j s p racademic.udayton.edu/matthewmontoya/pubs/montoya_horton...r. matthew montoya, university of...

Article

A meta-analyticinvestigation of theprocesses underlyingthe similarity-attractioneffect

R. Matthew Montoya1 and Robert S. Horton2

AbstractThis research investigated two competing explanations of the similarity effect: Byrne’s(1971) reinforcement model and the information processing perspective. Ameta-analysis of 240 laboratory-based similarity studies explored moderators importantto the similarity effect, including set size, proportion of similarity, centrality of attitudes,and information salience. Results indicated effects for proportion of similarity, centralityof attitudes, and information salience, and were largely consistent with predictions of theinformation processing perspective. We discuss the implications of these findings for thetwo perspectives, for other models for the similarity effect, and for the role of affect andcognition in the experience of interpersonal attraction.

Keywordssimilarity effect, interpersonal attraction, liking, reinforcement, cognition, affect

One of the most robust phenomena in attraction literature is the similarity effect (Byrne,

1997): Increased similarity with a target—with respect to attitudes, personality traits, or

a number of other attributes—is associated with increased attraction to the target. The

similarity effect has been observed in a multitude of different populations (e.g., Gaynor,

1 University of Dayton, USA2 Wabash College, USA

Corresponding author:

R. Matthew Montoya, University of Dayton, 300 College Park, Dayton, OH 45469, USA

Email: [email protected]

J S P R

Journal of Social andPersonal Relationships

30(1) 64–94ª The Author(s) 2012

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DOI: 10.1177/0265407512452989spr.sagepub.com

1971; Murstein & Beck, 1972; Tan & Singh, 1995) and has been observed in relation to

personality traits (e.g., Carli, Ganley, & Pierce-Otay, 1991; Steele & McGlynn, 1979),

attitudes (e.g., Bond, Byrne, & Diamond, 1968; Byrne & Blaylock, 1963), hobbies

(Jamieson, Lydon, & Zanna, 1987), and values (Davis, 1979; Lewis & Walsh, 1979),

among other attributes (e.g., Gillis & Avis, 1980; Hill, Rubin, & Peplau, 1976; Peterson

& Miller, 1980; Spuhler, 1968; Stevens, Owens, & Schaefer, 1990; Susanne & Lepage,

1988). A meta-analysis of over 300 similarity studies observed that similarity produces a

positive, moderately sized effect on attraction (Montoya, Horton, & Kirchner, 2008).

Despite the overwhelming evidence that individuals are attracted to others who are

similar to them, the explanation for this effect has been the subject of much debate. The

two models that have garnered the most empirical attention—the reinforcement model

popularized by Byrne and colleagues (e.g., Byrne, 1971; Byrne, Clore, Griffitt, Lam-

berth, & Mitchell, 1973; Byrne & Rhamey, 1965) and the information processing per-

spective (e.g., Ajzen, 1974; Fishbein & Ajzen, 1972; Kaplan & Anderson, 1973)—have

both received substantial support as well as criticism for their (in)ability to account for

the empirical evidence. The reinforcement model, for example, cannot readily explain

why attraction does not occur as often in field studies compared with laboratory studies

(Montoya et al., 2008; Sunnafrank, 1992) or the lack of attraction from similarity of

negative traits (e.g., Ajzen, 1974; Novak & Lerner, 1968). The information processing

perspective has been questioned as to why similarity on less important attitudes (i.e.,

peripheral attitudes) does not lead to less attraction than similarity on important attitudes

(i.e., central attitudes; Clore & Baldridge, 1968) and why the similarity effect is not

affected by the number of attitudes one is regarded as similar to a target (i.e., set size;

Byrne et al., 1973).

Although several other models have been posited to explain the similarity effect (e.g.,

the repulsion hypothesis; Rosenbaum, 1986), the two models outlined above have

generated the vast majority of empirical work and have been elaborated on in enough

detail to generate specific predictions for a diversity of findings associated with the

effect. In the current research, we conducted a meta-analysis of laboratory investigations

of the similarity effect, in order to (a) test the validity of the reinforcement and informa-

tion processing perspectives, and (b) investigate variables that moderate the similarity

effect, in the hope of understanding more clearly what is responsible for interpersonal

attraction.

Models of the similarity effect

Byrne’s reinforcement model

The early dominant model of the similarity effect was posited by Byrne (1971), who

borrowed concepts from cognitive dissonance theory (Festinger, 1957) and classical

conditioning to argue that similar attitudes serve as reinforcers. According to this per-

spective, individuals have a fundamental need for a logical and consistent view of the

world, a need that Byrne called the effectance motive. Individuals favor stimuli that

reinforce the logic and consistency of their world. People who agree with us validate our

ideas and attitudes and, in so doing, reinforce the logic and consistency of our world (i.e.,

Montoya and Horton 65

satisfy our effectance motive). Similar people are reinforcing and thus are associated

with positive feelings, which in turn lead to attraction. People who disagree with us

create inconsistency in our world (i.e., do not satisfy the effectance motive) and are

associated with anxiety and confusion—feelings that lead to repulsion or, at the very

least, lack of attraction. Importantly, such reinforcements, like other classically

conditioned associations, occur automatically and in the absence of conscious awareness

(Byrne & Clore, 1970; Byrne, Rasche, & Kelley, 1974; Clore & Gormly, 1974). Byrne

and colleagues labeled their theoretical account the reinforcement model (Byrne et al.,

1973).

