Rationale, procedures, and applications for decomposition of explained variance in multiple regression analyses

Robert D. McPhee, David R. Seibold

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Publication of studies utilizing multiple regression (MR) statistical techniques is on the rise in communication journals, but researchers have been remiss in reporting an important aspect of MR results: identification of the unique and non-unique (joint, incremental, or common) influences of independent variables on the dependent variable. This article explicates three methods of decomposing R2 and demonstrates, both by analogy and contrast to ANOVA, the importance of partitioning MR explained variance. Particular attention is paid to the relevance of variance decomposition techniques for explanation, prediction, and control in communication theory as well as the interpretive and the evaluative roles variance decomposition analyses should have in communication research. Finally, the specific steps entailed in performing each partitioning method are reviewed, and worked examples are supplied from the message/attitude/behavior research literature.

Original languageEnglish (US)
Pages (from-to)345-384
Number of pages40
JournalCommunication Research
Volume6
Issue number3
DOIs
StatePublished - 1979
Externally publishedYes

Fingerprint

Decomposition
regression
Communication
Information theory
Analysis of variance (ANOVA)
communication theory
communication research
communication
Multiple Regression
literature
Communication Theory
Communication Research
Prediction
Analysis of Variance

ASJC Scopus subject areas

  • Communication
  • Language and Linguistics
  • Linguistics and Language

Cite this

Rationale, procedures, and applications for decomposition of explained variance in multiple regression analyses. / McPhee, Robert D.; Seibold, David R.

In: Communication Research, Vol. 6, No. 3, 1979, p. 345-384.

Research output: Contribution to journalArticle

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