A Comparison of Person-Fit Indices to Detect Social Desirability Bias.

Abstract

Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the performance of 14 person-fit indices to identify SDB in simulated responses. The area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis was computed to evaluate the predictive power of these statistics. The findings showed that the agreement statistic $\left(A\right)$ outperformed all other person-fit indices, while the disagreement statistic $\left(D\right)$, dependability statistic $\left(E\right)$, and the number of Guttman errors $\left(G\right)$ also demonstrated high AUCs to detect SDB. Recommendations for practitioners to use these fit indices are provided.