Information processing

A second explanation of the similarity effect posits that it is a function of the valence and

weight of information that one infers about an individual based on similarity or dis-

similarity. According to this information processing perspective (e.g., Ajzen, 1974;

Kaplan & Anderson, 1973; Tesser, 1971), one person’s attraction to another is deter-

mined by the information one has about the other. The available information acts as a

direct and immediate influence on attraction. If the information is favorable, then

attraction results. The effect of similarity on attraction can be understood as a product of

the information implied by a target’s similar or dissimilar attitudes, personality traits, or

other attributes. In the words of Kaplan and Anderson (1973, p. 304), ‘‘when we are told

that X has similar attitudes, we like him not because that information acts as an

unconditioned stimulus, but because it leads us to expect that he has various positive

aspects to his personality.’’

First, the information inferred from an attribute (e.g., attitude or personality trait) is

assigned a valence. Information that is regarded as positive will lead to attraction.

Importantly, individuals use their own attributes as an anchor by which to assess the

information they infer about another (Ajzen, 1974; Insko et al., 1973). Because indi-

viduals evaluate their own attributes positively, attributes similar to their own are also

evaluated positively. Dissimilar attributes are judged less positively and result in dislike

(e.g., Stalling, 1970). In turn, we infer positive information about similar others and less

positive information (or even negative information) about dissimilar others, information

that translates into differential attraction to similar and dissimilar others.

Second, attributes and the information they imply are assigned a weight, or impor-

tance. That weight is at least partially a function of the amount of information one infers

about a target. The more information one infers about a target from a particular attribute,

the more important that attribute will be to determining one’s attraction to the target. As

such, more informative attributes should produce more polarized judgments than less

informative attributes. In this way, whereas informative positive and negative stimuli

should lead to relatively extreme evaluations and to attraction and repulsion, respec-

tively, non-informative stimuli, either of positive or negative valence, should have less of

an impact on interpersonal judgments.

Third, the salience of information is an important determinant of interpersonal

judgments. That is, the more attention one allocates to information, the more that

information will affect one’s judgments. This presumption is consistent with previous

66 Journal of Social and Personal Relationships 30(1)

work emphasizing the role of salience on judgments (e.g., Hastie & Kumar, 1979; Jones

& Davis, 1965), and several theorists have gone farther to specifically posit that it is the

reduced salience of the similarity/dissimilarity information that reduces similarity’s

impact outside of the laboratory setting (Montoya et al., 2008; Sunnafrank, 1992). In all,

it suggests that the impact of similarity on attraction should be particularly potent when

the information implied by such similarity is salient.

Different predictions?

The reinforcement model and the information processing perspective make different

predictions regarding factors affecting the magnitude of the similarity effect. In fact, the

empirical literature has identified multiple variables that affect the magnitude of the

similarity effect and about which these two perspectives make different predictions. As

is discussed in the following sections, these moderators provide the means by which to

compare the two explanations.

Moderators of the similarity effect

Type of stimuli

One frequently explored moderator of the similarity effect is the centrality, or the

importance, of the stimuli used in the description of the target. Newcomb (1956, p. 578)

argued that ‘‘the discovery of agreement between oneself and a new acquaintance

regarding some matter of only casual interest will probably be less rewarding than the

discovery of agreement concerning one’s own pet prejudices.’’ However, past research

related to attitude importance has been equivocal: Some studies have found an effect for

attitude importance (Davis, 1981; Cheney, 1975); others have not (Byrne & Nelson,

1964; 1965).

With respect to the reinforcement model, Byrne and colleagues (e.g., Byrne, London,

& Griffitt 1968; Clore & Baldridge, 1968, 1970) concluded, based on the evidence

available at the time, that there was no effect for attitude importance on the similarity

effect. However, it is important to note that Byrne and Rhamey (1965, p. 887, emphasis

added) acknowledge the possibility of such an effect by suggesting that ‘‘if such weights

can be empirically established, the [Byrne–Rhamey] attraction law should be rewritten’’

and claim that such an effect is not fundamentally inconsistent with the reinforcement

model. In such a case, Byrne and colleagues (1973) posited, central attitudes would be

associated with a greater weight than peripheral attitudes, and such additional weight

would then result in greater attraction (or repulsion).

With respect to the information processing perspective, attitude centrality should

influence the weight of the stimuli, such that central stimuli are likely to imply more

information about a target than peripheral stimuli. As such, the information processing

perspective posits that central attitudes should produce more liking from similarity (and

more disliking from dissimilarity) than do peripheral attitudes, because there is more

information implied about the target (Kaplan & Anderson, 1973; Tesser, 1971).


