Multivariate data analysis: a global perspective
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Upper Saddle River ; Munich [u.a.]
Pearson
2010
|
| Edition: | 7. ed., global ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXVIII, 800 S. graph. Darst. |
| ISBN: | 9780135153093 0135153093 |
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MARC
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| 245 | 1 | 0 | |a Multivariate data analysis |b a global perspective |c Joseph F. Hair ... |
| 250 | |a 7. ed., global ed. | ||
| 264 | 1 | |a Upper Saddle River ; Munich [u.a.] |b Pearson |c 2010 | |
| 300 | |a XXVIII, 800 S. |b graph. Darst. | ||
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| adam_text | BRIEF
CONTENTS
Chapter
1
Introduction: Methods and Model Building
1
SECTION I Understanding and Preparing For Multivariate
Analysis
31
Chapter
2
Cleaning and Transforming Data
33
Chapter
3
Factor Analysis
91
SECTION II Analysis Using Dependence Techniques
153
Chapter
4
Simple and Multiple Regression
155
Chapter
5
Canonical Correlation
235
Chapter
6
Conjoint Analysis
261
Chapter
7
Multiple Discriminant Analysis and Logistic Regression
335
Chapter
8
ANOVA and
MÁNOVA
439
SECTION III Analysis Using Interdependence Techniques
503
Chapter
9
Grouping Data with Cluster Analysis
505
Chapter
10
MDS and Correspondence Analysis
565
SECTION IV Structural Equations Modeling
627
Chapter
11
SEM:
An Introduction
629
Chapter
12
Applications of
SEM
687
VII
CONTENTS
Preface
xxv
About the Authors
xxvii
Chapter
1
Introduction: Methods and Model Building
1
What Is Multivariate Analysis?
3
Multivariate Analysis in Statistical Terms
4
Some Basic Concepts of Multivariate Analysis
4
The
Variate
4
Measurement Scales
5
Measurement Error and Multivariate Measurement
7
Statistical Significance Versus Statistical Power
8
Types of Statistical Error and Statistical Power
9
Impacts on Statistical Power
9
Using Power with Multivariate Techniques
11
A Classification of Multivariate Techniques
11
Dependence Techniques
14
Interdependence Techniques
14
Types of Multivariate Techniques
15
Principal Components and Common Factor Analysis
16
Multiple Regression
16
Multiple Discriminant Analysis and Logistic Regression
16
Canonical Correlation
17
Multivariate Analysis of Variance and Covariance
17
Conjoint Analysis
18
Cluster Analysis
18
Perceptual Mapping
19
Correspondence Analysis
19
Structural Equation Modeling and Confirmatory Factor
Analysis
19
Guidelines for Multivariate Analyses and Interpretation
20
Establish Practical Significance as Well as Statistical
Significance
20
Recognize That Sample Size Affects All Results
21
Know Your Data
21
Strive for Model Parsimony
21
Look at Your Errors
22
Validate Your Results
22
A Structured Approach to Multivariate Model Building
22
ix
Contents
Stage
1:
Define the Research Problem, Objectives,
and Multivariate Technique to Be Used
23
Stage
2:
Develop the Analysis Plan
23
Stage
3:
Evaluate the Assumptions Underlying the Multivariate
Technique
23
Stage
4:
Estimate the Multivariate Model and Assess Overall
Model Fit
23
Stage
5:
Interpret the Variate(s)
24
Stage
6:
Validate the Multivariate Model
24
A Decision Flowchart
24
Databases
24
Primary Database
25
Other Databases
27
Organization of the Remaining Chapters
28
Section I: Understanding and Preparing For Multivariate Analysis
28
Section II: Analysis Using Dependence Techniques
28
Section III: Interdependence Techniques
28
Section IV: Structural Equations Modeling
28
Summary
28 ·
Questions
30 ·
Suggested Readings
30
References
30
SECTION I Understanding and Preparing For Multivariate
Analysis
31
Chapter
2
Cleaning and Transforming Data
33
Introduction
36
Graphical Examination of the Data
37
Univariate Profiling: Examining the Shape of the
Distribution
38
Bivariate Profiling: Examining the Relationship Between
Variables
39
Bivariate Profiling: Examining Group Differences
40
Multivariate Profiles
41
Missing Data
42
The Impact of Missing Data
42
A Simple Example of a Missing Data Analysis
43
A Four-Step Process for Identifying Missing Data and Applying
Remedies
44
An Illustration of Missing Data Diagnosis with
the Four-Step Process
54
Outliers
64
Detecting and Handling Outliers
65
An Illustrative Example of Analyzing Outliers
68
Testing the Assumptions of Multivariate Analysis
70
Contents xi
Assessing Individual Variables Versus the
Variate
70
Four Important Statistical Assumptions
71
Data Transformations
77
An Illustration of Testing the Assumptions Underlying
Multivariate Analysis
79
Incorporating Nonmetric Data with Dummy Variables
86
Summary
88 ·
Questions
89 ·
Suggested Readings
89
References
90
Chapter
3
Factor Analysis
91
What Is Factor Analysis?
94
A Hypothetical Example of Factor Analysis
95
Factor Analysis Decision Process
96
Stage
1:
Objectives of Factor Analysis
96
Specifying the Unit of Analysis
98
Achieving Data Summarization Versus Data Reduction
98
Variable Selection
99
Using Factor Analysis with Other Multivariate Techniques
100
Stage
2:
Designing a Factor Analysis
100
Correlations Among Variables or Respondents
100
Variable Selection and Measurement Issues
101
Sample Size
102
Summary
102
Stage
3:
Assumptions in Factor Analysis
103
Conceptual Issues
103
Statistical Issues
103
Summary
104
Stage
4:
Deriving Factors and Assessing Overall Fit
105
Selecting the Factor Extraction Method
105
Criteria for the Number of Factors to Extract
108
Stage
5:
Interpreting the Factors
112
The Three Processes of Factor Interpretation
112
Rotation of Factors
113
Judging the Significance of Factor Loadings
116
Interpreting a Factor Matrix
118
Stage
6:
Validation of Factor Analysis
122
Use of a Confirmatory Perspective
122
Assessing Factor Structure Stability
122
Detecting Influential Observations
123
Stage
7:
Additional Uses of Factor Analysis Results
123
Selecting Surrogate Variables for Subsequent Analysis
123
Creating
Summated
Scales
124
xii Contents
Computing
Factor Scores
127
Selecting Among the Three Methods
128
An Illustrative Example
129
Stage
1:
Objectives of Factor Analysis
129
Stage
2:
Designing a Factor Analysis
129
Stage
3:
Assumptions in Factor Analysis
129
Component Factor Analysis: Stages
4
Through
7 132
Common Factor Analysis: Stages
4
and
5 144
A Managerial Overview of the Results
146
Summary
148 ·
Questions
150 ·
Suggested Readings
150
References
150
SECTION II Analysis Using Dependence Techniques
153
Chapter
4
Simple and Multiple Regression
155
What Is Multiple Regression Analysis?
