Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS

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Overview

Analysis of Variance Designs presents the foundations of experimental design: assumptions, statistical significance, strength of effect, and the partitioning of the variance. Exploring the effects of one or more independent variables on a single dependent variable as well as two-way and three-way mixed designs, this textbook offers an overview of traditionally advanced topics for progressive undergraduates and graduate students in the behavioral and social sciences. Separate chapters are devoted to multiple comparisons (post hoc and planned/weighted), ANCOVA, and advanced topics. Each of the design chapters contains conceptual discussions, hand calculations, and procedures for the omnibus and simple effects analyses in both SPSS and the new “click and shoot” SAS Enterprise Guide interface.

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Editorial Reviews

From the Publisher
"... readable but with a level of complexity commensurate with that of the topic."
J.T. Saccoman, Choice Magazine
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Product Details

  • ISBN-13: 9780521874816
  • Publisher: Cambridge University Press
  • Publication date: 9/1/2008
  • Edition description: New Edition
  • Pages: 594
  • Product dimensions: 7.20 (w) x 10.00 (h) x 1.50 (d)

Meet the Author

Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. He received his PhD from the University of Arkansas in experimental psychology. His research interests include the effects of multicultural variables such as client-therapist ethnic match, client acculturation status and ethnic identity, and therapist cultural competence on clinical outcomes. Additional research interests focus on conversation memory and discourse processing.

A. J. Guarino is on the faculty at Auburn University, teaching the statistics sequence of ANOVA, multiple regression, MANOVA, and structural equation modeling (SEM) in the College of Education. He received his bachelor's degree from the University of California, Berkeley. He earned a doctorate in statistics and research methodologies from the University of Southern California through the Department of Educational Psychology.

Lawrence S. Meyers is Professor of Psychology at Sacramento State University, where he teaches undergraduate and graduate courses in research design, data analysis, data interpretation, testing and measurement, and history and systems of psychology. He received his doctorate from Adelphi University and worked on the National Science Foundation Postdoctoral Fellowship at the University of Texas, Austin, and Purdue University.

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Table of Contents

1. ANOVA and research design; 2. Measurement, central tendency, and variability; 3. Elements of ANOVA; 4. The statistical significance and effect of strength; 5. Analysis of variance assumptions; 6. One-way between subjects design; 7. Multiple comparison procedures; 8. Two-way between subjects design; 9. Three-way between subjects design; 10. One-way within subject design; 11. Two-way within subjects design; 12. Three-way within subjects design; 13. Simple mixed design; 14. Complex mixed design: two between-subject factors and one within-subject factor; 15. Complex mixed design: one between-subject factor and two within-subject factors; 16. Analysis of covariance; 17. Advanced topics in analysis of variance; Appendix A. Primer on SPSS; Appendix B. Primer on SAS Enterprise Guide; Appendix C. Table of critical f values; Appendix D. Deviational formula for sums of squares.

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