Personality traits versus attitudes. A related question is whether the similarity effect

operates to a similar degree for personality traits as it does for attitudes. Previous

research found mixed results when investigating whether personality trait similarity

leads to attraction: Some researchers detected a relation (e.g., McLaughlin, 1970; 1971),

whereas others failed to (Hoffman & Maier, 1966; Katz, Cohen, & Castiglione, 1963;

Reilly, Commins, & Stefic, 1960).

For the reinforcement model, Byrne, Griffitt, and Stefaniak (1967, p. 83) stated that

‘‘similarity to self, whether involving attitudes or values or abilities. provides evidence

that one is functioning in a logical and meaningful manner’’ and, as a result, should cause

increased attraction to the target. In this way, the reinforcement model predicts that

personality trait similarity should result in increased attraction, as does attitude simi-

larity, but did not hypothesize that it should lead to more or less attraction than attitude

similarity (see also Byrne et al., 1973).

For the information processing perspective, attitude similarity should have a different

effect on attraction than personality trait similarity, to the extent that the amount of

information implied by attitudes is greater (or less) than it is for personality traits.

Though the question has rarely been explored empirically, direct and indirect evidence

indicates that attitudes are, in fact, more informative than personality traits: Individuals

are better able to evaluate others when the other is described in terms of the attitudes they

hold versus the personality traits they possess (e.g., Higgins & Winter, 1993; Reeder,

2009; Smith & Collins, 2009), and observers infer more information about targets from

their attitudes than from their personality traits (Horton & Montoya, 2012). As such, the

information processing perspective would posit that attitudes should be associated with a

larger effect for similarity.

Set size

Set size refers to the number of stimuli used to manipulate and assess similarity. Previous

research has been inconsistent regarding the impact of set size on attraction (Byrne,

1971). Whereas Byrne and Rhamey’s (1965) law of attraction stated that attraction is a

function of the proportion of similar attitudes, regardless of whether 10 or 100 attitudes

were used in the similarity manipulation (see also Byrne & Nelson, 1965; Rosenblood,

1970), other research has emphasized the influence of set size (e.g., Kaplan & Anderson,

1973).

As with the case of the Type of Stimuli moderator, the prediction of no effect for set

size is taken from the empirically derived Byrne–Rhamey (1965) law of attraction rather

than the reinforcement model. Had Byrne and colleagues identified an effect for set size

in past research, it is likely that the reinforcement model—and the law of attraction—

would have been modified to match the empirical findings. In this way, the reinforce-

ment model does not ‘‘rule out’’ an effect for set size per se, but nor does it predict one

(Byrne et al., 1973).

Alternatively, the information processing perspective posits that set size will have an

effect on the magnitude of the similarity effect because the amount of information

increases as set size increases (Kaplan & Anderson, 1973). Moreover, this perspective

posits that the relation between attraction and set size will increase linearly for small set


sizes, but will then asymptote for larger set size values (Kaplan & Anderson, 1973), as

each additional stimulus is relatively less informative of the other’s attributes.

Information salience

Information salience refers to the degree to which the information regarding the target is

consciously available immediately before the attraction assessment. Montoya and

Horton (2004), for instance, manipulated whether participants completed four ‘‘cogni-

tive evaluation’’ items prior to assessing attraction to a similar or dissimilar target.

Responding to the four evaluation items made salient the information implied by the

attitude similarity (or not) and resulted in increased attraction; not doing so left that

information relatively non-salient and eliminated the influence of similarity on attrac-

tion. Similarly, Simons (2008) manipulated the order in which interpersonal attraction

and cognitive evaluation items were assessed. He found that when the cognitive evalua-

tion items were assessed before the attraction assessment (high salience), the cognitive

evaluation items were significantly better mediators of the similarity effect than when

they were presented after the attraction assessment (low salience).

The reinforcement model—grounded in the principles of classical conditioning—

maintains that one’s assessment of attraction results from the amount of reinforcement

that is associated with the target—a process that is independent of one’s cognitive

processes. The reinforcement model suggests that such cognitive processes occur either

simultaneously with (Byrne et al., 1974) or after (e.g., Byrne & Clore, 1970; Clore &

Gormly, 1974) the experience of interpersonal attraction. By this reasoning, then, sal-

ience of information should not affect attraction.

The information processing perspective posits that salient information will have a

greater influence on attraction (Fishbein & Ajzen, 1975). As noted previously, this

perspective proposes that similarity drives a positive evaluation of the target other, which

then produces attraction. The more salient that evaluation is, the more powerful its

influence should be (Shaffer & Tabor, 1980; Snyder & Ebbesen, 1972).

Proportion of similarity

Proportion of similarity refers to the ratio of similar to dissimilar stimuli. This moderator

is the foundation of the similarity effect and is the defining characteristic of similarity

studies. The literature has consistently found an effect for proportion of similarity: Byrne

(1962), for example, manipulated seven progressive levels of proportion of similarity

and found that attraction increased linearly. In additional studies designed to investigate

different proportions of similarity, both Byrne and Nelson (1965) and Byrne and Rhamey

(1965) found similar results.

