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Exploratory factor analysis is a popular statistical technique used in communication research.Although exploratory factor analysis (EFA) and principal components analysis (PCA) are different techniques, PCA is often employed incorrectly to reveal latent constructs (i.e., factors) of observed variables, which is the purpose of EFA. PCA is more appropriate for reducing measured variables into a smaller set of variables (i.e., components) by keeping as much variance as possible out of the total variance in the measured variables. Furthermore, the popular use of varimax rotation raises some concerns about the relationships among the factors that researchers claim to discover. This paper discusses the distinct purposes of PCA and EFA, using two data sets as examples to highlight the differences in results between these procedures, and also reviews the use of each technique in three major communication journals: Communication Monographs, Human Communication Research, and Communication Research. Hee Sun Park (M.A., University of Hawai'i, 1998) is a visiting instructor in the Department of Communication at Michigan State University. René Dailey (M.A., University of Wyoming, 1998) and Daisy Lemus (B.A., University of Southern California, 2000) are graduate students in the

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Following Gouran (1994), the authors proposed four hypotheses that predict the probability of computer-mediated groups (CMGs) endorsing proposals based on (a) the number of reasons offered for them, (b) the number of members advancing these reasons, (c) the net number of positive reactions to the reasons advanced, and (d) the development of support for the reasons. Results from 11 groups that had long collaborated exclusively through computer-mediated means indicated that members in support of a proposal relative to those in opposition as well as the development of their arguments were significant predictors of decision outcomes. Moreover, the number of responses for/against a proposal and the difference in the positive and negative reactions to decision proposals were good independent predictors of decision outcomes.