Presentation on motivation and equity research by Kamden Strunk. Produced 1/5/16.

1. Motivation and Equity in
Education
KAMDEN K. STRUNK, PH.D.

2. Motivation Research
EXPECTANCY-VALUE, ACHIEVEMENT GOALS, AND TIME-RELATED
ACADEMIC BEHAVIOR

3. Motivation Research
Expectancy-Value and Achievement Goals
◦ Strunk, K. K., Lane, F. C., & Mwavita, M. (in progress). Situated motivational factors predict
changes in time-related academic behavior type over time.
◦ Thomas, J. T., & Strunk, K. K. (2015, December). Expectancy-value and children’s science
achievement: Parents matter. Manuscript submitted for publication. [Submitted to Journal of
Research on Science Teaching, Impact Factor = 3.16]
◦ Thomas, J. T., Hulings, M., Orona, C., & Strunk, K. K. (2015, November). How do low-income, rural
Oklahoma fifth graders develop STEM career aspirations? A Signals case study. [Submitted to
Science Education, Impact Factor = 2.85]
◦ Strunk, K. K., *Beswick, C. S., *Fowlkes, C., Cho, Y., & Mwarumba, M. (2015, October). Gendered
dynamics in student expectancy-value motivation for gateway STEM courses. Manuscript
submitted for publication. [Revise and resubmit to SAGE Open]
◦ Strunk, K. K. (2014). A factor analytic examination of the achievement goal questionnaire, revised:
A three-factor model. Psychological Reports, 115(2), 400-414.
◦ Gravina, V., *Besick, C., & Strunk, K. (2013). Teaching strategies and value for STEM courses.
Academic Exchange Quarterly, 17(1), 139-144.

4. Procrastination & Motivation Research
2x2 Theory of Time-Related Academic Behavior
◦ Strunk, K. K., Lane, F. C., & Mwavita, M. (in progress). Situated motivational factors predict changes in time-related
academic behavior type over time.
◦ Strunk, K. K., Lane, F. C., & Mwavita, M. (under review for August, 2016).Time-related academic behavior: State or
trait? A cluster invariance study. Presentation at the American Psychological Association conference in Denver, CO.
◦ Strunk, K. K., Cho, Y., Steele, M. R., & Bridges, S. L. (2013). Development and validation of a 2x2 model of time-
related academic behavior: Procrastination and timely engagement. Learning and Individual Differences, 25(1), 35-44.
Generalized Procrastination
◦ *Bobo, J. L., *Whitaker, K. C., & Strunk, K. K. (2013). Personality and student self-handicapping: A cross-validated
regression approach. Personality and Individual Differences, 55(5), 619-621.
◦ Strunk, K. K., & Spencer, J. M. (2012). A brief intervention for reducing procrastination. Academic Exchange Quarterly,
16(1), 91-96.
◦ Strunk, K. K., & Steele, M. R. (2011). Relative contributions of self-regulation, self-efficacy, and self-handicapping in
predicting student procrastination. Psychological Reports, 109(3), 983-989.

5. 2x2 Model of Time-Related
Academic Behavior

6. The Need for a New Model
What is procrastination?
◦ Traditionally, it has been defined as something that happens to the individual,
rather than something the individual chooses to perform.
◦ In the traditional model, all people procrastinate to one degree or another.
◦ Procrastination can be understood in this conceptualization as a deficit of the
individual, inherent in the person, which becomes explicit in their behavior in
the form of procrastination.

7. The Traditional Model
Personality:
◦ Neuroticism (Hess, Sherman, & Goodman, 2000; Johnson & Bloom, 1995; van Eerde, 2003)
◦ Perfectionism (Flett, Blanksten, Hewitt, & Koledin, 1992; Onwuegbuzie, 2000; Saddler & Buley, 1999)
◦ It is who the person is, not what the person does.
Self-protective mechanism:
◦ Self-handicapping (Beck, Koons & Milgram, 2000; van Eerde, 2003)
◦ Avoidant coping/cognitive style (Alexander & Onwuegbuzie, 2007; Burns, Dittmann, Nguyen, & Mitchelson, 2000; Carden, Bryant, & Moss, 2004;
Collins, Onwuegbuzie, & Jiao, 2008; Deniz, Tras, & Aydogan, 2009; Fritsche, Young & Hickson, 2003; Owens & Newbegin, 1997)
◦ It is unintentional as a result of a deficient thinking style.
Inability to Self-Regulate
◦ Low self-regulation (Brownlow & Reasinger, 2000; Senecal, Koestner, & Vallerand, 1995)
◦ Low self-efficacy for self-regulation (Klassen, Ang, Chong, Krawchuck, Huan, Wong, & Yeo, 2009; Klassen, Krawchuck, Lynch, & Rajani, 2008;
Klassen, Krawchuch, & Rajani, 2008)
◦ The behavior happens to the individual due to a constitutional inability to self-regulate.

