building a new model of time-related academic behavior: procrastination and timely engagement x...

BUILDING A NEW MODEL OF TIME-RELATED ACADEMIC BEHAVIOR: PROCRASTINATION AND TIMELY ENGAGEMENT × MOTIVATIONAL ORIENTATIONKamden K. Strunk

PROCRASTINATION IS A PROBLEM Researchers identify 40% to 60% of students as high

in procrastination (Onwuegbuzie, 2004; Ozer, Demir, & Ferrari, 2009; Rothblum, Solomon, & Murakami, 1986; Solomon & Rothblum, 1984).

The same levels are identified in cross-cultural studies, demonstrating this is not isolated to the U.S. or even Western contexts (Klassen, Ang, Chong, Krawchuch, Huan, Wong, & Yeo, 2009; Ozer, Demir, Ferrari, 2009).

Procrastination is association with negative health effects (Rothblum, Solomon, & Murakami, 1986; Tice & Baumeister, 1997), adverse psychological outcomes (Owens & Newbegin, 2000), and lowered academic performance (Owens & Newbegin, 2000; Tice & Baumeister, 1997).

THE TRADITIONAL MODEL What is procrastination?

Traditionally, it has been defined as something that happens to the individual, rather than something the individual chooses to perform.

In the traditional model, all people procrastinate to one degree or another.

Procrastination can be understood in this conceptualization as a deficit of the individual, inherent in the person, which becomes explicit in their behavior in the form of procrastination.

THE TRADITIONAL MODEL Personality:

Neuroticism (Hess, Sherman, & Goodman, 2000; Johnson & Bloom, 1995; van Eerde, 2003)

Perfectionism (Flett, Blanksten, Hewitt, & Koledin, 1992; Onwuegbuzie, 2000; Saddler & Buley, 1999)

It is who the person is, not what the person does. Self-protective mechanism:

Self-handicapping (Beck, Koons & Milgram, 2000; van Eerde, 2003)

Avoidant coping/cognitive style (Alexander & Onwuegbuzie, 2007; Burns, Dittmann, Nguyen, & Mitchelson, 2000; Carden, Bryant, & Moss, 2004; Collins, Onwuegbuzie, & Jiao, 2008; Deniz, Tras, & Aydogan, 2009; Fritsche, Young & Hickson, 2003; Owens & Newbegin, 1997)

It is unintentional as a result of a deficient thinking style.

THE TRADITIONAL MODEL Inability to Self-Regulate

Low self-regulation (Brownlow & Reasinger, 2000; Senecal, Koestner, & Vallerand, 1995)

Low self-efficacy for self-regulation (Klassen, Ang, Chong, Krawchuck, Huan, Wong, & Yeo, 2009; Klassen, Krawchuck, Lynch, & Rajani, 2008; Klassen, Krawchuch, & Rajani, 2008)

In either case, the dilatory behavior happens to the individual due to a constitutional inability to self-regulate.

THE PROBLEM Each of these approaches treats the

individual as self-unaware and deficient in the act of procrastination.

However, there is evidence to suggest that procrastination is a motivated behavior, that individuals engage in it for specific reasons to satisfy their goals.

For example, Schraw, Wadkins, and Olafson (2007) found in qualitative work that students procrastinate to avoid failure at times, and at times to increase their performance under more time pressure.

THE PROBLEM Others have suggested procrastination may

be motivated. Active versus Passive Procrastination (Choi &

Moran, 2009; Chu & Choi, 2005) Different Goals Relate Differently to

Procrastination (Howell & Buro, 2009; Seo, 2009). Any model of procrastination that does not

consider the motivation of the individual is incomplete and theoretically problematic.

TIMELY ENGAGEMENT So too is a model that does not consider

timely engagement, as current models do not.

ExtremeProcrastination

Little Procrastination

Started Work On The Way Out of

Class Today

Waited until the Day After the Due

Date to Start

Extreme Timely Engagement

THEORETICAL FRAMEWORK Building on the previous work in Active

Procrastination, Strunk, Cho, Steele, and Bridges (2012) developed a 2×2 model of time-related academic behavior.

This model integrates timely engagement and procrastination, and crosses those with motivational valence.

ProcrastinationTimely Engagement

Approach

Avoidance

Procrastination-Approach

Procrastination-Avoidance

Timely Engagement-Approach

Timely Engagement-Avoidance

NOT JUST WHAT, BUT WHY It is necessary to consider not only the

behavior, but the motivation. In order to understand students’ time-related

academic behavior, it is necessary to understand the underlying motivation.

In previous research, the 2×2 Measure of Time-Related Academic Behavior has been related to achievement goals, has shown convergent and divergent validity with traditional measures of procrastination, and is the best fit to observed data among competing models in confirmatory factor analyses.

PURPOSE A set of variables has been established that

seems useful in predicting procrastination: Personality Self-efficacy Self-regulation Self-efficacy for self-regulation Achievement goal orientation

However, how will these variables be related to the 2×2 model of time-related academic behavior, and how will these relationships build that model while providing insight for educational practice?

