Estimating Conditional Group Mean Difference: A Comparison of Four Approaches.
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| Title: | Estimating Conditional Group Mean Difference: A Comparison of Four Approaches. |
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| Authors: | Liu, Lu1 (AUTHOR) ll18g@fsu.edu, Yang, Yanyun1 (AUTHOR) |
| Source: | Journal of Experimental Education. 2026, Vol. 94 Issue 3, p658-680. 23p. |
| Subject Terms: | *Factor analysis, Structural equation modeling, Measurement errors, Errors-in-variables models, Estimation theory |
| Abstract: | The present study evaluated four alternative approaches to structural regression modeling in estimating conditional group mean differences under conditions with small samples, measurement errors, and measurement model misspecification. Two simulation studies were conducted: one considered unidimensional items and the other involved multidimensional items conformed to a bifactor model. Factor loadings, conditional group mean difference, and sample size were manipulated. The single-indicator approach using coefficient alpha yielded unbiased estimates when items were unidimensional or primarily measured a general factor. The traditional ANCOVA produced unbiased estimates when composite score reliability was high. ANCOVA using weighted composites or factor scores and the single-indicator approach using coefficient omega or omega hierarchical were not recommended as they were prone to inadmissible solutions in small samples. [ABSTRACT FROM AUTHOR] |
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| Database: | Education Research Complete |
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| Abstract: | The present study evaluated four alternative approaches to structural regression modeling in estimating conditional group mean differences under conditions with small samples, measurement errors, and measurement model misspecification. Two simulation studies were conducted: one considered unidimensional items and the other involved multidimensional items conformed to a bifactor model. Factor loadings, conditional group mean difference, and sample size were manipulated. The single-indicator approach using coefficient alpha yielded unbiased estimates when items were unidimensional or primarily measured a general factor. The traditional ANCOVA produced unbiased estimates when composite score reliability was high. ANCOVA using weighted composites or factor scores and the single-indicator approach using coefficient omega or omega hierarchical were not recommended as they were prone to inadmissible solutions in small samples. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00220973 |
| DOI: | 10.1080/00220973.2025.2522863 |