8. The Problem
Each of these approaches treats the individual as self-unaware and
deficient in the act of procrastination.
However, there is evidence to suggest that procrastination is a
motivated behavior, that individuals engage in it for specific reasons
to satisfy their goals.
For example, Schraw, Wadkins, and Olafson (2007) found in
qualitative work that students procrastinate to avoid failure at times,
and at other times to increase their performance under more time
pressure.

9. The Problem
Others have suggested procrastination may be motivated.
◦ Active versus Passive Procrastination (Choi & Moran, 2009; Chu & Choi, 2005)
◦ Different Goals Relate Differently to Procrastination (Howell & Buro, 2009;
Seo, 2009).
Any model of procrastination that does not consider the motivation
of the individual is incomplete and theoretically problematic.

10. Timely Engagement
So too is a model that does not consider timely engagement, as current models do not.
Extreme
Procrastination
Little
Procrastination
Started Work On The
Way Out of Class Today
Waited until the Day
After the Due Date to
Start
Extreme Timely
Engagement

12. 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.

13. Previous Findings
Among a sample of 1,496 undergraduate students, the relationship of
achievement goals to the 2x2 model of time-related academic behavior was
examined.
In the same study, the validity and suitability of the 2x2 measure was assessed:
◦ The model was a good fit for the observed data (CFI = .92, TLI = .91, RMSEA = .07, SRMR =
.05).
◦ It was superior to alternative models, using Δχ2 and ΔCFI.
◦ It correlated in the expected pattern with an existing measure of generalized procrastination.
◦ Both timely engagement-approach (r = −.61) and timely engagement-avoidance (r = −.60) correlated negatively
with the existing scale (Lay, 1986), while both procrastination-approach (r =.46) and procrastination-avoidance (r =
.46) correlated positively with the scale
◦ Coefficient alpha ranged from .76 to .87 for the four subscales of the measure.

15. Previous Findings, contd.
The 2×2 model of procrastination and timely engagement revealed more distinct and
differentiated relationships with achievement goals, providing empirical evidence that
considering the underlying motivation of procrastination helped illuminated the unique nature
and function of different types of procrastination and timely engagement.
Taken together, while previous research found that approach versus avoidance goals play a
determining role in predicting procrastination, these findings demonstrate that mastery versus
performance goals, as well as approach versus avoidance goals, play an important role in
predicting procrastination with distinct motivations.

16. Context Dependence
Because work in the traditional model has normally proceeded from the idea that
procrastination arises from relatively stable factors such as personality (specifically, neuroticism
and conscientiousness), researchers from this perspective have also tended to hypothesize
procrastination as relatively stable, arising from internal dispositions rather than from the
context.
In other words, procrastination is likely to be a trait rather than a state, in that model.
As such, if a new model like the 22 model of time-related behavior and its related critiques of
the traditional model are valid, it may be because procrastination and timely engagement are
actually states driven by more transient motivational factors.

