- Email: [email protected]
Exploring the dynamic nature of procrastination: A latent growth curve analysis of academic procrastination
Personality and Individual Differences 38 (2005) 297–309 www.elsevier.com/locate/paid
Exploring the dynamic nature of procrastination: A latent growth curve analysis of academic procrastination q Simon M. Moon a
a,*
, Alfred J. Illingworth
b,1
Department of Psychology, The University of Wisconsin-Oshkosh, Oshkosh, WI 54901, USA b 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 a curvilinear 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 relationship 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 negatively 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. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Procrastination; Latent growth curve modeling; Academic performance; Longitudinal design
q
An 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 Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2004.04.009
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 college students 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 behavior of college students, and its consequences, is not unfounded. Anecdotally, it has been suggested that approximately 95% of all college students procrastinate (Ellis & Knaus, 1977). Other researchers have estimated the prevalence of procrastination among college students to vary 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 negative correlation between academic procrastination and academic performance (e.g., Beswick et al., 1988; Steel, Brothen, & Wambach, 2001; Tice & Baumeister, 1997; Van Eerde, 2003). Given the frequency of academic procrastination, and its adverse effect on academic performance, researchers and clinicians have made a concerted effort to understand and ameliorate this behavior. In their attempts to understand academic procrastination, researchers have generally treated it as an immutable personality trait or disposition (Ferrari et al., 1995; Schouwenburg, 1995; Van Eerde, 2000). As a result, they implicitly assume that academic procrastination is stable across tasks, contexts, and time. This trait-based approach continues to drive the procrastination literature despite an abundance of contradicting theoretical and empirical work. For example, several models have been developed that provide a theoretical rationale for conceptualizing procrastination 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 situational variability 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 personality disposition, but is, in fact, a dynamic behavior that changes over time depending on the interaction of tasks and contexts. This conclusion, however, leads to several questions about academic procrastination that have yet to be answered. If procrastination changes over time, what is the shape of this change? Do all procrastinators change their behavior in the same way and to the same degree? What predicts changes in procrastination over time? And finally, are changes in procrastination related to important academic outcomes? Although several studies have attempted to answer these questions, 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 procrastination over time, (2) identify predictors of inter-individual differences in procrastination over time, and (3) examine the relationship between procrastination and academic performance over time. We begin by reviewing previous longitudinal studies of procrastination. We then introduce latent growth curve modeling (LGCM; Willett & Sayer, 1994), a structural equation technique used to model longitudinal data, as a viable statistical method for understanding the dynamic 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 our analyses, and a discussion of our findings. 1.1. Previous longitudinal studies of academic procrastination To date, several longitudinal studies investigating temporal changes in procrastination have been conducted (e.g., DeWitte & Schouwenburg, 2002; Pychyl et al., 2000; Schouwenburg, 1995; Schouwenburg & Groenewoud, 2001). Unlike previous longitudinal studies that only measured procrastination at two time points (e.g., Tice & Baumeister, 1997), these studies relied on multiple measurements to describe how the dilatory behavior of students fluctuated in the weeks leading up to an exam. By obtaining multiple measures of procrastination, it was possible to estimate the average trajectory that procrastinators tended to follow over time. Interestingly, all of these studies suggest that procrastination is characterized by curvilinear functions. For example, several early studies found that as deadlines associated with academic tasks approached, both high and low procrastinators exhibited decreased procrastination and increased study behavior that followed 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 his colleagues (DeWitte & Schouwenburg, 2002; Schouwenburg, 1995; Schouwenburg & Groenewoud, 2001). In his research, Schouwenburg hypothesizes that procrastination results from what is called the discounting principle: the longer people have to wait to receive a reward associated with a given behavior, the less attractive the behavior is perceived to be. With respect to procrastinators, the rewards associated with studying and achieving success on academic tasks is not realized until the deadline for initiating and completing the tasks is very near. Thus, the further 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, it follows that academic procrastination should exhibit a curvilinear trajectory over time, with a sharp 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 themselves studying at different times before an impending exam, ranging from 12 weeks before the exam to 1 week before the exam. For each time interval, the students were asked to indicate their motivation levels, 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 day given the proximity of the exam deadline. As expected, levels of motivation, the ability to resist social temptations, and actual hours spent studying were at their lowest levels when students perceived the exam deadline as being more than 4 weeks away. However, once the students were within 4 weeks of the deadline, the behavior associated with all three variables began to increase exponentially, reaching their highest levels the day before the exam. In a follow-up study, DeWitte and Schouwenburg (2002) measured the actual study behavior and intentions of students preparing for an exam to be administered at the end of the semester. As in Schouwenburg and Groenewoud (2001), they hypothesized that students would slowly increase their actual study behavior during the semester, followed by a burst of studying shortly before the exam. Consistent with their hypothesis, DeWitte and Schouwenburg found that both intentions to study, and number of hours studied, were best described by a hyperbolic or concave curve. In
300
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
addition, when DeWitte and Schouwenburg compared the predictive ability of the best fitting linear and hyperbolic curve for intentions to study and number of hours studied, they found that in 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 dynamic behavior. Following a curvilinear trajectory over time, academic procrastinators tend to delay working until the last minute. Under pressure to meet their deadlines or commitments, they compensate for their delays by increasing their study behavior. These longitudinal studies of academic procrastination have yielded some insight into how procrastination changes over time. However, the results of these studies are limited to a description of the trajectory procrastination follows, without any indication as to the causes, outcomes, or generalizability of this behavior. To further our understanding of longitudinal changes in procrastination, this research needs to be extended to identify inter-individual differences in procrastination over time, what individual difference variables might predict changes in procrastination over time, and how temporal changes in procrastination might be related to important academic outcomes. The application of a statistical technique called latent growth curve modeling may be the key to advancing our knowledge of temporal changes in procrastination. 1.2. Latent growth curve modeling Latent growth curve modeling (LGCM) is a powerful and flexible technique for understanding change 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 chronometric latent variables that may have structural relationships with other exogenous and endogenous latent factors (Chan, 1998; McArdle & Epstein, 1987). Central to LGCM is the identification of a general growth curve that accurately describes the trajectory for every member of a sample. Once this general growth curve has been established, it is then possible to determine whether there are inter-individual differences in the change captured by this curve, and the extent to which other variables predict these inter-individual differences (Muthen, 1991). The derivation of this general growth curve begins with the development of a mathematical model 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 general growth curve has been identified, the next step is to assess whether or not there is significant inter-individual variability around this trend line (Willett & Sayer, 1994, 1996). That is, it is possible to determine if every member of a sample followed a trajectory very similar to or very different from the general growth curve. If the variance associated with the growth parameters defining the growth curve is significant, it means that everyone in the sample did not follow the same trajectory over time. If, however, only chance variation is associated with the growth parameters, then everyone did follow a trajectory similar to the general growth curve. It is also important to note that there may be inter-individual differences on one or all of the growth parameters. Because LGCM is a covariance structure technique, a structural model can be tested in which time-invariant predictors of change, such as personality, are included as exogenous latent vari-
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
301
