Nonparametric Item Response Function Estimation for Assessing Parametric Model Fit
- 1 September 2001
- journal article
- research article
- Published by SAGE Publications in Applied Psychological Measurement
- Vol. 25 (3) , 234-243
- https://doi.org/10.1177/01466210122032046
Abstract
Methods are developed that investigate the fit of parametric item response models by comparing them to models fitted under nonparametric assumptions. The approach is primarily graphical, but is made inferential through resampling from an estimated parametric model. The identifiability and estimation consistency of item response theory models are discussed and shown to be vital to the interpretation of differences between two fitted item response theory models. Simulation studies and real-data examples illustrate these techniques.Keywords
This publication has 22 references indexed in Scilit:
- A characterization of monotone unidimensional latent variable modelsThe Annals of Statistics, 1997
- Joint Consistency of Nonparametric Item Characteristic Curve and Ability EstimationPsychometrika, 1997
- Rasch ModelsPublished by Springer Nature ,1995
- Kernel Smoothing Approaches to Nonparametric Item Characteristic Curve EstimationPsychometrika, 1991
- Applied Nonparametric RegressionPublished by Cambridge University Press (CUP) ,1990
- On the use of nonparametric regression for model checkingBiometrika, 1989
- Monotone Regression Splines in ActionStatistical Science, 1988
- The Analysis of Item-Ability Regressions: An Exploratory IRT Model Fit ToolApplied Psychological Measurement, 1985
- A Nonparametric Approach to the Analysis of Dichotomous Item ResponsesApplied Psychological Measurement, 1982
- Item characteristic curves estimated without knowledge of their mathematical form—a confrontation of Birnbaum's logistic modelPsychometrika, 1970