What do you think?
Rate this book
Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM's SPSS
Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally.
Features new to this edition include:
NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure
NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings
NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles
NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data
Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results
NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach
Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3)
A free online resources site at Routledge /9780415836661 with data sets and syntax from the text, additional data sets, and instructor's resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises).
Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.
Features new to this edition include:
NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure
NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings
NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles
NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data
Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results
NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach
Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3)
A free online resources site at Routledge /9780415836661 with data sets and syntax from the text, additional data sets, and instructor's resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises).
Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.
814 pages, Paperback
First published October 29, 2015
4 people are currently reading
8 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
April 10, 2020
2018.05.31–2018.06.11
Contents
Pituch KA & Stevens JP (2015) Applied Multivariate Statistics for the Social Sciences - Analyses with SAS and IBM’s SPSS, Sixth Edition
Preface
01. Introduction
• 1.01 Introduction
• 1.02 Type I Error, Type II Error, and Power
• 1.03 Multiple Statistical Tests and the Probability of Spurious Results
• 1.04 Statistical Significance Versus Practical Importance
• 1.05 Outliers
• 1.06 Missing Data
• 1.07 Unit or Participant Nonresponse
• 1.08 Research Examples for Some Analyses Considered in This Text
• 1.09 The SAS and SPSS Statistical Packages
• 1.10 SAS and SPSS Syntax
• 1.11 SAS and SPSS Syntax and Data Sets on the Internet
• 1.12 Some Issues Unique to Multivariate Analysis
• 1.13 Data Collection and Integrity
• 1.14 Internal and External Validity
• 1.15 Conflict of Interest
• 1.16 Summary
• 1.17 Exercises
02. Matrix Algebra
• 2.1 Introduction
• 2.2 Addition, Subtraction, and Multiplication of a Matrix by a Scalar
• 2.3 Obtaining the Matrix of Variances and Covariances
• 2.4 Determinant of a Matrix
• 2.5 Inverse of a Matrix
• 2.6 SPSS Matrix Procedure
• 2.7 SAS IML Procedure
• 2.8 Summary
• 2.9 Exercises
03. Multiple Regression for Prediction
• 3.01 Introduction
• 3.02 Simple Regression
• 3.03 Multiple Regression for Two Predictors: Matrix Formulation
• 3.04 Mathematical Maximization Nature of Least Squares Regression
• 3.05 Breakdown of Sum of Squares and F Test for Multiple Correlation
• 3.06 Relationship of Simple Correlations to Multiple Correlation
• 3.07 Multicollinearity
• 3.08 Model Selection
• 3.09 Two Computer Examples
• 3.10 Checking Assumptions for the Regression Model
• 3.11 Model Validation
• 3.12 Importance of the Order of the Predictors
• 3.13 Other Important Issues
• 3.14 Outliers and Influential Data Points
• 3.15 Further Discussion of the Two Computer Examples
• 3.16 Sample Size Determination for a Reliable Prediction Equation
• 3.17 Other Types of Regression Analysis
• 3.18 Multivariate Regression
• 3.19 Summary
• 3.20 Exercises
04. Two-Group Multivariate Analysis of Variance
• 4.01 Introduction
• 4.02 Four Statistical Reasons for Preferring a Multivariate Analysis
• 4.03 The Multivariate Test Statistic as a Generalization of the Univariate t Test
• 4.04 Numerical Calculations for a Two-Group Problem
• 4.05 Three Post Hoc Procedures
• 4.06 SAS and SPSS Control Lines for Sample Problem and Selected Output
• 4.07 Multivariate Significance but No Univariate Significance
• 4.08 Multivariate Regression Analysis for the Sample Problem
• 4.09 Power Analysis
• 4.10 Ways of Improving Power
• 4.11 A Priori Power Estimation for a Two-Group MANOVA
• 4.12 Summary
• 4.13 Exercises
05. K-Group MANOVA: A Priori and Post Hoc Procedures
• 5.01 Introduction
• 5.02 Multivariate Regression Analysis for a Sample Problem
• 5.03 Traditional Multivariate Analysis of Variance
• 5.04 Multivariate Analysis of Variance for Sample Data
• 5.05 Post Hoc Procedures
• 5.06 The Tukey Procedure
• 5.07 Planned Comparisons
• 5.08 Test Statistics for Planned Comparisons
• 5.09 Multivariate Planned Comparisons on SPSS MANOVA
• 5.10 Correlated Contrasts
• 5.11 Studies Using Multivariate Planned Comparisons
• 5.12 Other Multivariate Test Statistics
• 5.13 How Many Dependent Variables for a MANOVA?
