Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration and timeline of the critical events, which are also a binary and dichotomous measure. This paper introduces logistic and Cox regression models by illustrating examples, implementing step-by-step SPSS procedures, and further comparing the similarities and differences of the model characteristics. Logistic regression analysis was conducted to investigate the effects of the explanatory variables such as pre-admission variables, college cumulative GPAs, and curriculum tracks on student licensure examination. Moreover, logistic regression analysis was employed to quantify the effect (odds or odds ratio) of specific explanatory variables on the binary outcome holding other variables constant. With regards to Cox regression analysis, the outcome variable of interest was the timing of experiencing academic difficulty--dismissal, withdrawal, and leave of absence. The Cox regression model was used to detect when students were most likely to experience academic difficulty beyond their matriculation. The model also allowed the investigators to measure the effect (relative hazard or hazard ratio) of specific risk factors on the academic difficulty after adjusting for other factors. Identifying the occurrence of critical events along with the explanatory variables, college administrators and faculty could implement intervention strategies to ensure student success. (Contains 1 figure and 6 tables.)
Authors
- Authorizing Institution
- Association for Institutional Research
- Education Level
- ['Higher Education', 'Postsecondary Education']
- Peer Reviewed
- F
- Publication Type
- ['Collected Works - Serial', 'Reports - Descriptive']
- Published in
- United States of America
Table of Contents
- Volume 5 June 30 2005 1
- Abstract 1
- Introduction 1
- IR Applications Number 5 Analyzing Student Learning. . . 2
- IR Applications Number 5 Analyzing Student Learning. . . . 3
- Literature Review 3
- Logistic Regression Equation e e e 3
- IR Applications Number 5 Analyzing Student Learning. . . 4
- Interpretations of Odds Log Odds Odds Ratio and Delta P 4
- IR Applications Number 5 Analyzing Student Learning. . . . 5
- Example of Logistic Regression Analysis 5
- IR Applications Number 5 Analyzing Student Learning. . . 6
- SPSS PC Commands for Logistic Regression Analysis 6
- Major Findings for Logistic Regression Analysis 6
- IR Applications Number 5 Analyzing Student Learning. . . . 7
- Table 1 Logistic Regression Model for Predicting USMLE Step 1 Pass Status 7
- Survival Analysis 7
- IR Applications Number 5 Analyzing Student Learning. . . 8
- Table 2 Example of Uncensored and Censored Data for the Doctorate Program 8
- Relationship between Hazard and Survival Functions 8
- IR Applications Number 5 Analyzing Student Learning. . . . 9
- Kaplan-Meier Survival Analysis 9
- Cox Regression Equation 9
- Table 3 Example of Calculating the Estimated Survival Function St for the Doctorate Program 9
- IR Applications Number 5 Analyzing Student Learning. . . 10
- Interpretations of Relative Hazard and Hazard Ratio 10
- Example of Cox Regression Analysis 10
- IR Applications Number 5 Analyzing Student Learning. . . . 11
- SPSS PC Commands for Cox Regression Analysis 11
- Analyze Survival Cox Regression month difficult Define Event Continue ungbsa unggpa mcatvr mcatps mcatbs gender ethnic 11
- Major Findings for Cox Regression Analysis 11
- IR Applications Number 5 Analyzing Student Learning. . . 12
- Similarities between Logistic and Cox Regression Models 12
- Table 4 Cox Regression Model for Students Experiencing Academic Difficulty 12
- Figure 1 Hazard Curves for Students Experiencing Academic Difficulty 12
- IR Applications Number 5 Analyzing Student Learning. . . . 13
- Table 5 Summary of Similarities between Logistic and Cox Regression Models 13
- IR Applications Number 5 Analyzing Student Learning. . . 14
- Differences between Logistic and Cox Regression Models 14
- Table 6 Summary of Differences between Logistic and Cox Regression Models 14
- IR Applications Number 5 Analyzing Student Learning. . . . 15
- Summary 15
- Strengths 15
- IR Applications Number 5 Analyzing Student Learning. . . 16
- Limitations 16
- Major Alternatives 16
- Editors Notes 16
- IR Applications Number 5 Analyzing Student Learning. . . . 17
- References 17
- IR Applications Number 5 Analyzing Student Learning. . . 18
- . . 18
- IR Applications Number 5 Analyzing Student Learning. . . . 19
- IR Applications IR Applications IR Applications 19
- IR Applications 19