161
An Example of Simple and Multiple Regression
162
Prediction Using a Single Independent Variable:
Simple Regression
162
Prediction Using Several Independent Variables:
Multiple Regression
165
Summary
167
A Decision Process for Multiple Regression Analysis
167
Stage
1:
Objectives of Multiple Regression
169
Research Problems Appropriate for Multiple Regression
169
Specifying a Statistical Relationship
171
Selection of Dependent and Independent Variables
171
Stage
2:
Research Design of a Multiple Regression Analysis
173
Sample Size
174
Creating Additional Variables
176
Stage
3:
Assumptions in Multiple Regression Analysis
181
Assessing Individual Variables Versus the
Variate
182
Methods of Diagnosis
183
Linearity of the Phenomenon
183
Constant Variance of the Error Term
185
Independence of the Error Terms
185
Normality of the Error Term Distribution
185
Summary
186
Stage
4:
Estimating the Regression Model and Assessing Overall
Model Fit
186
Selecting an Estimation Technique
186
Testing the Regression
Variate
for Meeting the Regression
Assumptions
191
Contents xiii
Examining the Statistical Significance of Our Model
192
Identifying Influential Observations
194
Stage
5:
Interpreting the Regression
Variate
197
Using the Regression Coefficients
197
Assessing Multicollinearity
200
Stage
6:
Validation of the Results
206
Additional or Split Samples
206
Calculating the PRESS Statistic
206
Comparing Regression Models
206
Forecasting with the Model
207
Illustration of a Regression Analysis
207
Stage
1:
Objectives of Multiple Regression
207
Stage
2:
Research Design of a Multiple Regression Analysis
208
Stage
3:
Assumptions in Multiple Regression Analysis
208
Stage
4:
Estimating the Regression Model and Assessing
Overall Model Fit
208
Stage
5:
Interpreting the Regression
Variate
223
Stage
6:
Validating the Results
226
Evaluating Alternative Regression Models
227
A Managerial Overview of the Results
231
Summary
231 ·
Questions
234 ·
Suggested Readings
234
References
234
Chapter
5
Canonical Correlation
235
What Is Canonical Correlation?
237
Hypothetical Example of Canonical Correlation
238
Developing
a Variate
of Dependent Variables
238
Estimating the First Canonical Function
238
Estimating a Second Canonical Function
240
Relationships of Canonical Correlation Analysis to Other
Multivariate Techniques
241
Stage
1:
Objectives of Canonical Correlation Analysis
242
Selection of Variable Sets
242
Evaluating Research Objectives
242
Stage
2:
Designing a Canonical Correlation Analysis
243
Sample Size
243
Variables and Their Conceptual Linkage
243
Missing Data and Outliers
244
Stage
3:
Assumptions in Canonical Correlation
244
Linearity
244
Normality
244
Homoscedasticity and Multicollinearity
244
xiv Contents
Stage
4:
Deriving the Canonical Functions and Assessing
Overall Fit
245
Deriving Canonical Functions
246
Which Canonical Functions Should Be Interpreted?
246
Stage
5:
Interpreting the Canonical
Variate
250
Canonical Weights
250
Canonical Loadings
250
Canonical Cross-Loadings
250
Which Interpretation Approach to Use
251
Stage
6:
Validation and Diagnosis
251
An Illustrative Example
252
Stage
1:
Objectives of Canonical Correlation Analysis
253
Stages
2
and
3:
Designing a Canonical Correlation Analysis
and Testing the Assumptions
253
Stage
4:
Deriving the Canonical Functions and Assessing
Overall Fit
253
Stage
5:
Interpreting the Canonical
Variâtes
254
Stage
6:
Validation and Diagnosis
257
A Managerial Overview of the Results
258
Summary
258 ·
Questions
259 ·
References
260
Chapter
6
Conjoint Analysis
261
What Is Conjoint Analysis?
266
Hypothetical Example of Conjoint Analysis
267
Specifying Utility, Factors, Levels, and Profiles
267
Gathering Preferences from Respondents
268
Estimating Part-Worths
269
Determining Attribute Importance
270
Assessing Predictive Accuracy
270
The Managerial Uses of Conjoint Analysis
271
Comparing Conjoint Analysis with Other Multivariate
Methods
272
Compositional Versus Decompositional Techniques
272
Specifying the Conjoint
Variate
272
Separate Models for Each Individual
272
Flexibility in Types of Relationships
273
Designing a Conjoint Analysis Experiment
273
Stage
1:
The Objectives of Conjoint Analysis
276
Defining the Total Utility of the Object
276
Specifying the Determinant Factors
276
Stage
2:
The Design of a Conjoint Analysis
277
Selecting a Conjoint Analysis Methodology
278
Contents xv
Designing Profiles:
Selecting and Defining Factors
and Levels
278
Specifying the Basic Model Form
283
Data Collection
286
Stage
3:
Assumptions of Conjoint Analysis
293
Stage
4:
Estimating the Conjoint Model and Assessing Overall Fit
294
Selecting an Estimation Technique
294
Estimated Part-Worths
297
Evaluating Model Goodness-of-Fit
298
Stage
5:
Interpreting the Results
299
Examining the Estimated Part-Worths
300
Assessing the Relative Importance of Attributes
302
Stage
6:
Validation of the Conjoint Results
303
Managerial Applications of Conjoint Analysis
303
Segmentation
304
Profitability Analysis
304
Conjoint Simulators
305
Alternative Conjoint Methodologies
306
Adaptive/Self-Explicated Conjoint: Conjoint with
a Large Number of Factors
306
Choice-Based Conjoint: Adding Another Touch of Realism
308
Overview of the Three Conjoint Methodologies
312
An Illustration of Conjoint Analysis
312
Stage
1:
Objectives of the Conjoint Analysis
313
Stage
2:
Design of the Conjoint Analysis
313
Stage
3:
Assumptions in Conjoint Analysis
316
Stage
4:
Estimating the Conjoint Model and Assessing Overall
Model Fit
316
Stage
5:
Interpreting the Results
320
Stage
6:
Validation of the Results
324
A Managerial Application: Use of a Choice Simulator
325
Summary
327 ·
Questions
330 ·
Suggested Readings
330
References
330
Chapter
7
Multiple Discriminant Analysis and Logistic Regression
335
What Are Discriminant Analysis and Logistic Regression?
339
Discriminant Analysis
340
Logistic Regression
341
Analogy with Regression and
MÁNOVA
341
Hypothetical Example of Discriminant Analysis
342
A Two-Group Discriminant Analysis: Purchasers Versus
Nonpurchasers
342
xvi Contents
A
Geometrie
Representation of the Two-Group Discriminant
Function
345
A Three-Group Example of Discriminant Analysis: Switching
Intentions
346
The Decision Process for Discriminant Analysis
348
Stage
1:
Objectives of Discriminant Analysis
350
Stage
2:
Research Design for Discriminant Analysis
351
Selecting Dependent and Independent Variables
351
Sample Size
353
Division of the Sample
353
Stage
3:
Assumptions of Discriminant Analysis
354
Impacts on Estimation and Classification
354
Impacts on Interpretation
355
Stage
4:
Estimation of the Discriminant Model and Assessing
Overall Fit
356
Selecting an Estimation Method
356
Statistical Significance
358
Assessing Overall Model Fit
359
Casewise Diagnostics
368
Stage
5:
Interpretation of the Results
369
Discriminant Weights
369
Discriminant Loadings
370
Partial FValues
370
Interpretation of Two or More Functions
370
Which Interpretive Method to Use?