Proportion of similarity is a moderator about which the reinforcement model and

information processing perspective agree: Both predict an effect. The reinforcement

model regards a higher proportion of similarity as providing more reinforcement for

one’s own views, and thus more positive affect associated with a target. The information

processing perspective regards the higher proportion of similarity as providing a higher

proportion of positive, as opposed to negative, information about a target. Such relative


prevalence of positive information should produce more attraction. Although the two

perspectives make similar predictions, we included this in our analysis to confirm the

basic predictions of these models.

Purpose of this research

We conducted a meta-analysis of 240 laboratory studies of similarity effect to compare

the merit of the reinforcement and the information processing perspectives. A meta-

analysis provided the opportunity to test the different predictions of the two perspectives.

As summarized in Table 1, these predictions involved moderators of the similarity effect:

Type of stimuli, set size, and information salience. We also tested the moderating effect

of proportion of similarity to test a basic assumption of both perspectives.

Method

Meta-analysis sample

The studies included in this meta-analysis are a subset of those reported in Montoya et al.

(2008). Studies for the original sample were gathered in three ways. First, an electronic

literature search was conducted using the PsycINFO (1887 – July 2004) and Dissertation

Abstracts International (1861 – July 2004) databases. Keywords were ‘‘assumed,’’ ‘‘atti-

tude,’’ ‘‘attraction,’’ ‘‘complimentary,’’ ‘‘congruence,’’ ‘‘dissimilarity,’’ ‘‘homogamy,’’

‘‘ideal self,’’ ‘‘liking,’’ ‘‘perceived,’’ ‘‘personality,’’ ‘‘reinforcement-affect,’’ ‘‘repulsion,’’

and ‘‘similarity,’’ Second, the sent a request for relevant studies to an Internet discussion

forum; third, we contacted investigators who had frequently published research on the

similarity effect. We selected only laboratory-based studies that compared similar and

dissimilar attitudes or similar and dissimilar personality traits, resulting in 240 laboratory

studies. From these studies, we extracted 337 similarity–dissimilarity effect sizes with a

total sample size of 28,674 participants. Sample sizes ranged from 13 to 509

(M ¼ 83.84, SD ¼ 71.24). Inspection of the funnel plot of laboratory studies by Montoya

et al. (2008) provided no support for publication bias for this set of studies (see Montoya

et al., 2008, p. 889 for the funnel plot).

Data coding

Type of stimuli. Laboratory studies of the similarity effect most frequently manipulate

similarity using either attitudes or personality traits (Montoya et al., 2008). In order to

test predictions of the reinforcement and information processing perspectives using as

many studies as possible, we first coded this variable as a categorical variable with two

levels: Attitude study and personality study. A study was coded as an attitude study if

participants were asked to evaluate specific objects or issues (e.g., death penalty,

abortion, discotheques). We coded studies as a personality trait study if participants

completed either a personality trait assessment questionnaire (e.g., California Personality

Inventory, Minnesota Multiphasic Personality Inventory) or a specific personality trait

assessment (e.g., extraversion, agreeableness, hypertraditionality).


As a more sensitive test of this moderator, we further classified attitude studies into

one of three categories: Peripheral, central, and unclassified. Studies that used attitudes

to manipulate similarity in which the attitudes were defined by the authors as ‘‘central,’’

‘‘critical,’’ or ‘‘important’’ attitudes were coded as ‘‘central’’ attitude studies. Studies

that included attitudes that were described by the authors as ‘‘unimportant,’’ ‘‘irrele-

vant,’’ or ‘‘peripheral’’ were coded as ‘‘peripheral’’ attitude studies. A vast majority of

studies did not report their attitudes and therefore were labeled ‘‘unclassified.’’ Such a

process resulted in a four-level variable: personality trait studies, central attitude studies,

peripheral attitude studies, and unclassified attitude studies.

Set size. We coded set size as a continuous variable that was equivalent to the number of

items used to manipulate the degree of similarity. In any study in which the participant

received information from the target other, set size was defined as the number of stimuli

about which the participant received information.

Information salience. Information salience was coded as a categorical variable with two

levels: Salient evaluation present and salient evaluation absent. We coded each study by

noting the questions used to assess the target prior to the assessment of interpersonal

attraction. For example, the Interpersonal Judgment Scale (IJS, Byrne, 1971) includes

four ‘‘filler’’ questions that ask the participants about the attributes of the target other and

that precede the two attraction items. Montoya and Horton (2004) argued that the

‘‘filler’’ questions that ask participants to rate the target other’s intelligence, adjustment,

morality, and competence on a given task make salient their evaluation of the target

other. We coded as ‘‘information salient’’ those studies in which participants assessed

the overall quality of the target before indicating their attraction to the target. We coded

as ‘‘information not salient’’ those studies that did not meet this criterion.

