Statistical Methods for Communication Science
Routledge, 4 mar. 2009 - 536 páginas
Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication research. Writing in a straightforward, personal style, author Andrew F. Hayes offers this accessible and thorough introduction to statistical methods, starting with the fundamentals of measurement and moving on to discuss such key topics as sampling procedures, probability, reliability, hypothesis testing, simple correlation and regression, and analyses of variance and covariance. Hayes takes readers through each topic with clear explanations and illustrations. He provides a multitude of examples, all set in the context of communication research, thus engaging readers directly and helping them to see the relevance and importance of statistics to the field of communication.
Highlights of this text include:
*thorough and balanced coverage of topics;
*integration of classical methods with modern "resampling" approaches to inference;
*consideration of practical, "real world" issues;
*numerous examples and applications, all drawn from communication research;
*up-to-date information, with examples justifying use of various techniques; and
*a CD with macros, data sets, figures, and additional materials.
This unique book can be used as a stand-alone classroom text, a supplement to traditional research methods texts, or a useful reference manual. It will be invaluable to students, faculty, researchers, and practitioners in communication, and it will serve to advance the understanding and use of statistical methods throughout the discipline.
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Fundamentals of Measurement
ANOVA With Statistical Controls
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alternative hypothesis average causal Chapter communication researchers communication variables compute confidence interval critical value degrees of freedom demographics and political derived df beta discuss politics estimating political knowledge Figure homoscedasticity hypothesis is true hypothesis testing interpreted linear regression mean difference measures of partial multicollinearity multiple correlation multiple regression null and alternative null hypothesis obtained result one-sample t test outcome variable p-value p−value paired partial regression weight permutation test person’s political discussion political interest population inference population mean Predebate predictor variables proportion of variance quadratic regression model quantifies read the newspaper regression analysis regression model reject the null residuals sample mean sampling distribution simple regression SPSS SPSS output squared semipartial correlation SSresidual standard deviation standard error standardized regression statistically controlling Television Affinity Scale two-tailed variance in political Venn diagram X Y X Y zero α α μ μ μ μ σ π π π χ χ