A Conceptual Guide to Statistics Using SPSS

A Conceptual Guide to Statistics Using SPSS by Elliot T. Berkman, published by SAGE in 2012, is a comprehensive resource designed to help students grasp the underlying concepts of various statistical tests while utilizing SPSS software. This 296-page book is presented in English and focuses on linking statistical ideas with computational steps and outputs, enhancing the reader’s understanding of both statistics and SPSS.
Readers will find that each chapter begins with accessible explanations of statistical concepts, followed by detailed guidance on executing these tests in SPSS. The book emphasizes the connection between theoretical concepts and practical application, featuring annotated screenshots and highlighted output components to aid comprehension. Topics covered include custom hypothesis testing in ANOVA, MANOVA, ANCOVA, and regression, along with advanced matrix algebra functions available through SPSS syntax. This guide is suitable for advanced undergraduate and graduate-level courses in statistics, making it a valuable tool for students in psychology, social science, and research methodology.
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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.
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