373
Stage
6:
Validation of the Results
373
Validation Procedures
373
Profiling Group Differences
374
A Two-Group Illustrative Example
375
Stage
1:
Objectives of Discriminant Analysis
375
Stage
2:
Research Design for Discriminant Analysis
375
Stage
3:
Assumptions of Discriminant Analysis
376
Stage
4:
Estimation of the Discriminant Model
and Assessing Overall Fit
376
Stage
5:
Interpretation of the Results
387
Stage
6:
Validation of the Results
390
A Managerial Overview
391
A Three-Group Illustrative Example
391
Stage
1:
Objectives of Discriminant Analysis
391
Stage
2:
Research Design for Discriminant Analysis
392
Stage
3:
Assumptions of Discriminant Analysis
392
Contents xvii
Stage
4:
Estimation of the Discriminant Model and Assessing
Overall Fit
392
Stage
5:
Interpretation of Three-Group Discriminant Analysis
Results
404
Stage
6:
Validation of the Discriminant Results
410
A Managerial Overview
412
Logistic Regression: Regression with a Binary Dependent
Variable
413
Representation of the Binary Dependent Variable
414
Sample Size
415
Estimating the Logistic Regression Model
416
Assessing the Goodness-of-Fit of the Estimation Model
419
Testing for Significance of the Coefficients
421
Interpreting the Coefficients
422
Calculating Probabilities for a Specific Value of the Independent
Variable
425
Overview of Interpreting Coefficients
425
Summary
425
An Illustrative Example of Logistic Regression
426
Stages
1, 2,
and
3:
Research Objectives, Research Design,
and Statistical Assumptions
426
Stage
4:
Estimation of the Logistic Regression Model
and Assessing Overall Fit
426
Stage
5:
Interpretation of the Results
432
Stage
6:
Validation of the Results
433
A Managerial Overview
434
Summary
434 ·
Questions
437 ·
Suggested Readings
437
References
437
Chapter
8
ANOVA and
MÁNOVA
439
MÁNOVA:
Extending Univariate Methods for Assessing Group
Differences
443
Multivariate Procedures for Assessing Group Differences
444
A Hypothetical Illustration of
MÁNOVA
447
Analysis Design
447
Differences from Discriminant Analysis
448
Forming the
Variate
and Assessing Differences
448
A Decision Process for
MÁNOVA
449
Stage
1 :
Objectives of
MÁNOVA
450
When Should We Use
MÁNOVA?
450
Types of Multivariate Questions Suitable for
MÁNOVA
451
Selecting the Dependent Measures
452
xviii Contents
Stage
2:
Issues in the Research Design of
MÁNOVA
453
Sample Size Requirements
—
Overall and by Group
453
Factorial Designs
—
Two or More Treatments
453
Using Covariates—ANCOVA and MANCOVA
455
MÁNOVA
Counterparts of Other ANOVA Designs
457
A Special Case of
MÁNOVA:
Repeated Measures
457
Stage
3:
Assumptions of ANOVA and
MÁNOVA
458
Independence
458
Equality of Variance-Covariance Matrices
459
Normality
460
Linearity and Multicollinearity Among the Dependent Variables
460
Sensitivity to Outliers
460
Stage
4:
Estimation of the
MÁNOVA
Model and Assessing
Overall Fit
460
Estimation with the General Linear Model
462
Criteria for Significance Testing
463
Statistical Power of the Multivariate Tests
463
Stage
5:
Interpretation of the
MÁNOVA
Results
468
Evaluating Covariates
468
Assessing Effects on the Dependent
Variate
468
Identifying Differences Between Individual Groups
472
Assessing Significance for Individual Dependent Variables
474
Stage
6:
Validation of the Results
475
Summary
476
Illustration of a MANOVA Analysis
476
Example
1:
Difference Between Two Independent Groups
477
Stage
1:
Objectives of the Analysis
478
Stage
2:
Research Design of the MANOVA
478
Stage
3:
Assumptions in MANOVA
479
Stage
4:
Estimation of the MANOVA Model and Assessing
the Overall Fit
480
Stage
5:
Interpretation of the Results
482
Example
2:
Difference Between
К
Independent Groups
482
Stage
1 :
Objectives of the MANOVA
483
Stage
2:
Research Design of MANOVA
483
Stage
3:
Assumptions in MANOVA
484
Stage
4:
Estimation of the MANOVA Model and Assessing
Overall Fit
485
Stage
5:
Interpretation of the Results
485
Example
3:
A Factorial Design for MANOVA with Two Independent
Variables
488
Contents xix
Stage
1 :
Objectives of the
MÁNOVA
489
Stage
2:
Research Design of the
MÁNOVA
489
Stage
3:
Assumptions in
MÁNOVA
491
Stage
4:
Estimation of the
MÁNOVA
Model and Assessing
Overall Fit
492
Stage
5:
Interpretation of the Results
495
Summary
496
A Managerial Overview of the Results
496
Summary
498 ·
Questions
500 ·
Suggested Readings
500
References
500
SECTION III Analysis Using Interdependence Techniques
503
Chapter
9
Grouping Data with Cluster Analysis
505
What Is Cluster Analysis?
508
Cluster Analysis as a Multivariate Technique
508
Conceptual Development with Cluster Analysis
508
Necessity of Conceptual Support in Cluster Analysis
509
How Does Cluster Analysis Work?
510
A Simple Example
510
Objective Versus Subjective Considerations
515
Cluster Analysis Decision Process
515
Stage
1:
Objectives of Cluster Analysis
517
Stage
2:
Research Design in Cluster Analysis
518
Stage
3:
Assumptions in Cluster Analysis
526
Stage
4:
Deriving Clusters and Assessing Overall Fit
527
Stage
5:
Interpretation of the Clusters
538
Stage
6:
Validation and Profiling of the Clusters
539
An Illustrative Example
541
Stage
1:
Objectives of the Cluster Analysis
541
Stage
2:
Research Design of the Cluster Analysis
542
Stage
3:
Assumptions in Cluster Analysis
545
Employing Hierarchical and Nonhierarchical Methods
546
Step
1:
Hierarchical Cluster Analysis (Stage
4) 546
Step
2:
Nonhierarchical Cluster Analysis (Stages
4, 5,
and
6) 552
Summary
561 ·
Questions
563 ·
Suggested Readings
563
References
563
Chapter
10
MDS and Correspondence Analysis
565
What Is Multidimensional Scaling?
