Exploring the dynamic nature of procrastination: A latent growth curve analysis of academic procrastination

Personality and Individual Differences 38 (2005) 297–309www.elsevier.com/locate/paid

Exploring the dynamic nature of procrastination: Alatent growth curve analysis of academic procrastination q

Simon M. Moon a,*, Alfred J. Illingworth b,1

a Department of Psychology, The University of Wisconsin-Oshkosh, Oshkosh, WI 54901, USAb Department of Psychology, The University of Akron, Akron, OH 44325-4301, USA

Received 26 August 2003; received in revised form 25 February 2004; accepted 10 April 2004

Available online 8 June 2004

Abstract

A growing body of research suggests that academic procrastination is a dynamic behavior that follows acurvilinear trajectory over time. In this research, we examined whether there are inter-individual differences

in this trajectory, the extent to which these differences can be predicted by other variables, and the rela-

tionship between temporal changes in procrastination and academic outcomes. We collected multi-wave

data from 303 students regarding their actual procrastination behavior and test performance during an

academic semester, as well as single measurements of their self-reported levels of trait procrastination,

conscientiousness, and neuroticism. Using latent growth curve modeling, we found that high and low

procrastinators followed the same trajectory over time, that the self-report measures did not predict

temporal changes in procrastination and test performance, and that procrastination behavior was nega-tively related to test performance throughout the semester. The implications of these findings for trait-based

theories of procrastination, and the measurement of procrastination in general, are discussed.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Procrastination; Latent growth curve modeling; Academic performance; Longitudinal design

qAn earlier version of this paper was presented at the 2003 American Psychological Society convention in Atlanta,

Georgia.* Corresponding author. Tel.: +1-920-424-7175; fax: +1-920-424-1204.

E-mail addresses: [email protected] (S.M. Moon), [email protected] (A.J. Illingworth).1 Tel.: +1-330-972-7280; fax: +1-330-972-5174.

0191-8869/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.paid.2004.04.009

mail to: [email protected]

298 S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309

1. Introduction

The impetus for much of the procrastination literature originated in studies of collegestudents and their tendency to delay preparing for tests and completing course assignments(Burka & Yuen, 1983; Ellis & Knaus, 1977; Ferrari, Johnson, & McCown, 1995; Hill, Hill,Chabot, & Barrall, 1978; Solomon & Rothblum, 1984). This concern for the dilatory behaviorof college students, and its consequences, is not unfounded. Anecdotally, it has been sug-gested that approximately 95% of all college students procrastinate (Ellis & Knaus, 1977).Other researchers have estimated the prevalence of procrastination among college students tovary between 25% and 50% depending on the type of academic tasks being completed(Beswick, Rothblum, & Mann, 1988; Rothblum, Solomon, & Murakami, 1986; Solomon &Rothblum, 1984). Furthermore, several studies have found a moderate to strong negativecorrelation between academic procrastination and academic performance (e.g., Beswick et al.,1988; Steel, Brothen, & Wambach, 2001; Tice & Baumeister, 1997; Van Eerde, 2003). Giventhe frequency of academic procrastination, and its adverse effect on academic performance,researchers and clinicians have made a concerted effort to understand and ameliorate thisbehavior.

In their attempts to understand academic procrastination, researchers have generally treated itas an immutable personality trait or disposition (Ferrari et al., 1995; Schouwenburg, 1995; VanEerde, 2000). As a result, they implicitly assume that academic procrastination is stable acrosstasks, contexts, and time. This trait-based approach continues to drive the procrastination liter-ature despite an abundance of contradicting theoretical and empirical work. For example, severalmodels have been developed that provide a theoretical rationale for conceptualizing procrasti-nation as a situation specific behavior as opposed to a personality trait (Harris & Sutton, 1983;Rothblum, 1990; Van Eerde, 2000). Moreover, empirical support for temporal and situationalvariability in procrastination has been demonstrated in several studies (e.g., Blunt & Pychyl, 2000;Lonergan & Maher, 2000; Milgram, Sroloff, & Rosenbaum, 1988; Pychyl, Lee, Thibodeau, &Blunt, 2000; Senecal, Lavoie, & Koestner, 1997; Solomon & Rothblum, 1984; Tice & Baumeister,1997). Overall, the results of this research suggest that procrastination is not a stable personalitydisposition, but is, in fact, a dynamic behavior that changes over time depending on the inter-action of tasks and contexts.

