already familiar with one’s

advantages, self-boasting would lead to antipathy. However, this hypothesis has never been

empirically tested in this dissertation.

A further question following the motive and behavior issue is: are the self-enhancement

and self-verification measured in this dissertation motives or behavior? In Swann et al. (1989),

self-enhancement and self-verification were treated as psychological motives – “our findings

suggest that people possess at least two fundamental social motives: self-enhancement and self-

verification”. Study 2 and Study 3 of this dissertation used the same questions as Swann et al.

(1989) to measure self-enhancement and self-verification, assuming that the measures would also

work in the disclosure condition. However, as argued in the previous paragraph, when a person

actively discloses negative self-relevant feedback in public, which seems to be self-verifying, the

underlying intent could be the fact that the person wants others to think positively of him/her,

which is actually self-enhancing. Hence, the line is ambiguous between self-verification as a

motive and behavior in this dissertation. How do we know whether we are measuring self-

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verification as a motive or behavior? Are self-enhancement and self-verification measured in

Swann et al. (1989) indeed motives, or behavior?

To answer the questions, we shall start with the relationship between motive and behavior.

There are two renowned definitions of psychological motives – one is from Bartol and Martin

(1998), which defined motive as a force that energizes behavior and directs behavior towards

goals; the other definition was made by Greenberg and Baron (1997, p64), which defined motive

as “a set of processes that arouse, direct and maintain human behavior toward attaining a goal”.

It can be concluded from the definitions that there is a causal relationship between motive and

behavior – motive is why a person performs a behavior. Since psychological motives cannot be

touched or observed directly, researchers use affective (emotion), cognitive (e.g. recognition,

recall), behavioral (choice, task performance), as well as physiological responses to measure

motives (Touré-Tillery & Fishbach, 2014). In this dissertation, self-enhancement and self-

verification, as “psychological motives”, were measured with self-reported choices– choices on

self-relevant feedback disclosure. Hence, the dependent variables measured here are cognitions

rather than behavior.

Many of literature measured self-enhancement and self-verification with self-reported

choices. For example, Study 1 of Swann et al. (1989) used feedback-seeking questionnaire to

measure self-verification, which asked participants to indicate what kind of feedback they would

like to hear about. Some studies seemed to ask participants to make behavioral choices. However,

these “behavioral choices” actually measured cognitive responses, rather than behavioral

responses, because they did not ask participants to perform actual behavior, but to indicate their

behavior intention. For example, Swann et al. (1992) pre-selected negatively self-viewed

participants, and presented them with a set of three self-relevant feedback: a positive, a negative

136

and a neutral. They then told participants that the three feedback came from three different

evaluators, and asked them which evaluator they would like to interact with. Self-enhancement

was operationalized as a preference for the evaluator who provided a positive feedback, while

self-verification was operationalized as a preference for the negative-feedback-provider. In this

case, the evaluator choice is cognitive response, rather than behavioral response, because the

participants did not eventually choose an evaluator to interact – the questions only measured

preference, not behavior. Similarly, in this dissertation, since participants were only asked to

indicate which feedback they would like to disclose, rather than actually disclosing the feedback

on their social media, the measures used in this dissertation measured cognition (behavior

intention), rather than behavior.

The fourth question involves the operationalization of publicness. In Study 2, publicness

was simply operationalized as the “seeking condition” (seeking feedback from others to the self)

versus the “disclosure condition” (disclosing feedback from the self to others). However, the

operationalization of publicness might not be unidimensional – it could be bidimensional. There

would possibly be a situation where people want to seek certain feedback, but do not want to

disclose it. There also might be situations where people want to disclose feedback about certain

attributes, but do not care the exact content because they are extremely confident about that

attribute. This operationalization could better simulate the real-life situation where people

usually have the freedom to choose what to hear and what to disclose on social media. Future

research could further explore this area.

The last question is about the perceived relationship closeness between social media users

and their general social media friends. In Study 3, participants were primed with either close

friends or distant friends as the simulation to a real-life situation whether people set their social

137

media posts visible to either close-friend group or distant-friend group. However, based on the

results of the “social media use questions” in Study 3, only a quarter said that they usually

restrict the posts to certain groups of friends; almost a half of participants said that they usually

restrict the posts to their general friends, which include both close and distant friends; the rest

quarter said that they do not restrict the posts, thus usually setting the posts visible to the public.

