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ERIC Number: EJ1447915
Record Type: Journal
Publication Date: 2024-Dec
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1076-9986
EISSN: EISSN-1935-1054
The Use of Reparametrization and Constraints on Linear Models to Parse Qualitative and Quantitative Information for a Set of Predictors
Ernest C. Davenport Jr.; Mark L. Davison; Kyungin Park
Journal of Educational and Behavioral Statistics, v49 n6 p955-975 2024
The following study shows how reparameterizations and constraints of the general linear model can serve to parse quantitative and qualitative aspects of predictors. We demonstrate three different approaches. The study uses data from the High School Longitudinal Study of 2009 on mathematics course-taking and achievement as an example. Results show that all mathematics courses are not equally predictive of math achievement. Thus, taking into account qualitative aspects of mathematics courses is useful. The study ends with a justification of quantifying qualitative aspects of predictors relative to a criterion with extensions to other linear models.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://bibliotheek.ehb.be:2993
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education; Grade 9; Junior High Schools; Middle Schools
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A