Logistic Regression: Description, Examples, and Comparisons

Abstract

Family studies have seen a dramatic increase in the use of statistical tools for the analysis of nominal-level variables. Such models are categorized as log-linear models often known as logit models or logistic-regression models. Despite logistic regressions growing popularity there is still confusion about the nature and proper use in family studies. The authors present a nontechnical discussion of logistic regression with illustrations and comparisons to better-known procedures such as percentaging tables and ordinary least squares regression. They contend that logistic regression can be a powerful statistical procedure when used appropriately. Nominal-level dependent variables are common in family research and logistic-regression models appropriately model the impact of predictor variables on these outcomes. With the proliferation of computer software for estimating logistic-regression models use of logistic regression is likely to increase. Though some time and attention is required to master it the advantages of logistic regression make the effort worthwhile.