This conclusion, however, leads to several questions about academic procrastination that haveyet to be answered. If procrastination changes over time, what is the shape of this change? Do allprocrastinators change their behavior in the same way and to the same degree? What predictschanges in procrastination over time? And finally, are changes in procrastination related toimportant academic outcomes? Although several studies have attempted to answer these ques-tions, they suffer from methodological problems that make interpreting their results difficult.Therefore, the purpose of this research is to: (1) understand inter-individual differences in pro-crastination over time, (2) identify predictors of inter-individual differences in procrastinationover time, and (3) examine the relationship between procrastination and academic performanceover time. We begin by reviewing previous longitudinal studies of procrastination. We thenintroduce latent growth curve modeling (LGCM; Willett & Sayer, 1994), a structural equationtechnique used to model longitudinal data, as a viable statistical method for understanding thedynamic characteristics of academic procrastination. Next we develop several hypotheses within

S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309 299

the framework of LGCM. Finally, we present a description of our study, the results of ouranalyses, and a discussion of our findings.

1.1. Previous longitudinal studies of academic procrastination

To date, several longitudinal studies investigating temporal changes in procrastination havebeen conducted (e.g., DeWitte & Schouwenburg, 2002; Pychyl et al., 2000; Schouwenburg, 1995;Schouwenburg & Groenewoud, 2001). Unlike previous longitudinal studies that only measuredprocrastination at two time points (e.g., Tice & Baumeister, 1997), these studies relied on multiplemeasurements to describe how the dilatory behavior of students fluctuated in the weeks leading upto an exam. By obtaining multiple measures of procrastination, it was possible to estimate theaverage trajectory that procrastinators tended to follow over time. Interestingly, all of thesestudies suggest that procrastination is characterized by curvilinear functions. For example, severalearly studies found that as deadlines associated with academic tasks approached, both high andlow procrastinators exhibited decreased procrastination and increased study behavior that fol-lowed a curvilinear, hyperbolic trajectory over time (e.g., Rothblum et al., 1986).

Similar findings have also emerged from a research program initiated by Schouwenburg and hiscolleagues (DeWitte & Schouwenburg, 2002; Schouwenburg, 1995; Schouwenburg & Gro-enewoud, 2001). In his research, Schouwenburg hypothesizes that procrastination results fromwhat is called the discounting principle: the longer people have to wait to receive a rewardassociated with a given behavior, the less attractive the behavior is perceived to be. With respect toprocrastinators, the rewards associated with studying and achieving success on academic tasks isnot realized until the deadline for initiating and completing the tasks is very near. Thus, thefurther away procrastinators are from academic tasks, the less attractive is the study behavior,and as a result, the less likely they are to engage in study behavior. From this line of reasoning, itfollows that academic procrastination should exhibit a curvilinear trajectory over time, with asharp decrease in procrastination as the deadlines associated with academic tasks approach.

The results of two studies lend support to this hypothesis. Schouwenburg and Groenewoud(2001) asked students to participate in a mental simulation in which they imagined themselvesstudying at different times before an impending exam, ranging from 12 weeks before the exam to 1week before the exam. For each time interval, the students were asked to indicate their motivationlevels, how well they would be able to resist social temptations that could interfere with studying(e.g., watching television, social engagements), and how many hours they would study per daygiven the proximity of the exam deadline. As expected, levels of motivation, the ability to resistsocial temptations, and actual hours spent studying were at their lowest levels when studentsperceived the exam deadline as being more than 4 weeks away. However, once the students werewithin 4 weeks of the deadline, the behavior associated with all three variables began to increaseexponentially, reaching their highest levels the day before the exam.