Proportion of similarity. We coded proportion of similarity as a continuous variable with the

value assigned equal to the percentage of similar attributes. The proportion of similarity

was the percent of the partner’s attributes that were similar to those of the participant. For

example, in studies that were characterized by ‘‘75 vs. 25,’’ participants shared 75% of

attitudes in the similar condition and 25% of attitudes in the dissimilar condition, and

thus these studies were coded with a value of ‘‘75.’’ We excluded from this analysis any

Table 1. Predictions for the reinforcement and information integration models

Reinforcement model Information processing perspective

Set size No Yes, but curvilinearType of stimuli Noa Yes

Attitude vs. personality trait No YesInformational salience No YesProportion of similarity Yes Yes

a Later theorizing indicated that this prediction would change to ‘‘Yes’’ if empirical data were to becomeavailable.


study that failed to report the degree of similarity or was continuous in nature (i.e., the

percentage of similarity between individuals was derived from a post hoc comparison of

the participant’s attributes compared with another’s attributes).

Other variables. For each similarity–dissimilarity comparison we coded basic descriptive

information and additional variables for exploratory and sensitivity analyses. These

variables included: Author and full citation; source (journal, edited volume, thesis or

dissertation, or unpublished manuscript); sample (college students, adults, or school

children); year of publication; type of personality traits measured (specific personality

trait, complete scale); type of relationship (stranger, friend, boyfriend/girlfriend, mar-

riage partner); recruitment method (participant pool, monetary incentive, or volunteer);

sample size; and sex composition of the sample (all men, all women, men and women in

interactions that were homogenous with respect to sex, or men and women in interactions

that were heterogeneous with respect to sex).

Statistical methods

Effect sizes used

The effect size index was Fisher’s z (Fisher, 1928), calculated such that greater positive

values indicated greater attraction for similar others and negative values indicated

greater attraction for dissimilar others. An effect size of zero indicates no relation

between similarity and attraction. Following the recommendations of Rosenthal (1994),

we used the effect size z because of its conceptual superiority over effect size d for

studies involving continuous data.

Random-effects model

We selected a random-effects model in order to make unconditional inferences that gen-

eralized to the hypothetical population of all studies that could exist, rather than simply

to the studies included in the present sample (Hedges & Vevea, 1998). We used the

method of unconditional maximum likelihood to estimate model parameters. Effect sizes

were computed by weighting each individual effect size by the inverse of its variance.

We computed a Q-statistic to test the assumption of homogeneity and an I2 to test the

amount of heterogeneity.

For our analyses, we first tested an empty model to determine the average size of the

similarity effect. We then tested a model with the four proposed moderators of interest

entered simultaneously.

Sensitivity analyses. We made a number of assumptions in this meta-analysis. For example,

we identified several moderators for theoretical reasons (e.g., set size, proportion of simi-

larity) and, as such, implicitly assumed that others were less important. To investigate

the possible consequences of these assumptions, we performed a series of sensitivity

analyses. Our sensitivity analyses involved two processes. First, we tested the six two-

way interactions between the four moderators of interest to determine whether important


interactions would emerge. Second, to determine if a potentially important moderator

was excluded from our a priori analyses, we conducted additional analyses in which

other potential moderators (e.g., gender, sample, source) were included one at a time into

the a priori model.

Results

Overall model

A Q-test of the null hypothesis that it is plausible that the true variance component is zero

was significant (variance component ¼ 0.055), Q(337) ¼ 2886.41, p < .05. A test of

heterogeneity indicated substantial heterogeneity across studies, I2¼ 88% (95% confi-

dence interval [CI] ¼ 79% to 96%). Using a random-effects estimate, the effect size was

strong (r ¼ .59; 95% CI: .55, .63) and different from zero, z ¼ 29.60, p < .05.

Moderators

Type of stimuli. As noted in Table 2, the type of stimuli moderator reached significance.

The means for type of stimuli are presented in Table 3. We contrast coded this four-level

variable using three orthogonal contrasts. Contrasts were calculated to test two key

hypotheses: (a) whether central attitudes were associated with a larger effect than periph-

eral attitude studies, and (b) whether attitude studies, compared with personality trait

studies, were associated with a larger effect. The first contrast, which compared periph-

eral attitude studies to central attitudes, was significant, w2(1) ¼ 4.39, p < .05, and indi-

cated that central attitudes were associated with a larger effect than peripheral attitudes.

The second contrast detected a difference between all attitude studies and personality

trait studies, w2(1) ¼ 6.25, p < .05, revealing that attitude studies are associated with a

larger effect size than personality trait studies. The final contrast, which was included

without theoretical prediction, compared central and peripheral attitudes to personality

trait studies, and was not significant, w2(1) ¼ 0.41, p ¼ .52. The contrast indicated that

the averaged similarity effect for central and peripheral attitudes was not different from

that for the personality trait studies.

Set size. As presented in Table 2, the main effect for set size was not significant. Given

the predictions of the information integration perspective, we also tested set size as a

quadratic function. The effect was also not significant, w2(1) ¼ 1.51, p ¼ .21.

Information salience. The main effect for information salience was significant. The main

effect indicated that the similarity effect was more potent when the information was

made salient than when it was not.