568
Comparing Objects
568
Dimensions: The Basis for Comparison
569
xx Contents
A Simplified Look at How MDS Works
570
Gathering Similarity Judgments
570
Creating a Perceptual Map
570
Interpreting the Axes
571
Comparing MDS to Other Interdependence Techniques
572
Individual as the Unit of Analysis
573
Lack of
a Variate
573
A Decision Framework for Perceptual Mapping
573
Stage
1:
Objectives of MDS
573
Key Decisions in Setting Objectives
573
Stage
2:
Research Design of MDS
578
Selection of Either a Decompositional (Attribute-Free)
or Compositional (Attribute-Based) Approach
578
Objects: Their Number and Selection
580
Nonmetric Versus Metric Methods
581
Collection of Similarity or Preference Data
581
Stage
3:
Assumptions of MDS Analysis
584
Stage
4:
Deriving the MDS Solution and Assessing
Overall Fit
584
Determining an Object s Position in the Perceptual Map
584
Selecting the Dimensionality of the Perceptual Map
586
Incorporating Preferences into MDS
587
Stage
5:
Interpreting the MDS Results
592
Identifying the Dimensions
593
Stage
6:
Validating the MDS Results
594
Issues in Validation
594
Approaches to Validation
594
Overview of Multidimensional Scaling
595
Correspondence Analysis
595
Distinguishing Characteristics
595
Differences from Other Multivariate Techniques
596
A Simple Example of CA
596
A Decision Framework for Correspondence Analysis
600
Stage
1:
Objectives of CA
601
Stage
2:
Research Design of CA
601
Stage
3:
Assumptions in CA
602
Stage
4:
Deriving CA Results and Assessing Overall Fit
602
Stage
5:
Interpretation of the Results
603
Stage
6:
Validation of the Results
604
Overview of Correspondence Analysis
604
Illustrations of MDS and Correspondence Analysis
605
Contents xxi
Stage
1:
Objectives of Perceptual Mapping
606
Identifying Objects for Inclusion
606
Basing the Analysis on Similarity or Preference Data
607
Using a Disaggregate or Aggregate Analysis
607
Stage
2:
Research Design of the Perceptual Mapping Study
607
Selecting Decompositional or Compositional Methods
607
Selecting Firms for Analysis
608
Nonmetric Versus Metric Methods
608
Collecting Data for MDS
608
Collecting Data for Correspondence Analysis
609
Stage
3:
Assumptions in Perceptual Mapping
610
Multidimensional Scaling: Stages
4
and
5 610
Stage
4:
Deriving MDS Results and Assessing Overall Fit
610
Stage
5:
Interpretation of the Results
615
Overview of the Decompositional Results
616
Correspondence Analysis: Stages
4
and
5 617
Stage
4:
Estimating a Correspondence Analysis
617
Stage
5:
Interpreting CA Results
619
Overview of CA
621
Stage
6:
Validation of the Results
622
A Managerial Overview of MDS Results
622
Summary
623 ·
Questions
625 ·
Suggested Readings
625
References
625
SECTION IV Structural Equations Modeling
627
Chapter
11 SEM:
An Introduction
629
What Is Structural Equation Modeling?
634
Estimation of Multiple Interrelated Dependence
Relationships
635
Incorporating Latent Variables Not Measured Directly
635
Defining a Model
637
SEM
and Other Multivariate Techniques
641
Similarity to Dependence Techniques
641
Similarity to Interdependence Techniques
641
The Emergence of
SEM 642
The Role of Theory in Structural Equation Modeling
642
Specifying Relationships
642
Establishing Causation
643
Developing a Modeling Strategy
646
A Simple Example of
SEM 647
The Research Question
647
xxii Contents
Setting Up the Structural Equation Model
for Path Analysis
648
The Basics of
SEM
Estimation and Assessment
649
Six Stages in Structural Equation Modeling
653
Stage
1:
Defining Individual Constructs
655
Operationalizing the Construct
655
Pretesting
655
Stage
2:
Developing and Specifying the Measurement
Model
656
SEM
Notation
656
Creating the Measurement Model
657
Stage
3:
Designing a Study to Produce Empirical Results
657
Issues in Research Design
658
Issues in Model Estimation
662
Stage
4:
Assessing Measurement Model Validity
664
The Basics of Goodness-of-Fit
665
Absolute Fit Indices
666
Incremental Fit Indices
668
Parsimony Fit Indices
669
Problems Associated with Using Fit Indices
669
Unacceptable Model Specification to Achieve Fit
671
Guidelines for Establishing Acceptable and Unacceptable Fit
672
Stage
5:
Specifying the Structural Model
673
Stage
6:
Assessing the Structural Model Validity
675
Structural Model GOF
675
Competitive Fit
676
Comparison to the Measurement Model
676
Testing Structural Relationships
677
Summary
678 ·
Questions
680 ·
Suggested Readings
680
Appendix
1
1A: Estimating Relationships Using Path Analysis
681
Appendix
1
1B:
SEM
Abbreviations
683
Appendix 11C: Detail on Selected GOF Indices
684
References
685
Chapter^ Applications of
SEM 687
Part
1:
Confirmatory Factor Analysis
693
CFA
and Exploratory Factor Analysis
693
A Simple Example of
CFA
and
SEM 694
A Visual Diagram
694
SEM
Stages for Testing Measurement Theory Validation
with
CFA
695
Stage
1:
Defining Individual Constructs
696
Contents xxiii
Stage
2:
Developing the Overall Measurement Model
696
Unidimensionality
696
Congeneric Measurement Model
698
Items per Construct
698
Reflective Versus Formative Constructs
701
Stage
3:
Designing a Study to Produce Empirical Results
702
Measurement Scales in
CFA
702
SEM
and Sampling
703
Specifying the Model
703
Issues in Identification
704
Avoiding Identification Problems
704
Problems in Estimation
706
Stage
4:
Assessing Measurement Model Validity
707
Assessing Fit
707
Path Estimates
707
Construct Validity
708
Model Diagnostics
711
Summary Example
713
CFA
Illustration
715
Stage
1:
Defining Individual Constructs
716
Stage
2:
Developing the Overall Measurement Model
716
Stage
3:
Designing a Study to Produce Empirical Results
718
Stage
4:
Assessing Measurement Model Validity
719
H BAT CFA
Summary
727
Part
2:
What Is a Structural Model?
727
A Simple Example of a Structural Model
728
An Overview of Theory Testing with
SEM 729
Stages in Testing Structural Theory
730
One-Step Versus Two-Step Approaches
730
Stage
5:
Specifying the Structural Model
731
Unit of Analysis
731
Model Specification Using a Path Diagram
731
Designing the Study
735
Stage
6:
Assessing the Structural Model Validity
737
Understanding Structural Model Fit from
CFA Fit
737
Examine the Model Diagnostics
739
SEM
Illustration
740
Stage
5:
Specifying the Structural Model
740
Stage
6:
Assessing the Structural Model Validity
742
Part
3:
Extensions and Applications of
SEM 749
xxiv Contents
Reflective
Versus
Formative Measures
749
Reflective Versus Formative Measurement Theory
749
Operationalizing a Formative Construct
750
Distinguishing Reflective from Formative Constructs
751
Which to Use
—
Reflective or Formative?
753
Higher-Order Factor Analysis
754
Empirical Concerns
754
Theoretical Concerns
756
Using Second-Order Measurement Theories
756
When to Use Higher-Order Factor Analysis
757
Multiple Groups Analysis
758
Measurement Model Comparisons
758
Structural Model Comparisons
763
Measurement Bias
764
Model Specification
764
Model Interpretation
765
Relationship Types: Mediation and Moderation
766
Mediation
766
Moderation
770
Longitudinal Data
773
Additional Covariance Sources: Timing
773
Using Error Covariances to Represent Added Covariance
774
Partial Least Squares
775
Characteristics of PLS
775
Advantages and Disadvantages of PLS
776
Choosing PLS Versus
SEM 777
Summary
778 ·
Questions
781 ·
Suggested Readings
781
References
782
Index
785
|
| adam_txt |
BRIEF
CONTENTS
Chapter
1
Introduction: Methods and Model Building
1
SECTION I Understanding and Preparing For Multivariate
Analysis
31
Chapter
2
Cleaning and Transforming Data
33
Chapter
3
Factor Analysis
91
SECTION II Analysis Using Dependence Techniques
153
Chapter
4
Simple and Multiple Regression
155
Chapter
5
Canonical Correlation
235
Chapter
6
Conjoint Analysis
261
Chapter
7
Multiple Discriminant Analysis and Logistic Regression
335
Chapter
8
ANOVA and
MÁNOVA
439
SECTION III Analysis Using Interdependence Techniques
503
Chapter
9
Grouping Data with Cluster Analysis
505
Chapter
10
MDS and Correspondence Analysis
565
SECTION IV Structural Equations Modeling
627
Chapter
11
SEM:
An Introduction
629
Chapter
12
Applications of
SEM
687
VII
CONTENTS
Preface
xxv
About the Authors
xxvii
Chapter
1
Introduction: Methods and Model Building
1
What Is Multivariate Analysis?
