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

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  • Ronald C. Serlin
  • Joel R. Levin

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.

Suggested Citation

  • Ronald C. Serlin & Joel R. Levin, 1985. "Teaching How to Derive Directly Interpretable Coding Schemes for Multiple Regression Analysis," Journal of Educational and Behavioral Statistics, , vol. 10(3), pages 223-238, September.
    • Handle: RePEc:sae:jedbes:v:10:y:1985:i:3:p:223-238
    DOI: 10.3102/10769986010003223
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