This study examined whether time-related academic behavior is stable or context-dependent. Researchers analyzed data from over 450 undergraduate students who completed surveys in a fall semester and spring semester. They identified four clusters of time-related behavior: generalized timely engagement, timely engagement/approach, generalized procrastination, and timely engagement/avoidance. Most students (51%) changed clusters between semesters, indicating behavior is context-dependent. Motivation factors like self-efficacy, goal orientation, and self-regulation predicted changes in behavior, particularly increases in adaptive timely engagement and decreases in maladaptive procrastination avoidance. This suggests motivation can influence academic behavior and existing intervention strategies may help students adopt more productive habits.

1. Time-Related Academic
Behavior: State or Trait?
KAMDEN K. STRUNK, PH.D. – AUBURN UNIVERSITY
FORREST C. LANE, PH.D. – SAM HOUSTON STATE UNIVERSITY
MWARUMBA MWAVITA, PH.D. – OKLAHOMA STATE UNIVERSITY

2. 2x2 Model of Time-Related
Academic Behavior

4. Not just What, but Why
• It is necessary to consider not only the behavior, but the
motivation.
• In order to understand students’ time-related academic behavior, it
is necessary to understand the underlying motivation.

5. The Present Study
CONTEXTUAL CHANGES IN TIME-RELATED ACADEMIC BEHAVIOR

6. The Present Study
• Research Question: Is time-related academic behavior a function of the person
only, or are these behaviors, at least in part, contextually driven?
• Data were collected from 2,146 participants in face-to-face undergraduate
classes in a Fall semester.
• Follow up survey data was collected online during the Spring semester.
◦ In the follow-up survey, 453 participated and evaluated their new course.
◦ Some attrition was due to institutional retention. Others chose not to complete the follow-
up survey.

7. Participants
There were 453 participants, including 301 women and 152 men.
◦ The average age of participants was 20.56 (SD = 3.79).
◦ In terms of ethnicity, 75.5% were white, 8.2% multiracial, 4.9% Hispanic/Latino, 3.8%
Black/African American, 3.5% American Indian, 1.8% Asian, and 2.4% were ‘other’.
◦ In terms of academic standing, on average, participants in the sample had an ACT score of
25.20 (SD = 4.22), college grade point average of 3.26 (SD = .55), and had earned an average
of 72.61 college credit hours (SD = 38.30).

8. Measures
• 22 Measure of Time-Related Academic Behavior (Strunk, Cho, Steele &
Bridges, 2013)
◦ 25-item measure
◦ Reliability estimates using coefficient alpha ranged from .81 to .87
• Achievement Goal Questionnaire-Revised (Elliot & Murayama, 2008).
◦ This measure includes four subscales, including mastery-approach, mastery-avoidance,
performance-approach, and performance-avoidance.
◦ Reliability estimates using coefficient alpha ranged from .86 to .88.
• Motivated Strategies for Learning Questionnaire (Pintrich & DeGroot, 1990).
◦ Only the self-efficacy and self-regulation scales were used in the present study
◦ Both scales showed good score reliability, with coefficient alpha ranging from .79 to .84.

9. Results: Cluster Invariance
• Participants were classified on time-related academic behavior using
hierarchical cluster analysis.
◦ 22 Measure of Time-Related Academic Behavior as the clustering variables.
• Data from the initial collection and the one-semester follow up were clustered
simultaneously, to produce cluster solutions that were identical for both time
points.
• Cluster solutions (10 – 2 clusters) were examined the reverse scree method
(Lathrop & Williams, 1987; Lathrop & Williams, 1989; Lathrop & Williams,
1990.

10. Reverse Scree Analysis
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
10 9 8 7 6 5 4 3 2

11. Cluster solution
Four clusters were retained:
• Cluster 1 (Generalized Timely Engagement) - low means on both procrastination
subscales, and high means on both timely engagement subscales.
• Cluster 2 (Timely Engagement/Approach) – Higher means on timely engagement
subscales, but also the higher means in both procrastination and timely engagement
subscales with approach valence.
• Cluster 3 (Generalized Procrastination) – Opposite pattern to cluster one.
• Cluster 4 (Timely Engagement/Avoidance) – Higher means in timely engagement, but
also somewhat higher means in avoidance on both procrastination and timely
engagement subscales.