So, when people are thinking of their general friends, how do they perceive their relationship

closeness with them? Would it be less close than their “close friends”? Would it be closer than

“distant friends”? What about the general social media users, which consist of close friends,

distant friends as well as strangers? Moreover, since people may have different audiences on

different social media, would people have different types of self-presentation on different social

media? These questions await future research for answers.

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APPENDIX A. SELF-ATTRIBUTE QUESTIONNAIRE

(Adapted from Pelham and Swann, 1989)

This questionnaire has to do with your attitudes about some of your activities and

abilities. For the first ten items below, you should rate yourself relative to other people your own

age and same gender by using the following scale:

1 2 3 4 5 6 7 8 9 10

Bottom lower lower lower lower upper upper upper upper upper

5% 10% 20% 30% 50% 50% 30% 20% 10% 5%

An example of the way the scale works is as follows: if one of the traits that follows were

“height”, a woman who is just below average height would choose “5” for this question, whereas

a woman who is taller than 80% (but not taller than 90%) of women her age would mark “8”,

indicating that she is in the top 20% on this dimension.

a) intellectual  ability      

b)  social  skills/  social  competence  

c)  artistic  and/or  musical  ability    

d)  athletic  ability      

e)  physical  attractiveness    

2) Now rate how certain you are of your standing on each of the above traits (on a 9-point Likert

scale).

3) Now rate how personally important each of these domains is to you (on a 9-point Likert

scale).

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APPENDIX B. WITHIN-ATTRIBUTE FEEDBACK-SEEKING QUESTIONS

(Adapted from Swann et al., 1992)

We will be able to provide you with feedback about your personality in the form of

answering the following questions. Since open-ended questions will be individually scored for

content, we will be able to include a limited number of questions. You can help us in our

selection of questions by choosing, from each list of open-ended questions below, the two

questions, about which you would most like to learn.

Please read over the entire list in an area before you decide on your questions.

Remember, you are choosing the two questions you would like to learn about in each area.

Area I (Social)

1) What is some evidence that this person has good social skills?

A = yes, include this question B = no, do not include this question

2) What is some evidence that this person doesn’t have very good social skills?

3) What about this person shows s/he would be confident in social situations?

4) What about this person shows s/he doesn’t have much social confidence?

5) In terms of social competence, what is this person’s best asset?

6) In terms of social competence, what is this person’s worst asset?

Area II (Intellectual)

1) What are some signs that this person is above average in overall intellectual ability?

2) What are some signs that this person is below average in overall intellectual ability?

3) What about this person shows s/he will have academic problems?

4) What about this person shows s/he will do well academically?

5) What academic subjects would this person to be especially good at?

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6) What academic subjects would be expected to prove difficult for this person? Why?

Area III (Artistic/Musical)

1) What about some signs that show this would be a poor artist or musician?

2) What about some signs that show this person is musically or artistically talented?

3) What is this person’s greatest artistic or musical talent?

4) Why is this person unlikely to do well at creative activities?

5) What about this person shows s/he is very imaginative?

6) In the area of art or music, what is this person’s biggest limitation?

Area IV (Physical Appearance)

1) Why do people of the opposite sex would find this person attractive?

2) Why do you think people of the opposite sex would find this person unattractive?

3) What do you see as this person’s least physically attractive features?

4) What do you see as this person’s most physically attractive features?

5) Why should this person feel confident of his/her appearance?

6) Why might this person have little confidence in his/her appearance?

Area V (Sports)

1) What are some sports you would expect this person to be especially good at? Why?

2) What are some sports this person is expected to have problems with? Why?

3) What about this person allows him/her to be a good athlete?

4) What about this person prevents him/her from becoming a good athlete?

5) What is this person’s greatest natural athletic talent?

6) What natural athletic ability does this person possess least?

