Curr Psychol (2013) 32:175–185 DOI 10.1007/s12144-013-9171-8

Procrastination in Different Life-Domains: Is Procrastination Domain Specific? Katrin B. Klingsieck

Published online: 25 April 2013 # Springer Science+Business Media New York 2013

Abstract Procrastination, putting off until tomorrow what one had intended to do today, is a well-known phenomenon in everyday life. In an attempt to understand the character of procrastination, a large body of research has been accumulating over the last 40 years. The present study was to evaluate the need to distinguish between procrastination in different life-domains by gathering first hints as to whether procrastination is domain specific or domain general. In an online survey on 260 students (mean age= 23.56; SD = 3.74) the procrastination frequency in 6 different lifedomains (academic and work, everyday routines and obligations, health, leisure, family and partnership, social contacts) was examined. Confirmatory factor analysis (CFA) and the analysis of mean-level differences revealed that procrastination is domain specific, but not extremely so. The results encourage further investigations into the domain specificity of procrastination and suggest that future diagnoses of and interventions for procrastination will profit from considering the life-domain procrastination occurs in. Keywords Procrastination . Academic procrastination . Life-domains . Domain specificity

Procrastination, putting off until tomorrow what one had intended to do today, is a well-known and frequently experienced phenomenon. Procrastination is usually defined as “the purposive delay in the beginning and/or completion of an overt or covert act, typically accompanied by subjective discomfort” (Ferrari 1998, p. 281) or as “to voluntarily delay an intended course of action despite expecting to be worse off for the delay” (Steel 2007, p. 66). These two definitions reflect the widespread notion in procrastination research that procrastination represents a dysfunctional form of

K. B. Klingsieck (*) Universität Paderborn, Fakultät für Kulturwissenschaften, Fach Psychologie, Warburger Straße 100, 33098 Paderborn, Germany e-mail: [email protected]

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delay. Few studies have examined the functional aspects of delay (for example, Chu and Choi 2005; Schraw et al. 2007). A large body of research on this phenomenon has been accumulating in recent years. Researchers have focused on academic procrastination (delay of study-related activities in a student population). Estimates have indicated that up to 70 percent of college students consider themselves procrastinators (for example, Schouwenburg 1995) and that 50 percent procrastinate consistently and problematically (for example, Solomon and Rothblum 1984). Students were often engaged in such activities as sleeping, reading, or watching TV instead of studying (Pychyl et al. 2000). Studies have further shown that academic procrastination is related to poor academic performance (Ferrari et al. 1995; Tice and Baumeister 1997), lower selfefficacy (Steel 2007; Wolters 2003), higher stress levels (Tice and Baumeister 1997), and higher anxiety levels (for example, Rothblum et al. 1986). Although procrastination has also been found to chronically affect 20–25 percent of adults in the general population (Ferrari et al. 2007; Harriot and Ferrari 1996), few studies have examined procrastination in non-academic life-domains. Among those that have are studies that have compared the procrastination of non-academic and academic tasks among university students (Ferrari and Scher 2000; Milgram et al. 1998, 1988). Several studies have also investigated procrastination in the workplace (Ferrari 1992; Hammer and Ferrari 2002; Harriot and Ferrari 1996; Lonergan and Maher 2000) or among job-seekers (Lay and Brokenshire 1997; Senécal and Guay 2000). A few have examined procrastination with regard to filing taxes (Kasper 2004) and to preparing financially for retirement (Akerlof 1991; O’Donoghue and Rabin 1999). Further studies have dealt with the procrastination of health-related behavior among university students (Sirois et al. 2003; Stead et al. 2010; Tice and Baumeister 1997) and community-dwelling adults (Sirois 2007) and with the procrastination of leisure activities (Ferrari 1993; Shu and Gneezy 2010). Moreover, one study has examined the regret that adults felt because of their chronic procrastination in various life-domains (Ferrari et al. 2009).