17. Testing Context Dependence
Data were collected 453 students at a large public university.
◦ Data were collected twice: Once in a Fall semester, and the second time in a Spring semester.
◦ Students gave written descriptions about a particular course they were enrolled in at the time, and then
filled out a series of instruments related to that course.
Measures:
◦ 2x2 Measure of Time-Related Academic Behavior (Strunk, Cho, Steele, & Bridges, 2012)
◦ Achievement Goal Questionnaire, Revised (Elliot & Murayama, 2008)
◦ Motivated Strategies for Learning Questionnaire (Pintrich & DeGroot, 1990)
◦ Measures of Expectancy-Value motivation (Eccles & Wigfield, 1995; Wigfield & Eccles, 2000)

18. Cluster Analysis
Using hierarchical cluster analysis, we assigned participants to “types” on academic behavior.
The reverse scree analysis (Lathrop & Williams) suggested a four-cluster solution.
Figure 1.
Plot of Unexplained and Error Variance by Number of Clusters on Clustering Variables

19. Cluster Analysis
Cluster Procrastination-
Avoidance
Procrastination-
Approach
Timely Engagement-
Avoidance
Timely Engagement-
Approach
M SD M SD M SD M SD
1. Generalized Timely Engagement 1.61 .59 1.80 .70 5.70 .79 5.93 .67
2. Timely Engagement/Approach 2.07 .71 2.97 .79 3.64 .66 4.13 .64
3. Generalized Procrastination 3.26 1.18 4.34 1.28 2.49 .69 2.82 .81
4. Timely Engagement/Avoidance 3.47 1.07 3.38 1.10 4.80 .65 4.61 .67
1
2
3
4
5
6
7
Procrastination-Avoidance Procrastination-Approach Timely Engagement-Avoidance Timely Engagement-Approach

20. Cluster Analysis
Applying the four clusters across both Fall and Spring semesters, we tested whether participants
varied in cluster membership across the semester-long delay.
There was a significant difference in cluster membership across the semester-long delay (χ2
3 =
16.31, p < .001).
Specifically, 229 participants (50.55%) changed clusters.
These results suggested that time-related academic behavior “type” was not stable across time.
As a result, we tested whether the instability was predictable based on motivation variables.

21. Canonical Correlation Analysis
To better inform the results of the cluster analysis, only those students who changed clusters
over time were included in the Canonical correlation analysis (n= 229).
The CCA included change in the four subscales of the 2x2 measure of time-related academic
behavior as the criterion variables, and the motivation variables as predictors.
Only two functions were considered meaningful for further analysis, based on both probability
values and effect size estimates.
Root No. Eigenvalue % Cumulative % 𝑅𝑐 𝑅𝑐
2
1 .499 58.768 58.768 0.577 0.333
2 .246 28.977 87.745 0.444 0.197
3 .082 9.709 97.455 0.276 0.076
4 .022 2.545 100.000 0.145 0.021

22. Canonical Correlation Analysis
The first function was primarily predictive of timely engagement-approach.
This conclusion was supported by the variable’s squared structure coefficient (𝑟𝑠
2 = .750) which
was notably larger than any other criterion variable.
Of the predictor variables included in the analysis, mastery approach, self-efficacy, and self-
regulation seemed to get the most credit as predictors in this function.
Each of these predictors was positively related to changes in engagement approach.
These findings are particularly meaningful as timely engagement approach is thought to be the
most adaptive ‘type’ of behavior, and because prior researchers have identified interventions
that are effective for mastery-approach achievement goals, for self-efficacy, and for self-
regulation.

23. Canonical Correlation Analysis
For the second function, after disregarding relationships due to a suppression effect, the
function was primarily predictive of procrastination-avoidance.
Self-efficacy and self-regulation were the strongest predictors, and were negatively associated
with the criterion set.
Further, it appears that increases in self-efficacy were associated with increases in
procrastination-approach, and decreases in procrastination-avoidance. The same pattern was
found with self-regulation.
This is meaningful due to the maladaptive nature of procrastination-avoidance. However,
because increases in self-efficacy and self-regulation were also associated with increases in
procrastination-approach, there is need for caution in interpreting these motivation variables.

24. Understanding Changes in Time-Related
Academic Behavior
It appears time-related academic behavior is not a stable trait, but instead varies based on
context.
That variation is predictable based on motivation variables, suggesting the motivational
orientation toward a setting shapes the type of time-related academic behavior that will be
initiated.
Based on these results, we believe that particular classroom practices and contextual shifts
might be able to shape more adaptive time-related academic behavior among students.

