1. OSCAR MCKNIGHT PH.D. ASSISTANT DEAN FOR STUDENT AFFAIRS DIRECTOR OF PSYCHOLOGICAL COUNSELING SERVICES ASHLAND UNIVERSITY Student Affairs Program Evaluation: A Factor Analytic Solution

2. Program Overview 1. This program addresses the use of factor analysis in program evaluation. 2. Specific focus will highlight the development and selection of marker items. 3. The goal is to label and interpret factors according to targeted questions of interest. 4. Therefore, results are not only descriptive, but predictive - with practical application to student retention and satisfaction.

3. Student Affairs Assessment Benchmark best practices Measure student satisfaction and learning Track student participation Evaluate program results Determine future program or service needs Assess effectiveness of delivered programs

4. SA Assessment: Typical Survey Format

5. Typical Findings (example 1)

6. Typical Findings (example 2)

7. Using Factor Analysis In Student Affairs Factor analysis originated in the behavioral sciences - primarily psychometrics. Today factor analysis is equally useful in all social sciences; specifically, Student Affairs Assessment.

8. Exploratory Factor Analysis Factor analysis is a statistical method used to reduce the set of variables in a dataset; it can describe unobserved latent variables. Factor analysis is appropriate for program evaluation; given the use of created or identified marker items.  Varimax rotation: is an orthogonal rotation, minimize correlation between factors.  Direct oblimin rotation: factors are allowed to be correlated – resulting in diminished interpretation.

9. Marker Item(s) Individual Marker: an identified variable used to segment population data; for example, senior class member. Clustered Marker: jointing individual classifications into one conceptual grouping; for example, Student Affairs Divisions.

10. Correlation Relationships Correlation: a relationship in which two or more things are mutual or complementary - can indicate a predictive relationship; however, statistical dependence is not sufficient to demonstrate the presence of a causal relationship. Factor loadings*: are the correlation coefficients between the variables and factors (think Pearson's r)

11. Correlation Matrix: Marker Items (Ind.)

12. Marker Items (Cluster) Student Affairs Satisfaction = Community Service + Residence Life + Safety Services + Counseling + Health Center + Greek Life + Minority Services + Recreation Services + Career Services + Leadership + Commuter Services + Student Activities University Excitement = student activities, weekend life, Recreation Services Academic Satisfaction = major, faculty and advising Living Experience = Residence life and Convo

13. Correlation Matrix: Marker Items (cluster)

14. Factor Matrix (Clustered Marker): Total Variance

15. Rotated Factor Matrix (Clustered Marker)

16. Factor Matrix (Individual Marker): Total Variance

17. Rotated Factor Matrix (Individual Marker)

18. Factor Matrix (Individual Markers): Student Affairs - Total Variance

19. Rotated Factor Matrix (SA - Individual Markers)

20. Career Center: Promotions

21. Career Center: Satisfaction By College and Year

22. Career Center: Services

23. Career Center Services: Relative Importance

24. Summary Factor analysis is a viable research process for Student Affairs Assessment. Marker items have utility in developing conceptual areas of assessment. Answering specific questions of interest can be practical given the factor structure. Outcomes can describe a current situation. Results can be segmented and predictive. Overall, there are practical applications for Factor Analysis in Student Affairs – remember to combine it with other assessments!