• 5.14 Power Analysis—A Priori Determination of Sample Size
• 5.15 Summary
• 5.16 Exercises
06. Assumptions in MANOVA
• 6.01 Introduction
• 6.02 ANOVA and MANOVA Assumptions
• 6.03 Independence Assumption
• 6.04 What Should Be Done With Correlated Observations?
• 6.05 Normality Assumption
• 6.06 Multivariate Normality
• 6.07 Assessing the Normality Assumption
• 6.08 Homogeneity of Variance Assumption
• 6.09 Homogeneity of the Covariance Matrices
• 6.10 Summary
• 6.11 Complete Three-Group MANOVA Example
• 6.12 Example Results Section for One-Way MANOVA
• 6.13 Analysis Summary
• Appendix 6.1 Analyzing Correlated Observations
• Appendix 6.2 Multivariate Test Statistics for Unequal Covariance Matrices
• 6.14 Exercises
07. Factorial ANOVA and MANOVA
• 7.01 Introduction
• 7.02 Advantages of a Two-Way Design
• 7.03 Univariate Factorial Analysis
• 7.04 Factorial Multivariate Analysis of Variance
• 7.05 Weighting of the Cell Means
• 7.06 Analysis Procedures for Two-Way MANOVA
• 7.07 Factorial MANOVA With SeniorWISE Data
• 7.08 Example Results Section for Factorial MANOVA With SeniorWise Data
• 7.09 Three-Way MANOVA
• 7.10 Factorial Descriptive Discriminant Analysis
• 7.11 Summary
• 7.12 Exercises
08. Analysis of Covariance
• 8.01 Introduction
• 8.02 Purposes of ANCOVA
• 8.03 Adjustment of Posttest Means and Reduction of Error Variance
• 8.04 Choice of Covariates
• 8.05 Assumptions in Analysis of Covariance
• 8.06 Use of ANCOVA With Intact Groups
• 8.07 Alternative Analyses for Pretest–Posttest Designs
• 8.08 Error Reduction and Adjustment of Posttest Means for Several Covariates
• 8.09 MANCOVA—Several Dependent Variables and Several Covariates
• 8.10 Testing the Assumption of Homogeneous Hyperplanes on SPSS
• 8.11 Effect Size Measures for Group Comparisons in MANCOVA/ANCOVA
• 8.12 Two Computer Examples
• 8.13 Note on Post Hoc Procedures
• 8.14 Note on the Use of MVMM
• 8.15 Example Results Section for MANCOVA
• 8.16 Summary
• 8.17 Analysis Summary
• 8.18 Exercises
09. Exploratory Factor Analysis
• 9.01 Introduction
• 9.02 The Principal Components Method
• 9.03 Criteria for Determining How Many Factors to Retain Using Principal Components Extraction
• 9.04 Increasing Interpretability of Factors by Rotation
• 9.05 What Coefficients Should Be Used for Interpretation?
