Multivariate Analysis for the Behavioral Sciences 2nd Edition by Kimmo Vehkalahti, Brian Everitt ISBN 9781351202251 1351202251

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ISBN 10: 1351202251
ISBN 13: 9781351202251
Author: Kimmo Vehkalahti, Brian S. Everitt

Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter. After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis. Features: Presents an accessible introduction to multivariate analysis for behavioral scientists Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage Includes nearly 100 exercises for course use or self-study Supplemented by a GitHub repository with all datasets and R code for the examples and exercises Theoretical details are separated from the main body of the text Suitable for anyone working in the behavioral sciences with a basic grasp of statistics

Multivariate Analysis for the Behavioral Sciences 2nd Edition Table of contents:

1. Data, Measurement, and Models

1.1 Introduction

1.2 Types of Study

1.2.1 Surveys

1.2.2 Experiments

1.2.3 Observational Studies

1.2.4 Quasi-Experiments

1.3 Types of Measurement

1.3.1 Nominal or Categorical Measurements

1.3.2 Ordinal Scale Measurements

1.3.3 Interval Scales

1.3.4 Ratio Scales

1.3.5 Response and Explanatory Variables

1.4 Missing Values

1.5 The Role of Models in the Analysis of Data

1.6 Determining Sample Size

1.7 Significance Tests, p-Values, and Confidence Intervals

1.8 Summary

1.9 Exercises

2. Looking at Data

2.1 Introduction

2.2 Simple Graphics—Pie Charts, Bar Charts, Histograms, and Boxplots

2.2.1 Categorical Data

2.2.2 Interval/Quasi-Interval Data

2.3 The Scatterplot and beyond

2.3.1 The Bubbleplot

2.3.2 The Bivariate Boxplot

2.4 Scatterplot Matrices

2.5 Conditioning Plots and Trellis Graphics

2.6 Graphical Deception

2.7 Summary

2.8 Exercises

3. Simple Linear and Locally Weighted Regression

3.1 Introduction

3.2 Simple Linear Regression

3.2.1 Fitting the Simple Linear Regression Model to the Pulse Rates and Heights Data

3.2.2 An Example from Kinesiology

3.3 Regression Diagnostics

3.4 Locally Weighted Regression

3.4.1 Scatterplot Smoothers

3.5 Summary

3.6 Exercises

4. Multiple Linear Regression

4.1 Introduction

4.2 An Example of Multiple Linear Regression

4.3 Choosing the Most Parsimonious Model When Applying Multiple Linear Regression

4.3.1 Automatic Model Selection

4.3.2 Example of Application of the Backward Elimination

4.4 Regression Diagnostics

4.5 Multiple Linear Regression and Analysis of Variance

4.5.1 Analyzing the Fecundity of Fruit Flies by Regression

4.5.2 Multiple Linear Regression for Experimental Designs

4.5.3 Analyzing a Balanced Design

4.5.4 Analyzing an Unbalanced Design

4.6 Summary

4.7 Exercises

5. Generalized Linear Models

5.1 Introduction

5.2 Binary Response Variables

5.3 Response Variables That Are Counts

5.3.1 Overdispersion and Quasi-Likelihood

5.4 Summary

5.5 Exercises

6. Applying Logistic Regression

6.1 Introduction

6.2 Odds and Odds Ratios

6.3 Applying Logistic Regression to the GHQ Data

6.4 Selecting the Most Parsimonious Logistic Regression Model

6.5 Driving and Back Pain: A Matched Case–Control Study

6.6 Summary

6.7 Exercises

7. Survival Analysis

7.1 Introduction

7.2 The Survival Function

7.2.1 Age at First Sexual Intercourse for Women

7.3 The Hazard Function

7.4 Cox’s Proportional Hazards Model

7.4.1 Retention of Heroin Addicts in Methadone Treatment

7.5 Summary

7.6 Exercises

8. Analysis of Longitudinal Data I: Graphical Displays and Summary Measure Approach

8.1 Introduction

8.2 Graphical Displays of Longitudinal Data

8.3 Summary Measure Analysis of Longitudinal Data

8.3.1 Choosing Summary Measures

8.3.2 Applying the Summary Measure Approach

8.3.3 Incorporating Pre-Treatment Outcome Values into the Summary Measure Approach

8.3.4 Dealing with Missing Values When Using the Summary Measure Approach

8.4 Summary

8.5 Exercises

9. Analysis of Longitudinal Data II: Linear Mixed EffectsModels for Normal Response Variables

9.1 Introduction

9.2 Linear Mixed Effects Models for Repeated Measures Data

9.3 How Do Rats Grow?

9.3.1 Fitting the Independence Model to the Rat Data

9.3.2 Fitting Linear Mixed Models to the Rat Data

9.4 Computerized Delivery of Cognitive Behavioral Therapy—Beat the Blues

9.5 Summary

9.6 Exercises

10. Analysis of Longitudinal Data III: Non-Normal Responses

