This book helps students develop a conceptual understanding of a variety of statistical tests by linking the statistics with the computational steps and output from SPSS. Learning how statistical ideas map onto computation in SPSS will help students build a better understanding of both. For example, seeing exactly how the concept of variance is used in SPSS-how it is converted into a number based on real data, which other concepts it is associated with, and where it appears in various statistical tests-will not only help students understand how to use statistical tests in SPSS and how to interpret their output, but will also teach them about the concept of variance itself. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, pointing out wherever possible how the SPSS procedure and output connects back to the conceptual underpinnings of the test. Each of the steps is accompanied by annotated screen shots from SPSS, and relevant components of output are highlighted in both the text and in the figures.
Sections explain the conceptual machinery underlying the statistical tests. In contrast to merely presenting the equations for computing the statistic, these sections describe the idea behind each test in plain language and help students make the connection between the ideas and SPSS procedures. These include extensive treatment of custom hypothesis testing in ANOVA, MANOVA, ANCOVA, and regression, and an entire chapter on the advanced matrix algebra functions available only through syntax in SPSS. The book will be appropriate for both advanced undergraduate and graduate level courses in statistics.
Elliot T. Berkman is Assistant Professor of Psychology and director of the Social and Affective Neuroscience Laboratory at the University of Oregon. He has been teaching statistics to graduate students using SPSS for the past six years. In that time, he has been awarded the UCLA Distinguished Teaching Award and the Arthur J. Woodward Peer Mentoring Award. He has published numerous papers on the social psychological and neural processes involved in goal pursuit. His research on smoking cessation was recognized with the Joseph A. Gengerelli Distinguished Dissertation Award. He received his PhD in 2010 from the University of California, Los Angeles. Steve P. Reise is professor, chair of Quantitative Psychology, and co-director of the Advanced Quantitative Methods training program at University of California, Los Angeles. Dr. Reise is an internationally renowned teacher in quantitative methods; in particular, the application of item response theory models to personality, psychopathology, and patient reported outcomes. In recognition of his dedication to teaching, Dr. Reise was named "Professor of the Year" in 1995-96 by the graduate students in the psychology department at UC Riverside, and was awarded the 2008 Psychology Department Distinguished teaching award. Most recently, in recognition of his campus-wide and global contributions, Dr. Reise was awarded the University of California campus-wide distinguished teaching award. Dr. Reise has spent the majority of the last twenty years investigating the application of latent variable models in general and item response theory (IRT) models in particular to personality, psychopathology and health outcomes data. In 1998, Dr. Reise was recognized for his work and received the Raymond B. Cattell award for outstanding multivariate experimental psychologist. Along with Dr. Susan Embretson, Dr. Reise has the leading textbook on item response theory called "Item Response Theory for Psychologists" (2000 and forthcoming). He received his Ph.D. from the Department of Psychology at the University of Minnesota.
1. Introduction 2. Descriptive Statistics 3. Chi-Squared Test 4. Linear Correlation 5. One- and Two Sample T-Tests 6. One-way ANOVA 7. Two- and Higher-way ANOVA 8. Within-subject ANOVA 9. Mixed-model ANOVA 10. MANOVA 11. Regression 12. ANCOVA 13. Factor and Components Analysis 14. Psychometrics 15. Non-parametric Tests 16. Matrix Algebra 17. Appendix on the General Formulation of Custom Contrasts using Syntax
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