Berkman Elliot T. A Conceptual Guide to Statistics Using SPSS

SAGE Publications, Inc. 2012. — 312 p. — ISBN 1412974062.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.Preface
About the AuthorsGoal of This Book: Conceptual Understanding
Features That Will Help You in This Book
A Note on Data FilesDescriptive Statistics
Introduction to Descriptive Statistics
Computing Descriptive Statistics in SPSS
Interpreting the Output
A Closer Look: Eyeballing a Hypothesis Test
A Closer Look: Assessing for NormalityThe Chi-Squared Test for Contingency Tables
Introduction to the Chi-Squared Test
Computing the Chi-Squared Test in SPSS
A Closer Look: Fisher’s Exact Test
The Chi-Squared Test for Testing the Distribution of
One Categorical VariableCorrelation
Behind the Scenes: Conceptual Background of Correlation
Covariance Versus Correlation
Computing Correlation (and Covariance) in SPSS
Interpreting the Correlation Output
A Closer Look: Partial Correlations
Visualizing CorrelationsOne- and Two-Sample t-Tests
Conceptual Background of the t-Test
Behind the Scenes: The t-Ratio
Computing the One-Sample t-Test Using SPSS
Computing the Paired-Samples t-Test Using SPSSComputing the Independent-Samples t-Test Using SPSS
Connections: A Comparison of the Independent- and
Paired-Samples t-Tests
Visualizing the Results From the t-Test
A Closer Look: Testing the Assumptions Underlying the t-TestOne-Way ANOVA
Behind the Scenes: Conceptual Background of the Analysis of Variance (ANOVA)
Computing the One-Way ANOVA Using SPSS
Interpreting the ANOVA Output
A Closer Look: Custom Contrasts in One-Way ANOVA
Making the Most of Syntax: Custom Contrasts Using Syntax
Connections: On the Equivalence of One-Way ANOVA and t-Tests
Plotting the Results of the One-Way ANOVA
A Closer Look: Testing Assumptions in One-Way ANOVATwo- and Higher-Way ANOVA
Conceptual Background of the Higher-Order ANOVA
Behind the Scenes: Modeling Two- and Higher-Way ANOVA
With the GLM
Computing the Two-Way ANOVA Using SPSS
Interpreting the ANOVA Output
Making the Most of Syntax: Custom Contrasts in Two-Way ANOVA
A Closer Look: Multiple-Line Contrasts
Connections: Equivalence Between Main Effects Tests and Custom
Contrasts
Plotting the Results of the Two-Way ANOVAWithin-Subject ANOVA
Conceptual Background of the Within-Subjects ANOVA
Behind the Scenes: Modeling the Within-Subjects ANOVA
Computing the Within-Subjects ANOVA Using SPSS
Interpreting the ANOVA Output
Plotting the Results of Within-Subjects ANOVA
Making the Most of Syntax: Custom Contrasts in Within-Subjects ANOVA
Connections: Equivalence Between Within-Subjects ANOVA and
Paired-Samples t-TestsMixed-Model ANOVA
Conceptual Background of the Mixed-Model ANOVA
Computing the Mixed-Model ANOVA Using SPSS
A Closer Look: Testing the Assumptions of the Mixed-Model ANOVA
Interpreting the Output of the Mixed-Model ANOVA
Plotting the Results of the Mixed-Model ANOVA in SPSS
Making the Most of Syntax: Custom Contrast Tests in
Mixed-Model ANOVAMultivariate ANOVA
Conceptual Background of the Multivariate ANOVA
Computing MANOVA Using SPSS
Interpreting the SPSS Output
A Closer Look: Which Multivariate Test to Report?
Making the Most of Syntax: Testing Custom Contrasts in MANOVA
A Closer Look: When to Use MANOVA Versus Within-Subjects ANOVALinear Regression
Behind the Scenes: Conceptual Background of Linear Regression
Computing Linear Regression in SPSS
Interpreting the Linear Regression Output
Connections: Understanding the Meaning of Partial and Semipartial
Correlations
A Closer Look: Hierarchical Regression
A Closer Look: Testing Model AssumptionsAnalysis of Covariance
Conceptual Background of Analysis of Covariance (ANCOVA)
Computing ANCOVA in SPSS
Understanding the Output of ANCOVA
Visualizing Results of the ANCOVA
Making the Most of Syntax: Custom Hypothesis Testing in ANCOVA
A Closer Look: Evaluating the Assumptions of ANCOVAFactor and Components Analysis
Conceptual Background of the Factor Analysis
Background Issues
Computing the Factor Analysis in SPSS
Interpreting the SPSS Output of Factor Analysis
A Closer Look: Reporting the Results in a Research PaperPsychometrics
Conceptual Background of Psychometrics
Preliminary Psychometrics in SPSS
Computing Formal Psychometrics Analyses in SPSS
Interrater Reliability in SPSSNonparametric Tests
Conceptual Background of Nonparametric Tests
The Sign Test (for One-Sample Hypotheses)
The Wilcoxon Rank-Sum Test (for Independent Samples)
The Wilcoxon Signed-Rank Test (for Paired Samples)
Connections: A Comparison to the Paired-Samples t-Test
The Kruskal-Wallis Test (for Between-Subjects Comparisons)
Friedman’s Rank Test (for Within-Subject Comparisons)Matrix Algebra
Conceptual Background of Matrix Algebra
Overview of Matrix Algebra in SPSS
Making the Most of Syntax: Solving the General Linear Equation in SPSS
A Closer Look: Custom Hypothesis Testing Using Matrix AlgebraAppendix: Commented Syntax for One-Way ANOVA
Using Matrix Algebra in SPSSAppendix: General Formulation of
Custom Contrasts Using LMATRIXSubject Index
Syntax Index