A composite logistic regression approach for ordinal panel data regression | Blazingprojects Postgraduate Thesis
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A composite logistic regression approach for ordinal panel data regression

 

Table Of Contents


Chapter ONE

INTRODUCTION

  • 1.1Introduction
  • 1.2Background of Study
  • 1.3Problem Statement
  • 1.4Objective of Study
  • 1.5Limitation of Study
  • 1.6Scope of Study
  • 1.7Significance of Study
  • 1.8Structure of the Research
  • 1.9Definition of Terms

Chapter TWO

LITERATURE REVIEW

  • 2.1Overview of Logistic Regression
  • 2.2Ordinal Panel Data Analysis
  • 2.3Composite Regression Models
  • 2.4Applications of Logistic Regression
  • 2.5Panel Data Analysis Techniques
  • 2.6Composite Regression in Social Sciences
  • 2.7Challenges in Ordinal Panel Data Regression
  • 2.8Comparison of Regression Methods
  • 2.9Interpretation of Logistic Regression Results
  • 2.10Future Trends in Composite Regression

Chapter THREE

RESEARCH METHODOLOGY

  • 3.1Research Methodology Overview
  • 3.2Research Design and Approach
  • 3.3Data Collection Methods
  • 3.4Sampling Techniques
  • 3.5Variable Selection and Measurement
  • 3.6Model Specification
  • 3.7Data Analysis Procedures
  • 3.8Validation and Reliability Testing

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

  • 4.1Descriptive Analysis of Data
  • 4.2Regression Model Estimation
  • 4.3Interpretation of Regression Results
  • 4.4Comparison of Different Models
  • 4.5Robustness Checks and Sensitivity Analysis
  • 4.6Discussion of Findings
  • 4.7Implications for Theory and Practice
  • 4.8Recommendations for Future Research

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

  • 5.1Summary of Findings
  • 5.2Conclusions Drawn from the Study
  • 5.3Contribution to Knowledge
  • 5.4Practical Implications
  • 5.5Limitations and Suggestions for Future Research
  • 5.6Recommendations for Policy and Practice
  • 5.7Conclusion and Final Remarks

Thesis Abstract

We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001; Hsiao, 2003) can be directly applied. As a result, the new method allows the unobserved subject effects to be correlated with the observed predictors in an arbitrary manner. Computationally, the new method is able to profile out unobserved subject effects in a very neat manner. This not only makes computational implementation very easy but also makes theoretical treatment straightforward. In particular, we show theoretically that the resulting estimator is n-consistent and asymptotically normal. Both simulations and a real example are reported to demonstrate the usefulness of the new method.

Thesis Overview

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