Foundations of Behavioral Statistics: An Insight-Based Approach

With humor, extraordinary clarity, and carefully paced explanations and examples, Bruce Thompson shows readers how to use the latest techniques for interpreting research outcomes as well as how to make statistical decisions that result in better research. Utilizing the general linear model to demonstrate how different statistical methods are related to each other, Thompson integrates a broad array of methods involving only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA, multiple regression, loglinear analysis and logistic regression. Special features include SPSS and Excel demonstrations that offer opportunities, in the book’s datasets and on Thompson’s website, for further exploration of statistical dynamics.

Table of Contents:
Part I Introductory Terms and Concepts. Definitions of Some Basic Terms. Levels of Scale. Some Experimental Design Considerations. Some Key Concepts. Reflection Problems. Part II Location. Reasonable Expectations for Statistics. Location Concepts. Three Classical Location Descriptive Statistics. Four Criteria for Evaluating Statistics. Two Robust Location Statistics. Some Key Concepts. Reflection Problems. Part III Dispersion.Quality of Location Descriptive Statistics. Important in Its Own Right. Measures of Score Spread. Variance. Situation-Specific Maximum Dispersion. Robust Dispersion Descriptive Statistics. Standardized Score World. Some Key Concepts. Reflection Problems. Part IV Shape. Two Shape Descriptive Statistics. Normal Distributions. Two Additional Univariate Graphics. Some Key Concepts. Reflection Problems. Part V Bivariate Relationships. Pearson's r. Three Features of r. Three Interpretation Contextual Factors. Psychometrics of the Pearson r. Spearman's rho. Two Other r -Equivalent Correlation Coefficients. Bivariate Normality. Some Key Concepts. Reflection Problems. Part VI Statistical Significance. Sampling Distributions. Hypothesis Testing. Properties of Sampling Distributions. Standard Error/Sampling Error. Test Statistics. Statistical Precision and Power. pCALCULATED. Some Key Concepts. Reflection Problems. Part VII Practical Significance. Effect Sizes. Confidence Intervals. Confidence Intervals for Effect Sizes. Some Key Concepts. Reflection Problems. Part VIII Multiple Regression Analysis: Basic GLM Concepts. Purposes of Regression. Simple Linear Prediction. Case #1: Perfectly Uncorrelated Predictors. Case #2: Correlated Predictors, No Suppressor. Effects. Case #3: Correlated Predictors, Suppressor. Effects Present. b Weights versus Structure Coefficients. A Final Comment on Collinearity. Some Key Concepts. Reflection Problems. Part IX A GLM Interpretation Rubric. Do I Have Anything?Where Does My Something Originate? Stepwise Methods. Invoking Some Alternative Models. Some Key Concepts. Reflection Problems. Part X One-way Analysis of Variance (ANOVA). Experimentwise Type I Error. ANOVA Terminology. The Logic of Analysis of Variance. Practical and Statistical Significance. The "Homogeneity of Variance" Assumption. Post Hoc Tests. Some Key Concepts. Reflection Problems. Part XI Multiway and Alternative ANOVA Models. Multiway Models. Factorial versus Nonfactorial Analyses. Fixed-, Random-, and Mixed-Effects Models. Brief Comment on ANCOVA. Some Key Concepts. Reflection Problems. Part XII The General Linear Model (GLM): ANOVA via Regression. Planned Contrasts. Trend/Polynomial Planned Contrasts. Repeated Measures ANOVA via Regression. GLM Lessons. Some Key Concepts. Reflection Problems. Part XIII Some Logistic Models: Model Fitting in a Logistic Context. Logistic Regression. Loglinear Analysis. Some Key Concepts. Reflection Problems. Appendix: Scores (n = 100) with Near Normal Distributions.

About the Author :
Bruce Thompson is Distinguished Professor of Educational Psychology and Distinguished Professor of Library Sciences, Texas A&M University, and Adjunct Professor of Family and Community Medicine, Baylor College of Medicine, Houston. He is the coeditor of the teaching, learning, and human development section of the American Educational Research Journal and past editor of Educational and Psychological Measurement, the series Advances in Social Science Methodology, and two other journals. He is the author or editor of 10 books, has written over 175 research articles, and has made contributions that have been influential in promoting greater emphasis on effect size reporting and interpretation, and improved understanding of score reliability.

Review :
"I found the book very useful as an instructor, and the students really enjoyed the straightforward approach to explaining statistical methods. The accessible style made it easy for students to grasp and apply statistical concepts." - Tammy Kolbe, Department of Education Policy and Leadership, University of Maryland--College Park, USA "This is a very useful book that graduate students should read to help them understand and use their statistical tools. And not just grad students could stand to read it-some of the issues raised, such as statistical significance and size effects, plague the vast majority of social research. As editor of Social Problems, I very frequently came across sophisticated papers that simply reported the statistical significance of findings without saying a word about the magnitude of the effect purportedly being examined, or the importance or impact of the phenomenon under discussion." - "James A. Holstein, Department of Social and Cultural Sciences, Marquette University, USA "There are a lot of quite simple introductory statistics texts out there ('too cold!'), and a large number of really comprehensive ones, perhaps best digested over two semesters ('too hot!'). However, I found Thompson's treatment and coverage to be 'just right.' I especially appreciated his conservatism in using and interpreting statistics." - "Bruce Thyer, College of Social Work, Florida State University, USA "An important contribution to improving data analysis and interpretation methods in the social sciences...The Statistical Significance chapter is excellent and one of the crown jewels of this book. This is the kind of understanding of significance testing that we should seek to impart to students and researchers." - "Frank L. Schmidt, College of Business, University of Iowa, USA "This is an outstanding text that represents a new era in the learning and reporting of statistics in the behavioral sciences. Thompson focuses on analytic thinking rather than mathematical number-crunching, unlike others that emphasize calculations at the expense of critical thinking...This book is at the leading edge of methodological advances regarding the interpretation of research outcomes, and will serve a critical role in the continued evolution of the behavioral statistics field." - "Robin K. Henson, Department of Technology and Cognition, University of North Texas, USA