Enhanced descriptions from Syndetics:

With an approach that does not require formal mathematics (equations are accompanied by verbal explanations), this textbook provides a clear introduction to widely used topics in multivariate statistics, including Multiple Regression, Discriminant Analysis, MANOVA, Factor Analysis, and Binary Logistic Regression. Each chapter presents a complete empirical research example to illustrate the application of a specific method, such as Multiple Regression. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

Table of contents provided by Syndetics

• Preface
• Acknowledgments
• Chapter 1 Review of Basic Concepts
• 1.1 Introduction
• 1.2 A Simple Example of a Research Problem
• 1.3 Discrepancies Between Real and Ideal Research Situations
• 1.4 Samples and Populations
• 1.5 Descriptive Versus Inferential Uses of Statistics
• 1.6 Levels of Measurement and Types of Variables
• 1.7 The Normal Distribution
• 1.8 Research Design
• 1.9 Parametric Versus Nonparametric Statistics
• 1.10 Additional Implicit Assumptions
• 1.11 Selection of an Appropriate Bivariate Analysis
• 1.12 Summary
• Comprehension Questions
• Chapter 2 Introduction to SPSS: Basic Statistics, Sampling Error, and Confidence Intervals
• 2.1 Introduction
• 2.2 Research Example: Description of a Sample of HR Scores
• 2.3 Sample Mean (M)
• 2.4 Sum of Squared Deviations and Sample Variance (s2)
• 2.5 Degrees of Freedom (df) for a Sample Variance
• 2.6 Why Is There Variance?
• 2.7 Sample Standard Deviation (s)
• 2.8 Assessment of Location of a Single X Score Relative to a Distribution of Scores
• 2.9 A Shift in Level of Analysis: The Distribution of Values of M Across Many Samples From the Same Population
• 2.10 An Index of Amount of Sampling Error: The Standard Error of the Mean (oM)
• 2.11 Effect of Sample Size (N) on the Magnitude of the Standard Error (oM )
• 2.12 Sample Estimate of the Standard Error of the Mean (SEM)
• 2.13 The Family of t Distributions
• 2.14 Confidence Intervals
• 2.15 Summary
• Appendix on SPSS
• Comprehension Questions
• Chapter 3 Statistical Significance Testing
• 3.1 The Logic of Null Hypothesis Significance Testing (NHST)
• 3.2 Type I Versus Type II Error
• 3.3 Formal NHST Procedures: The z Test for a Null Hypothesis About One Population Mean
• 3.4 Common Research Practices Inconsistent With Assumptions and Rules for NHST
• 3.5 Strategies to Limit Risk of Type I Error
• 3.6 Interpretation of Results
• 3.7 When Is a t Test Used Instead of a z Test?
• 3.8 Effect Size
• 3.9 Statistical Power Analysis
• 3.10 Numerical Results for a One-Sample t Test Obtained From SPSS
• 3.11 Guidelines for Reporting Results
• 3.12 Summary
• Comprehension Questions
• Chapter 4 Preliminary Data Screening
• 4.1 Introduction: Problems in Real Data
• 4.2 Quality Control During Data Collection
• 4.3 Example of an SPSS Data Worksheet
• 4.4 Identification of Errors and Inconsistencies
• 4.5 Missing Values
• 4.6 Empirical Example of Data Screening for Individual Variables
• 4.7 Identification and Handling of Outliers
• 4.8 Screening Data for Bivariate Analyses
• 4.9 Nonlinear Relations
• 4.10 Data Transformations
• 4.11 Verifying That Remedies Had the Desired Effects
• 4.12 Multivariate Data Screening
• 4.13 Reporting Preliminary Data Screening
• 4.14 Summary and Checklist for Data Screening
• Comprehension Questions
• Chapter 5 Comparing Group Means Using the Independent Samples t Test
• 5.1 Research Situations Where the Independent Samples t Test Is Used
• 5.2 A Hypothetical Research Example
• 5.3 Assumptions About the Distribution of Scores on the Quantitative Dependent Variable
• 5.4 Preliminary Data Screening
• 5.5 Issues in Designing a Study
