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Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts, 3rded. Designed for the second coursein statistics it is one of the few texts that focuses just on intermediate statistics. The flexible coverage allows instructors to select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included. In addition to updating throughout, the new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. More on computing confidence intervals and conducting power analyses using G*Power. Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results. Extended sections on how to write-up statistical results in APA format. New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and allnew interpretative problems. A website that features Power Points, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more. Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Stop and Think Boxesprovide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are in the book. The first six chapters cover the basic and advanced analysis of variance models. The next three examine linear, multiple, and logistic regression models, topics that are often neglected in other texts. Intended for courses in intermediate statistics and/or statistics II taught in education and/or the behavioral sciences, predominantly at the master's or doctoral level. A rudimentary knowledge of algebra and introductory statistics is assumed.
Table of Contents
|One-Factor Analysis of Variance: Fixed-Effects Model||p. 1|
|Characteristics of One-Factor ANOVA Model||p. 2|
|Layout of Data||p. 6|
|ANOVA Theory||p. 6|
|ANOVA Model||p. 12|
|Assumptions and Violation of Assumptions||p. 19|
|Unequal n's or Unbalanced Procedure||p. 22|
|Alternative ANOVA Procedures||p. 22|
|SPSS and G*Power||p. 23|
|Template and APA-Style Write-Up||p. 44|
|Multiple Comparison Procedures||p. 51|
|Concepts of Multiple Comparison Procedures||p. 52|
|Selected Multiple Comparison Procedures||p. 58|
|Template and APA-Style Write-Up||p. 76|
|Factorial Analysis of Variance: Fixed-Effects Model||p. 81|
|Two-Factor ANOVA Model||p. 82|
|Three-Factor and Higher-Order ANOVA||p. 100|
|Factorial ANOVA With Unequal n's||p. 103|
|SPSS and G*Power||p. 105|
|Template and APA-Style Write-Up||p. 127|
|Introduction to Analysis of Covariance: One-Factor Fixed-Effects Model With Single Covariate||p. 137|
|Characteristics of the Model||p. 138|
|Layout of Data||p. 141|
|ANCOVA Model||p. 141|
|ANCOVA Summary Table||p. 142|
|Partitioning the Sums of Squares||p. 143|
|Adjusted Means and Related Procedures||p. 144|
|Assumptions and Violation of Assumptions||p. 146|
|ANCOVA Without Randomization||p. 153|
|More Complex ANCOVA Models||p. 154|
|Nonparametric ANCOVA Procedures||p. 154|
|SPSS and G*Power||p. 155|
|Template and APA-Style Paragraph||p. 179|
|Random- and Mixed-Effects Analysis of Variance Models||p. 187|
|One-Factor Random-Effects Model||p. 188|
|Two-Factor Random-Effects Model||p. 193|
|Two-Factor Mixed-Effects Model||p. 198|
|One-Factor Repeated Measures Design||p. 203|
|Two-Factor Split-Plot or Mixed Design||p. 210|
|SPSS and G*Power||p. 218|
|Template and APA-Style Write-Up||p. 258|
|Hierarchical and Randomized Block Analysis of Variance Models||p. 267|
|Two-Factor Hierarchical Model||p. 268|
|Two-Factor Randomized Block Design for n = 1||p. 276|
|Two-Factor Randomized Block Design for n > 1||p. 284|
|Friedman Test||p. 284|
|Comparison of Various ANOVA Models||p. 285|
|Template and APA-Style Write-Up||p. 313|
|Simple Linear Regression||p. 321|
|Concepts of Simple Linear Regression||p. 322|
|Population Simple Linear Regression Model||p. 324|
|Sample Simple Linear Regression Model||p. 325|
|Template and APA-Style Write-Up||p. 360|
|Multiple Regression||p. 367|
|Partial and Semipartial Correlations||p. 368|
|Multiple Linear Regression||p. 371|
|Methods of Entering Predictors||p. 386|
|Nonlinear Relationships||p. 389|
|Categorical Predictors||p. 390|
|Template and APA-Style Write-Up||p. 411|
|Logistic Regression||p. 419|
|How Logistic Regression Works||p. 420|
|Logistic Regression Equation||p. 421|
|Estimation and Model Fit||p. 425|
|Significance Tests||p. 426|
|Assumptions and Conditions||p. 431|
|Effect Size||p. 435|
|Methods of Predictor Entry||p. 436|
|Template and APA-Style Write-Up||p. 459|
|What Is Next?||p. 461|
|Appendix: Tables||p. 467|
|Odd-Numbered Answers to Problems||p. 501|
|Author Index||p. 507|
|Subject Index||p. 511|
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