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Levines Guide to Spss for Analysis of Variance by Melanie C.Page, Sanford L.Braver, David P.Mackinnon
Levines Guide to Spss for Analysis of Variance by Melanie C.Page, Sanford L.Braver, David P.Mackinnon
by Sanford L. Braver, David P. Mackinnon, and Melanie C. Page, Levines Guide to Spss for Analysis of Variance
This well-known manual offers instructions and clear examples for carrying out analyses of variance (ANOVA) and various other related statistical tests of significance with SPSS in a considerably extended and extensively rewritten second edition. The program statements needed for the more complex tests in analysis of variance are not provided by any other manual. Any SPSS version, including versions 11 and 11.5, may be used to execute any program in the book. The location of each sort of analysis (such as basic comparisons) for each type of design is shown in a table at the conclusion of the prologue (e.g., mixed two-factor design).
This is the only book available that offers thorough treatment of SPSS syntax, including the commands and subcommands that instruct SPSS what to do as well as the pull-down menu point-and-click approach. It also covers both basic and advanced subjects in ANOVA (PAC). Users can confirm the analysis being conducted by using a detailed description of the syntax, indicating what is required, preferred, and optional. Each design’s output is included in the book, along with a thorough description of the printing in question.
The new version has been restructured such that each design type’s analysis is covered in its own chapter. With additional chapters on designs with random variables, multivariate designs, the syntax used in PAC, and all-new examples of output with detailed explanations, it now includes extended treatment of analysis of covariance (ANCOVA) and mixed designs. The new version comes with a CD-ROM that has all of the data sets from the book as well as activities for each chapter.
Readers who are familiar with the fundamentals of the ANOVA approach, such as active researchers and data analysts, as well as advanced students learning analysis of variance, would benefit most from this book.
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