IBM SPSS for Intermediate Statistics

Use and Interpretation, Fourth Edition

By Nancy L. Leech, Karen C. Barrett, George A. Morgan

Routledge – 2011 – 312 pages

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    May 12th 2011


Designed to help readers analyze and interpret research data using IBM SPSS, this user-friendly book shows readers how to choose the appropriate statistic based on the design, perform intermediate statistics, including multivariate statistics, interpret output, and write about the results. The book reviews research designs and how to assess the accuracy and reliability of data: whether data meet the assumptions of statistical tests; how to calculate and interpret effect sizes for intermediate statistics, including odds ratios for logistic and discriminant analyses; how to compute and interpret post-hoc power; and an overview of basic statistics for those who need a review. Unique chapters on multilevel linear modeling, multivariate analysis of variance (MANOVA), assessing reliability of data, and factor analysis are provided. SPSS syntax, along with the output, is included for those who prefer this format.

The new edition features:

  • IBM SPSS version 19; although the book can be used with most older and newer versions
  • expanded discussion of assumptions and effect size measures in several chapters
  • expanded discussion of multilevel modeling
  • expansion of other useful SPSS functions in Appendix A
  • examples that meet the new formatting guidelines in the 6th edition of the APA Publication Manual (2010)
  • flowcharts and tables to help select the appropriate statistic and interpret statistical significance and effect sizes
  • multiple realistic data sets available on the website used to solve the chapter problems
  • password protected Instructor's Resource materials with PowerPoint slides, answers to interpretation questions and extra SPSS problems, and chapter outlines and study guides.

IBM SPSS for Intermediate Statistics, Fourth Edition provides helpful teaching tools:

  • all of the key SPSS windows needed to perform the analyses
  • outputs with call-out boxes to highlight key points
  • interpretation sections and questions to help students better understand and interpret the output
  • extra problems using multiple realistic data sets for practice in conducting analyses using intermediate statistics
  • helpful appendices on how to get started with SPSS, writing research questions, and review of basic statistics.

An ideal supplement for courses in either intermediate/advanced statistics or research methods taught in departments of psychology, education, and other social and health sciences, this book is also appreciated by researchers in these areas looking for a handy reference for SPSS.


"This book's clearly written content and illustrative examples will help students to develop the skills necessary to conduct statistical analyses and to interpret results, thereby making this an essential resource for intermediate statistics courses." - Kathleen M. T. Collins, University of Arkansas, USA

"This user-friendly textbook takes a holistic approach to statistical analyses, from exploratory data analysis to hierarchical linear modeling, by guiding readers from research question, to data entry, to data analysis, to interpreting outputs, and to writing results—brilliant!" - Tony Onwuegbuzie, Sam Houston State University, USA

"This book is a highly accessible asset to the applied researcher. It moves beyond compartmentalized sequences to run analyses and presents "big picture" considerations, situating analyses within research design and measurement, and then modeling the all-important step of writing up results." - Robin K. Henson, University of North Texas, USA

"The book presents a reasonable balance between theory and application moving from topical material to research situations and solution problems; exactly what students need. SPSS commands and related visuals enhance the book usability and make it a valued resource in teaching and learning statistics." - Nataliya V. Ivankova, University of Alabama at Birmingham, USA


1. Introduction: Measurement and Descriptive Statistics 2. Data Coding and Exploratory Analysis (EDA) 3. Several Measures of Reliability 4. Exploratory Factor Analysis and Principal Components Analysis 5. Selecting and Interpreting Inferential Statistics 6. Multiple Regression 7. Logistic Regression and Discriminant Analysis 8. Factorial ANOVA and ANCOVA 9. Repeated-Measures and Mixed ANOVAs 10. Multivariate Analysis of Variance (MANOVA) and Canonical Correlation 11. Multilevel Linear Modeling/Hierarchical Linear Modeling Appendices: Appendix A: Getting Started with SPSS and Other Useful Procedures. Appendix B: Review of Basic Statistics. Appendix C: Review Answers to Odd Interpretation Questions

Author Bio

Nancy Leech is an Associate Professor at the University of Colorado Denver. She received her Ph.D. in education with an emphasis on research and statistics from Colorado State University in 2002. Dr. Leech is currently teaching graduate level courses in research, statistics, and measurement. Her area of research is promoting new developments and better understandings in applied qualitative, quantitative, and mixed methodologies.

Karen C. Barrett is Professor of Human Development and Family Studies with a joint appointment in Psychology at Colorado State University. She received her Ph.D. from the University of Denver. She is Assistant Department head of HDFS and teaches graduate level research methods and statistics courses. Her research regards emotion regulation and its influence on development; social emotions such as guilt and shame; and family and cultural influences on emotions.

George A. Morgan is Emeritus Professor of Education and Human Development at Colorado State University. He received his Ph.D. in child development and psychology from Cornell University. In addition to writing textbooks on SPSS and research methods, he has advised many PhD students in education and related fields. Over the past 35 years, he has conducted a program of research on children’s motivation to master challenging tasks.

Data Sets

Sample Chapters