Nonparametric item response theory in action: An overview of the special issue (original) (raw)

2001, Applied Psychological Measurement

Although most item response theory (IRT) applications and related methodologies involve model fitting within a single parametric IRT (PIRT) family [e.g., the Rasch (1960) model or the threeparameter logistic model ( 3PLM; ], nonparametric IRT (NIRT) research has been growing in recent years. Three broad motivations for the development and continued interest in NIRT can be identified: 1. To identify a commonality among PIRT and IRT-like models, model features [e.g., local independence (LI), monotonicity of item response functions (IRFs), unidimensionality of the latent variable] should be characterized, and it should be discovered what happens when models satisfy only weakened versions of these features. Characterizing successful and unsuccessful inferences under these broad model features can be attempted in order to understand how IRT models aggregate information from data. All this can be done with NIRT. 2. Any model applied to data is likely to be incorrect. When a family of PIRT models has been shown (or is suspected) to fit poorly, a more flexible family of NIRT models often is desired. These NIRT models have been used to: (1) assess violations of LI due to nuisance traits (e.g., latent variable multidimensionality) or the testing context influencing test performance (e.g., speededness and question wording), (2) clarify questions about the sources and effects of differential item functioning, (3) provide a flexible context in which to develop methodology for establishing the most appropriate number of latent dimensions underlying a test, and (4) serve as alternatives for PIRT models in tests of fit. 3. In psychological and sociological research, when it is necessary to develop a new questionnaire or measurement instrument, there often are fewer examinees and items than are desired for fitting PIRT models in large-scale educational testing. NIRT provides tools that are easy to use in small samples. It can identify items that scale together well (follow a particular set of NIRT assumptions). NIRT also identifies several subscales with simple structure among the scales, if the items do not form a single unidimensional scale.