The Present Study Although the aforementioned studies have examined procrastination in other lifedomains than the academic, no study has yet evaluated the need to distinguish between procrastination in different life-domains. The purpose of the present study was to evaluate this need by gathering first clues with regard to the domain specificity, or domain generality, of procrastination. An extensive body of literature, primarily with regard to school subjects, has demonstrated the profitability of studying domain specificity. These studies have investigated the domain specificity of self-concept (Marsh 1990), goal orientation and self-efficacy (Bong 2001; Duda and Nicholls 1992), expectancy of success and task value (Eccles et al. 1993), and motivation and engagement (Martin 2008). Investigating the domain specificity of procrastination has implications for diagnoses of and interventions for procrastination. If procrastination is found to be domain general, then general instruments and interventions are appropriate. If, however, procrastination

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is found to be domain specific, then instruments and interventions should consider the life-domain procrastination occurs in. The present study focuses on procrastination in six different life-domains. I derived the six life-domains investigated by reviewing the literature on commonly applied classifications of life-domains, (for example, Gröpel and Kuhl 2006). They included academic and work (AW), everyday routines and obligations (EO), health (HE), leisure (LE), family and partnership (FP), and social contacts (SC). The academic and work domain refers to all study-related and job-related activities (for example, studying for an exam, returning a job-related phone call). The everyday routines and obligations domain refers to various kinds of activities that individuals have to do but that are not explicitly linked to the academic and work domain (for example, tax return, administrative task, chores around the house). Typical activities in the health domain are making a doctor’s appointment, redeeming a doctor’s prescription, or engaging in wellness behaviors (for example, dieting). The leisure domain comprises activities, such as getting tickets to a cultural event, pursuing ones’ hobbies regularly, or doing volunteer work. The family and partnership domain refers to activities that persons pursue with or for their partners and family members (for example, visiting parents, buying a Valentine’s Day gift for one’s partner). The final domain, social contacts, refers to different kinds of social activities (for example, returning a telephone call, writing an email, meeting friends). To investigate the domain specificity of procrastination, I followed Martin’s (2008) suggestions for assessing domain specificity. I first employed a factor analytical approach in which the model fit between a domain general and a domain specific model was compared via confirmatory factor analysis. Second, I assessed mean-level differences in procrastination frequency between domains and, thus, compared the frequency across the life-domains. If procrastination is domain specific, the procrastination frequency will vary as a function of the life-domains. If it is domain general, however, there should be few differences in frequency across life-domains.

Method Sample The sample consisted of 260 students, 66.9 percent of which were female. For the majority of participants (96.5 percent), German was their mother tongue. The mean age was 23.56 (SD=3.74) years. Participants were either single (64.6 percent) or lived with a partner (31.5 percent). Only 3.8 percent of the participants had children. Participants were enrolled in different fields of study (natural science: 41.8 percent; social science: 28.9 percent; engineering science: 27.3 percent; other: 2 percent) and had been studying for on average of five semesters (M=5.57, SD=3.93). Instruments The procrastination measure was included into a comprehensive survey on various aspects of life balance. The survey was composed of two parts. The first part comprised socio-demographic questions and several instruments concerning the

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different aspects. The second part of the survey comprised instruments that assessed the frequency and other aspects of procrastination in each domain. Before participants filled out the second part of the survey, they read a brief description of each domain’s content and rated how typical it was for them to procrastinate in each domain (for example, “How typical is it for you to procrastinate in this life-domain?”; 1 [very untypical] to 5 [very typical]). For the sake of valid answers in the following part of the survey, participants who rated their procrastination as very untypical (scale point: 1) did not answer the follow-up questions for the domain concerned. For the present study’s purpose, I only took the socio-demographic information and the procrastination measure into account. Seven items from an adapted version of the Procrastination Scale for Students (Glöckner-Rist et al. 2009) assessed the procrastination frequency among participants during the past two weeks ranging from 1([almost] never) to 5 ([almost] always). For instance, participants rated this item: “I delay the completion of certain things”. The reliability of this scale was good in all domains (AW: α=.90; EO: α=.90; HE: α=.92; LE: α=.91; FP: α=.93; SC=.93). Procedure Participants were recruited via an online link that was posted on different social networking websites (for example, Facebook) and could enter a lottery for gift certificates in compensation for their participation. I recruited university students, and thus only one sample, for this study to derive first clues to domain specificity while allowing for a sound comparison with the results of previous procrastination research, which has typically focused on academic procrastination. Overall, 279 students completed the survey, of whom 17 were not included in the final sample because they did not invest a reasonable amount of time (17 minutes; I derived this time from pretests) in completing the survey. Statistical Analysis Confirmatory Factor Analysis The first step for investigating domain specificity calls for finding the best fitting model by comparing a domain general and a domain specific model. In this study, this comparison was based upon the seven items measuring procrastination frequency (Glöckner-Rist et al. 2009) in each of the six domains. The first model, the domain generality model, was a 1-factor model in which these 42 items jointly constituted one procrastination factor. The second model, the domain specificity model, was a 6-factor model in which procrastination was freely estimated in each of the six domains (for example, academic procrastination, leisure procrastination). The six factors were correlated because they cannot be expected to be completely independent from each other. A confirmatory factor analysis (CFA), using AMOS 17.0, tested the hypothesized models. Maximum likelihood was the method of estimation. The root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the χ2 test statistic evaluated the goodness of fit of the models. For RMSEAs, values of less than .05 and .08 typically reflect a close and reasonable fit, respectively (Marsh et al. 1996). The CFI varies along a 0–1 continuum, in which values at or greater than .90 and .95 typically