In a follow-up study, DeWitte and Schouwenburg (2002) measured the actual study behaviorand intentions of students preparing for an exam to be administered at the end of the semester. Asin Schouwenburg and Groenewoud (2001), they hypothesized that students would slowly increasetheir actual study behavior during the semester, followed by a burst of studying shortly before theexam. Consistent with their hypothesis, DeWitte and Schouwenburg found that both intentions tostudy, and number of hours studied, were best described by a hyperbolic or concave curve. In


addition, when DeWitte and Schouwenburg compared the predictive ability of the best fittinglinear and hyperbolic curve for intentions to study and number of hours studied, they found thatin both cases the hyperbolic curve explained more variance in their data than the linear curve.Based on the results of these studies, academic procrastination does indeed appear to be a dy-namic behavior. Following a curvilinear trajectory over time, academic procrastinators tend todelay working until the last minute. Under pressure to meet their deadlines or commitments, theycompensate for their delays by increasing their study behavior.

These longitudinal studies of academic procrastination have yielded some insight into howprocrastination changes over time. However, the results of these studies are limited to adescription of the trajectory procrastination follows, without any indication as to the causes,outcomes, or generalizability of this behavior. To further our understanding of longitudinalchanges in procrastination, this research needs to be extended to identify inter-individual dif-ferences in procrastination over time, what individual difference variables might predict changesin procrastination over time, and how temporal changes in procrastination might be related toimportant academic outcomes. The application of a statistical technique called latent growthcurve modeling may be the key to advancing our knowledge of temporal changes in procras-tination.

1.2. Latent growth curve modeling

Latent growth curve modeling (LGCM) is a powerful and flexible technique for understandingchange over time in any variable for which multi-wave data is available (Chan, 1998; Willett &Sayer, 1994, 1996). Based on covariance structure analysis, LGCM represents change as chro-nometric latent variables that may have structural relationships with other exogenous andendogenous latent factors (Chan, 1998; McArdle & Epstein, 1987). Central to LGCM is theidentification of a general growth curve that accurately describes the trajectory for every memberof a sample. Once this general growth curve has been established, it is then possible to determinewhether there are inter-individual differences in the change captured by this curve, and the extentto which other variables predict these inter-individual differences (Muthen, 1991).

The derivation of this general growth curve begins with the development of a mathematicalmodel that describes how individuals change over time in the domain of interest (Willett &Sayer, 1996). This model includes intercept (i.e., initial levels) and growth parameters (e.g.,linear, quadratic, cubic) that characterize the nature of the change over time. Once the generalgrowth curve has been identified, the next step is to assess whether or not there is significantinter-individual variability around this trend line (Willett & Sayer, 1994, 1996). That is, it ispossible to determine if every member of a sample followed a trajectory very similar to or verydifferent from the general growth curve. If the variance associated with the growth parametersdefining the growth curve is significant, it means that everyone in the sample did not follow thesame trajectory over time. If, however, only chance variation is associated with the growthparameters, then everyone did follow a trajectory similar to the general growth curve. It is alsoimportant to note that there may be inter-individual differences on one or all of the growthparameters.

Because LGCM is a covariance structure technique, a structural model can be tested in whichtime-invariant predictors of change, such as personality, are included as exogenous latent vari-


ables with direct paths to the latent growth parameters (Muthen, 1991; Willett & Sayer, 1994). Inthis way, it is possible to predict any inter-individual differences that may be found in the generalgrowth curve. In addition, LGCM allows for the analysis of cross-domain relationships (Willett &Sayer, 1996). In these types of analyses, the extent to which changes in the growth parameters ofone variable predict changes in the growth parameters of a second variable can be estimated. Inother words, the relationship between the growth parameters of two domains, changing over time,can be analyzed.

