Item Response Theory Scoring and the Detection of Curvilinear Relationships

Psychologists are increasingly positing theories of behavior that suggest psychological constructs are curvilinearly related to outcomes. However, results from empirical tests for such curvilinear relations have been mixed. We propose that correctly identifying the response process underlying responses to measures is important for the accuracy of these tests. Indeed, past research has indicated that item responses to many self-report measures follow an ideal point response process-wherein respondents agree only to items that reflect their own standing on the measured variable-as opposed to a dominance process, wherein stronger agreement, regardless of item content, is always indicative of higher standing on the construct. We test whether item response theory (IRT) scoring appropriate for the underlying response process to self-report measures results in more accurate tests for curvilinearity. In 2 simulation studies, we show that, regardless of the underlying response process used to generate the data, using the traditional sum-score generally results in high Type 1 error rates or low power for detecting curvilinearity, depending on the distribution of item locations. With few exceptions, appropriate power and Type 1 error rates are achieved when dominance-based and ideal point-based IRT scoring are correctly used to score dominance and ideal point response data, respectively. We conclude that (a) researchers should be theory-guided when hypothesizing and testing for curvilinear relations; (b) correctly identifying whether responses follow an ideal point versus dominance process, particularly when items are not extreme is critical; and (c) IRT model-based scoring is crucial for accurate tests of curvilinearity.