25. Future Work
Based on identifying factors that are related to changes in time-
related academic behavior, future research will involve:
◦ Determining if mastery-supportive contexts are more likely to produce
adaptive time-related behavior.
◦ Determining if small changes in context (i.e., experimental or quasi-
experimental work) can result in changes to time-related academic behavior.
◦ Determining if larger contexts (beyond the classroom, such as department,
university) also play a role in determining behavior “type”. If so, how can
orientation to institution be formed in adaptive ways?

26. Equity Research
EQUITY, SOCIAL JUSTICE, AND EDUCATION

27. Race, Gender, and Education
Gender, Ethnicity, and STEM Education
◦ Strunk, K. K., & Thomas, J. T. (2015, November). Where do Gender Stereotyped Beliefs about Mathematics Come From? A
Longitudinal Analysis of Child, Parent, and Teacher Influences. Manuscript submitted for publication. [Revise and Resubmit to
Sex Roles, Impact Factor = 1.74]
◦ Thomas, J. T., Hulings, M., Orona, C., & Strunk, K. K. (2015, November). How do low-income, rural Oklahoma fifth graders
develop STEM career aspirations? A Signals case study. [Submitted to Science Education, Impact Factor = 2.85]
◦ Strunk, K. K., *Beswick, C. S., *Fowlkes, C., & Mwarumba, M. (2015, October). Gendered dynamics in student expectancy-
value motivation for gateway STEM courses. Manuscript submitted for publication. [Revise and resubmit to SAGE Open]
◦ Thomas, J., Orona, C.*, Hulings, M.*, & Strunk, K. (2013). What impacts 3rd-5th graders? Presentation at the Oklahoma State
Department of Education Vision 2020 Conference, Oklahoma City, OK.
Race and Ethnicity in Understanding U.S. Public Education
◦ Strunk, K. K., Locke, L. A., & Martin, G. L. (under contract, scheduled 2016). Oppression and resistance in Southern higher and
adult education: Mississippi and the dynamics of equity and social justice. New York, NY: Palgrave.
◦ Strunk, K. K., Locke, L. A., & *McGee, M. K. (2015). Neoliberalism and contemporary reform efforts in Mississippi’s public
education system. In M. Abendroth, & B. J. Porfilio (Eds.), Understanding neoliberal rule in K-12 schools: Educational fronts
for local and global justice (pp. 45-61). Charlotte, NC: Information Age Publishing.
◦ Strunk, K. K., *Suggs, J. R., & *Thompson, K. (2015). The USM campus climate survey: Findings and recommendations. The
University of Southern Mississippi & Research Initiative on Social Justice and Equity.

28. LGBTQ Students: Access, Equity, and Climate
LGBTQ Issues in Higher Education
◦ Strunk, K. K., & Bailey, L. E. (In press). The difference one word makes: Imagining sexual orientation in graduate school application
essays. Psychology of Sexual Orientation and Gender Diversity.
◦ Strunk, K. K., Bailey, L. E. (2016, April). “A question everybody danced around”: Self-identified gay men negotiating identity in
Christian colleges. Paper at the American Educational Research Association, Washington, DC.
◦ Strunk, K. K., Bailey, L. E., & *Takewell, W. C. (2014). “The enemy in the midst”: Gay identified men in Christian college spaces. In W.
M. Reynolds (Ed.), Critical studies of southern place: A reader (pp. 369-378). New York, NY: Peter Lang.
Educational Practice and Policy with LGBTQ Students
◦ Strunk, K. K., Bristol, D., & *Takewell, W. C. (in press). Queering South Mississippi: Simple and seemingly impossible work. In S. J.
Miller & N. M. Rodriguez. Educators queering academia. New York, NY: Palgrave Macmillan.
◦ Mattheis, A., Strunk, K. K., Greytak, E., & Garvey, J. (2016, April). Queering mixed methods research for social advocacy. Symposium at
the American Educational Research Association, Washington, DC.
◦ *Takewell, W. C., & Strunk, K. K. (2014). Gay students at Christian colleges: Implications for student affairs practice. NASPA Gay,
Bisexual, Lesbian, and Transgender Knowledge Community Research Summary and Compilation Whitepaper.
◦ Strunk, K. K., & *Suggs, J. R. (2014). Research update on findings from the USM campus climate survey: Results related to LGBTQ
students. Research Initiative on Social Justice and Equity Report.

29. Motivation and Equity in
Education