1.3. Hypotheses

Within the framework of LGCM, it is possible to develop and test hypotheses that previouslongitudinal studies of procrastination have ignored. As we indicated earlier, a number of lon-gitudinal studies have demonstrated that dilatory behavior does in fact follow a curvilinear trendover time (e.g., DeWitte & Schouwenburg, 2002). Thus, we propose the following hypothesis as areplication of these findings.

Hypothesis 1. Across time, procrastination will be best described by a curvilinear growth curve.

The evidence for inter-individual variability in procrastination over time has produced con-flicting results. For example, some studies have shown that high and low procrastinators exhibitdecreased levels of procrastination behavior leading up to a deadline (e.g., Rothblum et al., 1986;Schouwenburg, 1995). In contrast, other studies have found differences between procrastinatorsand non-procrastinators with respect to the curves describing changes in their dilatory behaviorover time (e.g., DeWitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001; Steel,Brothen, & Wambach, 2002). Thus, it is unclear to what extent there exist inter-individual dif-ferences in procrastination over time. Acknowledging the lack of a strong theoretical foundationto develop a testable hypothesis, we propose the following exploratory hypothesis.

Hypothesis 2. There will be significant inter-individual differences in the growth parametersdescribing the general growth curve of procrastination.

By delaying the completion of class assignments, and putting off their preparation for quizzesand tests, student procrastinators may negatively affect their academic performance. And in fact,this is what procrastination researchers tend to find (e.g., Beck, Koons, & Milgram, 2000; Owens& Newbegin, 1997; Tice & Baumeister, 1997). A recent meta-analysis by Van Eerde (2003)estimated the population level relationship between procrastination and two academic perfor-mance indicators: course grade ð�r ¼ �0:17Þ and grade point average ð�r ¼ �0:28Þ. Thus, thereappears to be a moderate, negative correlation between procrastination and academic perfor-mance.

Consequently, if procrastination is as dynamic as the work of Schouwenburg and Groenewoud(2001) and DeWitte and Schouwenburg (2002) suggests, then we would expect to see differentialrelationships between the growth parameters of academic procrastination and academic perfor-mance across time. In particular, given the negative relationship demonstrated by Van Eerde(2003), we would expect the results of a cross-domain analysis to indicate a negative relationship


between the growth parameters describing the general growth curves of academic procrastinationand academic performance.

Hypothesis 3. The growth parameters of the general growth curves for procrastination andacademic performance will be negatively related.

The procrastination literature is replete with studies investigating the correlates of procrasti-nation. We selected three time-invariant individual difference variables that have been found tohave the strongest relationships with procrastination and examined their ability to explain inter-individual differences in academic procrastination in the context of LGCM.

Self-reported procrastination. Measuring procrastination via self-report measures has been themethod of choice for assessing, diagnosing, and monitoring procrastination (Ferrari et al., 1995;Van Eerde, 2003, 2004). However, this method of measuring procrastination incorporates theassumptions of trait-based theories of procrastination. Following the rationale of researcherswho believe procrastination is a stable individual difference variable, we would expect to see apositive relationship between self-reported procrastination, measured once, and behavioralprocrastination, which has been measured multiple times. Thus, we propose the followinghypothesis.

Hypothesis 4. Self-reported procrastination will be positively related to the growth parametersdescribing the general growth curve of procrastination.

Conscientiousness and neuroticism. In the search for explanations of procrastination, researchershave investigated the utility of a wide variety of personality variables (Ferrari et al., 1995;Schouwenburg, 1995). Recently, a number of studies (e.g., Johnson & Bloom, 1995; Lay, 1997;Schouwenburg & Lay, 1995; Steel et al., 2001; Watson, 2001) have attempted to understandprocrastination using the five-factor model of personality (Costa & McCrae, 1992). The results ofthese studies suggest that conscientiousness and neuroticism have the strongest and most con-sistent relationships with procrastination. According to Van Eerde (2004), neuroticism tends tohave a small to moderate positive correlation with procrastination, whereas conscientiousnesstends to have a moderate to strong negative correlation with procrastination. Based on therelationships between procrastination, conscientiousness, and neuroticism described above, it ispossible that these personality variables might be related to inter-individual differences in pro-crastination over time. Thus, we propose the following hypotheses.

