ISBN-10:
0470148748
ISBN-13:
9780470148747
Pub. Date:
02/08/2010
Publisher:
Wiley
Statistics in the Social Sciences: Current Methodological Developments / Edition 1

Statistics in the Social Sciences: Current Methodological Developments / Edition 1

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Product Details

ISBN-13: 9780470148747
Publisher: Wiley
Publication date: 02/08/2010
Pages: 198
Product dimensions: 6.30(w) x 9.30(h) x 0.70(d)

About the Author

STANISLAV KOLENIKOV, PhD, is Adjunct Assistant Professor of Statistics at the University of Missouri. He served as Program Chair of the Sixth Winemiller Conference. Dr. Kolenikov is an applied statistician with interests in structural equation modeling, survey statistics, econometrics, and statistical programming.

DOUGLAS STEINLEY, PhD, is Associate Professor of Psychology at the University of Missouri. Dr. Steinley currently conducts research in the areas of multivariate statistical methodology, cluster analysis, and social network analysis.

LORI THOMBS, PhD, is Associate Professor of Statistics and Director of the Social Science Statistics Center at the University of Missouri. Dr. Thombs has more than twenty years of academic experience, and she currently focuses her research on the areas of time series, resampling methods for correlated variables, and statistics education.

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Table of Contents

List of Figures ix

List of Tables xi

Preface xiii

1 Analysis of Correlation Structures: Current Status and Open Problems 1

1.1 Introduction 1

1.2 Correlation versus Covariance Structures 2

1.3 Estimation and Model Testing 5

1.3.1 Basic Asymptotic Theory 5

1.3.2 Distribution of T Under Model Misspecification 6

1.3.3 Distribution of T Under Weight Matrix Misspecification 7

1.3.4 Estimation and Testing with Arbitrary Distributions 8

1.3.5 Tests of Model Fit Under Distributional Misspecification 12

1.3.6 Scaled and Adjusted Statistics 14

1.3.7 Normal Theory Estimation and Testing 15

1.3.8 Elliptical Theory Estimation and Testing 17

1.3.9 Heterogeneous Kurtosis Theory Estimation and Testing 19

1.3.10 Least Squares Estimation and Testing 21

1.4 Example 22

1.5 Simulations 24

1.5.1 Data 24

1.5.2 Correlation Structure with ADF Estimation and Testing 25

1.5.3 Correlation Structure with Robust Least Squares Methods 26

1.6 Discussion 27

References 28

2 Overview of Structural Equation Models and Recent Extensions 37

2.1 Model Specification and Assumptions 39

2.1.1 Illustration of Special Cases 39

2.1.2 Modeling Steps 41

2.2 Multilevel SEM 47

2.2.1 The Between-and-Within Specification 47

2.2.2 Random Effects as Factors Specification 49

2.2.3 Summary and Comparison 53

2.3 Structural Equation Mixture Models 53

2.3.1 The Model 54

2.3.2 Estimation 56

2.3.3 Sensitivity to Assumptions 56

2.3.4 Direct and Indirect Applications 58

2.3.5 Summary 59

2.4 Item Response Models 59

2.4.1 Categorical CFA 60

2.4.2 CCFA Estimation 61

2.4.3 Item Response Theory 62

2.4.4 CCFA and IRT 63

2.4.5 Advantages and Disadvantages 64

2.5 Complex Samples and Sampling Weights 65

2.5.1 Complex Samples and Their Features 65

2.5.2 Probability (Sampling) Weights. 67

2.5.3 Violations of SEM Assumptions 68

2.5.4 SEM Analysis Using Complex Samples with Unequal Probabilities of Selection 69

2.5.5 Future Research 72

2.6 Conclusion 73

References 73

3 Order-Constrained Proximity Matrix Representations 81

3.1 Introduction 81

3.1.1 Proximity Matrix for Illustration: Agreement Among Supreme Court Justices 83

3.2 Order-Constrained Ultrametrics 84

3.2.1 The M-file ultrafnd_confit.m 85

3.2.2 The M-file ultrafnd.confnd.m 87

3.2.3 Representing an (Order-Constrained) Ultrametric 88

3.2.4 Alternative (and Generalizable) Graphical Representation for an Ultrametric 91

3.2.5 Alternative View of Ultrametric Matrix Decomposition 93

3.3 Ultrametric Extensions by Fitting Partitions Containing Contiguous Subsets 95

3.3.1 Ordered Partition Generalizations 104

3.4 Extensions to Additive Trees: Incorporating Centroid Metrics 106

References 111

4 Multiobjective Multidimensional (City-Block) Scaling 113

4.1 Introduction 113

4.2 City-Block MDS 115

4.3 Multiobjective City-Block MDS 116

4.3.1 The Metric Multiobjective City-Block MDS Model 116

4.3.2 The Nonmetric Multiobjective City-Block MDS Model 118

4.4 Combinatorial Heuristic 119

4.5 Numerical Examples 121

4.5.1 Example 1 121

4.5.2 Example 2 124

4.6 Summary and Conclusions 128

References 130

5 Critical Differences in Bayesian and Non-Bayesian Inference 135

5.1 Introduction 135

5.2 The Mechanics of Bayesian Inference 137

5.2.1 Example with Count Data 139

5.2.2 Comments on Prior Distributions 141

5.3 Specific Differences Between Bayesians and non-Bayesians 142

5.4 Paradigms For Testing 143

5.5 Change-point Analysis of Thermonuclear Testing Data 148

5.6 Conclusion 151

References 153

6 Bootstrap Test of Shape Invariance Across Distributions 159

6.1 Lack of Robustness of a Parametric Test 161

6.2 Development of a Nonparametric Shape Test 163

6.3 Example 166

6.4 Extension of the Shape Test 166

6.5 Characteristics of the Bootstrap Shape Test 167

6.6 Application 169

6.7 Conclusion 171

References 172

7 Statistical Software for the Social Sciences 175

7.1 Social Science Research: Primary Capabilities 176

7.2 Statistical Social Science Statistical applications 178

7.2.1 R 178

7.2.2 SAS 179

7.2.3 SPSS 180

7.2.4 Stata 181

7.2.5 Statistica 183

7.2.6 StatXact/LogXact 184

7.3 Statistical Application Utilities 186

7.3.1 Stat/Transfer 186

7.3.2 ePrint Professional 186

7.3.3 nQuery Advisor 187

7.4 Summary Comments 188

References 189

8 Conclusion: Roundtable Discussion 191

Index 195

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