155

APPENDIX C. BETWEEN-ATTRIBUTE FEEDBACK-SEEKING QUESTIONS

(Swann et al., 1992)

Please rank the five areas below according to which areas you would most like to get

feedback about (we will give priority to the areas you are most interested in). Please give each of

the five areas below a ranking between 1 and 5 where 1 means you want most to hear about that

area and 5 means you want least to hear about that area. Please use a different number for each

area.

(1) I would like to hear about area I (social):

1 (most) 2 (2nd) 3 (3rd) 4 (4th) 5 (least)

(2) I would like to hear about area II (intellectual):

1 (most) 2 (2nd) 3 (3rd) 4 (4th) 5 (least)

(3) I would like to hear about area III (art/music):

1 (most) 2 (2nd) 3 (3rd) 4 (4th) 5 (least)

(4) I would like to hear about area IV (physical appearance):

1 (most) 2 (2nd) 3 (3rd) 4 (4th) 5 (least)

(5) I would like to hear about area V (sports):

1 (most) 2 (2nd) 3 (3rd) 4 (4th) 5 (least)

156

APPENDIX D. SHORTENED TEXAS SOCIAL BEHAVIOR INVENTORY

(Helmreich & Stapp, 1974)

This questionnaire consists of questions that allow us to more objectively evaluate your

personal attributes. In this test, there are no right or wrong answers. Simply answer in a way that

is most like you.

1. I am not likely to speak to people until they speak to me.

Not at all characteristic of me Not very Slightly Fairly Very characteristic of me

1 2 3 4 5

2. I would describe myself as self-confident.

3. I feel confident of my appearance.

4. I am a good mixer.

5. When in a group of people, I have trouble thinking of the right things to say.

6. When in a group of people, I usually do what the others want rather than make suggestions.

7. When I am in disagreement with other people, my opinion usually prevails.

8. I would describe myself as one who attempts to master situations.

9. Other people look up to me.

10. I enjoy social gatherings just to be with people.

11. I make a point of looking other people in the eye.

12. I cannot seem to get others to notice me.

13. I would rather not have very much responsibility for other people.

14. I feel comfortable being approached by someone in a position of authority.

15. I would describe myself as indecisive.

16. I have no doubts about my social competence.

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APPENDIX E. DEBRIEF

Thank you for participating in this study. The purpose of the study is to examine how

people solicit favorable and unfavorable feedback on social media. The study is important

because nowadays people often receive feedback on social media, and this feedback may be able

to influence people’s self- concepts and behavior.

In this study, you were told that you are going to receive the results of the personality

test. However, the questions in the personality test are just some filler questions, and you are not

going to receive the results of the personality test. The personality test is only a cover story.

What we want to examine is whether you would like to receive information about your negative

and positive attributes.

If you would like to learn more about this topic, please send us an email and we can give

you some references. If you have any questions, please contact the research assistant Anlan

Zheng at azheng6@illinois.edu. or Dr. Brittany Duff at bduff@illinois.edu.

158

APPENDIX F. FILLER QUESTIONS (IN THE PERSONALITY TEST)

1. What was your favorite subject at school?

a. Math; b. Sport; c. English; d. Biology; e. History; f. Music; g. Art; h. Psychology

2. Which color do you prefer?

3. What skill would you be most likely to learn in an evening class?

a. Poetry; b. Cooking; c. Another language; d. Playing the guitar; e. Some sort of sport

4. What would you consider the best compliment?

a. You are such a nice person

b. You are really good-looking

c. You have such good taste in music

d. You are so smart

e. You are really good at sports

159

f. You are so interesting

5. If you were an animal, what would you be?

6. Which male celebrity would you look best with? (do you prefer)

160

7. Which female celebrity would you look best with?

8. What time of the year gives you most energy to get moving?

a. Spring – not too hot, not too cold

b. Summer – I like it hot

c. Fall – crisp weather is prime for competition

d. Winter – time to burn off those calories

9. Which pair of sneakers would you rather wear while getting your sweat on?

161

10. Which work of art do you prefer?

11. Pick a building

162

12. Please write 3-5 sentences describing what you usually look like in your friends' eyes.

163

APPENDIX G. RELATIONSHIP CLOSENESS INVENTORY

(Berscheid, Snyder & Omoto, 1989)

