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Teaching How to Derive Directly Interpretable Coding Schemes for Multiple Regression Analysis

Ronald C. Serlin and Joel R. Levin
Journal of Educational Statistics
Vol. 10, No. 3, Special Issue: Teaching Statistics (Autumn, 1985), pp. 223-238
DOI: 10.2307/1164794
Stable URL: http://www.jstor.org/stable/1164794
Page Count: 16
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Teaching How to Derive Directly Interpretable Coding Schemes for Multiple Regression Analysis
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Abstract

Multiple linear regression is a versatile model for encompassing analysis of variance, analysis of covariance, and aptitude-by-treatment interaction designs. The question of how to teach the coding of levels of a qualitative variable is addressed in this paper. Although a variety of coding schemes will produce invariant omnibus statistical results for a given set of data, one's interpretation of treatment effects and treatment differences depends on the particular code values chosen. A general procedure is presented that allows the user to generate, on an a priori basis, code values that result in directly interpretable estimates of interest.

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