10.1 Introduction

10.2 Marginal Models and Conditional Models

10.2.1 Marginal Models

10.2.2 Conditional Models

10.3 Using Generalized Estimating Equations to Fit Marginal Models

10.3.1 Beat the Blues Revisited

10.3.2 Respiratory Illness

10.3.3 Epilepsy

10.4 Using Generalized Linear Mixed Effects Models to Fit Conditional Models

10.4.1 Respiratory Illness

10.4.2 Epilepsy

10.5 Summary

10.6 Exercises

11. Missing Values

11.1 Introduction

11.2 Missing Data Mechanisms

11.3 Dealing with Missing Values

11.4 Imputing Missing Values

11.5 Analyzing Multiply Imputed Data

11.6 Example of the Application of Multiple Imputation

11.6.1 Complete-Case Analysis

11.6.2 Mean Imputation

11.6.3 Multiple Imputation

11.7 Beat the Blues Revisited (Again)

11.8 Summary

11.9 Exercises

12. Multivariate Data and Multivariate Analysis

12.1 Introduction

12.2 The Initial Analysis of Multivariate Data

12.2.1 Summary Statistics for Multivariate Data

12.2.2 Graphical Descriptions of the Body Measurement Data

12.3 The Multivariate Normal Probability Density Function

12.3.1 Assessing Multivariate Data for Normality

12.4 Summary

12.5 Exercises

13. Principal Components Analysis

13.1 Introduction

13.2 Principal Components Analysis (PCA)

13.3 Finding the Sample Principal Components

13.4 Should Principal Components be Extracted fromthe Covariance or the Correlation Matrix?

13.5 Principal Components of Bivariate Data with Correlation Coefficient r

13.6 Rescaling the Principal Components

13.7 How the Principal Components Predict the Observed Covariance Matrix

13.8 Choosing the Number of Components

13.9 Calculating Principal Component Scores

13.10 Some Examples of the Application of PCA

13.10.1 Head Size of Brothers

13.10.2 Crime Rates in the United States

13.10.3 Drug Usage by American College Students

13.11 Using PCA to Select a Subset of the Variables

13.12 Summary

13.13 Exercises

14. Multidimensional Scaling and Correspondence Analysis

14.1 Introduction

14.2 Multidimensional Scaling

14.2.1 Classical Multidimensional Scaling

14.2.2 Connection to Principal Components

14.2.3 Road Distances in Finland

14.2.4 Mapping Composers of Classical Music

14.2.5 Nonmetric Multidimensional Scaling

14.2.6 Re-mapping Composers of Classical Music

14.3 Correspondence Analysis

14.3.1 Simple Example of the Application of Correspondence Analysis

14.3.2 Connections of Work Activities and Job Advantages

14.4 Summary

14.5 Exercises

15. Exploratory Factor Analysis

15.1 Introduction

15.2 The Factor Analysis Model

15.3 Estimating the Parameters in the Factor Analysis Model

15.4 Determining the Number of Factors

15.5 Fitting the Factor Analysis Model: An Example

15.6 Rotation of Factors

15.6.1 A Simple Example of Graphical Rotation

15.6.2 Numerical Rotation Methods

15.6.3 A Simple Example of Numerical Rotation

15.7 Estimating Factor Scores

15.7.1 Analyzing the Crime Rates by Factor Analysis

15.8 Exploratory Factor Analysis and Principal Component Analysis Compared

15.9 Summary

15.10 Exercises

16. Confirmatory Factor Analysis and Structural Equation Models

16.1 Introduction

16.2 Estimation, Identification, and Assessing the Fit for Confirmatory Factor Analysis and Structural Equation Models

16.2.1 Estimation

16.2.2 Identification

16.2.3 Assessing the Fit

16.3 Examples of Confirmatory Factor Analysis

16.3.1 Ability and Aspiration

16.3.2 Drug Usage among Students

16.4 Eight Factors of Systems Intelligence

16.4.1 Testing the Factorial Validity of the SI Inventory

16.5 Structural Equation Models

16.5.1 Example of a Structural Equation Model

16.6 Summary

16.7 Exercises

17. Cluster Analysis

17.1 Introduction

17.2 Cluster Analysis

17.3 Agglomerative Hierarchical Clustering

17.3.1 Clustering Individuals Based on Body Measurements

17.3.2 Clustering Countries on the Basis of Life Expectancy

17.4 k-Means Clustering

17.4.1 Clustering Crime Rates

17.5 Model-Based Clustering

17.5.1 Clustering European Countries

17.6 Summary

17.7 Exercises

18. Grouped Multivariate Data

18.1 Introduction

18.2 Two-Group Multivariate Data

18.2.1 Hotelling’s T2 Test

18.2.2 Fisher’s Linear Discriminant Function

18.3 More Than Two Groups

18.3.1 Multivariate Analysis of Variance (MANOVA)

18.3.2 Classification Functions

18.4 Summary

18.5 Exercises

References

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