• 5.6 Formulas for the Independent Samples t Test
• 5.7 Conceptual Basis: Factors That Affect the Size of the t Ratio
• 5.8 Effect Size Indexes for t
• 5.9 Statistical Power and Decisions About Sample Size for the Independent Samples t Test
• 5.10 Describing the Nature of the Outcome
• 5.11 SPSS Output and Model Results Section
• 5.12 Summary
• Comprehension Questions
• Chapter 6 One-Way Between-Subjects Analysis of Variance
• 6.1 Research Situations Where One-Way Between-Subjects Analysis of Variance (ANOVA) Is Used
• 6.2 Hypothetical Research Example
• 6.3 Assumptions About Scores on the Dependent Variable for One-Way Between-S ANOVA
• 6.4 Issues in Planning a Study
• 6.5 Data Screening
• 6.6 Partition of Scores Into Components
• 6.7 Computations for the One-Way Between-S ANOVA
• 6.8 Effect Size Index for One-Way Between-S ANOVA
• 6.9 Statistical Power Analysis for One-Way Between-S ANOVA
• 6.10 Nature of Differences Among Group Means
• 6.11 SPSS Output and Model Results
• 6.12 Summary
• Comprehension Questions
• Chapter 7 Bivariate Pearson Correlation
• 7.1 Research Situations Where Pearson r Is Used
• 7.2 Hypothetical Research Example
• 7.3 Assumptions for Pearson r
• 7.4 Preliminary Data Screening
• 7.5 Design Issues in Planning Correlation Research
• 7.6 Computation of Pearson r
• 7.7 Statistical Significance Tests for Pearson r
• 7.8 Setting Up CIs for Correlations
• 7.9 Factors That Influence the Magnitude and Sign of Pearson r
• 7.10 Pearson r and r2 as Effect Size Indexes
• 7.11 Statistical Power and Sample Size for Correlation Studies
• 7.12 Interpretation of Outcomes for Pearson r
• 7.13 SPSS Output and Model Results Write-Up
• 7.14 Summary
• Comprehension Questions
• Chapter 8 Alternative Correlation Coefficients
• 8.1 Correlations for Different Types of Variables
• 8.2 Two Research Examples
• 8.3 Correlations for Rank or Ordinal Scores
• 8.4 Correlations for True Dichotomies
• 8.5 Correlations for Artificially Dichotomized Variables
• 8.6 Assumptions and Data Screening for Dichotomous Variables
• 8.7 Analysis of Data: Dog Ownership and Survival After a Heart Attack
• 8.8 Chi-Square Test of Association (Computational Methods for Tables of Any Size)
• 8.9 Other Measures of Association for Contingency Tables
• 8.10 SPSS Output and Model Results Write-Up
• 8.11 Summary
• Comprehension Questions
• Chapter 9 Bivariate Regression
• 9.1 Research Situations Where Bivariate Regression Is Used
• 9.2 A Research Example: Prediction of Salary From Years of Job Experience
• 9.3 Assumptions and Data Screening
• 9.4 Issues in Planning a Bivariate Regression Study
• 9.5 Formulas for Bivariate Regression
• 9.6 Statistical Significance Tests for Bivariate Regression
• 9.7 Setting Up Confidence Intervals Around Regression Coefficients
• 9.8 Factors That Influence the Magnitude and Sign of b
• 9.9 Effect Size/Partition of Variance in Bivariate Regression
• 9.10 Statistical Power
• 9.11 Raw Score Versus Standard Score Versions of the Regression Equation
• 9.12 Removing the Influence of X From the Y Variable by Looking at Residuals From Bivariate Regression
• 9.13 Empirical Example Using SPSS
• 9.14 Summary
• Comprehension Questions
• Chapter 10 Adding a Third Variable: Preliminary Exploratory Analyses
• 10.1 Three-Variable Research Situations
• 10.2 First Research Example
• 10.3 Exploratory Statistical Analyses for Three-Variable Research Situations
• 10.4 Separate Analysis of X1, Y Relationship for Each Level of the Control Variable X2
• 10.5 Partial Correlation Between X1 and Y Controlling for X2
• 10.6 Understanding Partial Correlation as the Use of Bivariate Regression to Remove Variance Predictable by X2 From Both X1 and Y
• 10.7 Computation of Partial r From Bivariate Pearson Correlations