3
Multivariate Analysis in Statistical Terms
4
Some Basic Concepts of Multivariate Analysis
4
The
Variate
4
Measurement Scales
5
Measurement Error and Multivariate Measurement
7
Statistical Significance Versus Statistical Power
8
Types of Statistical Error and Statistical Power
9
Impacts on Statistical Power
9
Using Power with Multivariate Techniques
11
A Classification of Multivariate Techniques
11
Dependence Techniques
14
Interdependence Techniques
14
Types of Multivariate Techniques
15
Principal Components and Common Factor Analysis
16
Multiple Regression
16
Multiple Discriminant Analysis and Logistic Regression
16
Canonical Correlation
17
Multivariate Analysis of Variance and Covariance
17
Conjoint Analysis
18
Cluster Analysis
18
Perceptual Mapping
19
Correspondence Analysis
19
Structural Equation Modeling and Confirmatory Factor
Analysis
19
Guidelines for Multivariate Analyses and Interpretation
20
Establish Practical Significance as Well as Statistical
Significance
20
Recognize That Sample Size Affects All Results
21
Know Your Data
21
Strive for Model Parsimony
21
Look at Your Errors
22
Validate Your Results
22
A Structured Approach to Multivariate Model Building
22
ix
Contents
Stage
1:
Define the Research Problem, Objectives,
and Multivariate Technique to Be Used
23
Stage
2:
Develop the Analysis Plan
23
Stage
3:
Evaluate the Assumptions Underlying the Multivariate
Technique
23
Stage
4:
Estimate the Multivariate Model and Assess Overall
Model Fit
23
Stage
5:
Interpret the Variate(s)
24
Stage
6:
Validate the Multivariate Model
24
A Decision Flowchart
24
Databases
24
Primary Database
25
Other Databases
27
Organization of the Remaining Chapters
28
Section I: Understanding and Preparing For Multivariate Analysis
28
Section II: Analysis Using Dependence Techniques
28
Section III: Interdependence Techniques
28
Section IV: Structural Equations Modeling
28
Summary
28 ·
Questions
30 ·
Suggested Readings
30
References
30
SECTION I Understanding and Preparing For Multivariate
Analysis
31
Chapter
2
Cleaning and Transforming Data
33
Introduction
36
Graphical Examination of the Data
37
Univariate Profiling: Examining the Shape of the
Distribution
38
Bivariate Profiling: Examining the Relationship Between
Variables
39
Bivariate Profiling: Examining Group Differences
40
Multivariate Profiles
41
Missing Data
42
The Impact of Missing Data
42
A Simple Example of a Missing Data Analysis
43
A Four-Step Process for Identifying Missing Data and Applying
Remedies
44
An Illustration of Missing Data Diagnosis with
the Four-Step Process
54
Outliers
64
Detecting and Handling Outliers
65
An Illustrative Example of Analyzing Outliers
68
Testing the Assumptions of Multivariate Analysis
70
Contents xi
Assessing Individual Variables Versus the
Variate
70
Four Important Statistical Assumptions
71
Data Transformations
77
An Illustration of Testing the Assumptions Underlying
Multivariate Analysis
79
Incorporating Nonmetric Data with Dummy Variables
86
Summary
88 ·
Questions
89 ·
Suggested Readings
89
References
90
Chapter
3
Factor Analysis
91
What Is Factor Analysis?
94
A Hypothetical Example of Factor Analysis
95
Factor Analysis Decision Process
96
Stage
1:
Objectives of Factor Analysis
96
Specifying the Unit of Analysis
98
Achieving Data Summarization Versus Data Reduction
98
Variable Selection
99
Using Factor Analysis with Other Multivariate Techniques
100
Stage
2:
Designing a Factor Analysis
100
Correlations Among Variables or Respondents
100
Variable Selection and Measurement Issues
101
Sample Size
102
Summary
102
Stage
3:
Assumptions in Factor Analysis
103
Conceptual Issues
103
Statistical Issues
103
Summary
104
Stage
4:
Deriving Factors and Assessing Overall Fit
105
Selecting the Factor Extraction Method
105
Criteria for the Number of Factors to Extract
108
Stage
5:
Interpreting the Factors
112
The Three Processes of Factor Interpretation
112
Rotation of Factors
113
Judging the Significance of Factor Loadings
116
Interpreting a Factor Matrix
118
Stage
6:
Validation of Factor Analysis
122
Use of a Confirmatory Perspective
122
Assessing Factor Structure Stability
122
Detecting Influential Observations
123
Stage
7:
Additional Uses of Factor Analysis Results
123
Selecting Surrogate Variables for Subsequent Analysis
123
Creating
Summated
Scales
124
xii Contents
Computing
Factor Scores
127
Selecting Among the Three Methods
128
An Illustrative Example
129
Stage
1:
Objectives of Factor Analysis
129
Stage
2:
Designing a Factor Analysis
129
Stage
3:
Assumptions in Factor Analysis
129
Component Factor Analysis: Stages
4
Through
7 132
Common Factor Analysis: Stages
4
and
5 144
A Managerial Overview of the Results
146
Summary
148 ·
Questions
150 ·
Suggested Readings
150
References
150
SECTION II Analysis Using Dependence Techniques
153
Chapter
4
Simple and Multiple Regression
155
What Is Multiple Regression Analysis?
161
An Example of Simple and Multiple Regression
162
Prediction Using a Single Independent Variable:
Simple Regression
162
Prediction Using Several Independent Variables:
Multiple Regression
165
Summary
167
A Decision Process for Multiple Regression Analysis
167
Stage
1:
Objectives of Multiple Regression
169
Research Problems Appropriate for Multiple Regression
169
Specifying a Statistical Relationship
171
Selection of Dependent and Independent Variables
171
Stage
2:
Research Design of a Multiple Regression Analysis
173
Sample Size
174
Creating Additional Variables
176
Stage
3:
Assumptions in Multiple Regression Analysis
181
Assessing Individual Variables Versus the
Variate
182
Methods of Diagnosis
183
Linearity of the Phenomenon
183
Constant Variance of the Error Term
185
Independence of the Error Terms
185
Normality of the Error Term Distribution
185
Summary
186
Stage
4:
Estimating the Regression Model and Assessing Overall
Model Fit
186
Selecting an Estimation Technique
186
Testing the Regression
Variate
for Meeting the Regression
Assumptions
191
Contents xiii
Examining the Statistical Significance of Our Model
192
Identifying Influential Observations
194
Stage
5:
Interpreting the Regression
Variate
197
Using the Regression Coefficients
197
Assessing Multicollinearity
200
Stage
6:
Validation of the Results
206
Additional or Split Samples
206
Calculating the PRESS Statistic
206
Comparing Regression Models
206
Forecasting with the Model
207
Illustration of a Regression Analysis
207
Stage
1:
Objectives of Multiple Regression
207
Stage
2:
Research Design of a Multiple Regression Analysis
208
Stage
3:
Assumptions in Multiple Regression Analysis
208
Stage
4:
Estimating the Regression Model and Assessing
Overall Model Fit
208
Stage
5:
Interpreting the Regression
Variate
223
Stage
6:
Validating the Results
226
Evaluating Alternative Regression Models
227
A Managerial Overview of the Results
231
Summary
231 ·
Questions
234 ·
Suggested Readings
234
References
234
Chapter
5
Canonical Correlation
235
What Is Canonical Correlation?