PARTICIPANTS A total of 1,227 participants completed the

survey. They were recruited through emails to their student email accounts.

All participants were undergraduate students enrolled in at least one face-to-face class.

Emails were sent to 5,000 students randomly selected by IRIM for two consecutive semesters.

Age: M = 21.67, SD = 5.39 371 men, 752 women. 261 freshmen, 262 sophomores, 286 juniors,

and 301 seniors (17 reporting ‘other’) GPA: M = 3.28, SD = .53

PARTICIPANTS All academic majors were represented in the

sample. In terms of ethnicity, the sample did

significantly deviate from the total population at the university (χ2 = 43.687, p < .001). The sample contained an overrepresentation of those identified as ‘American Indian/Alaskan Native’ and ‘Asian or Pacific Islander’ underrepresentation of those identified as ‘Other’.

There was also a significant underrepresentation of men and overrepresentation of women (χ2 = 151.597, p < .001).

Overall response rate was 12%.

INSTRUMENTS 2×2 Measure of Time-Related Academic Behavior

25 items Previously showed reliability coefficients exceeding .8

and good fit in confirmatory analyses. MSLQ

56 item scale, only 18 are used here, as has been done in previous research (e.g. Howell & Watson, 2007, Klassen, et al., 2008).

Only the self-efficacy and self-regulation scales were used.

Mini-IPIP 20 items Reliabilities typically exceed .7, and correlates well

with longer measures of personality.

INSTRUMENTS AGQ-R

12 items The most popular measure for achievement

goals, and tends to show reliabilities above .8. The authors claim good fit in confirmatory analysis.

Self-Efficacy for Self-Regulation 11 items Part of a longer instrument containing 2 scales,

but is routinely separated. Shows reliabilities exceeding .85 in prior research.

Demographic Questionnaire

PROCEDURE The email contained a survey link, which

included the consent form. Each participant followed directions to create

a unique participant ID. Participants were entered for one of four

$50.00 cash awards for their participation. There was a new drawing each semester.

After approximately 15 weeks, students received a new email asking them to participate again for the longitudinal data collection. Of those who participated, 10.7% participated a second time.

DATA ANALYSIS Psychometric and Measurement Analyses

Structural Modeling within Time One data

Path Modeling for Longitudinal Data

Person-Centered Analysis (Cluster Analysis)

FOR BREVITY… In the CFA and Structural Analyses: The step-by-step analytic work of modeling

and refitting is skipped in this presentation of results as there are many models to present.

I will present only the final models used in interpretation here.

2×2 MEASURE OF TIME-RELATED ACADEMIC BEHAVIOR


ΔCFI < .01


ΔCFI < .001

CFA FOR OTHER KEY MEASURES MSLQ: The final model approached reasonably good fit

to the observed data, though it did not reach conventional cutoffs for fit indices (2

115 = 1037.71, 2/df = 9.02, CFI = .90, TLI = .88, RMSEA = .08, SRMR = .07).

Self-Efficacy for Self-Regulation: The final model fit the observed data reasonably well, though it was still not a good fit (2


AGQ-R: The final model was a reasonably good fit to the observed data (2


Mini-IPIP: The final model was a reasonably good fit to the data(2


Self-Regulation

Self-Efficacy

Self-Efficacy for

Self-Regulation




Timely Engagement-

Avoidance

.18

-.15

-.32

-.14

-.42

-.16.43

.29

-.22.41

.31

.64

.18

Conscientiousness

Agreeableness

Neuroticism

Extraversion




Timely Engagement-Avoidance

-.08

-.31

-.09

-.34

.22

-.07

.45

.42

-.06

-.21.30

.19-.33

-.11-.33

-.27.30.35

Agreeableness

Neuroticism

Extraversion

Imagination

Conscientiousness

Self-Efficacy Self-Regulation

Self-Efficacy for Self-

Regulation



Timely Engagement-

Approach

Timely Engagement-

Avoidance

Performance-Avoidance

Goals

Performance-Approach

Goals

Mastery-Avoidance

Goals

Mastery-Approach

Goals

.10-.19

-.20.24

.22

.34

-.06

-.12

.32.72 -.51

.60.24 -.11

.23-.10

.31 .20

.10-.12

.12 .23.16.28

.11 .09

.11

.13.11

.80

Agreeableness

Neuroticism

Extraversion

Imagination

Conscientiousness



Timely Engagement-

Approach

Timely Engagement-

Avoidance


Goals


Goals

Mastery-Avoidance

Goals

Mastery-Approach

Goals



Regulation

.80

-.33

-.33

.34

.35

Agreeableness

Neuroticism

Extraversion

Imagination

Conscientiousness



Timely Engagement-

Approach

Timely Engagement-

Avoidance


Goals


Goals

Mastery-Avoidance

Goals

Mastery-Approach

Goals



Regulation

.19

-.11

-.21

-.27

.32.72 -.51

.60.24 -.11

.80

Agreeableness

Neuroticism

Extraversion

Imagination

Conscientiousness



Timely Engagement-

Approach

Timely Engagement-

Avoidance


Goals


Goals

Mastery-Avoidance

Goals

Mastery-Approach

Goals



Regulation

-.33

.34

.35

.23-.10

.31 .20

.10-.12

.12 .23.16.28

.11 .09

.11

.13.11

.80

Self-Efficacy

Self-Regulation

Self-Efficacy for Self-Regulation

Procrastination-Approach, Time Two

Procrastination-Avoidance, Time Two

Timely Engagement-Approach, Time Two

Timely Engagement-Avoidance, Time Two

-.23

-.56

.42

.30

.13 (NS)