• 9.06 Sample Size and Reliable Factors
• 9.07 Some Simple Factor Analyses Using Principal Components Extraction
• 9.08 The Communality Issue
• 9.09 The Factor Analysis Model
• 9.10 Assumptions for Common Factor Analysis
• 9.11 Determining How Many Factors Are Present With Principal Axis Factoring
• 9.12 Exploratory Factor Analysis Example With Principal Axis Factoring
• 9.13 Factor Scores
• 9.14 Using SPSS in Factor Analysis
• 9.15 Using SAS in Factor Analysis
• 9.16 Exploratory and Confirmatory Factor Analysis
• 9.17 Example Results Section for EFA of Reactions-to-Tests Scale
• 9.18 Summary
• 9.19 Exercises
10. Discriminant Analysis
• 10.01 Introduction
• 10.02 Descriptive Discriminant Analysis
• 10.03 Dimension Reduction Analysis
• 10.04 Interpreting the Discriminant Functions
• 10.05 Minimum Sample Size
• 10.06 Graphing the Groups in the Discriminant Plane
• 10.07 Example With SeniorWISE Data
• 10.08 National Merit Scholar Example
• 10.09 Rotation of the Discriminant Functions
• 10.10 Stepwise Discriminant Analysis
• 10.11 The Classification Problem
• 10.12 Linear Versus Quadratic Classification Rule
• 10.13 Characteristics of a Good Classification Procedure
• 10.14 Analysis Summary of Descriptive Discriminant Analysis
• 10.15 Example Results Section for Discriminant Analysis of the National Merit Scholar Example
• 10.16 Summary
• 10.17 Exercises
11. Binary Logistic Regression
• 11.01 Introduction
• 11.02 The Research Example
• 11.03 Problems With Linear Regression Analysis
• 11.04 Transformations and the Odds Ratio With a Dichotomous Explanatory Variable
• 11.05 The Logistic Regression Equation With a Single Dichotomous Explanatory Variable
• 11.06 The Logistic Regression Equation With a Single Continuous Explanatory Variable
• 11.07 Logistic Regression as a Generalized Linear Model
• 11.08 Parameter Estimation
• 11.09 Significance Test for the Entire Model and Sets of Variables
• 11.10 McFadden’s Pseudo R-Square for Strength of Association
• 11.11 Significance Tests and Confidence Intervals for Single Variables
• 11.12 Preliminary Analysis
• 11.13 Residuals and Influence
• 11.14 Assumptions
• 11.15 Other Data Issues
• 11.16 Classification
• 11.17 Using SAS and SPSS for Multiple Logistic Regression
• 11.18 Using SAS and SPSS to Implement the Box–Tidwell Procedure
• 11.19 Example Results Section for Logistic Regression With Diabetes Prevention Study
• 11.20 Analysis Summary
• 11.21 Exercises
12. Repeated-Measures Analysis
• 12.01 Introduction
• 12.02 Single-Group Repeated Measures
• 12.03 The Multivariate Test Statistic for Repeated Measures
• 12.04 Assumptions in Repeated-Measures Analysis
• 12.05 Computer Analysis of the Drug Data
• 12.06 Post Hoc Procedures in Repeated-Measures Analysis
• 12.07 Should We Use the Univariate or Multivariate Approach?
