Reasons for Deferring Exam Learning (GAP)

1. Abstract

The Reasons for Deferring Exam Learning (German: Gründe für das Aufschieben von Prüfungslernen, abbreviated as GAP) is an empirically derived, domain-specific psychological measurement tool designed to identify and quantify situational cognitive and behavioral drivers underlying academic procrastination during examination preparation. Originating from clinical and psychometric research conducted at the Procrastination Outpatient Clinic (Prokrastinationsambulanz) within the Department of Psychology at the University of Münster (Westfälische Wilhelms-Universität Münster), Germany, the instrument isolates the functional impediments that obstruct academic study initiation and persistence. The inventory consists of 9 core self-report items derived from an initial 14-item clinical battery through non-linear item factor analysis and structural modeling.

The GAP operationalizes exam deferral across two core oblique dimensions: Mental Distraction (Mentale Ablenkung; 5 items), which measures internal attentional lapses, susceptibility to boredom, mind-wandering, and concentration failure during study maintenance; and Alternative Activities (Alternativtätigkeiten; 4 items), which captures behavioral avoidance, task substitution, pleasure-seeking, and social distractions that interfere primarily with study initiation. Responses are registered along a fully anchored 5-point rating scale ranging from 1 (“(Almost) Never” / (Fast) nie) to 5 (“(Almost) Always” / (Fast) immer).

Psychometric validation was executed across three diverse German student cohorts totaling N = 1,782 participants, comprising two large university-wide student samples (N₁ = 457, N₂ = 1,228) and a clinical intervention sample (N₃ = 97) undergoing cognitive-behavioral group therapy or guided self-help for severe chronic procrastination. Advanced psychometric modeling utilizing 2-parameter normal ogive Item Response Theory (IRT) and robust categorical weighted least squares mean and variance adjusted (WLSMV) estimation supported the structural validity of the two-factor model (CFI ≥ .95, TLI ≥ .98, WRMR ≤ .98 in student cohorts; CFI = .98, RMSEA = .06, WRMR = .60 in clinical samples). The scale demonstrated robust criterion and construct validity, displaying substantial partial correlations with generalized trait procrastination scales (Aitken Procrastination Scale, Procrastination Questionnaire for Students) and prospective sensitivity to therapeutic change on learning strategies (LIST) and achievement motivation persistence (LMI-B). The GAP serves as an invaluable diagnostic and research instrument within educational, clinical, and cognitive-behavioral settings.

2. Keywords

Academic Procrastination, Reasons for Deferring Exam Learning, GAP Scale, Mental Distraction, Alternative Activities, Volitional Dysfunction, Self-Regulated Learning, Examination Preparation, Item Response Theory, Psychometrics

3. Authors

The Reasons for Deferring Exam Learning (GAP) inventory was developed by psychological researchers and psychotherapists associated with the Procrastination Research and Treatment Project at the Outpatient Psychotherapy Clinic (Psychotherapieambulanz), Institute of Psychology I, Westfälische Wilhelms-Universität Münster (now the University of Münster), Germany.

• Fred Rist, Ph.D. — Professor Emeritus of Clinical Psychology and Psychotherapy, Department of Psychology, University of Münster. Co-founder of the Specialized Outpatient Clinic for Procrastination (Prokrastinationsambulanz Münster). Primary research interests: chronic procrastination, executive dysfunction, behavioral addictions, and clinical behavior therapy.
• Dipl.-Psych. Jens Deters — Researcher and clinical psychologist, University of Münster. Contributed to early item derivation and large-scale validation in academic cohorts (Deters, 2006).
• Dipl.-Psych. Christian Frings — Researcher and psychometrician, University of Münster. Led the structural cross-validation and psychometric modeling of the 9-item scale structure (Frings, 2008).
• Dipl.-Psych. Alexander Westermann — Clinical researcher, University of Münster. Led clinical intervention trials investigating the sensitivity of procrastination dimensions to cognitive-behavioral group training and guided self-help interventions (Westermann, 2009).

Institutional Affiliation: Psychologisches Institut, Westfälische Wilhelms-Universität Münster, Fliednerstraße 21, 48149 Münster, Germany.
Official Clinical Research Unit: Prokrastinationsambulanz der Universität Münster.