Hypothesis 5. Conscientiousness will be negatively related to the growth parameters describingthe general growth curve of procrastination.

Hypothesis 6. Neuroticism will be positively related to the growth parameters describing thegeneral growth curve of procrastination.

We will also conduct exploratory analyses to determine the relationship between our time-invariant predictor variables (self-reported procrastination, conscientiousness, and neuroticism)and the growth parameters of the general growth curve describing academic performance.


2. Method

2.1. Participants

Participants ðN ¼ 349Þwere drawn from introductory psychology courses at a largeMidwesternuniversity and offered partial course credit as compensation. Due to missing performance data, 46cases were excluded from our analyses, which resulted in a final sample of 303. The sample had amedian age of 19 (M ¼ 21:89, SD ¼ 6:66) and was primarily composed of women (64%) andCaucasians (80%), with a minority of participants self-identifying as African–American (14%),Asian (2%), and Other (4%). Most participants were either freshmen (67%) or sophomores (19%).

2.2. Measures

Academic procrastination. Academic procrastination was measured using the Aitken Procras-tination Inventory (API; Aitken, 1982). The API is a self-report inventory measuring trait pro-crastination among college students. It contains 19 items (e.g., ‘‘I delay starting things so long Idon’t get them done by the deadline’’) that use a 5-point scale ranging from 1 (False) to 5 (True).The scale was scored such that high scores identified students who were chronic procrastinators.

Conscientiousness and neuroticism. Conscientiousness and neuroticism were measured using the‘‘Mini-Markers’’ developed by Saucier (1994). The Mini-Markers is a 40-item adjective checklistthat represents the five-factor model of personality. For each adjective (e.g., ‘‘bashful’’), partici-pants described themselves on a 9-point scale ranging from 1 (Extremely Inaccurate) to 9 (Ex-tremely Accurate). Although participants completed the full Mini-Marker scale, only the resultspertaining to conscientiousness and neuroticism are reported below.

Test scores. All participants were students in introductory psychology courses. As part of theircourse requirements, they completed five 50-item, multiple-choice tests during the semester. Eachtest was computerized and based on a 100-point scale ranging from 0 to 100, with larger scoresindicating better test performance. For each participant, we collected five test scores.

Behavioral academic procrastination. Each introductory psychology test was administered viacomputer and included a 1-week window in which students could take the test at their conve-nience. We obtained the dates spanning each test window, as well as the actual dates participantscompleted the tests. Thus, for each test window we operationalized behavioral academic pro-crastination as the difference between the date the window opened and the date students took thetest, with larger differences indicating more procrastination. Scores for all five test windowsranged from 0 (i.e., took the test the same day the test window opened) to 6 (i.e., took the test thelast day available in the test window). For each participant, we collected five measures ofbehavioral academic procrastination.

2.3. Procedure

The data were collected during the spring semester of 2002 between the months of January andMay. After completing their second test, participants received a packet of materials containing aninformed consent form, a demographic questionnaire, the Aitken Procrastination Inventory, andthe Mini-Markers checklist. These self-report measures were completed only once. The packet


also included a permission form that allowed us to obtain all five of their test scores and testcompletion dates from the administrator overseeing the introductory psychology courses. Uponcompletion of the packet, participants were debriefed and thanked for their participation.

3. Results

Descriptive statistics and correlations among the study variables are presented in Table 1. Thereliabilities of the self-report measures are contained in the diagonals. As an indicator of thevalidity of our data, Table 1 reveals several relationships that have been found in prior research(e.g., Steel et al., 2001; Van Eerde, 2003). For instance, self-reported procrastination was nega-tively related to conscientiousness (r ¼ �0:66, p < 0:001) and positively related to every behav-ioral measure of procrastination. Furthermore, self-reported procrastination and measures ofbehavioral procrastination differed in their prediction of test performance. With the exception ofthe relationship between self-reported procrastination and performance on Test 4, self-reportedprocrastination did not predict test performance. In contrast, every behavioral measure of pro-crastination was a significant predictor of test performance throughout the semester.