• 12.08 One-Way Repeated Measures—A Trend Analysis
• 12.09 Sample Size for Power = .80 in Single-Sample Case
• 12.10 Multivariate Matched-Pairs Analysis
• 12.11 One-Between and One-Within Design
• 12.12 Post Hoc Procedures for the One-Between and One-Within Design
• 12.13 One-Between and Two-Within Factors
• 12.14 Two-Between and One-Within Factors
• 12.15 Two-Between and Two-Within Factors
• 12.16 Totally Within Designs
• 12.17 Planned Comparisons in Repeated-Measures Designs
• 12.18 Profile Analysis
• 12.19 Doubly Multivariate Repeated-Measures Designs
• 12.20 Summary
• 12.21 Exercises
13. Hierarchical Linear Modeling
• 13.1 Introduction
• 13.2 Problems Using Single-Level Analyses of Multilevel Data
• 13.3 Formulation of the Multilevel Model
• 13.4 Two-Level Model—General Formation
• 13.5 Example 1: Examining School Differences in Mathematics
• 13.6 Centering Predictor Variables
• 13.7 Sample Size
• 13.8 Example 2: Evaluating the Efficacy of a Treatment
• 13.9 Summary
14. Multivariate Multilevel Modeling
• 14.1 Introduction
• 14.2 Benefits of Conducting a Multivariate Multilevel Analysis
• 14.3 Research Example
• 14.4 Preparing a Data Set for MVMM Using SAS and SPSS
• 14.5 Incorporating Multiple Outcomes in the Level-1 Model
• 14.6 Example 1: Using SAS and SPSS to Conduct Two-Level Multivariate Analysis
• 14.7 Example 2: Using SAS and SPSS to Conduct Three-Level Multivariate Analysis
• 14.8 Summary
15. Canonical Correlation
• 15.01 Introduction
• 15.02 The Nature of Canonical Correlation
• 15.03 Significance Tests
• 15.04 Interpreting the Canonical Variates
• 15.05 Computer Example Using SAS CANCORR
• 15.06 A Study That Used Canonical Correlation
• 15.07 Using SAS for Canonical Correlation on Two Sets of Factor Scores
• 15.08 The Redundancy Index of Stewart and Love
• 15.09 Rotation of Canonical Variates
• 15.10 Obtaining More Reliable Canonical Variates
• 15.11 Summary
• 15.12 Exercises
16. Structural Equation Modeling
• 16.01 Introduction
• 16.02 Notation, Terminology, and Software
• 16.03 Causal Inference
• 16.04 Fundamental Topics in SEM
• 16.05 Three Principal SEM Techniques
• 16.06 Observed Variable Path Analysis
• 16.07 Observed Variable Path Analysis With the Mueller Study
• 16.08 Confirmatory Factor Analysis
• 16.09 CFA With Reactions-to-Tests Data
• 16.10 Latent Variable Path Analysis
• 16.11 Latent Variable Path Analysis With Exercise Behavior Study
• 16.12 SEM Considerations
• 16.13 Additional Models in SEM
• 16.14 Final Thoughts
Appendix 16.1 Abbreviated SAS Output for Final Observed Variable Path Model
Appendix 16.2 Abbreviated SAS Output for the Final Latent Variable Path Model for Exercise Behavior
Appendix A: Statistical Tables
Appendix B: Obtaining Nonorthogonal Contrasts in Repeated Measures Designs
Detailed Answers
Index
Contents
Pituch KA & Stevens JP (2015) Applied Multivariate Statistics for the Social Sciences - Analyses with SAS and IBM’s SPSS, Sixth Edition
Preface
01. Introduction
• 1.01 Introduction
• 1.02 Type I Error, Type II Error, and Power
• 1.03 Multiple Statistical Tests and the Probability of Spurious Results
• 1.04 Statistical Significance Versus Practical Importance
• 1.05 Outliers
• 1.06 Missing Data
• 1.07 Unit or Participant Nonresponse
• 1.08 Research Examples for Some Analyses Considered in This Text
• 1.09 The SAS and SPSS Statistical Packages
• 1.10 SAS and SPSS Syntax
• 1.11 SAS and SPSS Syntax and Data Sets on the Internet
• 1.12 Some Issues Unique to Multivariate Analysis
• 1.13 Data Collection and Integrity
• 1.14 Internal and External Validity
• 1.15 Conflict of Interest
• 1.16 Summary
• 1.17 Exercises
02. Matrix Algebra
• 2.1 Introduction
• 2.2 Addition, Subtraction, and Multiplication of a Matrix by a Scalar
