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ERIC Number: ED365712
Record Type: Non-Journal
Publication Date: 1993-Nov
Pages: 26
Abstractor: N/A
Reference Count: N/A
To Block or Covary a Concomitant Variable: Which Is Better?
Wu, Yi-Cheng; McLean, James E.
By employing a concomitant variable, researchers can reduce the error, increase the precision, and maximize the power of an experimental design. Blocking and analysis of covariance (ANCOVA) are most often used to harness the power of a concomitant variable. Whether to block or covary and how many blocks to be used if a block design is chosen become important. This paper provides an historical review of the problem and recommends future research to examine the problem based on how subjects are assigned, how data are analyzed, and the distributions of the variables. In this study, subjects were randomly assigned to treatments ignoring the concomitant variable, and data were analyzed by one-way analysis of variance (ANOVA), post-hoc two-block, four-block, and eight-block ANOVA and ANCOVA. Distributions of the concomitant and dependent variables were normal. The Monte Carlo method was used to generate 20,000 data sets for 8 experimental conditions (2 levels of subject and 4 levels of correlation between concomitant and dependent variables. The five analysis procedures were examined under each experimental condition. Results show that ANCOVA is more powerful than post-hoc rank blocking. Eight tables present analysis results. (Contains 36 references.) (Author/SLD)
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A