The Split Population Logit (SPopLogit): Modeling Measurement Bias in Binary Data

Abstract

Researchers frequently face applied situations where their measurement of a binary outcome suffers from bias. Social desirability bias in survey work is the most widely appreciated circumstance, but the strategic incentives of human beings similarly induce bias in many measures outside of survey research (e.g., whether the absence of an armed attack indicates a country’s satisfaction with the status quo or a calculation that the likely costs of war outweigh the likely benefits). In these circumstances the data we are able to observe do not reflect the distribution we wish to observe. This study introduces a statistical model that permits researchers to model the process that produces the bias, the split population logit (SPopLogit) model. It further presents a Monte Carlo simulation that demonstrates the ffectiveness of SPopLogit, and then reanalyzes a study of sexual infidelity to illustrate the richness of the quantities of (empirical and theoretical) interest that can be estimated with the model. Stata ado files that can be used to invoke SPopLogit, as well as batch files illustrating how to simulate commonly reported quantities of interest, are available for download from the WWW. The authors close by briefly identifying just a few of the many types of research projects that will benefit from abandoning logit and probit models in favor of SPopLogit.

Keywords: split population, bias, binary data, Social desirability bias, zero inflated

JEL Classification: C01, C35, C51