Journal of Data Science logo


Login Register

  1. Home
  2. Issues
  3. Volume 8, Issue 1 (2010)
  4. Contrast Coding in Multiple Regression A ...

Journal of Data Science

Submit your article Information
  • Article info
  • More
    Article info

Contrast Coding in Multiple Regression Analysis: Strengths, Weaknesses, and Utility of Popular Coding Structures
Volume 8, Issue 1 (2010), pp. 61–73
Matthew J. Davis  

Authors

 
Placeholder
https://doi.org/10.6339/JDS.2010.08(1).563
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

Abstract: The use of multiple regression analysis (MRA) has been on the rise over the last few decades in part due to the realization that analysis of variance (ANOVA) statistics can be advantageously completed using MRA. Given the limitations of ANOVA strategies it is argued that MRA is the better analysis; however, in order to use ANOVA in MRA coding structures must be employed by the researcher which can be confusing to understand. The present paper attempts to simplify this discussion by providing a description of the most popular coding structures, with emphasis on their strengths, limitations, and uses. A visual analysis of each of these strategies is also included along with all necessary steps to create the contrasts. Finally, a decision tree is presented that can be used by researchers to determine which coding structure to utilize in their current research project.

PDF XML
PDF XML

Copyright
No copyright data available.

Keywords
Analysis of variance contrast coding

Metrics
since February 2021
2744

Article info
views

1385

PDF
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

Journal of data science

  • Online ISSN: 1683-8602
  • Print ISSN: 1680-743X

About

  • About journal

For contributors

  • Submit
  • OA Policy
  • Become a Peer-reviewer

Contact us

  • JDS@ruc.edu.cn
  • No. 59 Zhongguancun Street, Haidian District Beijing, 100872, P.R. China
Powered by PubliMill  •  Privacy policy