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reflect acceptable and excellent fits to the data, respectively (McDonald and Marsh 1990). In CFA, parallel data (for example, parallel items in the six life-domains) pose statistical issues. Because the measurement errors for matching items across domains are likely to be correlated, parameter estimates are probably biased. Thus, in addition to assessing the fit of these two competing models, further analyses evaluated the decline in model fit when parallel correlations were constrained to be equal in addition to the factors being correlated. Martin (2008) has shown that this is the appropriate analytical means by which to assess domain specificity while controlling for correlated uniqueness. Mean-Level Differences For the second step (comparison of procrastination frequency across domains), an analysis of variance (repeated measurement design) and, thereafter, a series of paired-samples t-tests compared procrastination frequency across the different life-domains. Due to the special construction of the second part of the survey, participants who had rated their procrastination to be very untypical (scale point: 1) in one domain did not have to answer the follow-up questions for the domain concerned (that is, AW: n=16, EO: n=10, HE: n=9, LE: n=31, FP: n=30, SC: n=28). In the analyses that follow, I accounted for these participants by setting their frequency ratings equal to zero in the domain concerned because their ratings are essential to a full investigation of the domain specificity of procrastination.

Results Model Fit The 6-factor model yielded the best fit (χ2 =2165.80, df=805, CFI=.88, RMSEA=.08). The fit was significantly better (based on differences in χ2 and fit indices) than that of the 1-factor model (χ2 =9179.26, df=819, CFI=.27, RMSEA=.20). The seven procrastination frequency items were the same for each domain. To ensure that this parallel data did not result in biased parameter estimates, additional analyses evaluated the decline in model fit when parallel correlations were constrained to be equal (see Martin 2008). In such analysis, domain specificity is indicated by a significant decline in model fit of the model with no constraints. Domain generality is indicated if there is not a markedly significant decline. The correlations among the procrastination items in all six domains were set equal; that is, the within-domain correlations were constrained to equal the betweendomain correlations. The constrained model yielded a significantly poorer fit (χ2 =2421.33, df=846, CFI=.86, RMSEA=.09) than the model in which all of the correlations were freely estimated (χ2 =2165.80, df=805, CFI=.88, RMSEA=.08) based on the differences in χ2 (255.54) with 41 degrees of freedom (p<.01; see Martin 2008). Although these differences were not too extreme, they were present. Therefore, in terms of model fit, the data support the domain specificity of procrastination because the best fitting model was the one in which each domain was separated and the correlations were freely estimated.