ables with direct paths to the latent growth parameters (Muthen, 1991; Willett & Sayer, 1994). In this way, it is possible to predict any inter-individual differences that may be found in the general growth 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 of one variable predict changes in the growth parameters of a second variable can be estimated. In other 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 previous longitudinal studies of procrastination have ignored. As we indicated earlier, a number of longitudinal studies have demonstrated that dilatory behavior does in fact follow a curvilinear trend over time (e.g., DeWitte & Schouwenburg, 2002). Thus, we propose the following hypothesis as a replication 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 conflicting results. For example, some studies have shown that high and low procrastinators exhibit decreased 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 procrastinators and non-procrastinators with respect to the curves describing changes in their dilatory behavior over time (e.g., DeWitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001; Steel, Brothen, & Wambach, 2002). Thus, it is unclear to what extent there exist inter-individual differences in procrastination over time. Acknowledging the lack of a strong theoretical foundation to develop a testable hypothesis, we propose the following exploratory hypothesis. Hypothesis 2. There will be significant inter-individual differences in the growth parameters describing the general growth curve of procrastination. By delaying the completion of class assignments, and putting off their preparation for quizzes and 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 performance indicators: course grade ðr ¼ 0:17Þ and grade point average ðr ¼ 0:28Þ. Thus, there appears to be a moderate, negative correlation between procrastination and academic performance. 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 differential relationships between the growth parameters of academic procrastination and academic performance 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
302
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
between the growth parameters describing the general growth curves of academic procrastination and academic performance. Hypothesis 3. The growth parameters of the general growth curves for procrastination and academic performance will be negatively related. The procrastination literature is replete with studies investigating the correlates of procrastination. We selected three time-invariant individual difference variables that have been found to have the strongest relationships with procrastination and examined their ability to explain interindividual differences in academic procrastination in the context of LGCM. Self-reported procrastination. Measuring procrastination via self-report measures has been the method of choice for assessing, diagnosing, and monitoring procrastination (Ferrari et al., 1995; Van Eerde, 2003, 2004). However, this method of measuring procrastination incorporates the assumptions of trait-based theories of procrastination. Following the rationale of researchers who believe procrastination is a stable individual difference variable, we would expect to see a positive relationship between self-reported procrastination, measured once, and behavioral procrastination, which has been measured multiple times. Thus, we propose the following hypothesis. Hypothesis 4. Self-reported procrastination will be positively related to the growth parameters describing the general growth curve of procrastination. Conscientiousness and neuroticism. In the search for explanations of procrastination, researchers have 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 understand procrastination using the five-factor model of personality (Costa & McCrae, 1992). The results of these studies suggest that conscientiousness and neuroticism have the strongest and most consistent relationships with procrastination. According to Van Eerde (2004), neuroticism tends to have a small to moderate positive correlation with procrastination, whereas conscientiousness tends to have a moderate to strong negative correlation with procrastination. Based on the relationships between procrastination, conscientiousness, and neuroticism described above, it is possible that these personality variables might be related to inter-individual differences in procrastination over time. Thus, we propose the following hypotheses. Hypothesis 5. Conscientiousness will be negatively related to the growth parameters describing the general growth curve of procrastination. Hypothesis 6. Neuroticism will be positively related to the growth parameters describing the general growth curve of procrastination. We will also conduct exploratory analyses to determine the relationship between our timeinvariant predictor variables (self-reported procrastination, conscientiousness, and neuroticism) and the growth parameters of the general growth curve describing academic performance.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
303
2. Method 2.1. Participants Participants ðN ¼ 349Þ were drawn from introductory psychology courses at a large Midwestern university and offered partial course credit as compensation. Due to missing performance data, 46 cases were excluded from our analyses, which resulted in a final sample of 303. The sample had a median age of 19 (M ¼ 21:89, SD ¼ 6:66) and was primarily composed of women (64%) and Caucasians (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 Procrastination Inventory (API; Aitken, 1982). The API is a self-report inventory measuring trait procrastination among college students. It contains 19 items (e.g., ‘‘I delay starting things so long I don’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 checklist that represents the five-factor model of personality. For each adjective (e.g., ‘‘bashful’’), participants described themselves on a 9-point scale ranging from 1 (Extremely Inaccurate) to 9 (Extremely Accurate). Although participants completed the full Mini-Marker scale, only the results pertaining to conscientiousness and neuroticism are reported below. Test scores. All participants were students in introductory psychology courses. As part of their course requirements, they completed five 50-item, multiple-choice tests during the semester. Each test was computerized and based on a 100-point scale ranging from 0 to 100, with larger scores indicating better test performance. For each participant, we collected five test scores. Behavioral academic procrastination. Each introductory psychology test was administered via computer and included a 1-week window in which students could take the test at their convenience. We obtained the dates spanning each test window, as well as the actual dates participants completed the tests. Thus, for each test window we operationalized behavioral academic procrastination as the difference between the date the window opened and the date students took the test, with larger differences indicating more procrastination. Scores for all five test windows ranged from 0 (i.e., took the test the same day the test window opened) to 6 (i.e., took the test the last day available in the test window). For each participant, we collected five measures of behavioral academic procrastination. 2.3. Procedure The data were collected during the spring semester of 2002 between the months of January and May. After completing their second test, participants received a packet of materials containing an informed consent form, a demographic questionnaire, the Aitken Procrastination Inventory, and the Mini-Markers checklist. These self-report measures were completed only once. The packet
304
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
also included a permission form that allowed us to obtain all five of their test scores and test completion dates from the administrator overseeing the introductory psychology courses. Upon completion 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. The reliabilities of the self-report measures are contained in the diagonals. As an indicator of the validity 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 negatively related to conscientiousness (r ¼ 0:66, p < 0:001) and positively related to every behavioral measure of procrastination. Furthermore, self-reported procrastination and measures of behavioral procrastination differed in their prediction of test performance. With the exception of the relationship between self-reported procrastination and performance on Test 4, self-reported procrastination did not predict test performance. In contrast, every behavioral measure of procrastination 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 curve of behavioral procrastination. This indicated that procrastination during the semester followed a curvilinear, quadratic trajectory. To determine whether a linear or curvilinear function best described the general growth curve of procrastination, we conducted several nested model comparisons and assessed the relative improvement in the fit of each model. In our analyses we compared three models: a no growth curve model (Model 1), which was the most restricted and Table 1 Descriptive statistics and correlations among study variables 1. API 2. Consc 3. Neuro 4. Pro1 5. Pro2 6. Pro3 7. Pro4 8. Pro5 9. Test 1 10. Test 2 11. Test 3 12. Test 4 13. Test 5
M
SD
1
2
3
2.58 5.14 3.81 4.10 4.58 4.81 4.92 4.15 67.46 63.49 59.04 61.85 65.93
0.61 0.95 0.96 2.11 2.02 2.09 2.04 2.31 13.70 12.16 14.25 13.42 13.67
(0.87) )0.66 (0.80) 0.21 )0.20 (0.72) 0.25 )0.12 )0.01 0.22 )0.16 )0.05 0.21 )0.17 0.00 0.27 )0.24 0.04 0.19 )0.16 )0.02 )0.07 )0.01 0.10 )0.10 0.01 0.05 )0.11 0.07 0.05 )0.14 0.10 0.06 )0.10 0.04 0.08
4
5
6
7
8
9
) 0.52 0.49 0.51 0.31 )0.26 )0.28 )0.33 )0.29 )0.22
) 0.49 0.51 0.34 )0.18 )0.15 )0.28 )0.25 )0.17
– 0.56 0.39 )0.26 )0.26 )0.36 )0.34 )0.30
– 0.43 )0.24 )0.26 )0.33 )0.30 )0.27
– )0.13 – )0.14 0.64 )0.26 0.70 )0.21 0.66 )0.21 0.70
10
11
12
) 0.63 0.65 0.62
– 0.65 0.70
– 0.70
Note. N ¼ 297–303 due to missing data on the procrastination measures. API ¼ Aitken Procrastination Inventory; Consc ¼ Conscientiousness; Neuro ¼ Neuroticism; Pro1–Pro5 ¼ Behavioral Procrastination for all five test windows; Test1–Test5 ¼ Test scores for all five tests completed during the semester. Reliability coefficients are contained in the diagonals. * p < 0:01. ** p < 0:05.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
305
Table 2 Nested model comparisons and model fit indices for latent growth curve analyses of behavioral procrastination ðN ¼ 297Þ
Model 1 Model 2 Model 3
v2
df
75.21 62.85 11.96
13 10 6
Model Dv2 comparison 1 vs. 2 2 vs. 3
12.36 50.89
Ddf
NNFI
CFI
SRMR
RMSEA
3 4
0.89 0.88 0.98
0.86 0.88 0.99
0.09 0.09 0.04
0.13 0.13 0.05
Note. v2 ¼ Chi-square; df ¼ degrees of freedom; NNFI ¼ Non-Normed Fit Index; CFI ¼ Comparative Fit Index; SRMR ¼ Standardized Root Mean Square Residual; RMSEA ¼ Root Mean Square Error of Approximation. * p < 0:01.