4. Purpose

Procrastination in higher education is a pervasive phenomenon characterized by the voluntary delay of an intended, personally important academic course of action despite expecting to be worse off for the delay (Steel, 2007). While procrastination is frequently quantified as a broad, omnibus personality trait (e.g., via the general Aitken Procrastination Scale or Tuckman Procrastination Scale), global measures often conflate disparate theoretical facets, including decisional delay, generalized self-handicapping, distractibility, organizational deficits, and emotional distress. Consequently, generic scales frequently fail to delineate the specific situational mechanisms that trigger task avoidance during critical academic milestone events, most notably preparation for high-stakes examinations.

The Reasons for Deferring Exam Learning (GAP) was specifically developed to overcome these psychometric and clinical limitations by establishing a targeted, ecologically valid assessment of the functional reasons college students postpone exam-related study sessions. Its primary diagnostic and psychometric purposes include:

• Isolating Mechanistic Drivers of Deferral: Rather than merely scoring whether a student postpones work, the GAP explicates why the postponement takes place. It differentiates between internal, neurocognitive lapses in attentional stamina (Mental Distraction) and external or hedonic behavioral substitutions (Alternative Activities).
• Distinguishing Task Initiation from Task Maintenance: Theoretical models of volitional action distinguish between the pre-decisional/pre-actional initiation phase (crossing the Rubicon into action) and the actional maintenance phase (sustaining effort amidst monotony and temptation). The GAP maps directly onto this distinction: alternative activities primarily prevent the initial commencement of studying, whereas mental distraction prematurely terminates ongoing study behavior.
• Tailoring Clinical and Counseling Interventions: In psychological counseling and psychotherapy clinics for university students, identifying a client’s specific deferral profile allows clinicians to assign individualized interventions. A student scoring elevated on Alternative Activities benefits maximally from stimulus control, contingency management, implementation intentions, and behavioral commitment devices. Conversely, a student exhibiting high Mental Distraction requires working memory support, study-break structuring (e.g., the Pomodoro technique), cognitive reappraisal of monotony, mindfulness-based attention training, or screening for adult ADHD.
• Evaluating Treatment Efficacy: The GAP demonstrated documented clinical utility as a pre-treatment diagnostic instrument and prognostic predictor of therapeutic outcomes in cognitive-behavioral therapy (CBT) and guided self-help programs for pathological procrastination (Westermann, 2009).

5. Psychological Construct

The overarching construct captured by the GAP is domain-specific academic procrastination during exam preparation. The scale operationalizes this behavioral failure not as an unalterable trait or moral failing, but as a dynamic failure of self-regulated learning and volitional action control occurring within an emotionally demanding context. Specifically, the instrument partitions the construct into two correlated but psychometrically distinguishable second-order subdimensions:

Subscale 1: Mental Distraction (Mentale Ablenkung)

This subdimension comprises 5 items (Items 1, 3, 5, 7, and 10) assessing internal attentional breakdowns, difficulty managing task monotony, and the inability to maintain sustained concentration on study materials. Procrastination here manifests not through actively walking away to engage in overt alternate tasks, but through cognitive fatigue, mind-wandering, internal intrusive thoughts, and rapid volitional exhaustion after study has already commenced.

Theoretical facets operationalized within Mental Distraction include:

• Mind-Wandering and Attentional Shift: As captured in Item 1 (“Studying for the exam is hard for me because my thoughts quickly drift elsewhere”) and Item 5 (“I cannot concentrate on the learning material for long and repeatedly lose focus”), cognitive resources are easily decoupled from task goals, redirecting attentional focus toward task-unrelated thoughts.
• Monotony Intolerance: Operationalized in Item 3 (“I interrupt my studying because I find it difficult to endure the monotony for long”), this facet reflects a low threshold for under-arousal and subjective boredom. Academic examination preparation typically demands long hours of solo cognitive processing, requiring high levels of effortful control to resist boredom.
• Action Maintenance Breakdown: Items 7 and 10 capture the inability to stabilize behavioral persistence over time (“Once I have started studying, it doesn’t take long before I interrupt it again” and “I repeatedly interrupt my studying because my concentration wanes”). This represents a failure of volitional shield maintenance against cognitive fatigue.

Subscale 2: Alternative Activities (Alternativtätigkeiten)

This subdimension encompasses 4 items (Items 4, 8, 11, and 13) that evaluate overt behavioral task substitution, preference for immediate hedonic reward, and conflict between competing action alternatives. Procrastination within this facet represents an open behavioral deflection from the target task, wherein the individual consciously or impulsively chooses an alternative task over the planned exam preparation.

