Statistic lz based person-fit methods for non-cognitive multiscale measures
Conijn,J.M. Emons,W.H.M. Sijtsma,K.
Conijn,J.M.
Emons,W.H.M.
Sijtsma,K.
Abstract
Most person-fit statistics require long tests to reliably detect aberrant item-score vectors and are not readily applicable to noncognitive measures that consist of multiple short subscales. The authors propose combining subscale person-fit information to detect aberrant item-score vectors on noncognitive multiscale measures. They used a simulation study and three empirical personality and psychopathology test datasets to assess five multiscale person-fit methods based on the lz person-fit statistic with respect to (a) identifying aberrant item-score vectors, (b) improving accuracy of research results, and (c) understanding causes of aberrant responding. Simulated data analysis showed that the person-fit methods had good detection rates for substantially misfitting item-score vectors. Real-data person-fit analyses identified 4% to 17% misfitting item-score vectors. Removal of these vectors little improved model fit and test-score validity. The person-fit methods helped to understand causes of aberrant responding after controlling for response style on the explanatory variables. More real-data analyses are needed to demonstrate the usefulness of multiscale person-fit methods for noncognitive multiscale measures. Keywords: aberrant response behavior, lz person-fit statistic, multiscale person-fit analysis, personality measurement
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2014
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Conijn, J M, Emons, W H M & Sijtsma, K 2014, 'Statistic lz based person-fit methods for non-cognitive multiscale measures', Applied Psychological Measurement, vol. 38, pp. 122-136. https://doi.org/10.1177/0146621613497568