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

Authors:    Andreas Beger, Jacqueline DeMeritt, Will Moore,
Wonjae Hwang

Entrydate:  2009-02-03 20:43:05

Keywords:   binary, limited dependent variables, measurement
bias, unobservability

Abstract:   This study describes a split population logit model
that can be useful to researchers who are modeling a binary
dependent variable that is measured with a biased instrument. To
motivate the study we identify two common, yet widely
unrecognized, circumstances in which political scientists are
likely to study dichotomous variables that have been measured
with bias. In one such setting (e.g., surveys) the strategic
interests of actors will lead them to misrepresent an attitude
or behavior. In another such setting (e.g., content analysis of
events) researchers' instruments are unable to distinguish
between the absence of a characteristic or event and missing
data. We briefly argue that "unobservability," "zero-inflated,"
and other models form a single class of models that allow
researchers to model the bias in operational instruments, and
thus not only correct bias in statistical inference but, more
importantly, produce theoretical accounts of the bias and then
test the hypotheses that those accounts imply. We derive the
likelihood function for the split population logit model,
describe the properties of its MLEs, present the results from a
Monte Carlo study, and briefly describe code that researchers
can use to implement the model in the Stata statistical package.

http://polmeth.wustl.edu/retrieve.php?id=895

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