Mixed Model Generalizability Theory: A Case Study and Tutorial
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| Title: | Mixed Model Generalizability Theory: A Case Study and Tutorial |
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| Language: | English |
| Authors: | Alan Huebner, Gustaf B. Skar, Mengchen Huang |
| Source: | Practical Assessment, Research & Evaluation. 2025 30. |
| Availability: | University of Massachusetts Amherst Libraries. 154 Hicks Way, Amherst, MA 01003. e-mail: pare@umass.edu; Web site: https://openpublishing.library.umass.edu/pare/ |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2025 |
| Intended Audience: | Practitioners |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Generalizability Theory, Multivariate Analysis, Statistical Analysis, Writing Evaluation, Reliability |
| ISSN: | 1531-7714 |
| Abstract: | Generalizability theory is a modern and powerful framework for conducting reliability analyses. It is flexible to accommodate both random and fixed facets. However, there has been a relative scarcity in the practical literature on how to handle the fixed facet case. This article aims to provide practitioners a conceptual understanding and computational resources to deal with designs with a fixed facet in both univariate and multivariate generalizability theory settings. The analyses feature a real data set, which is available to readers along with the code to reproduce all analyses. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1482037 |
| Database: | ERIC |
| Abstract: | Generalizability theory is a modern and powerful framework for conducting reliability analyses. It is flexible to accommodate both random and fixed facets. However, there has been a relative scarcity in the practical literature on how to handle the fixed facet case. This article aims to provide practitioners a conceptual understanding and computational resources to deal with designs with a fixed facet in both univariate and multivariate generalizability theory settings. The analyses feature a real data set, which is available to readers along with the code to reproduce all analyses. |
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| ISSN: | 1531-7714 |