assumed no change in procrastination; a linear model (Model 2) that tested the assumption of a linear increase or decrease in procrastination; a quadratic model (Model 3) testing the assumption that 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 fluctuations. According to Dillon et al., sampling fluctuations cause Heywood cases when: (1) the model provides a reasonable fit, (2) the confidence interval for the offending estimate includes zero, and (3) the corresponding estimated standard error is roughly the same as the other estimated 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.’s suggestions, the first two conditions for sampling fluctuations were met. However, the estimated standard 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 randomly sampled approximately 70% of the cases 10 times (N ¼ 201–219), and found that in six random samples the variance estimates of both growth factors were not negative. Furthermore, the variances of the two growth factors were not significant in any of the resulting analyses. Thus, we believe that sampling fluctuations were the cause of the negative variances and the resulting Heywood case. 2 A summary of the model comparisons is presented in Table 2. Across all fit indices Model 3 was the best fitting model, and it fit the data significantly better than the other two models. Thus, Hypothesis 1 was supported. By examining the significance of the variances associated with the growth parameter factors contained in Model 3, we were able to test Hypothesis 2 and determine if all participants followed the same trajectory of procrastination during the semester. Surprisingly, the variances of the linear and quadratic growth parameter factors were not significant. These results suggest that there were no 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.
306
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
However, as indicated by significant variance on the intercept factor (t ¼ 4:587, p < 0:001), there were individual differences in baseline levels of procrastination. Therefore, although students may have differed in their initial levels of procrastination at the beginning of the semester (i.e., some students procrastinated more than others), all of them followed the same pattern of procrastination 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., negative variance problem) with the performance data. Subsequent post hoc analyses suggested the Heywood case might be caused by model misspecification. 3 However, inspection of the pattern of correlations between behavioral procrastination and test performance (see Table 1) at all time points indicated that they were negatively related. The more students procrastinated, the lower their grades tended to be. Therefore, Hypothesis 3 was supported. Our ability to test Hypotheses 4–6 was limited by several factors. First, the inclusion of behavioral procrastination data from Time 5 resulted in large residual variances that made a definite solution impossible once the individual difference variables were included. We believe this problem is due to the fact that the procrastination behavior of students at Time 5 is the result of factors other than differences in the tendency to procrastinate, which makes behavior at this point in time qualitatively different from earlier dilatory behavior. As a result, procrastination at Time 5 was dropped from our analysis of Hypotheses 4–6. Second, given the lack of variance on the linear and quadratic growth parameters, we could not test how well the individual difference variables predicted temporal changes in procrastination. We were, however, able to assess the degree to which the individual difference variables predicted initial levels (i.e., the intercept factor) of procrastination at the beginning of the semester. The standardized path coefficients in our structural model indicated that conscientiousness (b ¼ 0:23, p < 0:01) and self-reported procrastination (b ¼ 0:27, p < 0:01) predicted initial levels of procrastination among students, but neuroticism (b ¼ 0:04; ns) did not. Therefore, only partial support was found for Hypotheses 4 and 5 and no support was found for Hypothesis 6.
4. Discussion This research used LGCM to replicate and extend previous longitudinal studies of procrastination. As expected, we found that procrastination followed a curvilinear trend over time. However, in contrast to previous longitudinal studies of procrastination, dilatory behavior in our study actually increased over time and then dropped off suddenly at the end of the semester. This discrepancy may be due to several reasons. First, unlike other longitudinal studies, we used an actual behavioral measure of procrastination instead of a proxy for procrastination (e.g., number of hours studied). Second, we analyzed how procrastination changed over the course of a semester, which included multiple deadlines, as opposed to the weeks leading up to a single deadline such as an exam. Finally, the behavior measured in our study was qualitatively different from the behavior measured in other studies; we operationalized procrastination as a behavior associated with taking a test, whereas others have utilized behaviors specific to preparing for a
3
These analyses are available upon request from the authors.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
307
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 all followed the same trajectory during the semester. These results have important implications for the trait-based approach to understanding procrastination. According to trait-based explanations of procrastination, we would expect to see stable levels of procrastination across tasks, contexts, and time. Moreover, high and low procrastinators should follow distinctly different paths. Yet, as our research clearly shows, this is not the case. In fact, not only did mean levels of procrastination change over time, but students who differed significantly in their initial levels of procrastination followed the same trajectory. These findings contribute to a growing body of research that suggests trait-based explanations may not adequately describe the causal mechanisms underlying dilatory behavior. However, this does not mean that trait-based explanations of procrastination should be discarded. Instead, they need to be modified to reflect the complex interaction between personality and the environment. This would be consistent with trait-theorists who argue that some environments are capable of constraining the expression of personality (Magnusson & Torestad, 1992; Mischel, 1977; Mischel & Shoda, 1998; Reynolds & Karraker, 2003). From this perspective, our results do not seem so anomalous. Indeed, they suggest that there is something endemic to the academic context that dictates the behavior of procrastinators and non-procrastinators alike. As a result, individual differences in academic procrastination are suppressed by environmental factors. Alternatively, other types of procrastination (e.g., work procrastination) may not be as susceptible to environmental factors. Thus, individual differences may prevail in some types of procrastination while situational constraints may prevail in others. This is an important consideration, and one 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 procrastination, Steel et al. found that self-reported procrastination was generally unrelated to indicators of academic performance. In contrast, behavioral measures were significant predictors of academic performance. Similar results were found in our study. All of our behavioral measures of procrastination were better predictors of test performance across the semester than any of the selfreport trait-based measures. The consistent failure of self-report trait-based measures of procrastination to predict academic performance across time raises questions regarding their utility as accurate assessments of dilatory behavior and should be further investigated. In conclusion, latent growth curve modeling enabled us to describe the trajectory procrastination follows across time, and to reach some tentative conclusions regarding its causes and consequences. Furthermore, we were able to clarify several theoretical and methodological concerns regarding procrastination in general. As a result, we believe that researchers and practitioners 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 data analysis. We are also grateful to Kelly Zacharias and Brad Lenz for their assistance entering the
308
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
data. Both authors contributed equally to this manuscript. Order of authorship was determined arbitrarily.