As an initial test of Hypothesis 1, we began by examining a graph of the general growth curveof behavioral procrastination. This indicated that procrastination during the semester followed acurvilinear, quadratic trajectory. To determine whether a linear or curvilinear function best de-scribed the general growth curve of procrastination, we conducted several nested model com-parisons and assessed the relative improvement in the fit of each model. In our analyses wecompared three models: a no growth curve model (Model 1), which was the most restricted and

Table 1

Descriptive statistics and correlations among study variables

M SD 1 2 3 4 5 6 7 8 9 10 11 12

1. API 2.58 0.61 (0.87)

2. Consc 5.14 0.95 )0.66� (0.80)

3. Neuro 3.81 0.96 0.21� )0.20� (0.72)

4. Pro1 4.10 2.11 0.25� )0.12��)0.01 )5. Pro2 4.58 2.02 0.22� )0.16� )0.05 0.52� )6. Pro3 4.81 2.09 0.21� )0.17� 0.00 0.49� 0.49� –

7. Pro4 4.92 2.04 0.27� )0.24� 0.04 0.51� 0.51� 0.56� –

8. Pro5 4.15 2.31 0.19� )0.16� )0.02 0.31� 0.34� 0.39� 0.43� –

9. Test 1 67.46 13.70 )0.07 )0.01 0.10 )0.26� )0.18� )0.26� )0.24� )0.13�� –10. Test 2 63.49 12.16 )0.10 0.01 0.05 )0.28� )0.15� )0.26� )0.26� )0.14�� 0.64� )11. Test 3 59.04 14.25 )0.11 0.07 0.05 )0.33� )0.28� )0.36� )0.33� )0.26� 0.70� 0.63� –

12. Test 4 61.85 13.42 )0.14�� 0.10 0.06 )0.29� )0.25� )0.34� )0.30� )0.21� 0.66� 0.65� 0.65� –

13. Test 5 65.93 13.67 )0.10 0.04 0.08 )0.22� )0.17� )0.30� )0.27� )0.21� 0.70� 0.62� 0.70� 0.70�

Note. N ¼ 297–303 due to missing data on the procrastination measures. API¼Aitken Procrastination Inventory;

Consc¼Conscientiousness; Neuro¼Neuroticism; Pro1–Pro5¼Behavioral Procrastination for all five test windows;

Test1–Test5¼Test scores for all five tests completed during the semester. Reliability coefficients are contained in the

diagonals.* p < 0:01.** p < 0:05.

Table 2

Nested model comparisons and model fit indices for latent growth curve analyses of behavioral procrastination

ðN ¼ 297Þv2 df Model

comparison

Dv2 Ddf NNFI CFI SRMR RMSEA

Model 1 75.21� 13 0.89 0.86 0.09 0.13

Model 2 62.85� 10 1 vs. 2 12.36� 3 0.88 0.88 0.09 0.13

Model 3 11.96 6 2 vs. 3 50.89� 4 0.98 0.99 0.04 0.05

Note. v2 ¼Chi-square; df¼ degrees of freedom; NNFI¼Non-Normed Fit Index; CFI¼Comparative Fit Index;

SRMR¼ Standardized Root Mean Square Residual; RMSEA¼Root Mean Square Error of Approximation.* p < 0:01.


assumed no change in procrastination; a linear model (Model 2) that tested the assumption of alinear increase or decrease in procrastination; a quadratic model (Model 3) testing the assumptionthat procrastination followed a U-shaped (or inverted U) pattern.