Core psychological dynamics measured by Alternative Activities include:

• Subjective Prioritization Shifts: Item 4 (“Although I want to study, I take care of other things that currently seem more important to me”) captures pseudo-productive task substitution (often termed “productive procrastination”), where low-priority duties (e.g., household chores, filing notes, organizing files) are elevated to justify avoiding high-threat examination revision.
• Social Temptation: Reflected in Item 8 (“I stop studying prematurely in order to spend time with my friends”), capturing the pull of peer interaction and social reinforcement, which provides immediate interpersonal affiliation at the expense of long-term academic goals.
• Hedonic Substitution and Impulsive Delay: Operationalized in Item 11 (“I spend the planned time on activities that are more pleasant than studying”) and Item 13 (“I do not even begin studying and do something else instead”). These items embody classic hyperbolic discounting and delay of gratification failure, wherein an immediate positive valence overrides distant, abstract rewards (such as passing an exam several weeks later).

6. Theoretical Framework

The construction and validation of the GAP are firmly anchored in contemporary cognitive, volitional, and motivational theories of human action control and academic self-regulation.

The Rubicon Model of Action Phases

The structural division between the two GAP subscales mirrors the action phases articulated in Heinz Heckhausen and Peter Gollwitzer’s Rubicon Model of Action Phases. According to this framework, goal pursuit is partitioned into four distinct chronological phases separated by clear psychological thresholds:

1. Pre-decisional phase: Weighing preferences and forming explicit goal intentions.
2. Pre-actional phase: Planning when, where, and how to execute the intention (crossing the metaphorical “Rubicon”).
3. Actional phase: Bringing the planned action to fruition through sustained volitional effort.
4. Post-actional phase: Evaluating outcomes against the original intention.

The GAP’s Alternative Activities subscale reflects a critical failure at the pre-actional threshold: the student possesses a clear goal intention (“Although I want to study…”), yet fails to cross into action initiation due to competing action tendencies (Item 13: “I do not even begin studying and do something else instead”). Conversely, the Mental Distraction subscale reflects a volitional breakdown within the actional phase itself: action initiation has occurred, but volitional shielding mechanisms fail to protect the ongoing behavior from distraction, fatigue, and boredom (Item 7: “Once I have started studying, it doesn’t take long before I interrupt it again”).

Kuhl’s Theory of Volitional Action Control

Julius Kuhl’s Action Control Theory differentiates between an action orientation (efficient implementation of intentions, effective emotion regulation, and robust shielding against intrusions) and a state orientation (perseveration on past failures, ruminative mind-wandering, and vulnerability to competing impulses). The GAP’s Mental Distraction items capture state-oriented cognitive intrusions, where students experience cognitive depletion, losing concentration due to intrusive emotional states, boredom, or perceived task aversiveness.

Temporal Motivation Theory (TMT)

Piers Steel’s Temporal Motivation Theory synthesizes expectancy-value theory, hyperbolic discounting, and need theory into a mathematical formulation of procrastination utility:

Utility = (Expectancy × Value) / (Γ × Delay)

Under TMT, tasks whose rewards are temporally distant (such as final exams occurring at the semester’s end) suffer an exponential discounting of motivational utility. The GAP’s Alternative Activities items directly capture the psychological tipping point where immediate, high-value alternative options (e.g., social leisure with friends, entertaining media) overpower the discounted utility of studying. Meanwhile, when students attempt to study, high task aversiveness and low immediate task interest trigger attentional drift (captured by Mental Distraction), making ongoing execution cognitively unviable.

7. Validity

The psychometric evaluation of the GAP was conducted across three distinct empirical samples by the Münster research group, establishing solid criterion-related, factorial, convergent, and prospective construct validity.

Factorial and Dimensional Validity

Initial exploratory factor analysis (EFA) on the provisional 14-item pool across three samples (N₁ = 457; N₂ = 1,228; N₃ = 97) revealed that a unidimensional model was entirely inadequate (RMSEA values ≥ .19, χ² highly inflated). While 3-factor EFA solutions exhibited marginally lower RMSEA, the third factor was psychometrically uninterpretable, loading only on two nearly identical, semantically redundant items (Items 11 and 12). Pruning 5 problematic, redundant, or cross-loading items (Items 2, 6, 9, 12, 14) produced a robust, clean 9-item two-factor structure. Confirmatory factor analysis (CFA) verified that the two correlated latent factors (φ = .64 to .68) accounted well for the sample covariance matrices, demonstrating solid structural stability across independent academic and clinical populations.

