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Installieren Sie die genialokal App auf Ihrem Startbildschirm für einen schnellen Zugriff und eine komfortable Nutzung.
James Stevens' best-selling text, Intermediate Statistics, is written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results. SAS and SPSS are an integral part of each chapter. Definitional formulas are used on small data sets to provide conceptual insight into what is being measured. The assumptions underlying each analysis are emphasized and the reader is shown how to test the critical assumptions using SPSS or SAS. Printouts with annotations from SAS or SPSS show how to process the data for each analysis. The annotations highlight what the numbers mean and how to interpret the results. Numerical, conceptual, and computer exercises enhance understanding. Answers are provided for half of the exercises. The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts. Highlights of the Third Edition include: - A new chapter on hierarchical linear modeling using HLM6 - Downloadable resources containing all of the book's data sets - New coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Chapter 6) - More exercises in each chapter. Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required. An Instructor's Resource is available upon adoption. See www.researchmethodsarena.com .
James P. Stevens
Preface. 1. Introduction. 2. One Way Analysis of Variance. 3. Power Analysis. 4. Factorial Analysis of Variance. 5. Repeated Measures Analysis. 6. Simple and Mulitple Regression. 7. Analysis of Covariance. 8. Hierarchical Linear Modeling. Appendix A. Data Sets. Appendix B. Statistical Tables. Appendix C. Power Tables.
