Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts, 3rd ed. Designed for the second course in statistics, it is one of the few texts that focuses just on intermediate statistics. The book highlights how statistics work and what they mean to better prepare students to analyze their own data and interpret SPSS and research results. As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data. Determining appropriate sample sizes is emphasized throughout. Only crucial equations are included.
The new edition features:
Each chapter begins with an outline, a list of key concepts, and a research vignette related to the concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides tips for how to run SPSS and develop an APA style write-up. Tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter includes computational, conceptual, and interpretive problems. Answers to the odd-numbered problems are provided. The SPSS data sets that correspond to the book’s examples and problems are available on the web.
The book covers basic and advanced analysis of variance models and topics not dealt with in other texts such as robust methods, multiple comparison and non-parametric procedures, and multiple and logistic regression models. 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. Knowledge of introductory statistics is assumed.
"Useful … to psychologists beginning to find their feet in the world of research." - David J. Hand, Imperial College, UK, in the International Statistical Review
"I have been using this text to teach statistics to beginning and intermediate-level graduate students in education for years and have been extremely impressed with its readability and emphasis on conceptual understanding. I can’t wait to introduce my students to statistics with this new edition, as it addresses key concepts while also providing real-life examples that will aid them in learning to reason with statistics." --H. Michael Crowson, The University of Oklahoma, USA
"This is one of the most complete and lucid … statistics textbooks available for education and the social sciences: it blends theory and pragmatics seamlessly. The integration of SPSS into the chapters is a welcome addition. Each chapter describes what to do, why to do it, and how to do it." – Betsy McCoach, University of Connecticut, USA
"The unique blend of conceptual approaches to statistical learning, interpretative exercises, and APA styled write-ups sets this book apart from other texts. The broad coverage of statistical procedures, SPSS generation and interpretation, and emphasis on statistical assumptions will serve students and researchers." - C.Y. Joanne Peng, Indiana University, USA
"Combining theory and mathematical accessibility with examples, SPSS applications, and APA style write-ups, this is a fascinating book for … statistical courses in the social and behavioral sciences. It has a broad coverage of topics and should prove invaluable as a classroom text or as a reference for applied researchers. " - Feifei Ye, University of Pittsburgh, USA
"The clear content, easy-to-follow software examples, and comprehensive coverage of topics will be extremely useful for researchers in the early stages of their statistical development." - Brian F. French, USA Washington State University
"Writing statistical results in APA format is great for graduate students. … The … changes … make the book a better teaching tool. … 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. One-Factor Analysis of Variance - Fixed-Effects Model. 2. Multiple Comparison Procedures. 3. Factorial Analysis of Variance - Fixed-Effects Model. 4. Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model With a Single Covariate. 5. Random- and Mixed-Effects Analysis of Variance Models. 6. Hierarchical and Randomized Block Analysis of Variance Models. 7. Simple Linear Regression. 8. Multiple Regression. 9. Logistic Regression. Appendix Tables.