A. Frequency

We would like you to estimate the amount of time you typically spend alone with your

close/distant friend (referred to as CF/DF below) during the day. We would like you to make

these time estimates by breaking the day into morning, afternoon, and evening, although you

should interpret each of these time periods in terms of your own typical daily schedule. (For

example, if you work a night shift, "morning" may actually reflect time in the afternoon, but is

nevertheless time immediately after waking.) Think back over the past week and write in the

average amount of time, per day, that you spent alone with your CF/DF, both virtually and in the

real life, during each time period. If you did not spend any time with CF/DF in some time

periods, write 0 hour(s) and 0 minutes.

1. DURING THE PAST WEEK, what is the average amount of time per day that you spent alone

with CF/DF (both virtually and in the real life) in the MORNING (e.g. between the time you

wake up and 12 noon)?

2. DURING THE PAST WEEK, what is the average amount of time per day that you spent alone

with CF/DF(both virtually and in the real life) in the AFTERNOON (e.g. between 12 noon and 6

PM)?

3. DURING THE PAST WEEK, what is the average amount of time per day that you spent alone

with CF/DF (both virtually and in the real life) in the EVENING (e.g. between 6 PM and

bedtime)?

B. Diversity

164

The following is a list of different activities that people may engage in over the course of one

week. For each of the activities listed, please check all of those that you have engaged in alone

with CF/DF in the past week. Check only those activities that were done alone with CF and not

done with CF in the presence of others. In the past week, I did the following activities alone with

CF: (Check all that apply.)

_____did laundry _____talked on the phone _____prepared a meal _____went to a movie

_____watched TV _____ate a meal _____went to an auction/antique show _____participated in

a sporting activity _____attended a non-class lecture or presentation _____outdoor recreation

(e.g., sailing) _____went to a restaurant _____went to a play _____went to a grocery store

_____went to a bar _____went for a walk/drive _____visited family _____discussed things of a

personal nature _____visited friends _____went to a museum/art show _____went to a

department, book, hardware store, etc. _____planned a party/social event _____played

cards/board game _____attended class _____attended a sporting event _____went on a trip (e.g.,

vacation or weekend) _____exercised (e.g., jogging, aerobics) _____cleaned house/apartment

_____went on an outing (e.g. picnic, beach, zoo, winter carnival) _____went to church/religious

function _____wilderness activity (e.g., hunting, hiking, fishing) _____worked on homework

_____went to a concert _____engaged in sexual relations _____went dancing _____discussed

things of a non-personal nature _____went to a party _____went to a clothing store _____played

music/sang

C. Strength

The following questions concern the amount of influence CF/DF has on your thoughts,

feelings, and behavior. Using the 7-point scale below, please indicate the extent to which you

agree or disagree with each statement by placing an "X" over the appropriate circle.

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1. CF/DF will influence my future financial security.

2. CF/DF does not influence everyday things in my life.

3. CF/DF influences important things in my life.

4. CF/DF influences which parties and other social events I attend.

5. CF/DF influences the extent to which I accept responsibilities in our relationship.

6. CF/DF influences the way I feel about myself.

7. CF/DF does not influence my moods.

8. CF influences the basic values that I hold.

9. CF/DF does not influence the opinions that I have of other important people in my life.

10. CF/DF influences when I see, and the amount of time I spend with, my friends.

11. CF/DF does not influence which of my friends I see.

12. CF/DF does not influence the type of career I have/will have.

13. CF/DF influences or will influence how much time I devote to my career.

14. CF/DF does not influence my chances of getting a good job in the future.

15. CF/DF influences the way I feel about the future.

16. CF/DF does not have the capacity to influence how I act in various situations.

17. CF/DF influences and contributes to my overall happiness.

18. CF/DF influences how I spend my free time.

19. CF/DF influences when I see him/her and the amount of time the two of us spend together.

20. CF/DF does not influence how I dress.

21. CF/DF does not influence where I live.

Now we would like you to tell us how much CF/DF affects your future plans and goals. Using

the 7-point scale below, please indicate the degree to which your future plans and goals are

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affected by CF by placing an "X" over the appropriate circle for each item. If an area does not

apply to you (e.g. you have no plans or goals in that area), put an "X" over the circle for "1" (not

at all).