237
Hypothetical Example of Canonical Correlation
238
Developing
a Variate
of Dependent Variables
238
Estimating the First Canonical Function
238
Estimating a Second Canonical Function
240
Relationships of Canonical Correlation Analysis to Other
Multivariate Techniques
241
Stage
1:
Objectives of Canonical Correlation Analysis
242
Selection of Variable Sets
242
Evaluating Research Objectives
242
Stage
2:
Designing a Canonical Correlation Analysis
243
Sample Size
243
Variables and Their Conceptual Linkage
243
Missing Data and Outliers
244
Stage
3:
Assumptions in Canonical Correlation
244
Linearity
244
Normality
244
Homoscedasticity and Multicollinearity
244
xiv Contents
Stage
4:
Deriving the Canonical Functions and Assessing
Overall Fit
245
Deriving Canonical Functions
246
Which Canonical Functions Should Be Interpreted?
246
Stage
5:
Interpreting the Canonical
Variate
250
Canonical Weights
250
Canonical Loadings
250
Canonical Cross-Loadings
250
Which Interpretation Approach to Use
251
Stage
6:
Validation and Diagnosis
251
An Illustrative Example
252
Stage
1:
Objectives of Canonical Correlation Analysis
253
Stages
2
and
3:
Designing a Canonical Correlation Analysis
and Testing the Assumptions
253
Stage
4:
Deriving the Canonical Functions and Assessing
Overall Fit
253
Stage
5:
Interpreting the Canonical
Variâtes
254
Stage
6:
Validation and Diagnosis
257
A Managerial Overview of the Results
258
Summary
258 ·
Questions
259 ·
References
260
Chapter
6
Conjoint Analysis
261
What Is Conjoint Analysis?
266
Hypothetical Example of Conjoint Analysis
267
Specifying Utility, Factors, Levels, and Profiles
267
Gathering Preferences from Respondents
268
Estimating Part-Worths
269
Determining Attribute Importance
270
Assessing Predictive Accuracy
270
The Managerial Uses of Conjoint Analysis
271
Comparing Conjoint Analysis with Other Multivariate
Methods
272
Compositional Versus Decompositional Techniques
272
Specifying the Conjoint
Variate
272
Separate Models for Each Individual
272
Flexibility in Types of Relationships
273
Designing a Conjoint Analysis Experiment
273
Stage
1:
The Objectives of Conjoint Analysis
276
Defining the Total Utility of the Object
276
Specifying the Determinant Factors
276
Stage
2:
The Design of a Conjoint Analysis
277
Selecting a Conjoint Analysis Methodology
278
Contents xv
Designing Profiles:
Selecting and Defining Factors
and Levels
278
Specifying the Basic Model Form
283
Data Collection
286
Stage
3:
Assumptions of Conjoint Analysis
293
Stage
4:
Estimating the Conjoint Model and Assessing Overall Fit
294
Selecting an Estimation Technique
294
Estimated Part-Worths
297
Evaluating Model Goodness-of-Fit
298
Stage
5:
Interpreting the Results
299
Examining the Estimated Part-Worths
300
Assessing the Relative Importance of Attributes
302
Stage
6:
Validation of the Conjoint Results
303
Managerial Applications of Conjoint Analysis
303
Segmentation
304
Profitability Analysis
304
Conjoint Simulators
305
Alternative Conjoint Methodologies
306
Adaptive/Self-Explicated Conjoint: Conjoint with
a Large Number of Factors
306
Choice-Based Conjoint: Adding Another Touch of Realism
308
Overview of the Three Conjoint Methodologies
312
An Illustration of Conjoint Analysis
312
Stage
1:
Objectives of the Conjoint Analysis
313
Stage
2:
Design of the Conjoint Analysis
313
Stage
3:
Assumptions in Conjoint Analysis
316
Stage
4:
Estimating the Conjoint Model and Assessing Overall
Model Fit
316
Stage
5:
Interpreting the Results
320
Stage
6:
Validation of the Results
324
A Managerial Application: Use of a Choice Simulator
325
Summary
327 ·
Questions
330 ·
Suggested Readings
330
References
330
Chapter
7
Multiple Discriminant Analysis and Logistic Regression
335
What Are Discriminant Analysis and Logistic Regression?
339
Discriminant Analysis
340
Logistic Regression
341
Analogy with Regression and
MÁNOVA
341
Hypothetical Example of Discriminant Analysis
342
A Two-Group Discriminant Analysis: Purchasers Versus
Nonpurchasers
342
xvi Contents
A
Geometrie
Representation of the Two-Group Discriminant
Function
345
A Three-Group Example of Discriminant Analysis: Switching
Intentions
346
The Decision Process for Discriminant Analysis
348
Stage
1:
Objectives of Discriminant Analysis
350
Stage
2:
Research Design for Discriminant Analysis
351
Selecting Dependent and Independent Variables
351
Sample Size
353
Division of the Sample
353
Stage
3:
Assumptions of Discriminant Analysis
354
Impacts on Estimation and Classification
354
Impacts on Interpretation
355
Stage
4:
Estimation of the Discriminant Model and Assessing
Overall Fit
356
Selecting an Estimation Method
356
Statistical Significance
358
Assessing Overall Model Fit
359
Casewise Diagnostics
368
Stage
5:
Interpretation of the Results
369
Discriminant Weights
369
Discriminant Loadings
370
Partial FValues
370
Interpretation of Two or More Functions
370
Which Interpretive Method to Use?
373
Stage
6:
Validation of the Results
373
Validation Procedures
373
Profiling Group Differences
374
A Two-Group Illustrative Example
375
Stage
1:
Objectives of Discriminant Analysis
375
Stage
2:
Research Design for Discriminant Analysis
375
Stage
3:
Assumptions of Discriminant Analysis
376
Stage
4:
Estimation of the Discriminant Model
and Assessing Overall Fit
376
Stage
5:
Interpretation of the Results
387
Stage
6:
Validation of the Results
390
A Managerial Overview
391
A Three-Group Illustrative Example
391
Stage
1:
Objectives of Discriminant Analysis
391
Stage
2:
Research Design for Discriminant Analysis
392
Stage
3:
Assumptions of Discriminant Analysis
392
Contents xvii
Stage
4:
Estimation of the Discriminant Model and Assessing
Overall Fit
392
Stage
5:
Interpretation of Three-Group Discriminant Analysis
Results
404
Stage
6:
Validation of the Discriminant Results
410
A Managerial Overview
412
Logistic Regression: Regression with a Binary Dependent
Variable
413
Representation of the Binary Dependent Variable
414
Sample Size
415
Estimating the Logistic Regression Model
416
Assessing the Goodness-of-Fit of the Estimation Model
419
Testing for Significance of the Coefficients
421
Interpreting the Coefficients
422
Calculating Probabilities for a Specific Value of the Independent
Variable
425
Overview of Interpreting Coefficients
425
Summary
425
An Illustrative Example of Logistic Regression
426
Stages
1, 2,
and
3:
Research Objectives, Research Design,
and Statistical Assumptions
426
Stage
4:
Estimation of the Logistic Regression Model
and Assessing Overall Fit
426
Stage
5:
Interpretation of the Results
432
Stage
6:
Validation of the Results
433
A Managerial Overview
434
Summary
434 ·
Questions
437 ·
Suggested Readings
437
References
437
Chapter
8
ANOVA and
MÁNOVA
439
MÁNOVA:
Extending Univariate Methods for Assessing Group
Differences
443
Multivariate Procedures for Assessing Group Differences
444
A Hypothetical Illustration of
MÁNOVA
447
Analysis Design
447
Differences from Discriminant Analysis
448
Forming the
Variate
and Assessing Differences
448
A Decision Process for
MÁNOVA
449
Stage
1 :
Objectives of
MÁNOVA
450
When Should We Use
MÁNOVA?