• 10.8 Intuitive Approach to Understanding Partial r
• 10.9 Significance Tests, Confidence Intervals, and Statistical Power for Partial Correlations
• 10.10 Interpretation of Various Outcomes for rY1.2 and rY1
• 10.11 Two-Variable Causal Models
• 10.12 Three-Variable Models: Some Possible Patterns of Association Among X1, Y, and X2
• 10.13 Mediation Versus Moderation
• 10.14 Model Results
• 10.15 Summary
• Comprehension Questions
• Chapter 11 Multiple Regression With Two Predictor Variables
• 11.1 Research Situations Involving Regression With Two Predictor Variables
• 11.2 Hypothetical Research Example
• 11.3 Graphic Representation of Regression Plane
• 11.4 Semipartial (or "Part") Correlation
• 11.5 Graphic Representation of Partition of Variance in Regression With Two Predictors
• 11.6 Assumptions for Regression With Two Predictors
• 11.7 Formulas for Regression Coefficients, Significance Tests, and Confidence Intervals
• 11.8 SPSS Regression Results
• 11.9 Conceptual Basis: Factors That Affect the Magnitude and Sign of B and b Coefficients in Multiple Regression With Two Predictors
• 11.10 Tracing Rules for Causal Model Path Diagrams
• 11.11 Comparison of Equations for B, b, pr, and sr
• 11.12 Nature of Predictive Relationships
• 11.13 Effect Size Information in Regression With Two Predictors
• 11.14 Statistical Power
• 11.15 Issues in Planning a Study
• 11.16 Use of Regression With Two Predictors to Test Mediated Causal Models
• 11.17 Results
• 11.18 Summary
• Comprehension Questions
• Chapter 12 Dummy Predictor Variables and Interaction Terms in Multiple Regression
• 12.1 Research Situations Where Dummy Predictor Variables Can Be Used
• 12.2 Empirical Example
• 12.3 Screening for Violations of Assumptions
• 12.4 Issues in Planning a Study
• 12.5 Parameter Estimates and Significance Tests for Regressions With Dummy Variables
• 12.6 Group Mean Comparisons Using One-Way Between-S ANOVA
• 12.7 Three Methods of Coding for Dummy Variables
• 12.8 Regression Models That Include Both Dummy and Quantitative Predictor Variables
• 12.9 Tests for Interaction (or Moderation)
• 12.10 Interaction Terms That Involve Two Quantitative Predictors
• 12.11 Effect Size and Statistical Power
• 12.12 Nature of the Relationship and/or Follow-Up Tests
• 12.13 Results
• 12.14 Summary
• Comprehension Questions
• Chapter 13 Factorial Analysis of Variance
• 13.1 Research Situations and Research Questions
• 13.2 Screening for Violations of Assumptions
• 13.3 Issues in Planning a Study
• 13.4 Empirical Example: Description of Hypothetical Data
• 13.5 Computations for Between-S Factorial ANOVA
• 13.6 Conceptual Basis: Factors That Affect the Size of Sums of Squares and F Ratios in Factorial ANOVA
• 13.7 Effect Size Estimates for Factorial ANOVA
• 13.8 Statistical Power
• 13.9 Nature of the Relationships, Follow-Up Tests, and Information to Include in the Results
• 13.10 Factorial ANOVA Using the SPSS GLM Procedure
• 13.11 Summary
• Appendix: Nonorthogonal Factorial ANOVA (ANOVA With Unbalanced Numbers of Cases in the Cells or Groups)
• Comprehension Questions
• Chapter 14 Multiple Regression With More Than Two Predictors
• 14.1 Research Questions
• 14.2 Empirical Example
• 14.3 Screening for Violations of Assumptions
• 14.4 Issues in Planning a Study
• 14.5 Computation of Regression Coefficients With k Predictor Variables
• 14.6 Methods of Entry for Predictor Variables
• 14.7 Variance Partitioning in Regression for Standard or Simultaneous Regression Versus Regressions That Involve a Series of Steps
• 14.8 Significance Test for an Overall Regression Model
• 14.9 Significance Tests for Individual Predictors in Multiple Regression
• 14.10 Effect Size