References Aitken, M. (1982). A personality profile of the college student procrastinator. Unpublished doctoral dissertation, University of Pittsburgh. Beck, B. L., Koons, S. R., & Milgram, D. L. (2000). Correlates and consequences of behavioral procrastination: The effects of academic procrastination, self-consciousness, self-esteem and self-handicapping. Journal of Social Behavior and Personality, 15, 3–13. Beswick, G., Rothblum, E. D., & Mann, L. (1988). Psychological antecedents of student procrastination. Australian Psychologist, 23, 207–217. Blunt, A. K., & Pychyl, T. A. (2000). Task aversiveness and procrastination: A multi-dimensional approach to task aversiveness across stages of personal projects. Personality and Individual Differences, 28, 153–167. Burka, J. B., & Yuen, L. M. (1983). Procrastination: Why you do it, what to do about it. Reading, MA: AddissonWesley. Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421–483. Costa, P. T., & McCrae, R. R. (1992). Four ways five factors are basic. Personality and Individual Differences, 13, 653– 665. DeWitte, S., & Schouwenburg, H. C. (2002). Procrastination, temptations, and incentives: The struggle between the present and the future in procrastinators and the punctual. European Journal of Personality, 16, 469–489. Dillon, W. R., Kumar, A., & Mulani, N. (1987). Offending estimates in covariance structure analysis: Comments on the causes of and solutions to Heywood cases. Psychological Bulletin, 101, 126–135. Ellis, A., & Knaus, W. J. (1977). Overcoming procrastination or how to think and act rationally in spite of life’s inevitable hassles. New York, NY: Institute for Rational Living. Ferrari, J. R., Johnson, J. L., & McCown, W. G. (1995). Procrastination and task avoidance: Theory, research, and treatment. New York, NY: Plenum Press. Harris, N. N., & Sutton, R. I. (1983). Task procrastination in organizations: A framework for research. Human Relations, 11, 987–996. Hill, M. B., Hill, D. A., Chabot, A. E., & Barrall, J. F. (1978). A survey study of college faculty and student procrastination. College Student Journal, 12, 256–262. Johnson, J. L., & Bloom, M. (1995). An analysis of the contribution of the five factors of personality to variance in academic procrastination. Personality and Individual Differences, 18, 127–133. Lay, C. H. (1997). Explaining lower-order traits through higher-order factors: The case of trait procrastination, conscientiousness, and the specificity dilemma. European Journal of Personality, 11, 267–278. Lonergan, J. M., & Maher, K. J. (2000). The relationship between job characteristics and workplace procrastination as moderated by locus of control. Journal of Social Behavior and Personality, 15, 213–224. Magnusson, D., & Torestad, B. (1992). The individual as an interactive agent in the environment. In W. B. Walsh, K. H. Craik, & R. H. Price (Eds.), Person-environment psychology: Models and perspectives (pp. 89–126). Hillsdale, NJ: Erlbaum. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110–133. Milgram, N. A., Sroloff, B., & Rosenbaum, M. (1988). The procrastination of everyday life. Journal of Research in Personality, 22, 197–212. Mischel, W. (1977). On the future of personality measurement. American Psychologist, 32, 246–254. Mischel, W., & Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Psychology, 49, 229–258.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
309
Muthen, B. O. (1991). Analysis of longitudinal data using latent variable models with varying parameters. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 1–25). Washington, DC: American Psychological Association. Owens, A. M., & Newbegin, I. (1997). Procrastination in high school achievement: A causal structural model. Journal of Social Behavior and Personality, 12, 869–887. Pychyl, T. A., Lee, J. M., Thibodeau, R., & Blunt, A. (2000). Five days of emotion: An experience sampling study of undergraduate student procrastination. Journal of Social Behavior and Personality, 15, 239–254. Reynolds, B., & Karraker, K. (2003). A Big Five model of disposition and situation interaction: Why a helpful person may not always behave helpfully. New Ideas in Psychology, 21, 1–13. Rothblum, E. D. (1990). Fear of failure: The psychodynamic, need achievement, fear of success, and procrastination models. In H. Leitenberg (Ed.), Handbook of social and evaluation anxiety (pp. 497–537). New York, NY: Plenum Press. Rothblum, E. D., Solomon, L. J., & Murakami, J. (1986). Affective, cognitive, and behavioral differences between high and low procrastinators. Journal of Counseling Psychology, 33, 387–394. Saucier, G. (1994). Mini-Markers: A brief version of Goldberg’s unipolar big-five markers. Journal of Personality, 63, 506–516. Schouwenburg, H. C. (1995). Academic procrastination: Theoretical notions, measurement, and research. In J. R. Ferrari, J. L. Johnson, & W. G. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment (pp. 71–96). New York, NY: Plenum Press. Schouwenburg, H. C., & Groenewoud, J. T. (2001). Study motivation under social temptation: Effects of trait procrastination. Personality and Individual Differences, 30, 229–240. Schouwenburg, H. C., & Lay, C. H. (1995). Trait procrastination and the Big-Five factors of personality. Personality and Individual Differences, 18, 481–490. Senecal, C., Lavoie, K., & Koestner, R. (1997). Trait and situational factors in procrastination: An interactional model. Journal of Social Behavior and Personality, 12, 889–903. Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31, 503–509. Steel, P., Brothen, T., & Wambach, C. (2001). Procrastination and personality, performance, and mood. Personality and Individual Differences, 30, 95–106. Steel, P., Brothen, T., & Wambach, C. (2002). Procrastination and performance. Poster session presented at the annual meeting of the Society for Industrial and Organizational Psychology, Toronto, Ontario. Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science, 8, 454–458. Van Eerde, W. (2000). Procrastination: Self-regulation in initiating aversive goals. Applied Psychology: An International Review, 49, 372–389. Van Eerde, W. (2003). A meta-analytically derived nomological network of procrastination. Personality and Individual Differences, 35, 1401–1418. Van Eerde, W. (2004). Procrastination in academic settings and the Big Five Model of personality: A meta-analysis. In H. C. Schouwenburg, T. A. Pychyl, C. H. Lay, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings. Washington, DC: American Psychological Association. Watson, D. C. (2001). Procrastination and the five-factor model: A facet level analysis. Personality and Individual Differences, 30, 149–158. Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363–381. Willett, J. B., & Sayer, A. G. (1996). Cross-domain analyses of change over time: Combining growth modeling and covariance structure analysis. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 125–157). Mahwah, NJ: Erlbaum.
