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Time-Related Academic
Behavior: State or Trait?
KAMDEN K. STRUNK, PH.D. – AUBURN UNIVERSITY
FORREST C. LANE, PH.D. – SAM H...
2x2 Model of Time-Related
Academic Behavior
Not just What, but Why
• It is necessary to consider not only the behavior, but the
motivation.
• In order to understand s...
The Present Study
CONTEXTUAL CHANGES IN TIME-RELATED ACADEMIC BEHAVIOR
The Present Study
• Research Question: Is time-related academic behavior a function of the person
only, or are these behav...
Participants
There were 453 participants, including 301 women and 152 men.
◦ The average age of participants was 20.56 (SD...
Measures
• 22 Measure of Time-Related Academic Behavior (Strunk, Cho, Steele &
Bridges, 2013)
◦ 25-item measure
◦ Reliabi...
Results: Cluster Invariance
• Participants were classified on time-related academic behavior using
hierarchical cluster an...
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
Cluster solution
Four clusters were retained:
• Cluster 1 (Generalized Timely Engagement) - low means on both procrastinat...
Means by Cluster
1
2
3
4
5
6
7
Procrastination-Avoidance Procrastination-Approach Timely Engagement-Avoidance Timely Engag...
Cluster Invariance
Research Question: Would participants change in their basic ‘type’ of behavior
over time?
• Individuals...
Canonical Correlation
Research Question: Do motivational factors explain changes in time-related
academic behavior between...
CCA Results
• The canonical correlation analysis yielded four functions (Table 2).
• The full model was tested first (func...
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
...
Discussion
• Our first hypothesis was supported: ‘type’ of time-related academic behavior
was not stable across time and c...
Discussion
• The CCA had two meaningful functions.
• The first function primarily predicted changes in timely engagement-a...
Discussion
• The second function primarily predicted procrastination-avoidance.
• procrastination-avoidance is theoretical...
Time-Related Academic Behavior: State or Trait?
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Time-Related Academic Behavior: State or Trait?

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Presentation at the American Psychological Association Conference, Denver, CO. August, 2016.

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Time-Related Academic Behavior: State or Trait?

  1. 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. 2. 2x2 Model of Time-Related Academic Behavior
  3. 3. 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.
  4. 4. The Present Study CONTEXTUAL CHANGES IN TIME-RELATED ACADEMIC BEHAVIOR
  5. 5. 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.
  6. 6. 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).
  7. 7. 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.
  8. 8. 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.
  9. 9. 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
  10. 10. 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.
  11. 11. 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
  12. 12. 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.
  13. 13. 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)
  14. 14. 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.
  15. 15. 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
  16. 16. 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.
  17. 17. 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.
  18. 18. 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.

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