• 2.3 Obtaining the Matrix of Variances and Covariances
• 2.4 Determinant of a Matrix
• 2.5 Inverse of a Matrix
• 2.6 SPSS Matrix Procedure
• 2.7 SAS IML Procedure
• 2.8 Summary
• 2.9 Exercises
03. Multiple Regression for Prediction
• 3.01 Introduction
• 3.02 Simple Regression
• 3.03 Multiple Regression for Two Predictors: Matrix Formulation
• 3.04 Mathematical Maximization Nature of Least Squares Regression
• 3.05 Breakdown of Sum of Squares and F Test for Multiple Correlation
• 3.06 Relationship of Simple Correlations to Multiple Correlation
• 3.07 Multicollinearity
• 3.08 Model Selection
• 3.09 Two Computer Examples
• 3.10 Checking Assumptions for the Regression Model
• 3.11 Model Validation
• 3.12 Importance of the Order of the Predictors
• 3.13 Other Important Issues
• 3.14 Outliers and Influential Data Points
• 3.15 Further Discussion of the Two Computer Examples
• 3.16 Sample Size Determination for a Reliable Prediction Equation
• 3.17 Other Types of Regression Analysis
• 3.18 Multivariate Regression
• 3.19 Summary
• 3.20 Exercises
04. Two-Group Multivariate Analysis of Variance
• 4.01 Introduction
• 4.02 Four Statistical Reasons for Preferring a Multivariate Analysis
• 4.03 The Multivariate Test Statistic as a Generalization of the Univariate t Test
• 4.04 Numerical Calculations for a Two-Group Problem
• 4.05 Three Post Hoc Procedures
• 4.06 SAS and SPSS Control Lines for Sample Problem and Selected Output
• 4.07 Multivariate Significance but No Univariate Significance
• 4.08 Multivariate Regression Analysis for the Sample Problem
• 4.09 Power Analysis
• 4.10 Ways of Improving Power
• 4.11 A Priori Power Estimation for a Two-Group MANOVA
• 4.12 Summary
• 4.13 Exercises
05. K-Group MANOVA: A Priori and Post Hoc Procedures
• 5.01 Introduction
• 5.02 Multivariate Regression Analysis for a Sample Problem
• 5.03 Traditional Multivariate Analysis of Variance
• 5.04 Multivariate Analysis of Variance for Sample Data
• 5.05 Post Hoc Procedures
• 5.06 The Tukey Procedure
• 5.07 Planned Comparisons
• 5.08 Test Statistics for Planned Comparisons
• 5.09 Multivariate Planned Comparisons on SPSS MANOVA
• 5.10 Correlated Contrasts
• 5.11 Studies Using Multivariate Planned Comparisons
• 5.12 Other Multivariate Test Statistics
• 5.13 How Many Dependent Variables for a MANOVA?
• 5.14 Power Analysis—A Priori Determination of Sample Size
• 5.15 Summary
• 5.16 Exercises
06. Assumptions in MANOVA
• 6.01 Introduction
• 6.02 ANOVA and MANOVA Assumptions
• 6.03 Independence Assumption
• 6.04 What Should Be Done With Correlated Observations?
• 6.05 Normality Assumption
• 6.06 Multivariate Normality
• 6.07 Assessing the Normality Assumption
• 6.08 Homogeneity of Variance Assumption
• 6.09 Homogeneity of the Covariance Matrices
• 6.10 Summary
• 6.11 Complete Three-Group MANOVA Example
• 6.12 Example Results Section for One-Way MANOVA
• 6.13 Analysis Summary
• Appendix 6.1 Analyzing Correlated Observations
• Appendix 6.2 Multivariate Test Statistics for Unequal Covariance Matrices
• 6.14 Exercises
07. Factorial ANOVA and MANOVA
• 7.01 Introduction
• 7.02 Advantages of a Two-Way Design
• 7.03 Univariate Factorial Analysis
• 7.04 Factorial Multivariate Analysis of Variance
• 7.05 Weighting of the Cell Means
• 7.06 Analysis Procedures for Two-Way MANOVA
• 7.07 Factorial MANOVA With SeniorWISE Data
• 7.08 Example Results Section for Factorial MANOVA With SeniorWise Data
• 7.09 Three-Way MANOVA
• 7.10 Factorial Descriptive Discriminant Analysis
• 7.11 Summary
• 7.12 Exercises
08. Analysis of Covariance
• 8.01 Introduction
• 8.02 Purposes of ANCOVA
• 8.03 Adjustment of Posttest Means and Reduction of Error Variance
• 8.04 Choice of Covariates
