Procrastination in Different Life-Domains: Is Procrastination Domain Specific?

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  • 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 dotoday, is a well-known phenomenon in everyday life. In an attempt to understand thecharacter of procrastination, a large body of research has been accumulating over thelast 40 years. The present study was to evaluate the need to distinguish betweenprocrastination in different life-domains by gathering first hints as to whether pro-crastination 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 life-domains (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 isdomain specific, but not extremely so. The results encourage further investigationsinto the domain specificity of procrastination and suggest that future diagnoses of andinterventions for procrastination will profit from considering the life-domain procras-tination occurs in.

    Keywords Procrastination . Academic procrastination . Life-domains . Domainspecificity

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

    Curr Psychol (2013) 32:175185DOI 10.1007/s12144-013-9171-8

    K. B. Klingsieck (*)Universitt Paderborn, Fakultt fr Kulturwissenschaften, Fach Psychologie, Warburger Strae 100,33098 Paderborn, Germanye-mail:

  • delay. Few studies have examined the functional aspects of delay (for example, Chuand Choi 2005; Schraw et al. 2007).

    A large body of research on this phenomenon has been accumulating in recentyears. Researchers have focused on academic procrastination (delay of study-relatedactivities in a student population). Estimates have indicated that up to 70 percent ofcollege students consider themselves procrastinators (for example, Schouwenburg1995) and that 50 percent procrastinate consistently and problematically (for exam-ple, Solomon and Rothblum 1984). Students were often engaged in such activities assleeping, reading, or watching TV instead of studying (Pychyl et al. 2000).

    Studies have further shown that academic procrastination is related to pooracademic performance (Ferrari et al. 1995; Tice and Baumeister 1997), lower self-efficacy (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 2025 percent ofadults in the general population (Ferrari et al. 2007; Harriot and Ferrari 1996), fewstudies have examined procrastination in non-academic life-domains. Among thosethat have are studies that have compared the procrastination of non-academic andacademic tasks among university students (Ferrari and Scher 2000; Milgram et al.1998, 1988).

    Several studies have also investigated procrastination in the workplace (Ferrari1992; Hammer and Ferrari 2002; Harriot and Ferrari 1996; Lonergan and Maher2000) or among job-seekers (Lay and Brokenshire 1997; Sencal and Guay 2000). Afew have examined procrastination with regard to filing taxes (Kasper 2004) and topreparing financially for retirement (Akerlof 1991; ODonoghue and Rabin 1999).

    Further studies have dealt with the procrastination of health-related behavioramong university students (Sirois et al. 2003; Stead et al. 2010; Tice andBaumeister 1997) and community-dwelling adults (Sirois 2007) and with the pro-crastination of leisure activities (Ferrari 1993; Shu and Gneezy 2010). Moreover, onestudy has examined the regret that adults felt because of their chronic procrastinationin various life-domains (Ferrari et al. 2009).

    The Present Study

    Although the aforementioned studies have examined procrastination in other life-domains than the academic, no study has yet evaluated the need to distinguishbetween procrastination in different life-domains. The purpose of the present studywas to evaluate this need by gathering first clues with regard to the domain specific-ity, or domain generality, of procrastination.

    An extensive body of literature, primarily with regard to school subjects, hasdemonstrated the profitability of studying domain specificity. These studies haveinvestigated the domain specificity of self-concept (Marsh 1990), goal orientation andself-efficacy (Bong 2001; Duda and Nicholls 1992), expectancy of success and taskvalue (Eccles et al. 1993), and motivation and engagement (Martin 2008).

    Investigating the domain specificity of procrastination has implications for diagnosesof and interventions for procrastination. If procrastination is found to be domain general,then general instruments and interventions are appropriate. If, however, procrastination

    176 Curr Psychol (2013) 32:175185

  • is found to be domain specific, then instruments and interventions should consider thelife-domain procrastination occurs in.

    The present study focuses on procrastination in six different life-domains. Iderived the six life-domains investigated by reviewing the literature on commonlyapplied classifications of life-domains, (for example, Grpel and Kuhl 2006). Theyincluded 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 everydayroutines and obligations domain refers to various kinds of activities that individualshave to do but that are not explicitly linked to the academic and work domain (forexample, tax return, administrative task, chores around the house). Typical activitiesin the health domain are making a doctors appointment, redeeming a doctorsprescription, or engaging in wellness behaviors (for example, dieting). The leisuredomain comprises activities, such as getting tickets to a cultural event, pursuing oneshobbies regularly, or doing volunteer work. The family and partnership domain refersto activities that persons pursue with or for their partners and family members (forexample, visiting parents, buying a Valentines Day gift for ones partner). The finaldomain, 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 Martins (2008)suggestions for assessing domain specificity. I first employed a factor analyticalapproach in which the model fit between a domain general and a domain specificmodel was compared via confirmatory factor analysis. Second, I assessed mean-leveldifferences in procrastination frequency between domains and, thus, compared thefrequency across the life-domains. If procrastination is domain specific, the procras-tination 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.



