The Unique Contribution of Credit Information in the Selection Process

Abstract

The purpose of this study was to determine if credit information provided a unique contribution beyond the other selection predictors, such as criminal records, education, previous experience, or background checks. Ordinal logistic regression analyses were performed to compare two models: one without credit information (Model 1) and one with credit information (Model 2). Through likelihood ratio tests comparing both models, Model 2 was consistently found to be significant. Pseudo r-squared comparisons between the models showed that the Model 2 consistently explained more of the variability than Model 1. Significance tests with regression coefficient estimates showed the higher number of overdue accounts an applicant had, and the longer those accounts were past due, the lower the rating an applicant received in the selection process.