Proportion of similarity. We treated proportion of similarity as a continuous variable. The

similarity effect was stronger as the proportion of similarity increased. To illustrate the

effects of proportion on the similarity effect, Table 3 presents proportion of similarity as


a categorical variable using the commonly used operationalizations of proportion. There

was a clear trend for the similarity effect to increase as proportion of similarity increased.

Sensitivity analyses

Sensitivity analysis discovered that one additional factor, gender, was also associated

with the size of the similarity effect, w2(3) ¼ 12.39, p < .05. To account for gender’s

association with the similarity effect, we created a factor that accounted for not only the

gender of the participant, but also the gender of the target other. The factor included four

participant gender and target gender combinations: female participant–female target,

male participant–male target, unspecified participant (defined as groups of participants

Table 3. Mean effect sizes (in r) for each level of the similarity effect moderators

k Effect size (r)

OverallOverall effect 337 .59

Information salienceSalient 239 .60Not salient 98 .54

Type of stimulusPersonality trait 26 .48Unclassified attitude 244 .62Central attitude 50 .52Peripheral attitude 16 .41

Proportion of similarity75% similar–25% dissimilar 54 .4780% similar–20% dissimilar 42 .5483% similar–17% dissimilar 48 .6190% similar–10% dissimilar 19 .62100% similar–0% dissimilar 142 .62

Gender (Gender of participant–gender of target)Female–female 41 .69Male–male 54 .47Unspecified gender–same gender partner 197 .61Unspecified gender–opposite gender partner 45 .49

Note. Positive values indicate stronger relation between similarity and attraction. Numbers may not add up to337 due to missing values for some studies.

Table 2. Parameter estimates for similarity effect moderators

w2 d.f. P

Set size 0.21 1 .64Type of stimuli 9.05 3 .00Informational salience 4.11 1 .00Proportion of similarity 7.50 1 .00


whose gender was not specified, nor accounted for, in the original study)–matched gender

target, and unspecified participant–opposite gender target. To explore this moderator, we

created three orthogonal contrasts. The first contrast compared female–female interactions

to male–male interactions. This contrast revealed that female–female interactions were

associated with a stronger similarity effect than were male–male interactions, w2(1)¼ 4.87,

p < .05. The second contrast compared unspecified participant–matched gender target to the

male–male and female–female conditions, and was not significant, w2(1) ¼ 0.19, p ¼ .63.

The final contrast, which compared the unspecified participant–opposite gender target

condition to the three other levels (i.e., contexts in which the gender of the target matched

that of the participant), was significant, w2(1) ¼ 5.10, p < .05. This contrast indicated that

unspecified–matched interactions produced a weaker similarity effect than did the combi-

nation of all gender-specified interactions.

Information salience by proportion of similarity. Additional tests identified an Information

Salience� Proportion of Similarity interaction, w2(1)¼ 6.42, p < .05, such that increased

proportion of similarity was associated with a stronger similarity effect when informa-

tion was salient than when information was not salient. In other words, the slope for

proportion of similarity was greater when the available information was salient than

when it was not.

Information salience by type of stimuli. There was also an Information Salience � Type of

Stimuli interaction, w2(3) ¼ 9.11, p < .05, such that salient information led to a stronger

similarity effect in studies that used central attitudes or that were ‘‘unclassified,’’ but not

when the studies used peripheral attitudes or personality traits as stimuli.

Discussion

Our meta-analysis of 240 laboratory studies resulted in support for three of the four

effects proposed by the information processing perspective, and for two of the four

proposed by Byrne’s reinforcement model. More specifically, we observed effects for

two moderators which information processing predicted and the reinforcement model

did not: Type of stimuli and information salience. First, more informative stimuli

(e.g., central attitudes) were associated with a larger similarity effect than were less

informative stimuli (e.g., peripheral attitudes). Second, the similarity effect was greater

when similarity information was salient before the attraction assessment. Proportion of

similarity was also influential, but this was an effect that both the reinforcement model

and information processing perspective predicted.

In addition, two interactions identified during the sensitivity analyses were consistent

with the information processing perspective. Specifically, the type of stimulus and

proportion of similarity mattered more when the information about the target was made

salient compared to when it was not. Given the emphasis on cognitive processing in the

information processing perspective, having more information available and salient

should have a greater impact on the later experienced attraction. Alternatively, given the

emphasis and importance of affective processing of the reinforcement model (and the


related suggestion that any cognitive processes occur simultaneously or after the

affective processes), such findings would not necessarily be predicted by this approach.

Importantly, not all of the meta-analytic findings contradict Byrne’s reinforcement

model. First, Byrne et al. (1973) stated that if set size was found to be important to the

similarity effect, the model would require revision. However, we found no evidence that

the similarity effect is affected by the number of stimuli. Despite a positive slope within a

meta-analysis of a large sample (k¼ 337), the main effect for set size was not significant.