Exploring the dynamic nature of procrastination: A latent growth curve analysis of academic procrastination q Simon M. Moon a
a,*
, Alfred J. Illingworth
b,1
Department of Psychology, The University of Wisconsin-Oshkosh, Oshkosh, WI 54901, USA b 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 a curvilinear 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 relationship 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 negatively 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. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Procrastination; Latent growth curve modeling; Academic performance; Longitudinal design
q
An 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 Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2004.04.009
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 college students 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 behavior of college students, and its consequences, is not unfounded. Anecdotally, it has been suggested that approximately 95% of all college students procrastinate (Ellis & Knaus, 1977). Other researchers have estimated the prevalence of procrastination among college students to vary 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 negative correlation between academic procrastination and academic performance (e.g., Beswick et al., 1988; Steel, Brothen, & Wambach, 2001; Tice & Baumeister, 1997; Van Eerde, 2003). Given the frequency of academic procrastination, and its adverse effect on academic performance, researchers and clinicians have made a concerted effort to understand and ameliorate this behavior. In their attempts to understand academic procrastination, researchers have generally treated it as an immutable personality trait or disposition (Ferrari et al., 1995; Schouwenburg, 1995; Van Eerde, 2000). As a result, they implicitly assume that academic procrastination is stable across tasks, contexts, and time. This trait-based approach continues to drive the procrastination literature despite an abundance of contradicting theoretical and empirical work. For example, several models have been developed that provide a theoretical rationale for conceptualizing procrastination 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 situational variability 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 personality disposition, but is, in fact, a dynamic behavior that changes over time depending on the interaction of tasks and contexts. This conclusion, however, leads to several questions about academic procrastination that have yet to be answered. If procrastination changes over time, what is the shape of this change? Do all procrastinators change their behavior in the same way and to the same degree? What predicts changes in procrastination over time? And finally, are changes in procrastination related to important academic outcomes? Although several studies have attempted to answer these questions, 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 procrastination over time, (2) identify predictors of inter-individual differences in procrastination over time, and (3) examine the relationship between procrastination and academic performance over time. We begin by reviewing previous longitudinal studies of procrastination. We then introduce latent growth curve modeling (LGCM; Willett & Sayer, 1994), a structural equation technique used to model longitudinal data, as a viable statistical method for understanding the dynamic 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 our analyses, and a discussion of our findings. 1.1. Previous longitudinal studies of academic procrastination To date, several longitudinal studies investigating temporal changes in procrastination have been conducted (e.g., DeWitte & Schouwenburg, 2002; Pychyl et al., 2000; Schouwenburg, 1995; Schouwenburg & Groenewoud, 2001). Unlike previous longitudinal studies that only measured procrastination at two time points (e.g., Tice & Baumeister, 1997), these studies relied on multiple measurements to describe how the dilatory behavior of students fluctuated in the weeks leading up to an exam. By obtaining multiple measures of procrastination, it was possible to estimate the average trajectory that procrastinators tended to follow over time. Interestingly, all of these studies suggest that procrastination is characterized by curvilinear functions. For example, several early studies found that as deadlines associated with academic tasks approached, both high and low procrastinators exhibited decreased procrastination and increased study behavior that followed 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 his colleagues (DeWitte & Schouwenburg, 2002; Schouwenburg, 1995; Schouwenburg & Groenewoud, 2001). In his research, Schouwenburg hypothesizes that procrastination results from what is called the discounting principle: the longer people have to wait to receive a reward associated with a given behavior, the less attractive the behavior is perceived to be. With respect to procrastinators, the rewards associated with studying and achieving success on academic tasks is not realized until the deadline for initiating and completing the tasks is very near. Thus, the further 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, it follows that academic procrastination should exhibit a curvilinear trajectory over time, with a sharp 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 themselves studying at different times before an impending exam, ranging from 12 weeks before the exam to 1 week before the exam. For each time interval, the students were asked to indicate their motivation levels, 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 day given the proximity of the exam deadline. As expected, levels of motivation, the ability to resist social temptations, and actual hours spent studying were at their lowest levels when students perceived the exam deadline as being more than 4 weeks away. However, once the students were within 4 weeks of the deadline, the behavior associated with all three variables began to increase exponentially, reaching their highest levels the day before the exam. In a follow-up study, DeWitte and Schouwenburg (2002) measured the actual study behavior and intentions of students preparing for an exam to be administered at the end of the semester. As in Schouwenburg and Groenewoud (2001), they hypothesized that students would slowly increase their actual study behavior during the semester, followed by a burst of studying shortly before the exam. Consistent with their hypothesis, DeWitte and Schouwenburg found that both intentions to study, and number of hours studied, were best described by a hyperbolic or concave curve. In
300
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
addition, when DeWitte and Schouwenburg compared the predictive ability of the best fitting linear and hyperbolic curve for intentions to study and number of hours studied, they found that in 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 dynamic behavior. Following a curvilinear trajectory over time, academic procrastinators tend to delay working until the last minute. Under pressure to meet their deadlines or commitments, they compensate for their delays by increasing their study behavior. These longitudinal studies of academic procrastination have yielded some insight into how procrastination changes over time. However, the results of these studies are limited to a description of the trajectory procrastination follows, without any indication as to the causes, outcomes, or generalizability of this behavior. To further our understanding of longitudinal changes in procrastination, this research needs to be extended to identify inter-individual differences in procrastination over time, what individual difference variables might predict changes in procrastination over time, and how temporal changes in procrastination might be related to important academic outcomes. The application of a statistical technique called latent growth curve modeling may be the key to advancing our knowledge of temporal changes in procrastination. 1.2. Latent growth curve modeling Latent growth curve modeling (LGCM) is a powerful and flexible technique for understanding change 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 chronometric latent variables that may have structural relationships with other exogenous and endogenous latent factors (Chan, 1998; McArdle & Epstein, 1987). Central to LGCM is the identification of a general growth curve that accurately describes the trajectory for every member of a sample. Once this general growth curve has been established, it is then possible to determine whether there are inter-individual differences in the change captured by this curve, and the extent to which other variables predict these inter-individual differences (Muthen, 1991). The derivation of this general growth curve begins with the development of a mathematical model 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 general growth curve has been identified, the next step is to assess whether or not there is significant inter-individual variability around this trend line (Willett & Sayer, 1994, 1996). That is, it is possible to determine if every member of a sample followed a trajectory very similar to or very different from the general growth curve. If the variance associated with the growth parameters defining the growth curve is significant, it means that everyone in the sample did not follow the same trajectory over time. If, however, only chance variation is associated with the growth parameters, then everyone did follow a trajectory similar to the general growth curve. It is also important to note that there may be inter-individual differences on one or all of the growth parameters. Because LGCM is a covariance structure technique, a structural model can be tested in which time-invariant predictors of change, such as personality, are included as exogenous latent vari-
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
301