• 14.11 Changes in F and R as Additional Predictors Are Added to a Model in Sequential or Statistical Regression
• 14.12 Statistical Power
• 14.13 Nature of the Relationship Between Each X Predictor and Y (Controlling for Other Predictors)
• 14.14 Assessment of Multivariate Outliers in Regression
• 14.15 SPSS Example and Results
• 14.16 Summary
• Appendix 14.A A Review of Matrix Algebra Notation and Operations and Application of Matrix Algebra to Estimation of Slope Coefficients for Regression With More Than k Predictor Variables
• Appendix 14.B Tables for Wilkinson and Dallal (1981) Test of Significance of Multiple R2 in Method = Forward Statistical Regression
• Comprehension Questions
• Chapter 15 Analysis of Covariance
• 15.1 Research Situations and Research Questions
• 15.2 Empirical Example
• 15.3 Screening for Violations of Assumptions
• 15.4 Variance Partitioning in ANCOVA
• 15.5 Issues in Planning a Study
• 15.6 Formulas for ANCOVA
• 15.7 Computation of Adjusted Effects and Adjusted Y* Means
• 15.8 Conceptual Basis: Factors That Affect the Magnitude of SSAadj and SSresidual and the Pattern of Adjusted Group Means
• 15.9 Effect Size
• 15.10 Statistical Power
• 15.11 Nature of the Relationship and Follow-Up Tests: Information to Include in the Results Section
• 15.12 SPSS Analysis and Model Results
• 15.13 Additional Discussion of ANCOVA Results
• 15.14 Summary
• Appendix: Alternative Methods for the Analysis of Pretest/Posttest Data
• Comprehension Questions
• Chapter 16 Discriminant Analysis
• 16.1 Research Situations and Research Questions
• 16.2 Introduction of an Empirical Example
• 16.3 Screening for Violations of Assumptions
• 16.4 Issues in Planning a Study
• 16.5 Equations for Discriminant Analysis
• 16.6 Conceptual Basis: Factors That Affect the Magnitude of Wilks's Lambda
• 16.7 Effect Size
• 16.8 Statistical Power and Sample Size Recommendations
• 16.9 Follow-Up Tests to Assess What Pattern of Scores Best Differentiates Groups
• 16.10 Results
• 16.11 One-Way ANOVA on Scores on Discriminant Functions
• 16.12 Summary
• Appendix: Eigenvalue/Eigenvector Problem
• Comprehension Questions
• Chapter 17 Multivariate Analysis of Variance
• 17.1 Research Situations and Research Questions
• 17.2 Introduction of the Initial Research Example: A One-Way MANOVA
• 17.3 Why Include Multiple Outcome Measures?
• 17.4 Equivalence of MANOVA and DA
• 17.5 The General Linear Model
• 17.6 Assumptions and Data Screening
• 17.7 Issues in Planning a Study
• 17.8 Conceptual Basis of MANOVA and Some Formulas for MANOVA
• 17.9 Multivariate Test Statistics
• 17.10 Factors That Influence the Magnitude of Wilks's Lambda
• 17.11 Effect Size for MANOVA
• 17.12 Statistical Power and Sample Size Decisions
• 17.13 SPSS Output for a One-Way MANOVA: Career Group Data From Chapter 16
• 17.14 A 2 x 3 Factorial MANOVA of the Career Group Data
• 17.15 A Significant Interaction in a 3 x 6 MANOVA
• 17.16 Comparison of Univariate and Multivariate Follow-Up Analyses for MANOVA
• 17.17 Summary
• Comprehension Questions
• Chapter 18 Principal Components and Factor Analysis
• 18.1 Research Situations
• 18.2 Path Model for Factor Analysis
• 18.3 Factor Analysis as a Method of Data Reduction
• 18.4 Introduction of an Empirical Example
• 18.5 Screening for Violations of Assumptions
• 18.6 Issues in Planning a Factor Analytic Study
• 18.7 Computation of Loadings
• 18.8 Steps in the Computation of Principal Components or Factor Analysis
• 18.9 Analysis 1: Principal Components Analysis of Three Items Retaining All Three Components
• 18.10 Analysis 2: Principal Component Analysis of Three Items Retaining Only the First Component
• 18.11 Principal Components Versus Principal Axis Factoring