450
Types of Multivariate Questions Suitable for
MÁNOVA
451
Selecting the Dependent Measures
452
xviii Contents
Stage
2:
Issues in the Research Design of
MÁNOVA
453
Sample Size Requirements
—
Overall and by Group
453
Factorial Designs
—
Two or More Treatments
453
Using Covariates—ANCOVA and MANCOVA
455
MÁNOVA
Counterparts of Other ANOVA Designs
457
A Special Case of
MÁNOVA:
Repeated Measures
457
Stage
3:
Assumptions of ANOVA and
MÁNOVA
458
Independence
458
Equality of Variance-Covariance Matrices
459
Normality
460
Linearity and Multicollinearity Among the Dependent Variables
460
Sensitivity to Outliers
460
Stage
4:
Estimation of the
MÁNOVA
Model and Assessing
Overall Fit
460
Estimation with the General Linear Model
462
Criteria for Significance Testing
463
Statistical Power of the Multivariate Tests
463
Stage
5:
Interpretation of the
MÁNOVA
Results
468
Evaluating Covariates
468
Assessing Effects on the Dependent
Variate
468
Identifying Differences Between Individual Groups
472
Assessing Significance for Individual Dependent Variables
474
Stage
6:
Validation of the Results
475
Summary
476
Illustration of a MANOVA Analysis
476
Example
1:
Difference Between Two Independent Groups
477
Stage
1:
Objectives of the Analysis
478
Stage
2:
Research Design of the MANOVA
478
Stage
3:
Assumptions in MANOVA
479
Stage
4:
Estimation of the MANOVA Model and Assessing
the Overall Fit
480
Stage
5:
Interpretation of the Results
482
Example
2:
Difference Between
К
Independent Groups
482
Stage
1 :
Objectives of the MANOVA
483
Stage
2:
Research Design of MANOVA
483
Stage
3:
Assumptions in MANOVA
484
Stage
4:
Estimation of the MANOVA Model and Assessing
Overall Fit
485
Stage
5:
Interpretation of the Results
485
Example
3:
A Factorial Design for MANOVA with Two Independent
Variables
488
Contents xix
Stage
1 :
Objectives of the
MÁNOVA
489
Stage
2:
Research Design of the
MÁNOVA
489
Stage
3:
Assumptions in
MÁNOVA
491
Stage
4:
Estimation of the
MÁNOVA
Model and Assessing
Overall Fit
492
Stage
5:
Interpretation of the Results
495
Summary
496
A Managerial Overview of the Results
496
Summary
498 ·
Questions
500 ·
Suggested Readings
500
References
500
SECTION III Analysis Using Interdependence Techniques
503
Chapter
9
Grouping Data with Cluster Analysis
505
What Is Cluster Analysis?
508
Cluster Analysis as a Multivariate Technique
508
Conceptual Development with Cluster Analysis
508
Necessity of Conceptual Support in Cluster Analysis
509
How Does Cluster Analysis Work?
510
A Simple Example
510
Objective Versus Subjective Considerations
515
Cluster Analysis Decision Process
515
Stage
1:
Objectives of Cluster Analysis
517
Stage
2:
Research Design in Cluster Analysis
518
Stage
3:
Assumptions in Cluster Analysis
526
Stage
4:
Deriving Clusters and Assessing Overall Fit
527
Stage
5:
Interpretation of the Clusters
538
Stage
6:
Validation and Profiling of the Clusters
539
An Illustrative Example
541
Stage
1:
Objectives of the Cluster Analysis
541
Stage
2:
Research Design of the Cluster Analysis
542
Stage
3:
Assumptions in Cluster Analysis
545
Employing Hierarchical and Nonhierarchical Methods
546
Step
1:
Hierarchical Cluster Analysis (Stage
4) 546
Step
2:
Nonhierarchical Cluster Analysis (Stages
4, 5,
and
6) 552
Summary
561 ·
Questions
563 ·
Suggested Readings
563
References
563
Chapter
10
MDS and Correspondence Analysis
565
What Is Multidimensional Scaling?
568
Comparing Objects
568
Dimensions: The Basis for Comparison
569
xx Contents
A Simplified Look at How MDS Works
570
Gathering Similarity Judgments
570
Creating a Perceptual Map
570
Interpreting the Axes
571
Comparing MDS to Other Interdependence Techniques
572
Individual as the Unit of Analysis
573
Lack of
a Variate
573
A Decision Framework for Perceptual Mapping
573
Stage
1:
Objectives of MDS
573
Key Decisions in Setting Objectives
573
Stage
2:
Research Design of MDS
578
Selection of Either a Decompositional (Attribute-Free)
or Compositional (Attribute-Based) Approach
578
Objects: Their Number and Selection
580
Nonmetric Versus Metric Methods
581
Collection of Similarity or Preference Data
581
Stage
3:
Assumptions of MDS Analysis
584
Stage
4:
Deriving the MDS Solution and Assessing
Overall Fit
584
Determining an Object's Position in the Perceptual Map
584
Selecting the Dimensionality of the Perceptual Map
586
Incorporating Preferences into MDS
587
Stage
5:
Interpreting the MDS Results
592
Identifying the Dimensions
593
Stage
6:
Validating the MDS Results
594
Issues in Validation
594
Approaches to Validation
594
Overview of Multidimensional Scaling
595
Correspondence Analysis
595
Distinguishing Characteristics
595
Differences from Other Multivariate Techniques
596
A Simple Example of CA
596
A Decision Framework for Correspondence Analysis
600
Stage
1:
Objectives of CA
601
Stage
2:
Research Design of CA
601
Stage
3:
Assumptions in CA
602
Stage
4:
Deriving CA Results and Assessing Overall Fit
602
Stage
5:
Interpretation of the Results
603
Stage
6:
Validation of the Results
604
Overview of Correspondence Analysis
604
Illustrations of MDS and Correspondence Analysis
605
Contents xxi
Stage
1:
Objectives of Perceptual Mapping
606
Identifying Objects for Inclusion
606
Basing the Analysis on Similarity or Preference Data
607
Using a Disaggregate or Aggregate Analysis
607
Stage
2:
Research Design of the Perceptual Mapping Study
607
Selecting Decompositional or Compositional Methods
607
Selecting Firms for Analysis
608
Nonmetric Versus Metric Methods
608
Collecting Data for MDS
608
Collecting Data for Correspondence Analysis
609
Stage
3:
Assumptions in Perceptual Mapping
610
Multidimensional Scaling: Stages
4
and
5 610
Stage
4:
Deriving MDS Results and Assessing Overall Fit
610
Stage
5:
Interpretation of the Results
615
Overview of the Decompositional Results
616
Correspondence Analysis: Stages
4
and
5 617
Stage
4:
Estimating a Correspondence Analysis
617
Stage
5:
Interpreting CA Results
619
Overview of CA
621
Stage
6:
Validation of the Results
622
A Managerial Overview of MDS Results
622
Summary
623 ·
Questions
625 ·
Suggested Readings
625
References
625
SECTION IV Structural Equations Modeling
627
Chapter
11 SEM:
An Introduction
629
What Is Structural Equation Modeling?
