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# Categorical Data Analysis Survey Data

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Categorical Data Analysis of Student Satisfaction with Student Services at California State University East Bay: A Pedagogical Experience

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• 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:
• ### Categorical Data Analysis Survey Data

1. 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
2. 2. Outline <ul><li>Introduction to Survey Project </li></ul><ul><li>Background to Survey </li></ul><ul><li>Analysis </li></ul><ul><li>Pedagogical Perspective </li></ul><ul><li>Suggestions to Student Services Center </li></ul>
3. 3. Introduction to Survey Project <ul><li>Wanted real life experience of analyzing data </li></ul><ul><li>Was given data with no direction </li></ul><ul><ul><li>Did not see survey for weeks </li></ul></ul><ul><ul><li>Here is the data, analyze it! </li></ul></ul>
4. 4. Background to Survey <ul><li>Point of Service Survey for the Division of Student Affairs (SA). </li></ul><ul><ul><li>Dr. Coulman, Director of Student Affairs </li></ul></ul><ul><ul><li>Dr. Norton, Statistics and Biostatistics </li></ul></ul><ul><li>All Directors in the division assisted in developing the survey </li></ul><ul><ul><li>added specific questions to their individual unit. </li></ul></ul><ul><li>SA collected surveys </li></ul><ul><ul><li>Three weeks in May/June 2008 </li></ul></ul><ul><ul><li>470 surveys were collected. </li></ul></ul>
5. 5. Introduction/Background <ul><li>Web-based electronic surveys were collected and some paper surveys were collected. </li></ul><ul><ul><li>As similar as possible </li></ul></ul><ul><ul><li>Electronic surveys were set-up, overseen, and converted to data by Timothy Druley with assistance from Raechelle Clemmons. </li></ul></ul><ul><li>SA provided the completed Point of Service Surveys during June 2008 for analysis. </li></ul><ul><ul><li>Only the standard Student Affairs Survey items are discussed in this report. </li></ul></ul><ul><ul><li>Some surveys had unit-specific questions (not analyzed here) </li></ul></ul>
6. 6. Variable names and types collected: <ul><li>The following variables were on a 5 point Likert scale: </li></ul><ul><ul><li>Welcome: How welcome did the student feel at the Student Affairs Services Center </li></ul></ul><ul><ul><li>Waiting: How satisfied was the student with the overall waiting time for services after they arrived </li></ul></ul><ul><ul><li>Understand: As a result of this visit, how well did the student understand the information provided </li></ul></ul><ul><ul><li>Overall: Overall how satisfied was the student with their visit </li></ul></ul><ul><ul><li>Important: How important is it for the student to have this service available </li></ul></ul>
7. 7. Variable names and types collected – Demographic Variables: <ul><ul><li>Findout (“How did you find out about this service?”) </li></ul></ul><ul><ul><ul><li>5 Options: A friend, Email, Orientation , gs1010, Walked By, Internet, Other </li></ul></ul></ul><ul><ul><li>Age (“Student Age”) – Number </li></ul></ul><ul><ul><li>Gender (Male, Female) </li></ul></ul><ul><ul><li>Ethnicity (“Ethnicity”) </li></ul></ul><ul><ul><ul><li>5 Options: Black, Asian Pacific, Hispanic, White, International </li></ul></ul></ul><ul><ul><li>Level (“Level in College/Class Standing”) </li></ul></ul><ul><ul><ul><li>5 Options: Freshman, Sophomore, Junior, Senior, Graduate Student, Other </li></ul></ul></ul>
8. 8. Derived Categorical Variables: <ul><li>Agecat - Age category </li></ul><ul><ul><li>17-19, 20-24, 25+ </li></ul></ul><ul><li>Academic – Indicator Variable, Formed from Department </li></ul><ul><ul><li>Department in ( 'SAS_EOP','SCAA','Career','Advising‘)  1 </li></ul></ul><ul><li>Student Life – Indicator Variable, Formed from Department </li></ul><ul><ul><li>Department in ( 'CaPS','Health','SDRC‘)  1 </li></ul></ul><ul><li>Other – Indicator Variable, Formed from Department </li></ul><ul><ul><li>Department in ( Upolice, EXCEL, Project Impact)  1 </li></ul></ul><ul><li>Asian, International, White, Black, Hispanic grouped from Race. </li></ul><ul><li>WaittimeCat, OverallCat, ImportantCat, UnderstandCat – Binary, grouped from the Likert scale of 5: </li></ul><ul><ul><li>Not all satisfied, Somewhat dissatisfied, Neutral  Not Positive (0) </li></ul></ul><ul><ul><li>Somewhat satisfied, Very satisfied  Positive(1) </li></ul></ul>
9. 9. Correlation Analysis of Likert Scores <ul><li>The highlighted p-values are those which indicate the variables are highly correlated to Overall Satisfaction. The variables which are not correlated should not be included in the model selection process using Overall as the response variable. Females were more satisfied than males. </li></ul>
10. 10. Variables which are unbalanced <ul><li>The following pie charts shows the disparity between the amount of females vs. males in this sample and the two groups of location (Hayward vs. Concord campus). </li></ul>Male 149 342 Female FREQUENCY of location Concord Hayward FREQUENCY of gender