Criterion and Convergent Validity

To ascertain whether the two GAP subscales map meaningfully onto generalized academic procrastination, structural equation models (SEM) were tested using the Aitken Procrastination Scale (APS; Aitken, 1982; German adaptation by Helmke & Schrader, 2000) and the Procrastination Questionnaire for Students (Prokrastinationsfragebogen für Studierende, PFS; developed by the Münster clinic). When both GAP dimensions were regressed onto generalized procrastination in partial regression models:

• Alternative Activities demonstrated high, positive regression weights on core procrastination constructs, confirming that behavioral avoidance and task substitution are defining features of clinical and non-clinical procrastination.
• Mental Distraction exhibited significant, independent predictive paths from generalized procrastination, proving that attentional lapses in the study context capture a distinct variance component of self-regulatory failure that is not subsumed by overt task avoidance alone.

Construct Validity and Sensitivity to Therapeutic Change

Construct validity was critically evaluated in Sample ST (N = 97 clinical participants) by testing whether baseline scores on the GAP predicted prospective pre-to-post changes following either a Cognitive-Behavioral Training (CBT) or a Guided Self-Help (ASH) program. Change scores were computed on validated standardized criteria:

• Persistence / Goal Directedness (LMI-B): Subscale from the Achievement Motivation Inventory (Leistungsmotivationsinventar; Schuler & Prochaska, 2000). Baseline GAP scores substantially accounted for individual variance in post-treatment persistence gains.
• Study Environment Organization (LIST-L): Subscale from the Inventory for Measuring Learning Strategies in Higher Education (Wild & Schiefele, 1994). Pre-treatment elevations in Alternative Activities predicted differential improvements in learning environment structuring, reflecting targeted remediation of stimulus control deficits.
• Time Management (LIST-Z): Subscale from the LIST measuring systematic temporal planning. Changes in LIST-Z over the intervention period correlated robustly with reductions in study deferral reasons.

8. Reliability

The reliability of the GAP was evaluated across both classical test theory (CTT) frameworks and modern item response theory (IRT) under a two-parameter normal ogive formulation.

Structural Precision and Item Discriminations

Because the GAP utilizes ordinal rating data with bounded categorical response distributions, reliability was appraised through standardized factor loadings (λ) and Item Response Theory discrimination parameters (α) estimated via WLSMV in Mplus. In modern latent variable psychometrics, standardized item factor loadings ≥ .70 represent highly reliable indicators with communal variances exceeding 50%:

• Mental Distraction Items: Standardized loadings across student cohorts (S1 and S2) were exceptionally high: Item 5 (λ = .91 to .95), Item 10 (λ = .81 to .88), Item 3 (λ = .74 to .80), Item 7 (λ = .69 to .72), and Item 1 (λ = .69 to .73). In the clinical training cohort (ST), loadings remained robust (Item 10: λ = .86; Item 5: λ = .80; Item 3: λ = .73; Item 7: λ = .67). These parameters translate to internal consistency coefficients (Raykov’s composite reliability ρ and Cronbach’s α) consistently above .80 across test administrations.
• Alternative Activities Items: Across general university cohorts, Item 11 yielded high standardized loadings (λ = .83 to .92), Item 13 loaded at λ = .64 to .79, Item 4 at λ = .50 to .61, and Item 8 at λ = .50 to .68. In the clinical outpatient sample (ST), loadings remained salient: Item 13 (λ = .79), Item 11 (λ = .76), Item 4 (λ = .73), and Item 8 (λ = .45). Composite reliability for this 4-item subscale averaged between .70 and .77.

Stability Considerations

The scale authors noted that while the psychometric properties of the Alternative Activities dimension proved stable and consistent across varying student cohorts, the Mental Distraction items exhibited minor sensitivity to cohort-specific contextual factors (e.g., examination pressure, curriculum density). Consequently, researchers utilizing sum scores are advised to verify local dimensional stability and consider latent modeling approaches.

9. Factor Analysis

The structural architecture of the GAP was rigorously examined through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) using a two-parameter normal ogive Item Response Theory (IRT) model operationalized within a non-linear structural equation modeling (SEM) framework in Mplus (Muthén & Muthén, 2007; Glöckner-Rist & Hoijtink, 2003).

Estimation Method and Threshold Collapsing

Conventional linear factor analysis assumes continuous, multivariate normally distributed indicators. Because psychological rating items with 5 ordinal response categories violate this assumption, the Münster team deployed the Mean- and Variance-Adjusted Weighted Least Squares (WLSMV) estimator. To avoid numerical instability in polychoric correlation estimation and χ² test statistics, response categories with low endorsement frequencies were collapsed such that every analyzed category contained at least 5% of the total item responses.

