Modeling Dimensions Converging at the Upper Anchor in Learning Progressions: An Example of Micro-Evolution
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| Title: | Modeling Dimensions Converging at the Upper Anchor in Learning Progressions: An Example of Micro-Evolution |
|---|---|
| Language: | English |
| Authors: | Mingfeng Xue (ORCID |
| Source: | Applied Measurement in Education. 2024 37(1):71-87. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Learning Trajectories, Educational Assessment, Item Response Theory, Evolution, Measurement Techniques, Test Items, Multiple Choice Tests |
| DOI: | 10.1080/08957347.2024.2311934 |
| ISSN: | 0895-7347 1532-4818 |
| Abstract: | Multidimensionality is common in psychological and educational measurements. This study focuses on dimensions that converge at the upper anchor (i.e. the highest acquisition status defined in a learning progression) and compares different ways of dealing with them using the multidimensional random coefficients multinomial logit model and scale alignment methods. Assumptions underlying the four approaches studied are (a) ignoring the convergence, (b) recognizing the convergence of the dimensions, (c) treating convergence as a new dimension, and (d) separating the within-category multidimensionality and treating convergence as a new dimension. A learning progression about micro-evolution is used as an example, including model fits, step difficulties, and associations between dimensions, Wright maps are drawn, and inferences are made under the four building blocks of measurement development. Finally, the usefulness and weaknesses of the four approaches are discussed. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1413499 |
| Database: | ERIC |
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| Abstract: | Multidimensionality is common in psychological and educational measurements. This study focuses on dimensions that converge at the upper anchor (i.e. the highest acquisition status defined in a learning progression) and compares different ways of dealing with them using the multidimensional random coefficients multinomial logit model and scale alignment methods. Assumptions underlying the four approaches studied are (a) ignoring the convergence, (b) recognizing the convergence of the dimensions, (c) treating convergence as a new dimension, and (d) separating the within-category multidimensionality and treating convergence as a new dimension. A learning progression about micro-evolution is used as an example, including model fits, step difficulties, and associations between dimensions, Wright maps are drawn, and inferences are made under the four building blocks of measurement development. Finally, the usefulness and weaknesses of the four approaches are discussed. |
|---|---|
| ISSN: | 0895-7347 1532-4818 |
| DOI: | 10.1080/08957347.2024.2311934 |