11. 11. Descriptive Statistics – Frequency of Department
12. 12. Model selection for binary response variable: Overallcat ~ Bernoulli(  ) <ul><li>Model: </li></ul><ul><li>Using logistic regression with backward model selection at significance level of .05, including all possible predictors (continuous and binary) I obtain the model with the following predictors and p-values: </li></ul><.0001 Waittimecat <.0001 Understandcat P-Value Variable
13. 13. Understandcat and Waitimecat vs. Proportion Of Overall Satisfied Students
14. 14. Model Selection For Binary Response Variable – OverallCat <ul><li>The fitted model is: </li></ul><ul><li>Log(OverallCat odds) = 1.6579 + 1.3058 A + 0.8179 B, </li></ul><ul><ul><li>Where A = Understandcat(Understand), B = WaittimeCat (Positive) </li></ul></ul><ul><li>Interpretation of odds estimates: </li></ul><ul><ul><li>The odds of a student being overall satisfied with their visit is exp(1.6579)=5.25 when the student did not understand the information provided and the student was not satisfied with their waiting time. </li></ul></ul><ul><ul><li>The probability of a student being overall satisfied with their visit is 19.37 times the probability that a student was not satisfied for a student who understood the information provided than a student who did not understand the information provided. </li></ul></ul><ul><ul><li>The probability of a student being satisfied overall with their visit is 11.89 times the probability that a student was not satisfied for a student who was satisfied with their waiting time than a student who was not satisfied with their waiting time. </li></ul></ul><ul><ul><li>The Hosmer-Lemeshow statistic equals 0.4891 with df = 1 and p-value 0.4843 indicating an adequate fit. </li></ul></ul>
15. 15. Model selection for ordinal response variable: Overall <ul><li>Model: (Cumulative Logit model) </li></ul><ul><li>logit[P(Y ≤ j)] = </li></ul><ul><li>Using logistic regression with backward model selection including all possible predictors (continuous and ordinal) at significance level .05, I obtain the model with the following predictors and p-values: </li></ul>0.02 Welcome 0.01 Level <.0001 Understand <.0001 Waittime P-Value Variable
16. 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.
17. 17. Modeling with Academic Departments Only <ul><li>Using backwards model selection with significance level .05, I obtain Age category, Satisfaction with Waiting time, and how well the student understood the information provided as the significant variables in how satisfied the student was with the overall service. The students who are 25 and older were more likely to be satisfied overall. The students who are between 20 and 25 years of age were more likely to not be satisfied with the overall service. </li></ul><.0001 Understand <.0001 Waittime .04 Agecat P-value Variable
18. 18. Modeling Student Life Departments Only <ul><li>Using backwards model selection with significance level .05, I obtain Satisfaction with Waiting time and how well the student understood the information provided as the significant variables in how satisfied the student was with the overall service. </li></ul>.0023 Waittime .0025 Understand P-value Variable
19. 19. How did this Project help me as a student? (Pedagogical Benefits) <ul><li>Raw data: no specific assignment attached to it </li></ul><ul><ul><li>Response variable not specified </li></ul></ul><ul><ul><li>Data collection ??? </li></ul></ul><ul><ul><li>Explanatory variables mean? </li></ul></ul><ul><ul><li>What model is appropriate? </li></ul></ul><ul><ul><li>No guide! </li></ul></ul><ul><li>No directed assignment: In class subject studied, homework, then test </li></ul><ul><li>Dichotomizing – what to do with neutral, what to do with no response </li></ul><ul><ul><li>Missing data – Not included in analysis. </li></ul></ul><ul><ul><li>How data are stratified? (here, by student affairs department) </li></ul></ul><ul><li>Not told what to test for at each step. </li></ul><ul><li>This was very useful and I believe should be included in school curriculum. I am very grateful to have had this experience. Thank you for this opportunity! </li></ul>
20. 20. Suggestions for Student Affairs Services Center <ul><li>Change the variable Waittime to be quantitative: </li></ul><ul><ul><li>“ How long did you wait to be seen? (minutes)” </li></ul></ul><ul><li>Add a questions: </li></ul><ul><ul><li>“ Did you make an appointment(Yes/No)?” </li></ul></ul><ul><ul><li>“ Number of people waiting before you.” </li></ul></ul><ul><ul><li>These three should correlate </li></ul></ul><ul><li>Instead of the question: “How welcome did you feel at ___?” there could be questions such as “Did the receptionist smile at you today?” or “How long did you wait in line before the receptionist greeted you today?” </li></ul><ul><li>For the question “How did you find out about this service?” allow students to enter multiple responses. </li></ul><ul><li>For the level which the student is in (I.e. Freshman, Other), add 2 options: Other-Below college, Other-Past graduate, Other-Staff. </li></ul>