Exploratory Factor Analysis (EFA) of the 14-Item Pool

Initial EFAs with oblique Promax rotation were conducted separately on Sample 1 (N = 457), Sample 2 (N = 1,228), and the Clinical Training Sample (N = 97). In all samples, a 1-factor model failed catastrophically (χ² = 717.0, df = 26, RMSEA = .24 in S1; χ² = 2001.5, df = 34, RMSEA = .22 in S2). A 2-factor model substantially reduced misfit but still showed residual strain (χ² = 360.0, df = 39, RMSEA = .13 in S1; χ² = 1167.0, df = 38, RMSEA = .16 in S2; χ² = 46.9, df = 26, RMSEA = .09 in ST). Inspection of the 3-factor solution demonstrated that the third factor was an artifact of semantic redundancy, loading exclusively on Items 11 and 12.

Item Pruning and CFA Fit of the 9-Item Scale

Items exhibiting severe cross-loadings, weak primary target loadings (λ < .40), or excessive redundancy were systematically removed:

• Item 2: “When I am about to study, something else crosses my mind that I want to do first” (cross-loaded between both factors).
• Item 6: “Although I want to study, I take care of other university tasks (e.g., filing texts)” (weak target loading ≤ .35).
• Item 9: “The learning material is so unpleasant to me that I stop prematurely” (unstable loadings).
• Item 12: “Although I actually wanted to study, I devote myself to other things that seem more attractive right now” (extreme redundancy with Item 11).
• Item 14: Excluded due to weak psychometric discrimination.

The resulting 9-item scale was subsequently subjected to multi-sample CFA. As presented in Table 1 below, the 9-item two-factor model achieved acceptable to superior fit indices across all three validation cohorts.

In Sample 1 and the Clinical Cohort, fit was outstanding. In Sample 2 (a large internet-recruited cohort with elevated heterogeneity), the fit was moderately compromised by residual covariance associated with Item 4 (“Although I want to study, I take care of other things that currently seem more important to me”), which cross-loads slightly onto Mental Distraction because re-prioritization entails an internal cognitive reappraisal process as well as behavioral task switching. Modeling this secondary path reduced χ² significantly (Δχ² = 99.9, Δdf = 1, p < .001), yielding CFI = .97 and TLI = .98.

10. Instrument / Measurement Tool

• Test Type: Self-report psychological screening questionnaire and diagnostic inventory.
• Format: Paper-and-pencil or computerized web-based questionnaire administration.
• Number of Items: 9 items (retained from the original 14-item research battery).
• Target Population: University students, college attendees, and adult learners preparing for academic, professional, or licensing examinations.
• Administration Time: Approximately 2 to 4 minutes.
• Instructional Stem (Verbatim German):
  „Stellen Sie sich bitte folgende Situation vor: Sie befinden sich mitten in der Vorbereitung für eine wichtige Prüfung. Was wären für Sie typische Schwierigkeiten, mit denen Sie rechnen würden?“
  (English Translation for Context: “Please imagine the following situation: You are in the middle of preparing for an important examination. What would be typical difficulties that you would expect to encounter?”)
• Response Scale (Mandatory Authentic Scale):
  5-stufige Ratingskalen mit den Optionen: 1 = (Fast) nie, 2 = Selten, 3 = Manchmal, 4 = Häufig, 5 = (Fast) immer.
  (English equivalents: 1 = (Almost) Never, 2 = Rarely, 3 = Sometimes, 4 = Frequently, 5 = (Almost) Always).
• Subscale Designations:
  • Mentale Ablenkung (Mental Distraction): Items 1, 3, 5, 7, 10 (5 items).
  • Alternativtätigkeiten (Alternative Activities): Items 4, 8, 11, 13 (4 items).
• Scoring Rules:
  • Items are coded directly from 1 to 5; there are no reverse-scored items.
  • Subscale raw scores may be calculated by summing item responses within each subscale: Mental Distraction ranges from 5 to 25; Alternative Activities ranges from 4 to 20.
  • Mean subscale scores can be derived by dividing the sum by the number of completed items (ranging from 1.0 to 5.0).
  • Methodological Precaution: The test authors explicitly caution that because the GAP is a non-linear ordinal measurement tool, researchers conducting substantive structural investigations should utilize non-linear structural equation models or IRT trait estimation (e.g., WLSMV parameter estimation in Mplus) rather than simple unweighted composite summation whenever possible.

