Interpreting the results of studies using latent variable models to assess data quality: an empirical example using confirmatory factor analysis

摘要

Latent variable models (LVMs) offer one route to examine the quality of data collected in surveys. The possibility exists that individuals equivalent in their true level of a construct or variable being measured are unlikely to have equivalent observed responses as a function of an extraneous variable, e.g., group membership. This potential is labeled here as differential item functioning (DIF). Survey methods generally considers measurement bias to be estimators that do no not accurately reflect true values. DIF may be thought of as differential measurement bias, i.e., measurement bias conditional on group membership. As a function of group membership, the degree, amount, or type of measurement bias changes. DIF has the potential to negatively affect the quality of data. LVMs, e.g., confirmatory factor analysis (CFA), offer one tool to assess DIF. However, few published examples exist in the survey research field and training in the interpretation of these models is lacking. The purpose of the current paper is to describe CFA sufficiently for interpretive purposes and demonstrate an empirical application of CFA to assess survey data quality to provide further interpretive guidance. References are provided for analysts wishing to conduct analyses of this type.