As such, it appears that Byrne et al. (1973) were accurate in suggesting that any influence

of similarity information is as important for 10 stimuli as it is for 100 stimuli. Second,

Byrne and colleagues treated centrality as a hypothetical possibility that may need to

be included in the model if data indicated such need. In several papers (e.g., Byrne &

Clore, 1970), the researchers presented an alternative equation for the law of attraction

had there been sufficient empirical evidence to support ‘‘differential weighting in the

similarity effect:’’

AR ¼ f

PðPR�MÞP

ðPR�MÞ þPðNR�MÞ

� �ð1Þ

such that AR is the affective response (e.g., attraction), M is the Magnitude (i.e., the

weight associated with the reinforcements), and PR and NR represent the positive and

negative reinforcements, respectively. The current findings at the very least provide

empirical evidence to support the adoption of such a weighted model.

It should be noted that given the large sample size of the meta-analysis, it was likely

that significant effects were more ‘‘statistically’’ significant than they were ‘‘practically’’

important. For example, whereas we identified a significant effect for Information Sal-

ience, the mean difference between ‘‘salience’’ and ‘‘no salience’’ studies was .06, with

‘‘no salience’’ studies still producing a notable effect size. When comparing the predic-

tive ability of different theoretical models, however, the critical question is whether there

is a difference between models in their ability to explain the extant data; in that light,

such (perhaps small) effects remain meaningful.

Implications for other models of the similarity effect

It is important to acknowledge that the current work focused on only two explanations

for the similarity effect and excluded others, such as the rewards of interaction

(Berscheid & Walster, 1969), anticipation of liking (Condon & Crano, 1988), and

repulsion (Rosenbaum, 1986) perspectives. We focused on the reinforcement and

information processing perspectives because they specifically theorized about mod-

erators important to the similarity effect and have been explicitly tested in previous

work. But it is important to note that aspects of other models can be informed by the

findings of this meta-analysis.

Rewards of interaction. A rewards of interaction perspective (Berscheid & Walster, 1969)

posits that the degree of similarity between two persons predicts the number of rewards


an individual expects to experience in an interaction with another. One testable aspect of

this approach is that items that are more informative of the rewards (e.g., central

attitudes) should be more affected by similarity than less informative items (e.g.,

peripheral attitudes; Santee, 1976; see also Davis, 1981). From the rewards of interaction

perspective, similarity of central attitudes should have a greater influence on attraction

because they are more informative about the rewards that might be expected in the

future. The results of this meta-analysis are consistent with this prediction.

Second, the rewards of interaction approach has its roots in equity theory (Walster,

Walster, & Berscheid, 1978), and as such, includes an emphasis on the cognitive pro-

cesses that determine benefits and costs. In this light, salience of the anticipated

rewards—particularly those inferred from central attitudes—should be more influential

on attraction. As described earlier, we found a main effect for salience, in addition to two

interactions involving the salience variable that indicate that the effects for similarity are

stronger for salient similarity information. In this way, many of the predictions of the

rewards of interaction approach would be supported by the results of this meta-analysis.

Repulsion hypothesis. Rosenbaum (1986) proposed that the positive relation between simi-

larity and attraction may result from the repulsion caused by the dissimilar attitudes—the

greater the number of dissimilar attitudes, the greater the repulsion. Although this is a tes-

table proposition for a meta-analysis, to our knowledge only four studies have independently

varied the number of similar and dissimilar attitudes, making a meta-analysis impractical.

Additionally, results of those studies are mixed: Rosenbaum found that the number of dis-

similar attitudes had an effect on attraction but the number of similar attitudes did not, while

Smeaton, Byrne, and Murnen (1989, Study 1) held the number of dissimilar attitudes con-

stant but changed the number of similar attitudes. Contrary to Rosenbaum’s (1986) findings,

attraction increased with an increasing number of similar attitudes.

However, what is really at the heart of this debate is whether dissimilar attitudes have

a disproportionate influence on attraction when compared with similar attitudes. Indeed,

research has repeatedly demonstrated that dissimilar attitudes affect attraction more than

similar attitudes do (e.g., Singh & Teoh, 1999). Whereas the notion that dissimilar

attitudes contributes more to the attraction process than do similar attitudes was dis-

missed out of hand by Byrne and colleagues (Smeaton et al., 1989), it has become clear

that dissimilar stimuli—due to a multitude of different affective and cognitive pro-

cesses—contribute more to the experience of attraction than do similar stimuli (e.g., Jia

& Singh, 2009; Singh & Ho, 2000; Singh, Lin, Tan & Ho, 2008; Singh & Teoh, 1999).

Such an explanation could be incorporated into both the information processing and

reinforcement perspectives: (a) for the reinforcement perspective, it would necessitate a

different weight for positive versus negative reinforcements (called ‘‘magnitude’’ in

Equation 1 above); and (b) for the information processing perspective, it would simply

involve more information being inferred from negative stimuli than from positive

stimuli. Such an interpretation indicates that both models—and thus the present

meta-analytic findings—can incorporate the expectations of the repulsion hypothesis.