634
Estimation of Multiple Interrelated Dependence
Relationships
635
Incorporating Latent Variables Not Measured Directly
635
Defining a Model
637
SEM
and Other Multivariate Techniques
641
Similarity to Dependence Techniques
641
Similarity to Interdependence Techniques
641
The Emergence of
SEM 642
The Role of Theory in Structural Equation Modeling
642
Specifying Relationships
642
Establishing Causation
643
Developing a Modeling Strategy
646
A Simple Example of
SEM 647
The Research Question
647
xxii Contents
Setting Up the Structural Equation Model
for Path Analysis
648
The Basics of
SEM
Estimation and Assessment
649
Six Stages in Structural Equation Modeling
653
Stage
1:
Defining Individual Constructs
655
Operationalizing the Construct
655
Pretesting
655
Stage
2:
Developing and Specifying the Measurement
Model
656
SEM
Notation
656
Creating the Measurement Model
657
Stage
3:
Designing a Study to Produce Empirical Results
657
Issues in Research Design
658
Issues in Model Estimation
662
Stage
4:
Assessing Measurement Model Validity
664
The Basics of Goodness-of-Fit
665
Absolute Fit Indices
666
Incremental Fit Indices
668
Parsimony Fit Indices
669
Problems Associated with Using Fit Indices
669
Unacceptable Model Specification to Achieve Fit
671
Guidelines for Establishing Acceptable and Unacceptable Fit
672
Stage
5:
Specifying the Structural Model
673
Stage
6:
Assessing the Structural Model Validity
675
Structural Model GOF
675
Competitive Fit
676
Comparison to the Measurement Model
676
Testing Structural Relationships
677
Summary
678 ·
Questions
680 ·
Suggested Readings
680
Appendix
1
1A: Estimating Relationships Using Path Analysis
681
Appendix
1
1B:
SEM
Abbreviations
683
Appendix 11C: Detail on Selected GOF Indices
684
References
685
Chapter^ Applications of
SEM 687
Part
1:
Confirmatory Factor Analysis
693
CFA
and Exploratory Factor Analysis
693
A Simple Example of
CFA
and
SEM 694
A Visual Diagram
694
SEM
Stages for Testing Measurement Theory Validation
with
CFA
695
Stage
1:
Defining Individual Constructs
696
Contents xxiii
Stage
2:
Developing the Overall Measurement Model
696
Unidimensionality
696
Congeneric Measurement Model
698
Items per Construct
698
Reflective Versus Formative Constructs
701
Stage
3:
Designing a Study to Produce Empirical Results
702
Measurement Scales in
CFA
702
SEM
and Sampling
703
Specifying the Model
703
Issues in Identification
704
Avoiding Identification Problems
704
Problems in Estimation
706
Stage
4:
Assessing Measurement Model Validity
707
Assessing Fit
707
Path Estimates
707
Construct Validity
708
Model Diagnostics
711
Summary Example
713
CFA
Illustration
715
Stage
1:
Defining Individual Constructs
716
Stage
2:
Developing the Overall Measurement Model
716
Stage
3:
Designing a Study to Produce Empirical Results
718
Stage
4:
Assessing Measurement Model Validity
719
H BAT CFA
Summary
727
Part
2:
What Is a Structural Model?
727
A Simple Example of a Structural Model
728
An Overview of Theory Testing with
SEM 729
Stages in Testing Structural Theory
730
One-Step Versus Two-Step Approaches
730
Stage
5:
Specifying the Structural Model
731
Unit of Analysis
731
Model Specification Using a Path Diagram
731
Designing the Study
735
Stage
6:
Assessing the Structural Model Validity
737
Understanding Structural Model Fit from
CFA Fit
737
Examine the Model Diagnostics
739
SEM
Illustration
740
Stage
5:
Specifying the Structural Model
740
Stage
6:
Assessing the Structural Model Validity
742
Part
3:
Extensions and Applications of
SEM 749
xxiv Contents
Reflective
Versus
Formative Measures
749
Reflective Versus Formative Measurement Theory
749
Operationalizing a Formative Construct
750
Distinguishing Reflective from Formative Constructs
751
Which to Use
—
Reflective or Formative?
753
Higher-Order Factor Analysis
754
Empirical Concerns
754
Theoretical Concerns
756
Using Second-Order Measurement Theories
756
When to Use Higher-Order Factor Analysis
757
Multiple Groups Analysis
758
Measurement Model Comparisons
758
Structural Model Comparisons
763
Measurement Bias
764
Model Specification
764
Model Interpretation
765
Relationship Types: Mediation and Moderation
766
Mediation
766
Moderation
770
Longitudinal Data
773
Additional Covariance Sources: Timing
773
Using Error Covariances to Represent Added Covariance
774
Partial Least Squares
775
Characteristics of PLS
775
Advantages and Disadvantages of PLS
776
Choosing PLS Versus
SEM 777
Summary
778 ·
Questions
781 ·
Suggested Readings
781
References
782
Index
785 |
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| id | DE-604.BV023279572 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:39:16Z |
| indexdate | 2024-07-09T21:14:49Z |
| institution | BVB |
| isbn | 9780135153093 0135153093 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016464401 |
| oclc_num | 317669474 |
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| physical | XXVIII, 800 S. graph. Darst. |
| publishDate | 2010 |
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| publisher | Pearson |
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| spelling | Multivariate data analysis a global perspective Joseph F. Hair ... 7. ed., global ed. Upper Saddle River ; Munich [u.a.] Pearson 2010 XXVIII, 800 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Data-analyse gtt Multivariate analyse gtt Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Multivariate Analyse (DE-588)4040708-1 s Datenanalyse (DE-588)4123037-1 s b DE-604 Hair, Joseph F. 1944- Sonstige (DE-588)135615828 oth Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016464401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Multivariate data analysis a global perspective Data-analyse gtt Multivariate analyse gtt Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4123037-1 (DE-588)4123623-3 |
| title | Multivariate data analysis a global perspective |
| title_auth | Multivariate data analysis a global perspective |
| title_exact_search | Multivariate data analysis a global perspective |
| title_exact_search_txtP | Multivariate data analysis a global perspective |
| title_full | Multivariate data analysis a global perspective Joseph F. Hair ... |
| title_fullStr | Multivariate data analysis a global perspective Joseph F. Hair ... |
| title_full_unstemmed | Multivariate data analysis a global perspective Joseph F. Hair ... |
| title_short | Multivariate data analysis |
| title_sort | multivariate data analysis a global perspective |
| title_sub | a global perspective |
| topic | Data-analyse gtt Multivariate analyse gtt Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Data-analyse Multivariate analyse Multivariate analysis Multivariate Analyse Datenanalyse Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016464401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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