This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors 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 occasionally 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:
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' boxes provide 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 given in the book.
The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed.
Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required.
"Lomax and Hahs-Vaughn write clearly. … Writing statistical results in APA format is great for graduate students. … The … changes … make the book a better teaching tool. … The level is appropriate for graduate and doctoral students in psychology, sociology, and education. … The basic terms and concepts are defined and developed clearly, accurately, and in an interesting manner." - Robert P. Conti, Sr., Mount Saint Mary College, USA
1. Introduction. 2. Data Representation. 3. Univariate Population Parameters and Sample Statistics. 4. The Normal Distribution and Standard Scores. 5. Introduction to Probability and Sample Statistics. 6. Introduction to Hypothesis Testing: Inferences About a Single Mean. 7. Inferences About the Difference Between Two Means. 8. Inferences About Proportions. 9. Inferences About Variances. 10. Bivariate Measures of Association. 11. One-Factor Analysis of Variance - Fixed-Effects Model. 12. Multiple Comparison Procedures. 13. Factorial Analysis of Variance - Fixed-Effects Model. 14. Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model With a Single Covariate. 15. Random- and Mixed-Effects Analysis of Variance Models. 16. Hierarchical and Randomized Block Analysis of Variance Models. 17. Simple Linear Regression. 18. Multiple Regression. 19. Logistic Regression. Appendix Tables.