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Mean-Level Differences In the case of mean-level differences in procrastination frequency across domains, an analysis of variance (repeated measurement design) revealed significant mean-level differences across domains, F (5, 1295) = 50.82, p<.01, η2 =0.16. Accordingly, a series of paired-samples t-tests examined procrastination frequency in the different lifedomains in detail. Significant differences (p<.003 after Bonferroni correction) emerged for all comparisons but for EO-HE, LE-FP, and FP-SC, indicating mean-level differences in which the AW-mean (M=3.13, SD=1.19) was highest and the LE-mean (M=2.01, SD=1.09) was lowest. Table 1 summarizes the effects and presents the means (and standard deviations) of each domain. The data on the mean-level differences of procrastination frequency again points toward the domain specificity of procrastination. In addition, a glance at the participant’s ratings of the question “How typical is procrastination for you in each domain?” also provided clues in favor of the domain specificity of procrastination. An analysis of variance (repeated measurement design) revealed significant and large mean-level differences, F (5, 1295) = 50.22, p<.01, η2 =0.16. A series of paired t-tests provided a more detailed picture of these differences. While no significant mean-level differences emerged for the comparisons AW-EO, AW-HE, EO-HE, LE-FP, and LE-SC, significant (p<.003 after Bonferroni correction) differences emerged between AW/EO/HE and LE/FP/SC. It seems that procrastination is more typical in AW (M=3.55, SD=1.25), EO (M=3.45, SD=1.12), and HE (M=3.59, SD=1.10) and less typical in LE (M=2.64, SD=1.05), FP (M=2.58, SD=.97), and SC (M=2.88, SD=1.16). This observation matched the distinctive pattern that emerged when the percentages for scale point 4 (typical) and 5 (very typical) for each domain were evaluated. While the number of participants who evaluated procrastination as typical or very typical were above 50 percent for AW (56.3 percent), EO (51.9 percent), and HE (57.3 percent), the number was below 30 percent in LE (20.4 percent), FP (16.2 percent), and SC (28.8 percent). Table 1 Mean-level differences of procrastination frequency across domains M (SD)

AW

EO

HE

LE

FP

AW

3.13 (1.19)

–

EO

2.84 (1.05)

Mdiff =0.29**, T=3.69

–

HE

2.76 (1.07)

Mdiff =0.37**, T=4.13

Mdiff =0.08, T=1.06

–

LE

2.01 (1.09)

Mdiff =1.11**, T=11.26

Mdiff =0.82**, T=9.95

Mdiff =0.74**, T=9.31

–

FP

2.25 (1.18)

Mdiff =0.88**, T=9.20

Mdiff =0.59**, T=7.20

Mdiff =0.51**, T=6.27

Mdiff =−0.23*, T=−2.64

–

SC

2.27 (1.17)

Mdiff =0.85**, T=8.70

Mdiff =0.56**, T=6.76

Mdiff =0.48**, T=5.93

Mdiff =−0.26*, T=−3.44

Mdiff =−0.03, T=−0.35

AW academic and work; EO everyday routines and obligations; HE health; LE leisure; FP family and partnership; SC social contacts. Degrees of freedom = 259; N=260; *p<.003 **p<.0007 (Bonferroni corrected)

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Discussion The present study was intended to gather first clues with regard to the domain specificity, or domain generality, of procrastination. The study examined the academic and work (AW), everyday routines and obligations (EO), health (HE), leisure (LE), family and partnership (FP), and social contacts (SC) life-domains. The results demonstrated that procrastination exists in all six life-domains. Few participants rated procrastination as very untypical for them. Procrastination, however, was more typical for the academic and work, everyday routines and obligations, and health domains than for the leisure, family and partnership, and social contacts domains. Altogether, the results provided first clues with regard to the question of procrastination’s domain specificity or domain generality. First, the domain specific model yielded a better fit in the confirmatory factor analysis than the domain general model. Second, procrastination presented itself differently in each life-domain with regard to its frequency. Its frequency was highest in AW and lowest in LE. (Considering the fact that the AW is possibly the one domain characterized by external and concrete deadlines, the existence or non-existence of deadlines in a domain could possibly account for these frequency differences.) Both results support the notion of procrastination to be domain specific and, by that, encourage the differentiation between procrastination in different life-domains in the realm of theoretical approaches, diagnostic tools, and intervention programs. The results illustrate that further research to differentiate procrastination’s characteristics in various life-domains will be worthwhile. I believe the following three lines of research will be fruitful. First, a broader choice of life-domains will strengthen future studies. Additionally, the life-domains that I have chosen for this study need to be critically reviewed and revised in future studies. For instance, in this study, I combined the academic and the job domains. In a sample of (European) university students, whose jobs are often related to their studies, this category might be tenable. Differentiating this domain into study-related and job-related work might have produced more face validity. In addition, it seems possible that collapsing the family and the social contacts domain into one domain might result in a more economical presentation. Moreover, I asked participants to name a domain that they felt was missing. Only a few made use of this opportunity. The results suggested that a domain reflecting the religious and spiritual aspects of life and a domain focusing on “time for myself” could be included in a future study. Second, the two steps taken in this study (confirmatory factor analysis, mean-level differences) could be enriched. Strictly speaking, the present study limits the investigation of domain specificity to the self-reported frequency of procrastination, thereby missing the opportunity to investigate how procrastination itself may vary across domains. For example, the reasons for and consequences of procrastination and the correlation of procrastination with constructs that are usually associated with procrastination (for example, Big Five, van Eerde 2003; Watson 2001; selfregulation, Dewitte and Lens 2000; Dietz et al. 2007; Wolters 2003) could be compared across domains. Third, investigating the domain specificity of procrastination in a non-student sample would strengthen future studies on the domain specificity of procrastination.

