On the Performance of Several Approaches to Obtain Standardized Logistic Regression Coefficients
In general regression analysis, standardized beta weights are often used to compare strength of prediction across variables. In order to consider obtaining an estimation of standardized logistic regression coefficients, the model must be rescaled to include such coefficients. One of the reasons to have the coefficient standardized is we will have more informative coefficients compared to unstandardized coefficients, especially for variables which have no natural metric. Several approaches of obtaining standardized coefficients in logistic regression are available in literatures. This article studies the performance of the existing approaches to obtain standardized logistic regression coefficient based on real data.
The authors are grateful to University Grants Scheme by Universiti Putra Malaysia for awarding a research grant for supporting the research.
- 1.Menard, S. (2002). Applied logistic regression analysis (Quantitative applications in the social sciences, no. 106). Thousand Oaks: Sage.Google Scholar
- 3.Kim, J., & Ferree, G. D. (1996). Standardization in causal analysis. Sociological Methods and Research, 10, 187–210.Google Scholar
- 7.Menard, S. (1995). Applied logistic regression analysis. Thousand Oaks: Sage.Google Scholar
- 9.Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.Google Scholar