    The sample consisted of 260 students, 66.9 percent of which were female. For themajority of participants (96.5 percent), German was their mother tongue. The meanage was 23.56 (SD=3.74) years. Participants were either single (64.6 percent) or livedwith 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) andhad been studying for on average of five semesters (M=5.57, SD=3.93).


    The procrastination measure was included into a comprehensive survey on variousaspects of life balance. The survey was composed of two parts. The first partcomprised socio-demographic questions and several instruments concerning the

    Curr Psychol (2013) 32:175185 177

  • different aspects. The second part of the survey comprised instruments that assessed thefrequency and other aspects of procrastination in each domain. Before participants filledout the second part of the survey, they read a brief description of each domains contentand 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 notanswer the follow-up questions for the domain concerned.

    For the present studys purpose, I only took the socio-demographic informa-tion and the procrastination measure into account. Seven items from an adaptedversion of the Procrastination Scale for Students (Glckner-Rist et al. 2009)assessed the procrastination frequency among participants during the past twoweeks ranging from 1([almost] never) to 5 ([almost] always). For instance,participants rated this item: I delay the completion of certain things. The reliabilityof this scale was good in all domains (AW: =.90; EO:=.90; HE: =.92; LE: =.91;FP: =.93; SC=.93).


    Participants were recruited via an online link that was posted on different socialnetworking websites (for example, Facebook) and could enter a lottery for giftcertificates in compensation for their participation. I recruited university students,and thus only one sample, for this study to derive first clues to domain specificitywhile allowing for a sound comparison with the results of previous procrastinationresearch, which has typically focused on academic procrastination. Overall, 279students completed the survey, of whom 17 were not included in the final samplebecause they did not invest a reasonable amount of time (17 minutes; I derived thistime from pretests) in completing the survey.

    Statistical Analysis

    Confirmatory Factor Analysis The first step for investigating domain specificity callsfor finding the best fitting model by comparing a domain general and a domainspecific model. In this study, this comparison was based upon the seven itemsmeasuring procrastination frequency (Glckner-Rist et al. 2009) in each of the sixdomains. The first model, the domain generality model, was a 1-factor model inwhich these 42 items jointly constituted one procrastination factor. The secondmodel, the domain specificity model, was a 6-factor model in which procrastinationwas freely estimated in each of the six domains (for example, academic procrastina-tion, leisure procrastination). The six factors were correlated because they cannot beexpected to be completely independent from each other.

    A confirmatory factor analysis (CFA), using AMOS 17.0, tested the hypothesizedmodels. Maximum likelihood was the method of estimation. The root mean square errorof approximation (RMSEA), the comparative fit index (CFI), and the 2 test statisticevaluated the goodness of fit of the models. For RMSEAs, values of less than .05 and .08typically reflect a close and reasonable fit, respectively (Marsh et al. 1996). The CFIvaries along a 01 continuum, in which values at or greater than .90 and .95 typically

    178 Curr Psychol (2013) 32:175185

  • reflect acceptable and excellent fits to the data, respectively (McDonald and Marsh1990).

    In CFA, parallel data (for example, parallel items in the six life-domains) posestatistical issues. Because the measurement errors for matching items across domainsare likely to be correlated, parameter estimates are probably biased. Thus, in additionto assessing the fit of these two competing models, further analyses evaluated thedecline in model fit when parallel correlations were constrained to be equal inaddition to the factors being correlated. Martin (2008) has shown that this is theappropriate analytical means by which to assess domain specificity while controllingfor correlated uniqueness.

    Mean-Level Differences For the second step (comparison of procrastination frequen-cy across domains), an analysis of variance (repeated measurement design) and,thereafter, a series of paired-samples t-tests compared procrastination frequencyacross the different life-domains. Due to the special construction of the second partof 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 thedomain 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 settingtheir frequency ratings equal to zero in the domain concerned because their ratings areessential to a full investigation of the domain specificity of procrastination.


    Model Fit

    The 6-factor model yielded the best fit (2=2165.80, df=805, CFI=.88, RMSEA=.08).The fit was significantly better (based on differences in2 and fit indices) than that of the1-factor model (2=9179.26, df=819, CFI=.27, RMSEA=.20).

    The seven procrastination frequency items were the same for each domain. Toensure that this parallel data did not result in biased parameter estimates, additionalanalyses evaluated the decline in model fit when parallel correlations wereconstrained to be equal (see Martin 2008). In such analysis, domain specificity isindicated 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. Thecorrelations among the procrastination items in all six domains were set equal;that is, the within-domain correlations were constrained to equal the between-domain correlations. The constrained model yielded a significantly poorer fit(2=2421.33, df=846, CFI=.86, RMSEA=.09) than the model in which all of thecorrelations were freely estimated (2=2165.80, df=805, CFI=.88, RMSEA=.08)based on the differences in 2 (255.54) with 41 degrees of freedom (p

  • Mean-Level Differences

    In the case of mean-level differences in procrastination frequency across domains, ananalysis of variance (repeated measurement design) revealed significant mean-leveldifferences across domains, F (5, 1295) = 50.82, p

  • Discussion

    The present study was intended to gather first clues with regard to the domainspecificity, or domain generality, of procrastination. The study examined the...


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