On the Utility of Indirect Methods for Detecting Faking

Indirect indices for faking detection in questionnaires make use of a respondent's deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher's choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.

Automated summary

Indirect indices for faking detection in questionnaires use a respondent's deviant or unlikely response pattern to identify them as a faker, offering advantages over direct faking indices. This study compared the performance of different indirect faking detection indices and found that the Likert-type item response process tree model, proportion of desirable scale endpoint responses, and covariance index performed the best. Using indirect indices in combination resulted in comparable or better detection rates than direct faking measures. Some effective indirect indices had minimal correlation with substantive scales, making them useful for detecting faking without losing substance. Researchers are encouraged to use indirect indices for detecting faking in their data.