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• W51715909 abstract "Summary A number of exact person fit indices for the Rasch model is adapted to the problem of detecting faked personality inventories. Detailed alternative hypotheses are presented and discussed which are based on apriori reasoning or on empirical information about aberrant response patterns. A simulation study demonstrates that powerful indices are possible, but only under certain conditions like large calibration samples consisting of honest and faking persons as well as specific item construction. An empirical application supports this conclusion. Menu-driven software is available. Key words: faking, item response theory, person fit, Rasch model, personality measurement 1. Introduction Confronted with faking personality inventories, a basic question is which part of the data can be trusted and which not. Since asking examinees whether they faked data is not a sensible option, all our conclusions must rely on the evidence given by the persons' response vectors. A well-known strategy is to use lying scales or social desirability scales in order to provoke obviously dubious responses; however, a clever (and possibly prepared) examinee may be careful enough not to agree to very unrealistic statements and thus avoid arousing suspicion. The same argument disfavors looking exclusively at unrealistic raw scores. More structure-oriented procedures can be found within the framework of Item Response Theory because of its strict model assumptions; the more detailed the relationship between latent trait and response behavior is specified, the more stringent methods for detecting faking can be developed (provided the model assumptions hold). As shown by Zickar and Drasgow (1996), IRT techniques may in fact be superior to the conventional approach. The present article adopts their attempt to supply the traditional methods with more subtle cues of faking. Thereby the focus does not lie primarily on single conspicuous answers, but on the whole structure of response patterns: A certain response is not doubtful by itself, but because it does not fit the person's other responses well; for example, it might be inconspicuous given high score, but not in the context of medium score. This approach automatically involves structural considerations: A response vector is suspected of being faked not so much on the basis of the number of positive answers but rather because of their pattern. It seems to be a reasonable assumption that a faking examinee is less aware of the latter possibility than of the former. Of course every attempt at detecting faking only works if a dishonest examinee gives improper responses; on the other hand, without this assumption there is no chance of detecting aberrant response patterns at all because no data analysis will discriminate between true and perfectly simulated answers. Detecting structure-violating response patterns has some tradition in item response theory connected with notions of caution index (Tatsuoka, 1984), appropriateness indices (Levine & Rubin, 1979, Levine & Drasgow, 1982), or person fit indices (Wright, 1977). For comprehensive surveys see Levine and Drasgow (1982, 1988). The ideas about non-normal response patterns also refer to genuine cheating like copying answers from one's neighbor, to small likelihoods of response vectors, to random behavior, or to improbable answers like agreements to difficult items but disagreements to easy ones; however, deliberately faking response behavior should be characterized even more specifically (Zickar & Drasgow, 1996; the more stringently suspicions are formulated, the more readily deviations from honest behavior can be detected, see Klauer, 1995). For personality questionnaire situations, a person is unlikely to cheat by copying or giving random responses; the more frequent faking strategy will be to judge for which items the answer yes will produce a favorable or unfavorable impression and then to consciously distort some responses. …" @default.
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• W51715909 title "Towards Identification of Unscalable Personality Questionnaire Respondents: The Use of Person Fit Indices" @default.
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