An Introduction to Statistical Concepts
Published March 1st 2012 by Routledge – 838 pages
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.
"[This text] would provide an accessible first introduction to statistics to psychologists. … It covers the basic topics needed at this level and so would give a broad grounding. … Teachers … should certainly examine a copy." - 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 introductory 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. Not only is this book ideal for introductory statistics classes, it is clear and comprehensive enough to allow students to teach themselves statistics." – 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 introductory 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
"Practical throughout, this edition delivers a nice balance between technical detail and understandable explanations of basic statistical methods appropriate for graduate students in the social sciences. The book recognizes the importance of effect sizes, offers screen shots of how to execute a statistical analysis in SPSS with annotated output, provides graphs of sampling distributions to help users make correct decisions regarding their own research, and features APA guidelines with sample write-ups." - Jeffrey R. Harring, University of Maryland, USA
"This book is a refreshing treatment of introductory statistical concepts. 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
"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.
Richard G. Lomax is a Professor in the School of Educational Policy and Leadership at The Ohio State University. He received his Ph.D. in Educational Research Methodology from the University of Pittsburgh. His research focuses on models of literacy acquisition, multivariate statistics, and assessment. He has twice served as a Fulbright Scholar and is a Fellow of the American Educational Research Association.
Debbie L. Hahs-Vaughn is an Associate Professor in the College of Education at the University of Central Florida. She received her Ph.D. in Educational Research from the University of Alabama. Her research focuses on methodological and substantive research using complex survey data, program evaluation, and practitioner use of research to inform their practice. Dr. Hahs-Vaughn was the recipient of the 2007 College of Education Excellence in Graduate Teaching Award, 2009 College of Education Distinguished Researcher Award, 2009 Teaching Incentive Program Award, and 2009 Research Incentive Award. She is currently the Executive Editor of the Measurement, Statistics, and Research Design section of the Journal of Experimental Education.