Kernel Smoothing Item Response Theory in R: A Didactic
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| Title: | Kernel Smoothing Item Response Theory in R: A Didactic |
|---|---|
| Language: | English |
| Authors: | Effatpanah, Farshad (ORCID |
| Source: | Practical Assessment, Research & Evaluation. May 2023 28. |
| Availability: | Center for Educational Assessment. 813 North Pleasant Street, Amherst, MA 01002. e-mail: pare@umass.edu; Tel: 413-577-2180; Web site: https://scholarworks.umass.edu/pare |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2023 |
| Intended Audience: | Researchers |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Item Response Theory, Feedback (Response), Mathematical Models, Item Analysis, Psychological Testing, Educational Assessment, Test Anxiety, Children, Measures (Individuals), Factor Analysis |
| ISSN: | 1531-7714 |
| Abstract: | Item response theory (IRT) refers to a family of mathematical models which describe the relationship between latent continuous variables (attributes or characteristics) and their manifestations (dichotomous/polytomous observed outcomes or responses) with regard to a set of item characteristics. Researchers typically use parametric IRT (PIRT) models to measure educational and psychological latent variables. However, PIRT models are based on a set of strong assumptions that often are not satisfied. For this reason, non-parametric IRT (NIRT) models can be more desirable. An exploratory NIRT approach is kernel smoothing IRT (KS-IRT; Ramsay, 1991) which estimates option characteristic curves by non-parametric kernel smoothing technique. This approach only gives graphical representations of item characteristics in a measure and provides preliminary feedback about the performance of items and measures. Although KS-IRT is not a new approach, its application is far from widespread, and it has limited applications in psychological and educational testing. The purpose of the present paper is to give a reader-friendly introduction to the KS-IRT, and then use the KernSmoothIRT package (Mazza et al., 2014, 2022) in R to straightforwardly demonstrate the application of the approach using data of Children's Test Anxiety scale. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1392871 |
| Database: | ERIC |
| Abstract: | Item response theory (IRT) refers to a family of mathematical models which describe the relationship between latent continuous variables (attributes or characteristics) and their manifestations (dichotomous/polytomous observed outcomes or responses) with regard to a set of item characteristics. Researchers typically use parametric IRT (PIRT) models to measure educational and psychological latent variables. However, PIRT models are based on a set of strong assumptions that often are not satisfied. For this reason, non-parametric IRT (NIRT) models can be more desirable. An exploratory NIRT approach is kernel smoothing IRT (KS-IRT; Ramsay, 1991) which estimates option characteristic curves by non-parametric kernel smoothing technique. This approach only gives graphical representations of item characteristics in a measure and provides preliminary feedback about the performance of items and measures. Although KS-IRT is not a new approach, its application is far from widespread, and it has limited applications in psychological and educational testing. The purpose of the present paper is to give a reader-friendly introduction to the KS-IRT, and then use the KernSmoothIRT package (Mazza et al., 2014, 2022) in R to straightforwardly demonstrate the application of the approach using data of Children's Test Anxiety scale. |
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| ISSN: | 1531-7714 |