There was a Heywood case (i.e., negative variance) in Model 3. Dillon, Kumar, and Mulani(1987) suggest that Heywood cases may be caused by model misspecification or sampling fluc-tuations. According to Dillon et al., sampling fluctuations cause Heywood cases when: (1) themodel provides a reasonable fit, (2) the confidence interval for the offending estimate includeszero, and (3) the corresponding estimated standard error is roughly the same as the other esti-mated standard errors. In Model 3, the linear (d2 ¼ �0:072, SE ¼ 0:379) and quadratic(d2 ¼ �0:009, SE ¼ 0:021) growth factors had small negative variances. Based on Dillon et al.’ssuggestions, the first two conditions for sampling fluctuations were met. However, the estimatedstandard error of the quadratic effect was much smaller than the other standard error estimates.To assess whether sampling fluctuations were the cause of the negative variance, we randomlysampled approximately 70% of the cases 10 times (N ¼ 201–219), and found that in six randomsamples the variance estimates of both growth factors were not negative. Furthermore, thevariances of the two growth factors were not significant in any of the resulting analyses. Thus, webelieve that sampling fluctuations were the cause of the negative variances and the resultingHeywood case. 2 A summary of the model comparisons is presented in Table 2. Across all fitindices Model 3 was the best fitting model, and it fit the data significantly better than the othertwo models. Thus, Hypothesis 1 was supported.

By examining the significance of the variances associated with the growth parameter factorscontained in Model 3, we were able to test Hypothesis 2 and determine if all participants followedthe same trajectory of procrastination during the semester. Surprisingly, the variances of the linearand quadratic growth parameter factors were not significant. These results suggest that there wereno individual differences in the pattern of procrastination students followed during the semester.

2 A simple solution in the event of a Heywood case is to fix the offending value at zero. However, because the purpose

of growth curve modeling is to estimate the variance of each growth parameter, this strategy does not provide a logical

solution. Therefore, we ran the analysis in two ways. We first fixed the variance of the linear and quadratic growth

factors to zero. We then compared these results to the results from the six random samples that did not show negative

variance. The two analysis strategies produced similar results, although the second strategy provided slightly better fit

indices.


However, as indicated by significant variance on the intercept factor (t ¼ 4:587, p < 0:001), therewere individual differences in baseline levels of procrastination. Therefore, although students mayhave differed in their initial levels of procrastination at the beginning of the semester (i.e., somestudents procrastinated more than others), all of them followed the same pattern of procrasti-nation over the course of the semester. Thus, Hypothesis 2 was not supported.

We were unable to test Hypothesis 3 using LGCM due to a Heywood case (i.e., negativevariance problem) with the performance data. Subsequent post hoc analyses suggested theHeywood case might be caused by model misspecification. 3 However, inspection of the pattern ofcorrelations between behavioral procrastination and test performance (see Table 1) at all timepoints indicated that they were negatively related. The more students procrastinated, the lowertheir grades tended to be. Therefore, Hypothesis 3 was supported.

Our ability to test Hypotheses 4–6 was limited by several factors. First, the inclusion ofbehavioral procrastination data from Time 5 resulted in large residual variances that made adefinite solution impossible once the individual difference variables were included. We believe thisproblem is due to the fact that the procrastination behavior of students at Time 5 is the result offactors other than differences in the tendency to procrastinate, which makes behavior at this pointin time qualitatively different from earlier dilatory behavior. As a result, procrastination at Time 5was dropped from our analysis of Hypotheses 4–6. Second, given the lack of variance on the linearand quadratic growth parameters, we could not test how well the individual difference variablespredicted temporal changes in procrastination. We were, however, able to assess the degree towhich the individual difference variables predicted initial levels (i.e., the intercept factor) ofprocrastination at the beginning of the semester. The standardized path coefficients in ourstructural model indicated that conscientiousness (b ¼ �0:23, p < 0:01) and self-reported pro-crastination (b ¼ 0:27, p < 0:01) predicted initial levels of procrastination among students, butneuroticism (b ¼ �0:04; ns) did not. Therefore, only partial support was found for Hypotheses 4and 5 and no support was found for Hypothesis 6.

