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Partially and Fully Noncompensatory Response Models for Dichotomous and Polytomous Items.

ABSTRACT: This article extends Sympson's partially noncompensatory dichtomous response model to ordered response data, and introduces a set of fully noncompensatory models for dichotomous and polytomous response data. The theoretical properties of the partially and fully noncompensatory response models are contrasted, and a small set of Monte Carlo simulations are presented to evaluate their parameter recovery performance. Results indicate that the respective models fit the data similarly when correctly matched to their respective population generating model. The fully noncompensatory models, however, demonstrated lower sampling variability and smaller degrees of bias than the partially noncompensatory counterparts. Based on the theoretical properties and empirical performance, it is argued that the fully noncompensatory models should be considered in item response theory applications when investigating conjunctive response processes.

SUBMITTER: Chalmers RP

PROVIDER: S-EPMC7383690 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Partially and Fully Noncompensatory Response Models for Dichotomous and Polytomous Items.

Chalmers R Philip RP

Applied psychological measurement 20200508 6

This article extends Sympson's partially noncompensatory dichtomous response model to ordered response data, and introduces a set of fully noncompensatory models for dichotomous and polytomous response data. The theoretical properties of the partially and fully noncompensatory response models are contrasted, and a small set of Monte Carlo simulations are presented to evaluate their parameter recovery performance. Results indicate that the respective models fit the data similarly when correctly m ...[more]

PMID: 32788814

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Project description:Mixture models of item response theory (IRT) can be used to detect inappropriate category use. Data collected by panel surveys where attitudes and traits are typically assessed by short scales with many response categories are prone to response styles indicating inappropriate category use. However, the application of mixed IRT models to this data type can be challenging because of many threshold parameters within items. Up to now, there is very limited knowledge about the sample size required for an appropriate performance of estimation methods as well as goodness-of-fit criteria of mixed IRT models in this case. The present Monte Carlo simulation study examined these issues for two mixed IRT models [the restricted mixed generalized partial credit model (rmGPCM) and the mixed partial credit model (mPCM)]. The population parameters of the simulation study were taken from a real application to survey data which is challenging (a 5-item scale with an 11-point rating scale, and three latent classes). Additional data conditions (e.g., long tests, a reduced number of response categories, and a simple latent mixture) were included in this simulation study to improve the generalizability of the results. Under this challenging data condition, for each model, data were generated based on varying sample sizes (from 500 to 5,000 observations with a 500-step). For the additional conditions, only three sample sizes (consisting of 1,000, 2,500, and 4,500 observations) were examined. The effect of sample size on estimation problems and accuracy of parameter and standard error estimates were evaluated. Results show that the two mixed IRT models require at least 2,500 observations to provide accurate parameter and standard error estimates under the challenging data condition. The rmGPCM produces more estimation problems than the more parsimonious mPCM, mostly because of the sparse tables arising due to many response categories. These models exhibit similar trends of estimation accuracy across sample sizes. Under the additional conditions, no estimation problems are observed. Both models perform well with a smaller sample size when long tests were used or a true latent mixture includes two classes. For model selection, the AIC3 and the SABIC are the most reliable information criteria.

Dataset Information

Partially and Fully Noncompensatory Response Models for Dichotomous and Polytomous Items.

Publications

Partially and Fully Noncompensatory Response Models for Dichotomous and Polytomous Items.

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