Anticipation of liking. Condon and Crano (1988) argued that a person’s attraction toward

another is a function of the extent to which the other is assumed to like that person. From


this perspective, people anticipate that those with similar attitudes will like them, and

those with dissimilar attitudes will dislike them. In effect, people like similar others

because they expect those others to like them. This explanation mirrors another classic

finding in the attraction literature: The reciprocity of liking effect (Gouldner, 1960). It is

interesting to note that past research has found that the reciprocity of liking effect is

approximately three times stronger than that of the similarity effect (e.g., Bell & Baron,

1974; Byrne & Ervin, 1969; Clore & Baldridge, 1970).

There is likely one prediction of this approach that is inconsistent with the meta-

analytic results: Specifically, set size. It may be hypothesized that this approach would

argue that increased set size should be associated with a smaller similarity effect. With

respect to the influence of a specific stimulus, similarity researchers have concluded that

the effects of similarity information are additive (Byrne, 1971), whereas the same addi-

tive rules may not apply to liking information. In other words, inconsistent liking infor-

mation (i.e., ‘‘I like you. I don’t like you. I like you.’’) may influence the evaluations of

others differently from similarity information’s influence on the evaluations of others.

There are two reasons for this difference: (a) As noted within the gain–loss literature

(Aronson, 1969; Aronson & Linder, 1965), not all expressions of (dis)liking are

weighted equally, such that expressions of dislike after liking have a disproportionate

influence on evaluations; and (b) expressions of liking are informative of another’s

‘‘benevolent’’ orientation toward the other (Montoya & Insko, 2008) and situations in

which a benevolent orientation is repeatedly violated may lead to reduced attraction.

Such a difference would be most prominent with greater set sizes, such that whereas

similarity models would posit that such reinforcements/punishments would accumulate

additively, the reciprocity of liking literature would posit that there is a greater opportu-

nity for benevolent expectations to be repeatedly violated. In this way, it is plausible to

hypothesize that a reciprocity of liking approach would posit that increased set size

would be associated with a smaller similarity effect—a result inconsistent with the find-

ings of this meta-analysis.

The bigger picture

The reinforcement model and the information processing perspective represent two

distinct perspectives on the experience of attraction: At their ideological cores, the

information processing perspective posits a principally cognitive approach, whereas the

reinforcement model posits a fundamentally affective approach. Our findings not only

provide evidence regarding the efficacy of these models for explaining the similarity

effect, but also provide additional evidence for the processes that underlie the broader

experience of attraction. As noted earlier, the findings were largely consistent with the

information processing perspective: We found a main effect for salience, and discovered

that the salience variable interacted with two variables important for the operation of the

similarity effect.

Indeed, the current findings are consistent with recent explanations of other attraction

phenomena that focus on underlying cognitive processes. For example, whereas initial

explanations of the pratfall effect focused on affective forces (e.g., Aronson, Willerman,

& Floyd, 1966), more recent explanations have focused on a combination of cognitive


and affective processes (e.g., Herbst, Insko, & Gaertner, 2003). Similarly, initial

explanations of the reciprocity of liking effect focused considerably on affective pro-

cesses (e.g., Adams, 1963); but subsequent research posited that cognitive processes

played a dominant role in the effect (e.g., Montoya & Insko, 2008). Further, initial

explanations of increased attraction to others who are present during an anxiety-

producing event focused on affect (Dutton & Aron, 1974), but later interpretations

focused on the influential role of cognitive processes (e.g., Foster, Witcher, Campbell,

& Green, 1998). Specific to the similarity effect, research has provided evidence that

cognitive processes, compared to affective processes, do a better job at mediating the

effect. In a series of studies that specifically tested the different mediators of the similar-

ity effect, Singh and colleagues (Singh et al., 2007; Singh, Ng, Ong, & Lin, 2008) found

that affect did not mediate the influence of similarity on attraction when cognitive med-

iators were included in the model, whereas cognitive processes consistently mediated the

effect. Overall, then, cognitive processes effectively explain a variety of attraction phe-

nomena, including the similarity effect, many of which were originally presumed to be a

result of affective processes.

It is noteworthy that this cognitive perspective can account effectively for not only the

current meta-analytic findings, but also some interesting inconsistencies in the similarity

effect. As an example, take the finding that similarity on negative attributes does not lead

to attraction (Novak & Lerner, 1968). Though other models struggle to explain this find-

ing (e.g., Ajzen, 1974), the information processing perspective argues that similarity of

negative attributes conveys negative information about the target. This negative informa-

tion would not lead to attraction, but to avoidance. The fact that the information process-

ing perspective explains diverse, and sometimes disparate, findings through the same

cognitive model speaks to its value as a more general theory of attraction.

Conclusion

This meta-analysis explored four critical moderators of the similarity effect. The key

findings included significant main effects for type of stimulus, proportion of similarity,

and information salience. The main effect for information salience, in addition to two

interactions involving this variable, provided evidence for the role of cognitive processes

in the experience of attraction. In this way, these results provided more support for the

information processing perspective than for the reinforcement model and, more gener-

ally, provided more support for a cognitive approach than for an affective-based

approach.

Acknowledgements

We are grateful to all the authors who made additional information available on their studies. We

also thank the SCAR Group for their comments and assistance with this research.

Funding

This research received no specific grant from any agency in the public, commercial, or not-for-

profit sectors.


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