4. Discussion

This research used LGCM to replicate and extend previous longitudinal studies of procrasti-nation. As expected, we found that procrastination followed a curvilinear trend over time.However, in contrast to previous longitudinal studies of procrastination, dilatory behavior in ourstudy actually increased over time and then dropped off suddenly at the end of the semester. Thisdiscrepancy may be due to several reasons. First, unlike other longitudinal studies, we used anactual behavioral measure of procrastination instead of a proxy for procrastination (e.g., numberof hours studied). Second, we analyzed how procrastination changed over the course of asemester, which included multiple deadlines, as opposed to the weeks leading up to a singledeadline such as an exam. Finally, the behavior measured in our study was qualitatively differentfrom the behavior measured in other studies; we operationalized procrastination as a behaviorassociated with taking a test, whereas others have utilized behaviors specific to preparing for a

3 These analyses are available upon request from the authors.


test. We also found no inter-individual variability in the trend of procrastination over time.Regardless of whether students started out with high or low levels of procrastination, they allfollowed the same trajectory during the semester.

These results have important implications for the trait-based approach to understandingprocrastination. According to trait-based explanations of procrastination, we would expect to seestable levels of procrastination across tasks, contexts, and time. Moreover, high and low pro-crastinators should follow distinctly different paths. Yet, as our research clearly shows, this is notthe case. In fact, not only did mean levels of procrastination change over time, but students whodiffered significantly in their initial levels of procrastination followed the same trajectory. Thesefindings contribute to a growing body of research that suggests trait-based explanations may notadequately describe the causal mechanisms underlying dilatory behavior.

However, this does not mean that trait-based explanations of procrastination should be dis-carded. Instead, they need to be modified to reflect the complex interaction between personalityand the environment. This would be consistent with trait-theorists who argue that some envi-ronments are capable of constraining the expression of personality (Magnusson & Torestad, 1992;Mischel, 1977; Mischel & Shoda, 1998; Reynolds & Karraker, 2003). From this perspective, ourresults do not seem so anomalous. Indeed, they suggest that there is something endemic to theacademic context that dictates the behavior of procrastinators and non-procrastinators alike. As aresult, individual differences in academic procrastination are suppressed by environmental factors.Alternatively, other types of procrastination (e.g., work procrastination) may not be as susceptibleto environmental factors. Thus, individual differences may prevail in some types of procrastina-tion while situational constraints may prevail in others. This is an important consideration, andone that should be examined in future studies of procrastination.

Our results also speak to the measurement of procrastination. Recent work by Steel et al. (2001)questions the utility of relying exclusively on trait-based self-report measures of procrastination.In their comparison of the predictive ability of self-report and behavioral measures of procras-tination, Steel et al. found that self-reported procrastination was generally unrelated to indicatorsof academic performance. In contrast, behavioral measures were significant predictors of aca-demic performance. Similar results were found in our study. All of our behavioral measures ofprocrastination were better predictors of test performance across the semester than any of the self-report trait-based measures. The consistent failure of self-report trait-based measures of pro-crastination to predict academic performance across time raises questions regarding their utility asaccurate assessments of dilatory behavior and should be further investigated.

In conclusion, latent growth curve modeling enabled us to describe the trajectory procrasti-nation follows across time, and to reach some tentative conclusions regarding its causes andconsequences. Furthermore, we were able to clarify several theoretical and methodological con-cerns regarding procrastination in general. As a result, we believe that researchers and practi-tioners are now better equipped to understand this dynamic behavior.

Acknowledgements

We would like to thank Rosalie Hall and David Chan for their assistance with the dataanalysis. We are also grateful to Kelly Zacharias and Brad Lenz for their assistance entering the


data. Both authors contributed equally to this manuscript. Order of authorship was determinedarbitrarily.

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Exploring the dynamic nature of procrastination: A latent growth curve analysis of academic procrastination