Pairwise Regression Weight Contrasts: Models for Allocating Psychological Resources

Mark L. Davison, Hao Jia and Ernest C. Davenport
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Ernest C. Davenport: University of Minnesota

Journal of Educational and Behavioral Statistics, 2024, vol. 49, issue 4, 533-564

Abstract: Researchers examine contrasts between analysis of variance (ANOVA) effects but seldom contrasts between regression coefficients even though such coefficients are an ANOVA generalization. Regression weight contrasts can be analyzed by reparameterizing the linear model. Two pairwise contrast models are developed for the study of qualitative differences among predictors. One leads to tests of null hypotheses that the regression weight for a reference predictor equals each of the other weights. The second involves ordered predictors and null hypotheses that the weight for a predictor equals that for the variables just above or below in the ordering. As illustration, qualitative differences in high school math course content are related to math achievement. The models facilitate the study of qualitative differences among predictors and the allocation of resources. They also readily generalize to moderated, hierarchical, and generalized linear forms.

Keywords: linear regression; generalized linear regression; randomized experiments; hierarchical linear regression; mathematics coursework; mathematics achievement; ANOVA (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:49:y:2024:i:4:p:533-564

DOI: 10.3102/10769986231200155

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