• 8.05 Assumptions in Analysis of Covariance
• 8.06 Use of ANCOVA With Intact Groups
• 8.07 Alternative Analyses for Pretest–Posttest Designs
• 8.08 Error Reduction and Adjustment of Posttest Means for Several Covariates
• 8.09 MANCOVA—Several Dependent Variables and Several Covariates
• 8.10 Testing the Assumption of Homogeneous Hyperplanes on SPSS
• 8.11 Effect Size Measures for Group Comparisons in MANCOVA/ANCOVA
• 8.12 Two Computer Examples
• 8.13 Note on Post Hoc Procedures
• 8.14 Note on the Use of MVMM
• 8.15 Example Results Section for MANCOVA
• 8.16 Summary
• 8.17 Analysis Summary
• 8.18 Exercises
09. Exploratory Factor Analysis
• 9.01 Introduction
• 9.02 The Principal Components Method
• 9.03 Criteria for Determining How Many Factors to Retain Using Principal Components Extraction
• 9.04 Increasing Interpretability of Factors by Rotation
• 9.05 What Coefficients Should Be Used for Interpretation?
• 9.06 Sample Size and Reliable Factors
• 9.07 Some Simple Factor Analyses Using Principal Components Extraction
• 9.08 The Communality Issue
• 9.09 The Factor Analysis Model
• 9.10 Assumptions for Common Factor Analysis
• 9.11 Determining How Many Factors Are Present With Principal Axis Factoring
• 9.12 Exploratory Factor Analysis Example With Principal Axis Factoring
• 9.13 Factor Scores
• 9.14 Using SPSS in Factor Analysis
• 9.15 Using SAS in Factor Analysis
• 9.16 Exploratory and Confirmatory Factor Analysis
• 9.17 Example Results Section for EFA of Reactions-to-Tests Scale
• 9.18 Summary
• 9.19 Exercises
10. Discriminant Analysis
• 10.01 Introduction
• 10.02 Descriptive Discriminant Analysis
• 10.03 Dimension Reduction Analysis
• 10.04 Interpreting the Discriminant Functions
• 10.05 Minimum Sample Size
• 10.06 Graphing the Groups in the Discriminant Plane
• 10.07 Example With SeniorWISE Data
• 10.08 National Merit Scholar Example
• 10.09 Rotation of the Discriminant Functions
• 10.10 Stepwise Discriminant Analysis
• 10.11 The Classification Problem
• 10.12 Linear Versus Quadratic Classification Rule
• 10.13 Characteristics of a Good Classification Procedure
• 10.14 Analysis Summary of Descriptive Discriminant Analysis
• 10.15 Example Results Section for Discriminant Analysis of the National Merit Scholar Example
• 10.16 Summary
• 10.17 Exercises
11. Binary Logistic Regression
• 11.01 Introduction
• 11.02 The Research Example
• 11.03 Problems With Linear Regression Analysis
• 11.04 Transformations and the Odds Ratio With a Dichotomous Explanatory Variable
• 11.05 The Logistic Regression Equation With a Single Dichotomous Explanatory Variable
• 11.06 The Logistic Regression Equation With a Single Continuous Explanatory Variable
• 11.07 Logistic Regression as a Generalized Linear Model
• 11.08 Parameter Estimation
• 11.09 Significance Test for the Entire Model and Sets of Variables
• 11.10 McFadden’s Pseudo R-Square for Strength of Association
• 11.11 Significance Tests and Confidence Intervals for Single Variables
• 11.12 Preliminary Analysis
• 11.13 Residuals and Influence
• 11.14 Assumptions
• 11.15 Other Data Issues
• 11.16 Classification
• 11.17 Using SAS and SPSS for Multiple Logistic Regression
• 11.18 Using SAS and SPSS to Implement the Box–Tidwell Procedure
• 11.19 Example Results Section for Logistic Regression With Diabetes Prevention Study
• 11.20 Analysis Summary
• 11.21 Exercises
12. Repeated-Measures Analysis
• 12.01 Introduction
• 12.02 Single-Group Repeated Measures
• 12.03 The Multivariate Test Statistic for Repeated Measures
• 12.04 Assumptions in Repeated-Measures Analysis
• 12.05 Computer Analysis of the Drug Data
• 12.06 Post Hoc Procedures in Repeated-Measures Analysis
• 12.07 Should We Use the Univariate or Multivariate Approach?