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Because student life is only a marginal representation of non-student life conditions, it would be illustrative to see whether a different pattern emerges in a non-student population. All in all, a study that compares a wide range of procrastination aspects across a wide range of life-domains in a diverse sample will contribute more deeply to our understanding of whether procrastination presents itself similarly in different lifedomains or whether its characteristics vary as a function of life-domain. The study’s limitations lie in the sampling and statistical procedures. The use of a student sample does not allude to the extent to which the results generalize. Thus, the use of non-student sample would have provide a more reasonable answer to the question of domain specificity of procrastination. Furthermore, the results may not generalize to individuals from other contexts because the sample was not random but rather convenient and respondents were limited to those with access to the Internet. The analyses relied on self-reported data only, thereby excluding insights that could be gained from actual behavioral indices of procrastination. The handling of the missing values that resulted from the construction of the survey poses a statistical problem. I set ratings of participants who had rated their procrastination to be very untypical (scale point: 1) in one domain equal to zero for the procrastination frequency measure in the domain concerned. I am aware that this procedure has artificially inflated the results. The exploratory purpose of this study might make this procedure more acceptable. Furthermore, making participants answer questions that they could not answer, did not make sense. However, in future studies, I suggest that researchers use techniques of adaptive testing to avoid having to resort to this strategy. I also acknowledge that the comparative fit index (CFI) of the confirmatory factor analyses hardly represents an acceptable fit. However, in light of the acceptable RMSEA, this should not cast doubt on the results. Against the background of these limitations, it cannot be stressed enough that this study was meant to be a preliminary study on the domain specificity of procrastination. Yet, even as a preliminary study, the results have the potential to advance procrastination research and practice. Previous research has suggested that procrastination has sufficient cross-temporal and situational stability to be considered a trait (Steel 2007). The present results suggest that this stability is at least partly explained by what may be a procrastination-factor (pfactor), analogous to the g-factor of intelligence (Spearman 1904), that is incorporated in all domains. At the same time, differences between domains, however, might be due to the special procrastination characteristics of the life-domains and, therefore, to a domain-factor (d-factor). The p-factor might pertain to self-regulation, conscientiousness, and motivation, for example, while the d-factor might pertain to aspects unique to the domain, such as task aversiveness, social affiliation, or body image. The results also suggest that an instrument that assesses procrastination as a dispositional variable without differentiating between domains might not capture the complex nature of procrastination. Individuals might display unique profiles of procrastination in each of the life-domains. Accordingly, there seems to be merit in developing measurements that tap procrastination in different life-domains. Moreover, procrastination interventions would benefit from a more differentiated approach to overcoming procrastination by, for example, focusing on motivational strategies for procrastination in one domain (for example, AW) and on strategies of social integration in others (for example, LE, SC).

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In conclusion, the results seem to suggest that—in addition to investigating dispositional procrastination types, such as arousal, avoidance, and decisional (Steel 2010)—the investigation of procrastination types based on life-domains could expand procrastination research. Introducing domain-specific differences into procrastination research will not only enrich the theoretical framework but will also provide more valid instruments for assessing procrastination, by that supporting the development of effective interventions. Acknowledgments I would like to thank Carola Grunschel for her insightful and helpful comments concerning an earlier version of this article and Laura Thau for her support in collecting the data.

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