Dr. Beeper, an Educational Psychologists who studies issues related to higher education, is interested in studying key factors that impact year to year persistence among college students. His review of the literature identifies several factors that appear to be causally related to persistence. Specifically, academic aptitude, goal commitment, institutional commitment, and the number of work hours.
To test the importance of these factors, Dr. Beeper administers a set of questionnaires to 100 randomly selected first-time, full-time freshmen college students (50 male and 50 female) that attended the Freshmen Orientation in the Fall of 2016, at Newton Young University (NYU) in Nebraska.
Measures:
Institutional Commitment (IC) represents the importance that students place on graduating from the college they are currently attending. Institutional Commitment was measured with five-item questionnaire. Each item was rated on a 0, 1, or 2 scale. The possible range of scale scores are zero to 10, where values close to zero indicate little to no importance, and values close to 10 indicate high importance.
Goal Commitment (GC) represents the importance that students place on obtaining a college degree. Goal Commitment was also measured with five-item questionnaire. Each item was rated on a 0, 1, or 2 scale. The possible range of scale scores are zero to 10; where values close to zero indicated little to no importance to obtaining a college degree, and values close to 10 indicated a high importance to graduating from college.
Academic Aptitude was represented as scores on both the SAT-Math and the SAT-Verbal tests. SAT scores for all participants were obtained from high school transcripts.
Hours works, represents the anticipated number of hours the student expected to work throughout the semester.
Finally, Year-to-year persistence was determined by examining the enrollment records for the sample of 100 students. A student that was registered for registered for the Fall 2017 classes was classified as a “Persister”, and given a code of 1, a student that did not re-enroll for classes at NYU, or any other college/university (based on follow-up phone interviews) was considered a “Non-persister”, and was given a code of 0. Therefore, the SPSS variable Persist has two levels, 0 and 1.
The assignment is, using the attached SPSS data file, conduct a binary logistical regression analysis in which IC, GC, SAT-Math, SAT-Verbal, and Hours Worked are the predictor. variables (covariates in SPSS), and the variable Persist is the outcome (DV in SPSS). Use my sample summary as a model for your summary.
The specific elements of the assignment are:
1) Create a Null and Alternative Hypotheses for the Logistical Regression Analysis
2) State the Goals of the analysis
3) Summarize the results and interpret findings the overall model (for example the Chi Square results, Nagelkerke R-Square or Cox Snell R-Square).
4) Summarize and interpret the results for each predictor; and present, summarize and interpret the results for each significant predictor (i.e., B, Wald’s test, df, p and OR (ExpB). Interpret the significant OR using the effect size conventions I posted in last week’s (8) discussion board.
5) Include and refer to the appropriate tables within the summary.
Please read my sample summary see what statistics to report, and how to report and interpret them in correct APA style, as well as the tables to include.
You’ll see that in my sample summary I also include t-tests. You may want to conduct t-tests that compare “persisters” and non-persisters, on the predictor variables (covariates). Please note that the t-test are optional, and will have no impact on your grade whether you include them or not. The t-test are very informative about the bivariate relationship between the predictor variables (covariates in SPSS) and the binomial outcome (DV in SPSS) .
Please note that you are not required to conduct the t-tests, or to compute and report Cohen’s d.
Here’s the syntax for my sample summary.
T-TEST GROUPS=BO(0 1)
/MISSING=ANALYSIS
/VARIABLES=teachsat ressat wkoverld
/CRITERIA=CI(.95).
LOGISTIC REGRESSION VARIABLES BO
/METHOD=ENTER teachsat ressat wkoverld
/PRINT=GOODFIT CI(95)
/CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).
