Detecting Faking on Self-Report Measures Using the Balanced Inventory of Desirable Responding

Abstract

We compared three methods for scoring the Balanced Inventory of Desirable Responding (BIDR) to detect faked responses on self-report measures: (1) polytomous, (2) dichotomous emphasizing exaggerating endorsement of socially desirable behaviors, and (3) dichotomous emphasizing exaggerating denial of such behaviors. The results revealed that respondents on average were able to fake good or fake bad and that faking markedly affected score distributions, subscale score intercorrelations, and overall model fits. When using the Impression Management scale, polytomous and dichotomous exaggerated endorsement scoring were best for detecting faking good, whereas polytomous and dichotomous exaggerated denial scoring were best for detecting faking bad. When using the Self-Deceptive Enhancement scale, polytomous and dichotomous exaggerated endorsement scoring again were best for detecting faking good, but dichotomous exaggerated denial scoring was best for detecting faking bad. Percentages of correct classification of honest and faked responses for the most effective methods for any given scale ranged from 85% to 93%, with accuracy on average in detecting faking bad greater than in detecting faking good and greater when using the Impression Management than using the Self-Deceptive Enhancement scale for both types of faking. Overall, these results best support polytomous scoring of the BIDR Impression Management scale as the single most practical and efficient means to detect faking. Cut scores that maximized classification accuracy for all scales and scoring methods are provided for future use in screening for possible faking within situations in which relevant local data are unavailable.