ables with direct paths to the latent growth parameters (Muthen, 1991; Willett & Sayer, 1994). In this way, it is possible to predict any inter-individual differences that may be found in the general growth 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 of one variable predict changes in the growth parameters of a second variable can be estimated. In other 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 previous longitudinal studies of procrastination have ignored. As we indicated earlier, a number of longitudinal studies have demonstrated that dilatory behavior does in fact follow a curvilinear trend over time (e.g., DeWitte & Schouwenburg, 2002). Thus, we propose the following hypothesis as a replication 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 conflicting results. For example, some studies have shown that high and low procrastinators exhibit decreased 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 procrastinators and non-procrastinators with respect to the curves describing changes in their dilatory behavior over time (e.g., DeWitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001; Steel, Brothen, & Wambach, 2002). Thus, it is unclear to what extent there exist inter-individual differences in procrastination over time. Acknowledging the lack of a strong theoretical foundation to develop a testable hypothesis, we propose the following exploratory hypothesis. Hypothesis 2. There will be significant inter-individual differences in the growth parameters describing the general growth curve of procrastination. By delaying the completion of class assignments, and putting off their preparation for quizzes and 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 performance indicators: course grade ðr ¼ 0:17Þ and grade point average ðr ¼ 0:28Þ. Thus, there appears to be a moderate, negative correlation between procrastination and academic performance. 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 differential relationships between the growth parameters of academic procrastination and academic performance 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
302
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
between the growth parameters describing the general growth curves of academic procrastination and academic performance. Hypothesis 3. The growth parameters of the general growth curves for procrastination and academic performance will be negatively related. The procrastination literature is replete with studies investigating the correlates of procrastination. We selected three time-invariant individual difference variables that have been found to have the strongest relationships with procrastination and examined their ability to explain interindividual differences in academic procrastination in the context of LGCM. Self-reported procrastination. Measuring procrastination via self-report measures has been the method of choice for assessing, diagnosing, and monitoring procrastination (Ferrari et al., 1995; Van Eerde, 2003, 2004). However, this method of measuring procrastination incorporates the assumptions of trait-based theories of procrastination. Following the rationale of researchers who believe procrastination is a stable individual difference variable, we would expect to see a positive relationship between self-reported procrastination, measured once, and behavioral procrastination, which has been measured multiple times. Thus, we propose the following hypothesis. Hypothesis 4. Self-reported procrastination will be positively related to the growth parameters describing the general growth curve of procrastination. Conscientiousness and neuroticism. In the search for explanations of procrastination, researchers have 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 understand procrastination using the five-factor model of personality (Costa & McCrae, 1992). The results of these studies suggest that conscientiousness and neuroticism have the strongest and most consistent relationships with procrastination. According to Van Eerde (2004), neuroticism tends to have a small to moderate positive correlation with procrastination, whereas conscientiousness tends to have a moderate to strong negative correlation with procrastination. Based on the relationships between procrastination, conscientiousness, and neuroticism described above, it is possible that these personality variables might be related to inter-individual differences in procrastination over time. Thus, we propose the following hypotheses. Hypothesis 5. Conscientiousness will be negatively related to the growth parameters describing the general growth curve of procrastination. Hypothesis 6. Neuroticism will be positively related to the growth parameters describing the general growth curve of procrastination. We will also conduct exploratory analyses to determine the relationship between our timeinvariant predictor variables (self-reported procrastination, conscientiousness, and neuroticism) and the growth parameters of the general growth curve describing academic performance.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
303
2. Method 2.1. Participants Participants ðN ¼ 349Þ were drawn from introductory psychology courses at a large Midwestern university and offered partial course credit as compensation. Due to missing performance data, 46 cases were excluded from our analyses, which resulted in a final sample of 303. The sample had a median age of 19 (M ¼ 21:89, SD ¼ 6:66) and was primarily composed of women (64%) and Caucasians (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 Procrastination Inventory (API; Aitken, 1982). The API is a self-report inventory measuring trait procrastination among college students. It contains 19 items (e.g., ‘‘I delay starting things so long I don’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 checklist that represents the five-factor model of personality. For each adjective (e.g., ‘‘bashful’’), participants described themselves on a 9-point scale ranging from 1 (Extremely Inaccurate) to 9 (Extremely Accurate). Although participants completed the full Mini-Marker scale, only the results pertaining to conscientiousness and neuroticism are reported below. Test scores. All participants were students in introductory psychology courses. As part of their course requirements, they completed five 50-item, multiple-choice tests during the semester. Each test was computerized and based on a 100-point scale ranging from 0 to 100, with larger scores indicating better test performance. For each participant, we collected five test scores. Behavioral academic procrastination. Each introductory psychology test was administered via computer and included a 1-week window in which students could take the test at their convenience. We obtained the dates spanning each test window, as well as the actual dates participants completed the tests. Thus, for each test window we operationalized behavioral academic procrastination as the difference between the date the window opened and the date students took the test, with larger differences indicating more procrastination. Scores for all five test windows ranged from 0 (i.e., took the test the same day the test window opened) to 6 (i.e., took the test the last day available in the test window). For each participant, we collected five measures of behavioral academic procrastination. 2.3. Procedure The data were collected during the spring semester of 2002 between the months of January and May. After completing their second test, participants received a packet of materials containing an informed consent form, a demographic questionnaire, the Aitken Procrastination Inventory, and the Mini-Markers checklist. These self-report measures were completed only once. The packet
304
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
also included a permission form that allowed us to obtain all five of their test scores and test completion dates from the administrator overseeing the introductory psychology courses. Upon completion 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. The reliabilities of the self-report measures are contained in the diagonals. As an indicator of the validity 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 negatively related to conscientiousness (r ¼ 0:66, p < 0:001) and positively related to every behavioral measure of procrastination. Furthermore, self-reported procrastination and measures of behavioral procrastination differed in their prediction of test performance. With the exception of the relationship between self-reported procrastination and performance on Test 4, self-reported procrastination did not predict test performance. In contrast, every behavioral measure of procrastination 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 curve of behavioral procrastination. This indicated that procrastination during the semester followed a curvilinear, quadratic trajectory. To determine whether a linear or curvilinear function best described the general growth curve of procrastination, we conducted several nested model comparisons and assessed the relative improvement in the fit of each model. In our analyses we compared three models: a no growth curve model (Model 1), which was the most restricted and Table 1 Descriptive statistics and correlations among study variables 1. API 2. Consc 3. Neuro 4. Pro1 5. Pro2 6. Pro3 7. Pro4 8. Pro5 9. Test 1 10. Test 2 11. Test 3 12. Test 4 13. Test 5
M
SD
1
2
3
2.58 5.14 3.81 4.10 4.58 4.81 4.92 4.15 67.46 63.49 59.04 61.85 65.93
0.61 0.95 0.96 2.11 2.02 2.09 2.04 2.31 13.70 12.16 14.25 13.42 13.67
(0.87) )0.66 (0.80) 0.21 )0.20 (0.72) 0.25 )0.12 )0.01 0.22 )0.16 )0.05 0.21 )0.17 0.00 0.27 )0.24 0.04 0.19 )0.16 )0.02 )0.07 )0.01 0.10 )0.10 0.01 0.05 )0.11 0.07 0.05 )0.14 0.10 0.06 )0.10 0.04 0.08
4
5
6
7
8
9
) 0.52 0.49 0.51 0.31 )0.26 )0.28 )0.33 )0.29 )0.22
) 0.49 0.51 0.34 )0.18 )0.15 )0.28 )0.25 )0.17
– 0.56 0.39 )0.26 )0.26 )0.36 )0.34 )0.30
– 0.43 )0.24 )0.26 )0.33 )0.30 )0.27
– )0.13 – )0.14 0.64 )0.26 0.70 )0.21 0.66 )0.21 0.70
10
11
12
) 0.63 0.65 0.62
– 0.65 0.70
– 0.70
Note. N ¼ 297–303 due to missing data on the procrastination measures. API ¼ Aitken Procrastination Inventory; Consc ¼ Conscientiousness; Neuro ¼ Neuroticism; Pro1–Pro5 ¼ Behavioral Procrastination for all five test windows; Test1–Test5 ¼ Test scores for all five tests completed during the semester. Reliability coefficients are contained in the diagonals. * p < 0:01. ** p < 0:05.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
305
Table 2 Nested model comparisons and model fit indices for latent growth curve analyses of behavioral procrastination ðN ¼ 297Þ
Model 1 Model 2 Model 3
v2
df
75.21 62.85 11.96
13 10 6
Model Dv2 comparison 1 vs. 2 2 vs. 3
12.36 50.89
Ddf
NNFI
CFI
SRMR
RMSEA
3 4
0.89 0.88 0.98
0.86 0.88 0.99
0.09 0.09 0.04
0.13 0.13 0.05
Note. v2 ¼ Chi-square; df ¼ degrees of freedom; NNFI ¼ Non-Normed Fit Index; CFI ¼ Comparative Fit Index; SRMR ¼ Standardized Root Mean Square Residual; RMSEA ¼ Root Mean Square Error of Approximation. * p < 0:01.
