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Categorical Data Analysis Survey Data
- 1. Categorical Data Analysis of Student Satisfaction with Student Services at California State University East Bay: A Pedagogical Experience Monica Anand, M.S. Department of Statistics and Biostatistics California State University East Bay
- 11. Descriptive Statistics – Frequency of Department
- 13. Understandcat and Waitimecat vs. Proportion Of Overall Satisfied Students
- 16. Modeling with Important as the ordinal response Using backwards model selection with significance level .05, I obtain Understand and gender as the significant variables in how important it is for students to have the service. Females find it more important to have these services. Department was not significant in how important it is for students to have these services.
Notas del editor
- Make it clear academic, student life and other are dummy variables SAS_EOP = Student Academic Services/Equal Opportunity Program SCAA = Student Center for Academic Achievement Career = Career Development Center Advising = University Advisement Center CaPS = Counseling and Psychological Services Health = Student Health Center SDRC = Student Disabilities Resource Center
- More people from hayward answered. Student population at Hayward campus is larger than concord campus. Women make up a little over 2 times as much of the surveys as men.
- Academic departments are more frequented than student life (A little more than 2 times Student Life).
- Note: used the condensed explanatory variables (… cat) because of small sample size
- Understand=0 is Did not Understand, 1=Understood Waittime =0 is Student was not satisfied with the waiting time for their service, 1= Student was satisfied with waiting time for their service.
- Forward selection provided the same model. It is great if the student is satisfied with their waiting time but it’s even better if the student understood the information provided! Note: No demographic variable is significant :( Note: There was no significant department effect, even when combining academic departments and student life departments
- Note only ordinal not binary derived Same model forward selection selected. Interesting note: When only looking at the academic departments, the significant variables are: Waittime and Understand When only looking at Student Life departments, the significant variables are: Waittime, Understand and Welcome.
- Same model is obtained from forward selection.
- Helped me to see real life situations. After careful deliberation, I decided on the response variable, and using correlation information and assumption testing, did categorical data analysis on the data using Overall satisfaction as the response variable.
- In conclusion, to help Student Affairs centers perform better, these are my suggestions: