Part I. The Basics of Multivariate Design
Chapter 1. An Introduction to Multivariate Design Chapter 2. Some Fundamental Research Design Concepts Chapter 3A. Data Screening Chapter 3B. Data Screening Using IBM SPSS
Part II. Comparisons of Means
Chapter 4A. Univariate Comparison of Means Chapter 4B. Univariate Comparison of Means Using IBM SPSS Chapter 5A. Multivariate Analysis of Variance (MANOVA) Chapter 5B. Multivariate Analysis of Variance (MANOVA) Using IBM SPSS
Part III. Predicting the Value of a Single Variable
Chapter 6A. Bivariate Correlation and Simple Linear Regression Chapter 6B. Bivariate Correlation and Simple Linear Regression Using IBM SPSS Chapter 7A. Multiple Regression: Statistical Methods Chapter 7B. Multiple Regression: Statistical Methods Using IBM SPSS Chapter 8A. Multiple Regression: Beyond Statistical Regression Chapter 8B. Multiple Regression: Beyong Statistical Regression Using IBM SPSS Chapter 9A. Multilevel Modeling Chapter 9B. Multilevel Modeling Using IBM SPSS Chapter 10A. Binary and Multinomial Logistic Regression and ROC Analysis Chapter 10B. Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS
Part IV. Analysis of Structure
Chapter 11A. Discriminant Function Analysis Chapter 11B. Discriminant Function Analysis Using IBM SPSS Chapter 12A. Principal Components and Exploratory Factor Analysis Chapter 12B. Principal Components and Exploratory Factor Analysis Using IBM SPSS Chapter 13A. Canonical Correlation Analysis Chapter 13B. Canonical Correlation Analysis Using IBM SPSS Chapter 14A. Multidimensional Scaling Chapter 14B. Multidimensional Scaling Using IBM SPSS Chapter 15A. Cluster Analysis Chapter 15B. Cluster Analysis Using IBM SPSS
Part V. Fitting Models to Data
Chapter 16A. Confirmatory Factor Analysis Chapter 16B. Confirmatory Factor Analysis Using Amos Chapter 17A. Path Analysis: Multiple Regression Chapter 17B. Path Analysis: Multiple Regression Using IBM SPSS Chapter 18A. Path Analysis: Structural Modeling Chapter 18B. Path Analysis: Structural Modeling Using Amos Chapter 19A. Structural Equation Modeling Chapter 19B. Structural Equation Modeling Using Amos Chapter 20A. Model Invariance: Applying a Model to Different Groups Chapter 20B. Assessing Model Invariance Using Amos

PART I: THE BASICS OF
MULTIVARIATE DESIGN 1
Chapter 1: An Introduction to
Multivariate Design 2
1.1: The Use of Multivariate
Designs 2
1.2: The Definition of the Multivariate
Domain 2
1.3: The Importance of Multivariate
Designs 3
1.4: The General Form of a Variate 4
1.5: The Type of Variables Combined
to Form a Variate 5
1.6: The General Organization of the
Book 6
1.7: Recommended Readings 10
Chapter 2: Some Fundamental Research
Design Concepts 11
2.1: Populations and Samples 11
2.2: Scales of Measurement 12
2.3: Independent Variables, Dependent
Variables, and Covariates 18
2.4: Between-Subjects and
Within-Subjects Independent
Variables 21
2.5: Latent Variables or Variates and
Measured Variables 22
2.6: Endogenous and Exogenous
Variables 24
2.7: Statistical Significance 24
2.8: Statistical Power 32
2.9: Recommended Readings 36
Chapter 3A: Data Screening 37
3A.1: Overview 37
3A.2: Value Cleaning 38
3A.3: Patterns of Missing
Values 44
3A.4: Overview of Methods of
Handling Missing Data 48
3A.5: Deletion Methods of Handling
Missing Data 49
3A.6: Single Imputation
Methods of Handling Missing
Data 51
3A.7: Modern Imputation Methods of
Handling Missing Data 54
3A.8: Recommendations for Handling
Missing Data 57
3A.9: Outliers 57
3A.10: Using Descriptive Statistics in
Data Screening 63
3A.11: Using Pictorial Representations
in Data Screening 64
3A.12: Multivariate Statistical
Assumptions Underlying the
General Linear Model 67
3A.13: Data Transformations 72
3A.14: Recommended Readings 73
Chapter 3B: Data Screening Using IBM
SPSS 75
3B.1: The Look of IBM SPSS 75
3B.2: Data Cleaning: All
Variables 76
3B.3: Screening Quantitative
Variables 80
3B.4: Missing Values: Overview 81
3B.5: Missing Value Analysis 81
3B.6: Multiple Imputation 89
3B.7: Mean Substitution as a Single
Imputation Approach 102
3B.8: Univariate Outliers 106
3B.9: Normality 109
3B.10: Linearity 120
3B.11: Multivariate Outliers 122
3B.12: Screening Within Levels of
Categorical Variables 128
3B.13: Reporting the Results 136
PART II: COMPARISONS OF
MEANS 139
Chapter 4A: Univariate Comparison of
Means 140
4A.1: Overview 140
4A.2: Means are Compared With
Respect to Their Associated
Variability 141
4A.3: The t and F Tests 143
4A.4: One-Way Between-Subjects
Designs 144
4A.5: Two-Way (Factorial) BetweenSubjects
Design 148
4A.6: One-Way Within-Subjects
Design 152
4A.7: Two-Way Simple Mixed
Design 153
4A.8: One-Way Between-Subjects
ANCOVA 156
4A.9: The General Linear Model 162
4A.10: Recommended Readings 164
Chapter 4B: Univariate
Comparison of Means
Using IBM SPSS 165
4B.1: One-Way Between-Subjects
Design 165
4B.2: Two-Way Between-Subjects
Design 172
4B.3: One-Way Within-Subjects
Design 181
4B.4: Simple Mixed Design 187
4B.5: Trend Analysis 196
4B.6: Analysis of Covariance 201
4B.7: One-Way Between-Subjects
Design Using Generalized Linear
Models 211
4B.8: Simple Mixed Design
Using Generalized Linear
Models 215
Chapter 5A: Multivariate Analysis of
Variance 224
5A.1: Overview 224
5A.2: Working With Multiple
Dependent Variables 224
5A.3: Benefits of and Drawbacks to
Using MANOVA 227
5A.4: Hotelling’s T2 229
5A.5: Multivariate Significance
Testing With More Than Two
Groups 232
5A.6: What to Do After a Significant
Multivariate Effect 235
5A.7: Advantages of Multivariate
Factorial Designs 237
5A.8: A Strategy For Examining TwoWay
Between-Subjects MANOVA
Results 238
5A.9: The Time Dimension
in Multivariate Data
Analysis 242
5A.10: Recommended Readings 245
Chapter 5B: Multivariate Analysis of
Variance Using IBM SPSS 247
5B.1: Numerical Example 247
5B.2: Alternatives to Performing a
MANOVA Analysis 248
5B.3: Two-Group
MANOVA 248
5B.4: k-Group MANOVA 257
5B.5: Two-Way Between-Subjects
Factorial MANOVA 269
PART III: PREDICTING THE VALUE
OF A SINGLE VARIABLE 283
Chapter 6A: Bivariate Correlation and
Simple Linear Regression 284
6A.1: The Concept of
Relationship 284
6A.2: Different Types of
Relationships 285
6A.3: Statistical Significance
of the Correlation
Coefficient 292
6A.4: Strength of
Relationship 294
6A.5: Pearson Correlation Using a
Quantitative Variable and a
Dichotomous Nominal
Variable 298
6A.6: Simple Linear
Regression 302
6A.7: Statistical Error in
Prediction: Why Bother With
Regression? 309
6A.8: How Simple Linear Regression Is
Used 311
6A.9: Factors Affecting the Computed
Pearson r and Regression
Coefficients 311
6A.10: Recommended
Readings 314
Chapter 6B: Bivariate Correlation and
Simple Linear Regression Using IBM
SPSS 315
6B.1: Bivariate Correlation: Analysis
Setup 315
6B.2: Simple Linear Regression 319
6B.3: Reporting Results 323
Chapter 7A: Multiple Regression:
Statistical Methods 324
7A.1: General Considerations 324
7A.2: A Range of Regression
Methods 325
7A.3: The Variables in a Multiple
Regression Analysis 325
7A.4: Multiple Regression
Research 327
7A.5: The Regression Equations 329
7A.6: The Variate in Multiple
Regression 332
7A.7: The Standard (Simultaneous)
Regression Method 333
7A.8: Partial Correlation 337
7A.9: The Squared Multiple
Correlation 338
7A.10: The Squared Semipartial
Correlation 339
7A.11: Structure Coefficients 344
7A.12: Statistical Summary of the
Regression Solution 345
7A.13: Evaluating the Overall
Model 346
7A.14: Evaluating the Individual
Predictor Results 351
7A.15: Step Methods of Building the
Model 357
7A.16: The Forward Method 357
7A.17: The Backward Method 358
7A.18: The Backward Versus Forward
Solutions 358
7A.19: The Stepwise Method 359
7A.20: Evaluation of the Statistical
Methods 361
7A.21: Collinearity and
Multicollinearity 363
7A.22: Recommended Readings 365
Chapter 7B: Multiple Regression:
Statistical Methods Using IBM
SPSS 366
7B.1: Standard Multiple
Regression 366
7B.2: Stepwise Multiple
Regression 372
Chapter 8A: Multiple Regression: Beyond
Statistical Regression 382
8A.1: A Larger World of
Regression 382
8A.2: Hierarchical Linear
Regression 382
8A.3: Suppressor Variables 386
8A.4: Linear and Nonlinear
Regression 388
8A.5: Dummy and Effect
Coding 391
8A.6: Moderator Variables and
Interactions 396
8A.7: Simple Mediation 399
8A.8: Recommended Readings 411
Chapter 8B: Multiple Regression: Beyond
Statistical Regression Using IBM
SPSS 413
8B.1: Hierarchical Linear
Regression 413
8B.2: Polynomial Regression 419
8B.3: Dummy and Effect Coding 428
8B.4: Interaction Effects of
Quantitative Variables in
Regression 439
8B.5: Mediation 457
Chapter 9A: Multilevel Modeling 466
9A.1: The Name of the
Procedure 466
9A.2: The Rise of Multilevel
Modeling 466
9A.3: The Defining Feature of
Multilevel Modeling: Hierarchically
Structured Data 467
9A.4: Nesting and the Independence
Assumption 468
9A.5: The Intraclass Correlation as an
Index of Clustering 469
9A.6: Consequences of Violating
the Independence
Assumption 470
9A.7: Some Ways in Which Level 2
Groups Can Differ 472
9A.8: The Random Coefficient
Regression Model 474
9A.9: Centering the Variables 476
9A.10: The Process of Building the
Multilevel Model 479
9A.11: Recommended
Readings 483
Chapter 9B: Multilevel Modeling Using
IBM SPSS 484
9B.1: Numerical Example 484
9B.2: Assessing the Unconditional
Model 484
9B.3: Centering the Variables 490
9B.4: Building the Multilevel Models:
Overview 493
9B.5: Building the First Model 496
9B.6: Building the Second
Model 504
9B.7: Building the Third Model 509
9B.8: Building the Fourth Model 515
9B.9: Reporting Multilevel Modeling
Results 519
Chapter 10A: Binary and Multinomial
Logistic Regression and ROC
Analysis 522
10A.1: Overview 522
10A.2: The Variables in Logistic
Regression Analysis 523
10A.3: Assumptions of Logistic
Regression 524
10A.4: Coding of the Binary Variables
in Logistic Regression 524
10A.5: The Logistic Regression
Model 528
10A.6: Logistic Regression and Odds 530
10A.7: The Logistic Regression
Model 532
10A.8: Calculating the Changes of
Cases Belonging to the Target
Group 534
10A.9: Binary Logistic Regression With
a Single Binary Predictor 534
10A.10: Binary Logistic Regression
With a Single Quantitative
Predictor 536
10A.11: Binary Logistic Regression
With a Categorical and a
Quantitative Predictor 540
10A.12: Evaluating the Logistic
Model 541
10A.13: Strategies For Building the
Logistic Regression Model 544
10A.14: ROC Analysis 545
10A.15: Recommended Readings 556
Chapter 10B: Binary and Multinomial
Logistic Regression and ROC
Analysis Using IBM SPSS 557
10B.1: Binary Logistic Regression 557
10B.2: ROC Analysis 565
10B.3: Multinomial Logistic
Regression 575
PART IV: ANALYSIS OF
STRUCTURE 585
Chapter 11A: Discriminant Function
Analysis 586
11A.1: Overview 586
11A.2: Discriminant Function
Analysis and Logistic Analysis
Compared 588
11A.3: Discriminant Function
Analysis and
MANOVA 588
11A.4: Assumptions Underlying
Discriminant Function
Analysis 589
11A.5: Sample Size for Discriminant
Analysis 590
11A.6: The Discriminant
Function 590
11A.7: The Number of Discriminant
Functions That Can Be
Extracted 592
11A.8: Dynamics of Extracting
Discriminant Functions 593
11A.9: Testing Statistical
Significance 594
11A.10: Evaluating the Quality of the
Solution 596
11A.11: Coefficients Associated With
the Interpretation of Discriminant
Functions 601
11A.12: Different Discriminant
Function Methods 606
11A.13: Recommended Readings 608
Chapter 11B: Discriminant Function
Analysis Using IBM SPSS 609
11B.1: Two-Group Disciminant
Function Analysis Setup 609
11B.2: Two-Group Discriminant
Function Analysis Output 613
11B.3: Reporting the Results of a TwoGroup
Discriminant Function
Analysis 620
11B.4: Three-Group Discriminant
Function Analysis Setup 622
11B.5: Three-Group Discriminant
Function Analysis Output 625
11B.6: Reporting the Results of a
Three-Group Discriminant
Function Analysis 637
Chapter 12A: Principal Components
Analysis and Exploratory Factor
Analysis 640
12A.1: Orientation and
Terminology 640
12A.2: How Factor Analysis Is Used in
Psychological Research 641
12A.3: Origins of Factor
Analysis 641
12A.4: The General Organization of
This Chapter 642
12A.5: Where the Analysis Begins: The
Correlation Matrix 642
12A.6: Acquiring Perspective on Factor
Analysis 648
12A.7: Distinctions Within Factor
Analysis 651
12A.8: The First Phase: Component
Extraction 652
12A.9: Distances of Variables From a
Component 658
12A.10: Principal Components Analysis
Versus Factor Analysis 662
12A.11: Different Extraction
Methods 664
12A.12: Recommendations
Concerning Extraction 666
12A.13: The Rotation Process 667
12A.14: Orthogonal Factor
Rotation 672
12A.15: Oblique Factor Rotation 673
12A.16: Choosing Between Orthogonal
and Oblique Rotation
Strategies 674
12A.17: The Factor Analysis
Printout 676
12A.18: Interpreting Factors 680
12A.19: Selecting the Factor
Solution 683
12A.20: Sample Size Issues 686
12A.21: Recommended Readings 687
Chapter 12B: Principal Components
Analysis and Exploratory Factor
Analysis Using IBM SPSS 688
12B.1: Numerical Example 688
12B.2: Preliminary Principal
Components Analysis 690
12B.3: Principal Components
Analysis With a Promax Rotation:
Two-Component Solution 700
12B.4: ULS Analysis With
a Promax Rotation: Two-Factor
Solution 704
12B.5: Wrap-Up of the Two-Factor
Solution 708
12B.6: Looking For Six
Dimensions 708
12B.7: Principal Components Analysis
With a Promax Rotation: SixComponent
Solution 708
12B.8: ULS Analysis With a Promax
Rotation: Six-Component
Solution 713
12B.9: Principal Axis Factor Analysis
With a Promax Rotation: SixComponent
Solution 717
12B.10: Wrap-Up of the Six-Factor
Solution 720
12B.11: Assessing Reliability: General
Principles 721
12B.12: Assessing Reliability: The
Global Domains 724
12B.13: Assessing Reliability: The Six
Item Sets Based on the ULS/Promax
Structure 729
12B.14: Computing Scales
Based on the ULS Promax
Structure 729
12B.15: Using the Computed Variables
in Further Analyses 736
12B.16: Reporting the Results 745
Chapter 13A: Canonical Correlation
Analysis 750
13A.1: Overview 750
13A.2: Canonical Functions or
Roots 751
13A.3: The Index of Shared
Variance 752
13A.4: The Dynamics of Extracting
Canonical Functions 753
13A.5: Testing Statistical
Significance 754
13A.6: The Multivariate
Tests 755
13A.7: Redundancy Index 756
13A.8: Coefficients Associated With
the Canonical Functions 757
13A.9: Interpreting the Canonical
Functions 758
13A.10: Recommended Readings 758
Chapter 13B: Canonical Correlation
Analysis Using IBM SPSS 759
13B.1: Canonical Correlation: Analysis
Setup 759
13B.2: Canonical Correlation:
Overview of Output 760
13B.3: Canonical Correlation:
Multivariate Tests of
Significance 761
13B.4: Canonical Correlation:
Eigenvalues and Canonical
Correlations 761
13B.5: Canonical Correlation:
Dimension Reduction
Analysis 763
13B.6: Canonical Correlation: How
Many Functions Should Be
Interpreted? 764
13B.7: Canonical Correlation:
The Coefficients in the
Output 764
13B.8: Canonical Correlation:
Interpreting the Dependent
Variates 765
13B.9: Canonical Correlation:
Interpreting the Predictor
Variates 766
13B.10: Canonical Correlation:
Interpreting the Canonical
Functions 767
13B.11: Reporting Canonical
Correlation Analysis
Results 768
Chapter 14A: Multidimensional
Scaling 770
14A.1: Overview 770
14A.2: The Paired Comparison
Method 771
14A.3: Dissimilarity Data in
MDS 772
14A.4: Similarity/Dissimilarity
Conceived as an Index of
Distance 773
14A.5: Dimensionality in MDS 774
14A.6: Data Collection Methods 775
14A.7: Similarity Versus
Dissimilarity 777
14A.8: Distance Models 778
14A.9: A Classification Schema for
MDS Techniques 780
14A.10: Types of MDS Models 782
14A.11: Assessing Model Fit 784
14A.12: Recommended Readings 788
Chapter 14B: Multidimensional Scaling
Using IBM SPSS 790
14B.1: The Structure of This
Chapter 790
14B.2: Metric CMDS 790
14B.3: Nonmetric CMDS 799
14B.4: Metric WMDS 807
Chapter 15A: Cluster Analysis 818
15A.1: Introduction 818
15A.2: Two Types of Clustering 818
15A.3: Hierarchical Clustering 819
15A.4: k-Means Clustering 829
15A.5: Recommended Readings 832
Chapter 15B: Cluster Analysis Using IBM
SPSS 833
15B.1: Hierarchical Cluster
Analysis 833
15B.2: k-Means Cluster Analysis 841
PART V: FITTING MODELS TO
DATA 849
Chapter 16A: Confirmatory Factor
Analysis 850
16A.1: Overview 850
16A.2: The General Form of a
Confirmatory Model 851
16A.3: The Difference Between Latent
and Indicator Variables 852
16A.4: Contrasting Principal
Components Analysis, Exploratory
Factor Analysis, and Confirmatory
Factor Analysis 853
16A.5: Confirmatory Factor Analysis
Is Theory Based 860
16A.6: The Logic of Performing a
Confirmatory Factor Analysis 861
16A.7: Model Specification 861
16A.8: Model Identification 862
16A.9: Model Estimation 866
16A.10: Model Evaluation
Overview 867
16A.11: Assessing Fit of Hypothesized
Models 868
16A.12: Model Estimation: Assessing
Pattern/Structure
Coefficients 873
16A.13: Model Respecification 874
16A.14: General
Considerations 878
16A.15: Recommended Readings 879
Chapter 16B: Confirmatory Factor
Analysis Using Amos 880
16B.1: Using Amos 880
16B.2: Numerical Example 880
16B.3: Model Specification 881
16B.4: Model Identification 885
16B.5: Performing the Analysis 888
16B.6: Working With the Analysis
Output 890
16B.7: Considering the Respecification
of the Model 894
16B.8: Respecifying the Model 898
16B.9: Output From the
Respecification 898
16B.10: Reporting Confirmatory
Factor Analysis Results 901
Chapter 17A: Path Analysis: Multiple
Regression 903
17A.1: Overview 903
17A.2: Principles of Path Analysis 904
17A.3: Causality and Path
Analysis 905
17A.4: The Concept of a Path
Model 907
17A.5: The Roles Played by Variables
in a Path Structure 907
17A.6: The Assumptions of Path
Analysis 909
17A.7: Missing Values in Path
Analysis 910
17A.8: Analyzing the Path
Structure 911
17A.9: The Multiple Regression
Approach to Path Analysis 911
17A.10: Indirect and Total Effects 913
17A.11: Comparing Multiple
Regression and Model-Fitting
Approaches 914
17A.12: A Path Analysis
Example 914
17A.13: The Multiple Regression
Strategy to Perform a Path
Analysis 916
17A.14: Examining Mediation
Effects 917
17A.15: Respecifying the Model 919
17A.16: Recommended Readings 920
Chapter 17B: Path Analysis: Multiple
Regression Using IBM SPSS 921
17B.1: The Data Set and Model Used
in Our Example 921
17B.2: Specifying the Variables in Each
Analysis 921
17B.3: Predicting Exercise 923
17B.4: Predicting Diet 925
17B.5: Predicting Social Desirability 926
17B.6: Predicting Acceptance 927
17B.7: Mediation Effects in the Larger
Model 929
17B.8: Reporting Path Analysis
Results 934
Chapter 18A: Path Analysis: Structural
Modeling 937
18A.1: The Model-Fitting Approach to
Path Analysis 937
18A.2: Comparing Multiple Regression
and Model-Fitting Approaches 938
18A.3: The Model-Fitting Strategy to
Perform a Path Analysis With Only
Measured Variables 940
18A.4: Differences Between Regression
and Structural Equations 940
18A.5: The Analysis of a Structural
Model 941
18A.6: Configuring the Structural
Model 942
18A.7: Identifying the Structural
Model 942
18A.8: The Model Results 944
18A.9: Respecifying the Model 946
18A.10: Respecified Model
Results 948
18A.11: Recommended Readings 949
Chapter 18B: Path Analysis: Structural
Modeling Using Amos 951
18B.1: Overview 951
18B.2: The Data Set and Model Used
in Our Example 951
18B.3: Drawing the Model 952
18B.4: Model Identification 954
18B.5: Performing the Analysis 955
18B.6: The Analysis Output 956
18B.7: The Structural Model 961
18B.8: Specification Search to Delete
Paths 961
18B.9: Reporting Path Analysis
Results 972
Chapter 19A: Structural Equation
Modeling 974
19A.1: Overview 974
19A.2: The Measurement and
Structural Models 974
19A.3: From Path Analysis to
SEM 975
19A.4: Building a Structural Model
From Our Path Model 977
19A.5: Results for our Structural
Model 979
19A.6: Recommended Readings 981
Chapter 19B: Structural Equation
Modeling Using Amos 982
19B.1: Overview 982
19B.2: The Example
We Use 983
19B.3: The Variables in Our Example
Model 984
19B.4: The Measurement
Model 984
19B.5: The Variables Configured
in the Full Structural
Model 988
19B.6: Performing the
Analysis 988
19B.7: Output for the Full Structural
Model 990
19B.8: Respecification of the
Model 994
19B.9: Output for the Full Respecified
Structural Model 995
19B.10: Reporting SEM Analysis
Results 998
Chapter 20A: Model Invariance:
Applying a Model to Different
Groups 1001
20A.1: Overview 1001
20A.2: The General Strategy Used to
Compare Groups 1002
20A.3: The Omnibus Model
Comparison Phase 1002
20A.4: The Coefficient Comparison
Phase 1005
20A.5: Recommended Readings 1005
Chapter 20B: Assessing Model Invariance
Using Amos 1007
20B.1: Overview 1007
20B.2: Confirmatory Factor
Analysis 1007
20B.3: Path Analysis 1018