assumed no change in procrastination; a linear model (Model 2) that tested the assumption of a linear increase or decrease in procrastination; a quadratic model (Model 3) testing the assumption that 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 fluctuations. According to Dillon et al., sampling fluctuations cause Heywood cases when: (1) the model provides a reasonable fit, (2) the confidence interval for the offending estimate includes zero, and (3) the corresponding estimated standard error is roughly the same as the other estimated 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.’s suggestions, the first two conditions for sampling fluctuations were met. However, the estimated standard 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 randomly sampled approximately 70% of the cases 10 times (N ¼ 201–219), and found that in six random samples the variance estimates of both growth factors were not negative. Furthermore, the variances of the two growth factors were not significant in any of the resulting analyses. Thus, we believe that sampling fluctuations were the cause of the negative variances and the resulting Heywood case. 2 A summary of the model comparisons is presented in Table 2. Across all fit indices Model 3 was the best fitting model, and it fit the data significantly better than the other two models. Thus, Hypothesis 1 was supported. By examining the significance of the variances associated with the growth parameter factors contained in Model 3, we were able to test Hypothesis 2 and determine if all participants followed the same trajectory of procrastination during the semester. Surprisingly, the variances of the linear and quadratic growth parameter factors were not significant. These results suggest that there were no 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.
306
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
However, as indicated by significant variance on the intercept factor (t ¼ 4:587, p < 0:001), there were individual differences in baseline levels of procrastination. Therefore, although students may have differed in their initial levels of procrastination at the beginning of the semester (i.e., some students procrastinated more than others), all of them followed the same pattern of procrastination 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., negative variance problem) with the performance data. Subsequent post hoc analyses suggested the Heywood case might be caused by model misspecification. 3 However, inspection of the pattern of correlations between behavioral procrastination and test performance (see Table 1) at all time points indicated that they were negatively related. The more students procrastinated, the lower their grades tended to be. Therefore, Hypothesis 3 was supported. Our ability to test Hypotheses 4–6 was limited by several factors. First, the inclusion of behavioral procrastination data from Time 5 resulted in large residual variances that made a definite solution impossible once the individual difference variables were included. We believe this problem is due to the fact that the procrastination behavior of students at Time 5 is the result of factors other than differences in the tendency to procrastinate, which makes behavior at this point in time qualitatively different from earlier dilatory behavior. As a result, procrastination at Time 5 was dropped from our analysis of Hypotheses 4–6. Second, given the lack of variance on the linear and quadratic growth parameters, we could not test how well the individual difference variables predicted temporal changes in procrastination. We were, however, able to assess the degree to which the individual difference variables predicted initial levels (i.e., the intercept factor) of procrastination at the beginning of the semester. The standardized path coefficients in our structural model indicated that conscientiousness (b ¼ 0:23, p < 0:01) and self-reported procrastination (b ¼ 0:27, p < 0:01) predicted initial levels of procrastination among students, but neuroticism (b ¼ 0:04; ns) did not. Therefore, only partial support was found for Hypotheses 4 and 5 and no support was found for Hypothesis 6.
4. Discussion This research used LGCM to replicate and extend previous longitudinal studies of procrastination. As expected, we found that procrastination followed a curvilinear trend over time. However, in contrast to previous longitudinal studies of procrastination, dilatory behavior in our study actually increased over time and then dropped off suddenly at the end of the semester. This discrepancy may be due to several reasons. First, unlike other longitudinal studies, we used an actual behavioral measure of procrastination instead of a proxy for procrastination (e.g., number of hours studied). Second, we analyzed how procrastination changed over the course of a semester, which included multiple deadlines, as opposed to the weeks leading up to a single deadline such as an exam. Finally, the behavior measured in our study was qualitatively different from the behavior measured in other studies; we operationalized procrastination as a behavior associated with taking a test, whereas others have utilized behaviors specific to preparing for a
3
These analyses are available upon request from the authors.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
307
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 all followed the same trajectory during the semester. These results have important implications for the trait-based approach to understanding procrastination. According to trait-based explanations of procrastination, we would expect to see stable levels of procrastination across tasks, contexts, and time. Moreover, high and low procrastinators should follow distinctly different paths. Yet, as our research clearly shows, this is not the case. In fact, not only did mean levels of procrastination change over time, but students who differed significantly in their initial levels of procrastination followed the same trajectory. These findings contribute to a growing body of research that suggests trait-based explanations may not adequately describe the causal mechanisms underlying dilatory behavior. However, this does not mean that trait-based explanations of procrastination should be discarded. Instead, they need to be modified to reflect the complex interaction between personality and the environment. This would be consistent with trait-theorists who argue that some environments are capable of constraining the expression of personality (Magnusson & Torestad, 1992; Mischel, 1977; Mischel & Shoda, 1998; Reynolds & Karraker, 2003). From this perspective, our results do not seem so anomalous. Indeed, they suggest that there is something endemic to the academic context that dictates the behavior of procrastinators and non-procrastinators alike. As a result, individual differences in academic procrastination are suppressed by environmental factors. Alternatively, other types of procrastination (e.g., work procrastination) may not be as susceptible to environmental factors. Thus, individual differences may prevail in some types of procrastination while situational constraints may prevail in others. This is an important consideration, and one 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 procrastination, Steel et al. found that self-reported procrastination was generally unrelated to indicators of academic performance. In contrast, behavioral measures were significant predictors of academic performance. Similar results were found in our study. All of our behavioral measures of procrastination were better predictors of test performance across the semester than any of the selfreport trait-based measures. The consistent failure of self-report trait-based measures of procrastination to predict academic performance across time raises questions regarding their utility as accurate assessments of dilatory behavior and should be further investigated. In conclusion, latent growth curve modeling enabled us to describe the trajectory procrastination follows across time, and to reach some tentative conclusions regarding its causes and consequences. Furthermore, we were able to clarify several theoretical and methodological concerns regarding procrastination in general. As a result, we believe that researchers and practitioners 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 data analysis. We are also grateful to Kelly Zacharias and Brad Lenz for their assistance entering the
308
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
data. Both authors contributed equally to this manuscript. Order of authorship was determined arbitrarily.