• 18.12 Analysis 3: PAF of Nine Items, Two Factors Retained, No Rotation
• 18.13 Geometric Representation of Correlations Between Variables and Correlations Between Components or Factors
• 18.14 The Two Multiple Regressions
• 18.15 Analysis 4: PAF With Varimax Rotation
• 18.16 Questions to Address in the Interpretation of Factor Analysis
• 18.17 Results Section for Analysis 4: PAF With Varimax Rotation
• 18.18 Factor Scores Versus Unit-Weighted Composites
• 18.19 Summary of Issues in Factor Analysis
• 18.20 Optional: Brief Introduction to Concepts in Structural Equation Modeling
• Appendix: The Matrix Algebra of Factor Analysis
• Comprehension Questions
• Chapter 19 Reliability, Validity, and Multiple-Item Scales
• 19.1 Assessment of Measurement Quality
• 19.2 Cost and Invasiveness of Measurements
• 19.3 Empirical Examples of Reliability Assessment
• 19.4 Concepts From Classical Measurement Theory
• 19.5 Use of Multiple-Item Measures to Improve Measurement Reliability
• 19.6 Three Methods for the Computation of Summated Scales
• 19.7 Assessment of Internal Homogeneity for Multiple-Item Measures
• 19.8 Correlations Among Scores Obtained Using Different Methods of Summing Items
• 19.9 Validity Assessment
• 19.10 Typical Scale Development Study
• 19.11 Summary
• Appendix: The CESD Scale
• Comprehension Questions
• Chapter 20 Analysis of Repeated Measures
• 20.1 Introduction
• 20.2 Empirical Example: Experiment to Assess Effect of Stress on Heart Rate
• 20.3 Discussion of Sources of Within-Group Error in Between-S Versus Within-S Data
• 20.4 The Conceptual Basis for the Paired Samples t Test and One-Way Repeated Measures ANOVA
• 20.5 Computation of a Paired Samples t Test to Compare Mean HR Between Baseline and Pain Conditions
• 20.6 SPSS Example: Analysis of Stress/HR Data Using a Paired Samples t Test
• 20.7 Comparison Between Independent Samples t Test and Paired Samples t Test
• 20.8 SPSS Example: Analysis of Stress/HR Data Using a Univariate One-Way Repeated Measures ANOVA
• 20.9 Using the SPSS GLM Procedure for Repeated Measures ANOVA
• 20.10 Screening for Violations of Assumptions in Univariate Repeated Measures
• 20.11 The Greenhouse-Geisser e and Huynh Feldt e Correction Factors
• 20.12 MANOVA Approach to Analysis of Repeated Measures Data
• 20.13 Effect Size
• 20.14 Statistical Power
• 20.15 Planned Contrasts
• 20.16 Results
• 20.17 Design Problems in Repeated Measures Studies
• 20.18 More Complex Designs
• 20.19 Alternative Analyses for Pretest and Posttest Scores
• 20.20 Summary
• Comprehension Questions
• Chapter 21 Binary Logistic Regression
• 21.1 Research Situations
• 21.2 Simple Empirical Example: Dog Ownership and Odds of Death
• 21.3 Conceptual Basis for Binary Logistic Regression Analysis
• 21.4 Definition and Interpretation of Odds
• 21.5 A New Type of Dependent Variable: The Logit
• 21.6 Terms Involved in Binary Logistic Regression Analysis
• 21.7 Analysis of Data for First Empirical Example: Dog Ownership/Death Study
• 21.8 Issues in Planning and Conducting a Study
• 21.9 More Complex Models
• 21.10 Binary Logistic Regression for Second Empirical Analysis: Drug Dose and Gender as Predictors of Odds of Death
• 21.11 Comparison of Discriminant Analysis to Binary Logistic Regression
• 21.12 Summary
• Comprehension Questions
• Appendix A Proportions of Area Under Standard Normal Curve
• Appendix B Critical Values for t Distribution
• Appendix C Critical Values of F
• Appendix D Critical Values of Chi-Square
• Appendix E Critical Values of the Correlation Coefficient
• Appendix F Critical Values of the Studentized Range Statistic
• Appendix G Transformation of r (Pearson Correlation) to Fisher Z
• Glossary
• References
• Index
• About the Author