12. Means by Cluster
1
2
3
4
5
6
7
Procrastination-Avoidance Procrastination-Approach Timely Engagement-Avoidance Timely Engagement-Approach
Generalized Timely Engagement Timely Engagement/Approach Generalized Procrastination Timely Engagement/Avoidance

13. Cluster Invariance
Research Question: Would participants change in their basic ‘type’ of behavior
over time?
• Individuals were tested to determine if their cluster membership varied from
the initial to the follow-up survey.
• There was a significant difference in cluster membership across the semester-
long delay (χ2
3 = 16.31, p < .001).
o Specifically, 229 participants (50.55%) changed clusters.

14. Canonical Correlation
Research Question: Do motivational factors explain changes in time-related
academic behavior between semesters?
• A canonical correlation analysis (CCA) was performed on the data from
participants who changed clusters (N = 228).
• Seven predictor variables were included representing the difference scores
between the first and second survey administrations.
o Achievement Goal Questionnaire-Revised (AGQ-R), Motivated Strategies for Learning Questionnaire
(MSLQ), a more general measure of academic self-efficacy (SSE), and a measure of subjective task
value (utility value and intrinsic value).
• Four criterion variables were included representing the change in each of the
four group cluster scores for participants (e.g. procrastination-avoidance)

15. CCA Results
• The canonical correlation analysis yielded four functions (Table 2).
• The full model was tested first (functions 1 to 4) and determined to be
statistically significant (F36,799.95 = 5.309, p <.001).
o This collective model explained 55% of the variance across all predictor and
criterion variable sets (Wilks’ λ = .448).
• The model’s subsequent functions were then tested hierarchically through a
dimension reduction analysis.
• Only function 2 (F24,621.27 = 3.456, p <.001) and function 3 (F14,430.00 = 2.245, p =
.006) resulted in statistically significant relationships.

16. CCA Results
Variables Coef 𝑟𝑠 𝑟𝑠
2 Coef 𝑟𝑠 𝑟𝑠
2
ℎ2
Predictor
AGQ-R
Mastery Approach 0.210 .678 .460 0.161 -.032 .001 .461
Mastery Avoidance 0.089 .474 .224 -0.404 -.163 .027 .251
Performance Approach 0.034 .521 .271 -0.403 -.245 .060 .332
Performance Avoidance 0.079 .459 .211 0.358 .076 .006 .216
MSLQ
Self-Efficacy 0.267 .741 .549 -0.734 -.385 .148 .697
Self-Regulation 0.291 .738 .545 0.962 .545 .297 .843
Bandura
Self-Efficacy 0.299 .646 .417 -0.018 -.023 .001 .418
Subjective Task Value
Utility Value -0.247 .460 .211 0.141 -.051 .003 .214
Task Value 0.417 .647 .419 -0.142 -.092 .008 .427
𝑅 𝐶
2 .355 .199
Criterion
Procrastination Approach 0.542 -.226 .051 -.969 -.835 .698 .749
Procrastination Avoidance -0.617 -.601 .362 .427 -.074 .005 .367
Engagement Approach 0.882 .831 .691 -.469 .441 .194 .885
Engagement Avoidance 0.029 .644 .414 .664 .646 .417 .832

17. Discussion
• Our first hypothesis was supported: ‘type’ of time-related academic behavior
was not stable across time and context, and the majority of participants
changed ‘type’ of behavior over the course of a semester.
o These results support the notion that time-related academic may not be stable, or tied to
personality and genetic disposition as previously supposed.
• Our second hypothesis was also supported: changes in time-related academic
behavior were associated with changes in motivation variables.
o Changes in motivation variables may result in changes to time-related academic behavior.

18. Discussion
• The CCA had two meaningful functions.
• The first function primarily predicted changes in timely engagement-approach
o Timely engagement-approach is the most adaptive ‘type’ of behavior.
o Understanding predictors of change in timely engagement-approach behaviors may be useful
in devising intervention strategies to encourage more adaptive academic behavior.
- Increases in mastery approach goal orientation, self-efficacy, and self-regulation all predicted increases in
timely engagement-approach behavior.

19. Discussion
• The second function primarily predicted procrastination-avoidance.
• procrastination-avoidance is theoretically the most maladaptive ‘type’ of time-
related academic behavior.
- The primary predictors were self-efficacy and self-regulation, which, as noted above, have shown
malleability to intervention in prior research.
• It may, then, be possible that existing intervention strategies might also prove
useful in decreasing procrastination-avoidance.