References Aitken, M. (1982). A personality profile of the college student procrastinator. Unpublished doctoral dissertation, University of Pittsburgh. Beck, B. L., Koons, S. R., & Milgram, D. L. (2000). Correlates and consequences of behavioral procrastination: The effects of academic procrastination, self-consciousness, self-esteem and self-handicapping. Journal of Social Behavior and Personality, 15, 3–13. Beswick, G., Rothblum, E. D., & Mann, L. (1988). Psychological antecedents of student procrastination. Australian Psychologist, 23, 207–217. Blunt, A. K., & Pychyl, T. A. (2000). Task aversiveness and procrastination: A multi-dimensional approach to task aversiveness across stages of personal projects. Personality and Individual Differences, 28, 153–167. Burka, J. B., & Yuen, L. M. (1983). Procrastination: Why you do it, what to do about it. Reading, MA: AddissonWesley. Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421–483. Costa, P. T., & McCrae, R. R. (1992). Four ways five factors are basic. Personality and Individual Differences, 13, 653– 665. DeWitte, S., & Schouwenburg, H. C. (2002). Procrastination, temptations, and incentives: The struggle between the present and the future in procrastinators and the punctual. European Journal of Personality, 16, 469–489. Dillon, W. R., Kumar, A., & Mulani, N. (1987). Offending estimates in covariance structure analysis: Comments on the causes of and solutions to Heywood cases. Psychological Bulletin, 101, 126–135. Ellis, A., & Knaus, W. J. (1977). Overcoming procrastination or how to think and act rationally in spite of life’s inevitable hassles. New York, NY: Institute for Rational Living. Ferrari, J. R., Johnson, J. L., & McCown, W. G. (1995). Procrastination and task avoidance: Theory, research, and treatment. New York, NY: Plenum Press. Harris, N. N., & Sutton, R. I. (1983). Task procrastination in organizations: A framework for research. Human Relations, 11, 987–996. Hill, M. B., Hill, D. A., Chabot, A. E., & Barrall, J. F. (1978). A survey study of college faculty and student procrastination. College Student Journal, 12, 256–262. Johnson, J. L., & Bloom, M. (1995). An analysis of the contribution of the five factors of personality to variance in academic procrastination. Personality and Individual Differences, 18, 127–133. Lay, C. H. (1997). Explaining lower-order traits through higher-order factors: The case of trait procrastination, conscientiousness, and the specificity dilemma. European Journal of Personality, 11, 267–278. Lonergan, J. M., & Maher, K. J. (2000). The relationship between job characteristics and workplace procrastination as moderated by locus of control. Journal of Social Behavior and Personality, 15, 213–224. Magnusson, D., & Torestad, B. (1992). The individual as an interactive agent in the environment. In W. B. Walsh, K. H. Craik, & R. H. Price (Eds.), Person-environment psychology: Models and perspectives (pp. 89–126). Hillsdale, NJ: Erlbaum. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110–133. Milgram, N. A., Sroloff, B., & Rosenbaum, M. (1988). The procrastination of everyday life. Journal of Research in Personality, 22, 197–212. Mischel, W. (1977). On the future of personality measurement. American Psychologist, 32, 246–254. Mischel, W., & Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Psychology, 49, 229–258.
S.M. Moon, A.J. Illingworth / Personality and Individual Differences 38 (2005) 297–309
309
Muthen, B. O. (1991). Analysis of longitudinal data using latent variable models with varying parameters. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 1–25). Washington, DC: American Psychological Association. Owens, A. M., & Newbegin, I. (1997). Procrastination in high school achievement: A causal structural model. Journal of Social Behavior and Personality, 12, 869–887. Pychyl, T. A., Lee, J. M., Thibodeau, R., & Blunt, A. (2000). Five days of emotion: An experience sampling study of undergraduate student procrastination. Journal of Social Behavior and Personality, 15, 239–254. Reynolds, B., & Karraker, K. (2003). A Big Five model of disposition and situation interaction: Why a helpful person may not always behave helpfully. New Ideas in Psychology, 21, 1–13. Rothblum, E. D. (1990). Fear of failure: The psychodynamic, need achievement, fear of success, and procrastination models. In H. Leitenberg (Ed.), Handbook of social and evaluation anxiety (pp. 497–537). New York, NY: Plenum Press. Rothblum, E. D., Solomon, L. J., & Murakami, J. (1986). Affective, cognitive, and behavioral differences between high and low procrastinators. Journal of Counseling Psychology, 33, 387–394. Saucier, G. (1994). Mini-Markers: A brief version of Goldberg’s unipolar big-five markers. Journal of Personality, 63, 506–516. Schouwenburg, H. C. (1995). Academic procrastination: Theoretical notions, measurement, and research. In J. R. Ferrari, J. L. Johnson, & W. G. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment (pp. 71–96). New York, NY: Plenum Press. Schouwenburg, H. C., & Groenewoud, J. T. (2001). Study motivation under social temptation: Effects of trait procrastination. Personality and Individual Differences, 30, 229–240. Schouwenburg, H. C., & Lay, C. H. (1995). Trait procrastination and the Big-Five factors of personality. Personality and Individual Differences, 18, 481–490. Senecal, C., Lavoie, K., & Koestner, R. (1997). Trait and situational factors in procrastination: An interactional model. Journal of Social Behavior and Personality, 12, 889–903. Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31, 503–509. Steel, P., Brothen, T., & Wambach, C. (2001). Procrastination and personality, performance, and mood. Personality and Individual Differences, 30, 95–106. Steel, P., Brothen, T., & Wambach, C. (2002). Procrastination and performance. Poster session presented at the annual meeting of the Society for Industrial and Organizational Psychology, Toronto, Ontario. Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science, 8, 454–458. Van Eerde, W. (2000). Procrastination: Self-regulation in initiating aversive goals. Applied Psychology: An International Review, 49, 372–389. Van Eerde, W. (2003). A meta-analytically derived nomological network of procrastination. Personality and Individual Differences, 35, 1401–1418. Van Eerde, W. (2004). Procrastination in academic settings and the Big Five Model of personality: A meta-analysis. In H. C. Schouwenburg, T. A. Pychyl, C. H. Lay, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings. Washington, DC: American Psychological Association. Watson, D. C. (2001). Procrastination and the five-factor model: A facet level analysis. Personality and Individual Differences, 30, 149–158. Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363–381. Willett, J. B., & Sayer, A. G. (1996). Cross-domain analyses of change over time: Combining growth modeling and covariance structure analysis. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 125–157). Mahwah, NJ: Erlbaum.