A generalized partial credit FACETS model for investigating order effects in self-report personality data
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Despite its convenience, the process of self-report in personality testing can be impacted by a variety of cognitive and perceptual biases. One bias that violates local independence, a core criterion of modern test theory, is the order effect. In this bias, characteristics of an item response are impacted not only by the content of the current item but also the accumulated exposure to previous, similar-content items. This bias is manifested as increasingly stable item responses for items that appear later in a test. Previous investigations of this effect have been rooted in classical test theory (CTT) and have consistently found that item reliabilities, or corrected item-total score correlations, increase with the item's serial position in the test. The purpose of the current study was to more rigorously examine order effects via item response theory (IRT). To this end, the FACETS modeling approach (Linacre, 1989) was combined with the Generalized Partial Credit model (GPCM; Muraki, 1992) to produce a new model, the Generalized Partial Credit FACETS model (GPCFM). Serial position of an item serves as a facet that contributes to the item response, not only via its impact on an item's location on the latent trait continuum, but also its discrimination. Thus, the GPCFM differs from previous generalizations of the FACETS model (Wang&Liu, 2007) in that the item discrimination parameter is modified to include a serial position effect. This parameter is important because it reflects the extent to which the purported underlying trait is represented in an item score. Two sets of analyses were conducted. First, a simulation study demonstrated effective parameter recovery, though measurements of error were impacted by sample size for all parameters, test length for trait level estimates, and the size of the order effect for trait level estimates, and an interaction between sample size and test length for item discrimination. Secondly, with respect to real self-report personality data, the GPCFM demonstrated good fit as well as superior fit relative to competing, nested models while also identifying order effects in some traits, particularly Neuroticism, Openness, and Agreeableness.