• 12.08 One-Way Repeated Measures—A Trend Analysis
• 12.09 Sample Size for Power = .80 in Single-Sample Case
• 12.10 Multivariate Matched-Pairs Analysis
• 12.11 One-Between and One-Within Design
• 12.12 Post Hoc Procedures for the One-Between and One-Within Design
• 12.13 One-Between and Two-Within Factors
• 12.14 Two-Between and One-Within Factors
• 12.15 Two-Between and Two-Within Factors
• 12.16 Totally Within Designs
• 12.17 Planned Comparisons in Repeated-Measures Designs
• 12.18 Profile Analysis
• 12.19 Doubly Multivariate Repeated-Measures Designs
• 12.20 Summary
• 12.21 Exercises
13. Hierarchical Linear Modeling
• 13.1 Introduction
• 13.2 Problems Using Single-Level Analyses of Multilevel Data
• 13.3 Formulation of the Multilevel Model
• 13.4 Two-Level Model—General Formation
• 13.5 Example 1: Examining School Differences in Mathematics
• 13.6 Centering Predictor Variables
• 13.7 Sample Size
• 13.8 Example 2: Evaluating the Efficacy of a Treatment
• 13.9 Summary
14. Multivariate Multilevel Modeling
• 14.1 Introduction
• 14.2 Benefits of Conducting a Multivariate Multilevel Analysis
• 14.3 Research Example
• 14.4 Preparing a Data Set for MVMM Using SAS and SPSS
• 14.5 Incorporating Multiple Outcomes in the Level-1 Model
• 14.6 Example 1: Using SAS and SPSS to Conduct Two-Level Multivariate Analysis
• 14.7 Example 2: Using SAS and SPSS to Conduct Three-Level Multivariate Analysis
• 14.8 Summary
15. Canonical Correlation
• 15.01 Introduction
• 15.02 The Nature of Canonical Correlation
• 15.03 Significance Tests
• 15.04 Interpreting the Canonical Variates
• 15.05 Computer Example Using SAS CANCORR
• 15.06 A Study That Used Canonical Correlation
• 15.07 Using SAS for Canonical Correlation on Two Sets of Factor Scores
• 15.08 The Redundancy Index of Stewart and Love
• 15.09 Rotation of Canonical Variates
• 15.10 Obtaining More Reliable Canonical Variates
• 15.11 Summary
• 15.12 Exercises
16. Structural Equation Modeling
• 16.01 Introduction
• 16.02 Notation, Terminology, and Software
• 16.03 Causal Inference
• 16.04 Fundamental Topics in SEM
• 16.05 Three Principal SEM Techniques
• 16.06 Observed Variable Path Analysis
• 16.07 Observed Variable Path Analysis With the Mueller Study
• 16.08 Confirmatory Factor Analysis
• 16.09 CFA With Reactions-to-Tests Data
• 16.10 Latent Variable Path Analysis
• 16.11 Latent Variable Path Analysis With Exercise Behavior Study
• 16.12 SEM Considerations
• 16.13 Additional Models in SEM
• 16.14 Final Thoughts
Appendix 16.1 Abbreviated SAS Output for Final Observed Variable Path Model
Appendix 16.2 Abbreviated SAS Output for the Final Latent Variable Path Model for Exercise Behavior
Appendix A: Statistical Tables
Appendix B: Obtaining Nonorthogonal Contrasts in Repeated Measures Designs
Detailed Answers
Index
Displaying 1 of 1 review