1. My vacation plans

2. My plans to have children

3. My plans to make major investments (house, car, etc.)

4. My plans to join a club, social organization, church, etc.

5. My school-related plans

6. My plans for achieving a particular financial standard of living

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APPENDIX H. TIE-STRENGTH MANIPULATION BY WILCOX AND STEPHEN

(2012)

1. Name listing task:

1) Please list five names of five friends you have on Facebook who you consider as close (weak)

friends.

2) Please rate how much the friend’s opinion matters to you, from 1 (opinion doesn’t matter at

all) to 7 (opinion matters a lot).

2. Manipulation check:

From 1(strongly disagree) to 7 (strongly agree) how much you agree with the following

statement

1) I thought about my close friends

2) I thought about friends whose opinions matter

3) I thought about friends who are influential to me

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APPENDIX I. TIE-STRENGTH MANIPULATION FOR STUDY 3

1. Name listing task:

1) Please list the names of five Facebook friends with who you have a good relationship that

bears high level of closeness, depth, involvement and intimacy, and follow and interact with you

on Facebook

2) Please rate how much each friend’s opinion matters to you, from 1 (opinion doesn’t matter at

all) to 7 (opinion matters a lot).

2. Manipulation check:

From 1(strongly disagree) to 7 (strongly agree) how much you agree with the following

statement

1) I thought about my close (distant) friends

2) I thought about friends whose opinions (doesn’t) matter

3) I thought about friends who are (not) influential to me

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APPENDIX J. THE UNIDIMENSIONAL RELATIONSHIP CLOSENESS SCALE

(Dibble et al., 2012)

Instructions: The following questions refer to your relationship with your romantic

partner [friend, family member, etc.]. Please think about your relationship with your romantic

partner [friend, family member, etc.] when responding to the following questions. Please respond

to the following statements using this scale:

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree

1. My relationship with my _____ is close.

2. When we are apart, I miss my _____ a great deal.

3. My _____ and I disclose important personal things to each other.

4. My _____ and I have a strong connection.

5. My _____ and I want to spend time together.

6. I’m sure of my relationship with my _____.a

7. My _____ is a priority in my life.

8. My _____ and I do a lot of things together.

9. When I have free time I choose to spend it alone with my _____.

10. I think about my _____ a lot.

11. My relationship with my _____ is important in my life.

12. I consider my _____ when making important decisions.

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APPENDIX K. CONSENT FORM

You are invited to participate in a research study on personality. This study is conducted

by Dr. Brittany Duff (associate professor, Institute of Communication Research) from the

University of Illinois Urbana Champaign.

This study will take approximately 15 minutes of your time. You will be asked to

complete an online survey about your perception about some personal attributes. You will

receive 1 dollar after completing the survey.

Your decision to participate or decline participation in this study is completely voluntary

and you have the right to terminate your participation at any time without penalty. If you do not

wish to complete this survey just close your browser.

Your participation in this research will be confidential. Faculty, students, and staff who

may see your information will maintain confidentiality to the extent of laws and university

policies. Personal identifiers will not be published or presented. Data will be averaged and

reported in aggregate. Possible outlets of dissemination may be journal articles and academic

papers. Although your participation in this research may not benefit you personally, it will help

us understand the relationship between self-concept and social media use.

There are no risks to individuals participating in this survey beyond those that exist in

daily life.

If you have questions about this project, you may contact the research assistant, Anlan

Zheng via azheng6@illinois.edu, or Brittany Duff via bduff@illinois.edu. If you have any

questions about your rights as a participant in this study or any concerns or complaints, please

contact the University of Illinois Office for the Protection of Research Subjects at 217-333-2670

or via email at irb@illinois.edu.

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Please print a copy of this consent form for your records, if you so desire.

I have read and understand the above consent form, I certify that I am 18 years old or

older and, by clicking the submit button to enter the survey, I indicate my willingness voluntarily

take part in the study.