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Categorical Statistics for Communication Research by Bryan E. Denham

By Bryan E. Denham

Categorical facts for CommunicationResearch provides students with a discipline-specific consultant to specific information research. The textual content blends invaluable history details and formulation for statistical tactics with info analyses illustrating suggestions reminiscent of log- linear modeling and logistic regression analysis.

  • Provides thoughts for interpreting express facts from a verbal exchange reports perspective
  • Provides an obtainable presentation of thoughts for examining express info for verbal exchange students and different social scientists operating on the complex undergraduate and graduate educating levels
  • Illustrated with examples from sorts of communique study comparable to healthiness, political and activities conversation and entertainment
  • Includes routines on the finish of every bankruptcy and a better half site containing workout solutions and chapter-by-chapter PowerPoint slides

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Extra resources for Categorical Statistics for Communication Research

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To illustrate the McNemar procedure, one might consider a hypothetical study addressing fear appeals. A researcher is interested in whether graphic footage of an auto accident will deter adolescents (N = 72) from texting while driving. The researcher designs an experiment and develops a pretest and posttest instrument for measuring possible changes in attitudes. 4) and uses McNemar’s test to analyze the data. 4, cell a contains individuals who recognized texting dangers in both the pretest and the posttest, while cell b contains individuals who recognized dangers in the pretest but did not identify dangers in the posttest.

The sidebar accompanying this section of the chapter addresses the reluctance of research methodologist Paul Lazarsfeld to test bivariate relationships for statistical significance. One of his primary concerns was that introducing a­ dditional 30 Categorical Statistics for Communication Research variables in a statistical system might eliminate significance at the bivariate level, and while the current text does not advocate the elimination of significance testing, it does caution researchers about the myriad of ways in which chi‐square, in particular, can indicate significant findings when none may actually exist.

1207/ s15327906mbr3103_4. Cochran, William G. 1952. ” The Annals of Mathematical Statistics, 23: 315–345. Cramér, Harald. 1946. Mathematical Methods of Statistics. Princeton, NJ: Princeton University Press. Delucchi, Kevin L. 1983. ” Psychological Bulletin, 94: 166–175. Denham, Bryan E. 2014. S. ” Journalism & Mass Communication Quarterly, 91: 17–37. 1177/1077699013514415. Fienberg, Stephen E. 2007. The Analysis of Cross‐Classified Categorical Data, 2nd ed. New York: Springer. Fisher, R. A. 1934.

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