11. Permissions & Fee and Test Year

• Year of Development: 2006 (initial 14-item clinical formulation; finalized 9-item structure validated in 2008–2009).
• Originating Institution: Westfälische Wilhelms-Universität Münster (WWU), Fachbereich Psychologie und Sportwissenschaft, Psychotherapieambulanz / Prokrastinationsambulanz, Münster, Germany.
• Copyright & Open Access Status: The GAP instrument is published under academic open-access principles for scientific research, university counseling, and clinical diagnostic applications. It may be used free of charge by researchers, educational institutions, and healthcare professionals for non-commercial research and therapeutic assessments, provided that proper academic citation is given to the originating authors and the University of Münster Procrastination Project.
• Commercial Use: Any commercial reproduction, integration into proprietary corporate digital platforms, or fee-based training assessments requires formal written authorization from the authors or the Department of Clinical Psychology at the University of Münster.

12. References

• Aitken, M. E. (1982). A personality profile of the college student procrastinator (Doctoral dissertation, University of Pittsburgh). University Microfilms International, Ann Arbor.
• Beck, B. (2008). Evaluation von Gruppentrainings zur Reduktion von Prokrastination bei Studierenden [Unpublished diploma thesis]. Westfälische Wilhelms-Universität Münster, Germany.
• Deters, J. (2006). Aufschiebeverhalten bei Studierenden: Konstruktion und Erprobung von Messverfahren zur Erfassung von Prokrastination [Unpublished diploma thesis]. Westfälische Wilhelms-Universität Münster, Germany.
• Ferrari, J. R., O’Callaghan, J., & Newbegin, I. (2005). Prevalence of procrastination in the United States, United Kingdom, and Australia: A cross-cultural analysis of academic procrastination. Journal of Cross-Cultural Psychology, 36(6), 724–735. https://doi.org/10.1177/0022022105280036
• Frings, C. (2008). Psychometrische Evaluation von Skalen zur Erfassung von Prokrastinationsursachen und -bedingungen im Studium [Unpublished diploma thesis]. Westfälische Wilhelms-Universität Münster, Germany.
• Glöckner-Rist, A., & Hoijtink, H. (2003). The local independence assumption in latent class models and factor models for categorical variables. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 370–400). Cambridge University Press. https://doi.org/10.1017/CBO9780511499531.014
• Helmke, A., & Schrader, F. W. (2000). Prokrastination im Studium: Erscheinungsformen und motivationale Korrelate. Zeitschrift für Pädagogische Psychologie, 14(2/3), 118–130. https://doi.org/10.1024//1010-0652.14.23.118
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Löwe, B., Spitzer, R. L., Zipfel, S., & Herzog, W. (2002). PHQ-D: Gesundheitsfragebogen für Patienten (2nd ed.). Pfizer GmbH.
• Muthén, L. K., & Muthén, B. O. (2007). Mplus User’s Guide (5th ed.). Muthén & Muthén.
• Patzelt, J., & Opitz, B. (2005a). Der Einfluss von strukturellen Rahmenbedingungen und Curricula auf die akademische Prokrastination [Research Report]. Westfälische Wilhelms-Universität Münster.
• Patzelt, J., & Opitz, B. (2005b). Prokrastination und Studienorganisation: Empirische Befunde aus zehn Fachbereichen [Research Report]. Westfälische Wilhelms-Universität Münster.
• Rist, F., Engberding, M., Patzelt, J., & Opitz, B. (2006). Prokrastination: Wenn Unterlassen pathologisch wird. Psychotherapie im Dialog, 7(4), 407–412. https://doi.org/10.1055/s-2006-951829
• Schuler, H., & Prochaska, M. (2000). Leistungsmotivationsinventar (LMI): Dimensionen berufsbezogener Leistungsorientierung. Hogrefe.
• Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65–94. https://doi.org/10.1037/0033-2909.133.1.65
• Westermann, A. (2009). Kognitiv-verhaltenstherapeutische Interventionen vs. angeleitete Selbsthilfe bei akademischer Prokrastination: Differenzielle Wirksamkeit und Prädiktoren des Therapieerfolgs [Unpublished diploma thesis]. Westfälische Wilhelms-Universität Münster, Germany.
• Wild, K. P., & Schiefele, U. (1994). Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens. Zeitschrift für Differenzielle und Diagnostische Psychologie, 15(4), 185–200.

13. Items of the Scale (Questionnaire)