.57





Performance Approach Goal Orientation

Performance Avoidance Goal Orientation

Mastery Approach Goal Orientation

Mastery Avoidance Goal Orientation

.09

-.06

.05

.03-.12

.11

.06

.11

-.02

.03

.05-.02





Conscientiousness

Neuroticism

-.25

-.26

.18

.30

.25

Procrastination-Approach, Time

Two

Procrastination-Avoidance, Time

Two

Timely Engagement-

Approach, Time Two

Timely Engagement-

Avoidance, Time Two

Self-Efficacy for Self-Regulation


Performance Approach Goals

Mastery Approach Goals

Neuroticism Imagination/ Intellect

-.22 .18 -.52 .40 .29

.14 .59

.25 .49

.18.19

2 3 4 5 6 7 8 9 100.625

0.675

0.725

0.775

0.825

0.875

0.925

0.975

2 3 4 5 6 7 8 9 103.75

4.25

4.75

5.25

5.75

6.25

6.75

MSE

R2

Procra

stina

tion-A

pproa

ch

Procra

stina

tion-A

voida

nce

Timely

Enga

gemen

t-Appr

oach

Timely

Enga

gemen

t-Avoi

dance

Self-E

fficacy

Self-R

egulat

ion

Self-E

fficacy f

or Sel

f-Regu

lation

Mastery

Appro

ach

Perfor

mance A

pproac

h

Self-H

andic

appin

g1

2

3

4

5

6

7

Cluster 1Cluster 2Cluster 3Cluster 4

DISCUSSION The 2×2 Measure of Time-Related Academic

Measure Appears to have a structure that is invariant and

reliable. Is useful in understanding time-related academic

behavior. Unit-weighted scores should be useful for other

researchers (as supported by the minute differences between congeneric and tau-equivalent reliabilities)

Is also stable across time, making it useful for longitudinal research.

Offers empirical advantages over existing measures.

DISCUSSION Other Measures

Were relative good-fitting in CFA models. The AGQ needs more research… especially given

the lower reliability in the congeneric model, which is a troubling finding for the dimensionality of the measure.

In general, measures produced good fit and moderate-to-good reliabilities.

DISCUSSION Structural Modeling

Self-efficacy was only associated with lower levels of procrastination-avoidance. This is significant, and shows the need for differentiation by valence.

Self-efficacy leads to higher procrastination-avoidance. Shows a balance with self-efficacy, where too little leads to one type of procrastination, too much to another, perhaps.

Worth noting that the influence was stronger for self-efficacy for self-regulation. This variable is emerging as perhaps one of the most important for time-related academic behavior research.

DISCUSSION Structural Modeling

The pattern with achievement goals again emerged.

Procrastination may be a performance-enhancement strategy, while timely engagement is a mastery attainment strategy.

This points to the idea of a context-dependence in time-related academic behaviors, and is supported by the longitudinal analyses.

DISCUSSION Structural Analyses

Personality showed the expected associations in time one data.

However, it is worth noting the differentiation by type was again observed with these associations.

Again here, the longitudinal prediction is quite weak, suggesting the hypothesis that procrastination is who someone is rather than what he/she does is flawed.

Additionally, in the integrated model, the predictive influence flows through goals. So personality may influence goal structure, which influences behavioral choices.

DISCUSSION Cluster Analysis (Person-Centered Analysis)

Each of the four clusters offers insight on ‘groups’ of people, and how motivational variables may operate around time-related academic behavior.

It is worth noting the way that goals cluster around behaviors, with self-efficacy for self-regulation again emerging as much higher in procrastination profiles and much lower in timely engagement profiles.

These profiles again lend themselves to the hypothesis of context-dependence in time-related academic behavior.

Further research could determine if people ‘jump’ profile from course to course, semester to semester.

CONCLUSIONS Time-related academic behaviors are strategies. They must be viewed holistically, including timely

engagement and procrastination together and understanding the underlying motivation.

Deficit theory in this area offers an incomplete and perhaps incorrect understanding.

Intervention research seeking to increase self-efficacy may result in unintended consequences.

Self-regulation may also be insufficient in intervention.

Goals may be the key in intervention…I.e., students need to have goals that drive timely engagement strategy use, but also the self-efficacy to use those self-regulated behaviors.

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