Statistics in the Social Sciences Current

Fashioned after the 2006 Winemiller Symposium on Recent Advances in Social Science Statistics, and bringing together top researchers from around the world, this book provides overviews of the most important advanced techniques in the social sciences, including latent variable models, multilevel models, social networks, missing data techniques, educational measurement, cluster analysis, and treatment effects. The aim of the book is to foster collaboration among mathematical statisticians and quantitatively oriented social science researchers and to highlight the interface between developments in each area.

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 networks analysis. Lori Thombs PhD, is Associate Professor of Statistics and Director of the Social Science Statistics Centre at the University of Missouri. Dr. Thombs has more than twenty years of academic experience, and the currently focuses her research on the areas of times series, resampling methods for correlated variables, and statistics education.

List of Figures	p. ix
List of Tables	p. xi
Preface	p. xiii
Analysis of Correlation Structures: Current Status and Open Problems	p. 1
Introduction	p. 1
Correlation versus Covariance Structures	p. 2
Estimation and Model Testing	p. 5
Basic Asymptotic Theory	p. 5
Distribution of T Under Model Misspecification	p. 6
Distribution of T Under Weight Matrix Misspecification	p. 7
Estimation and Testing with Arbitrary Distributions	p. 8
Tests of Model Fit Under Distributional Misspecification	p. 12
Scaled and Adjusted Statistics	p. 14
Normal Theory Estimation and Testing	p. 15
Elliptical Theory Estimation and Testing	p. 17
Heterogeneous Kurtosis Theory Estimation and Testing	p. 19
Least Squares Estimation and Testing	p. 21
Example	p. 22
Simulations	p. 24
Data	p. 24
Correlation Structure with ADF Estimation and Testing	p. 25
Correlation Structure with Robust Least Squares Methods	p. 26
Discussion	p. 27
References	p. 28
Overview of Structural Equation Models and Recent Extensions	p. 37
Model Specification and Assumptions	p. 39
Illustration of Special Cases	p. 39
Modeling Steps	p. 41
Multilevel SEM	p. 47
The Between-and-Within Specification	p. 47
Random Effects as Factors Specification	p. 49
Summary and Comparison	p. 53
Structural Equation Mixture Models	p. 53
The Model	p. 54
Estimation	p. 56
Sensitivity to Assumptions	p. 56
Direct and Indirect Applications	p. 58
Summary	p. 59
Item Response Models	p. 59
Categorical CFA	p. 60
CCFA Estimation	p. 61
Item Response Theory	p. 62
CCFA and IRT	p. 63
Advantages and Disadvantages	p. 64
Complex Samples and Sampling Weights	p. 65
Complex Samples and Their Features	p. 65
Probability (Sampling) Weights.	p. 67
Violations of SEM Assumptions	p. 68
SEM Analysis Using Complex Samples with Unequal Probabilities of Selection	p. 69
Future Research	p. 72
Conclusion	p. 73
References	p. 73
Order-Constrained Proximity Matrix Representations	p. 81
Introduction	p. 81
Proximity Matrix for Illustration: Agreement Among Supreme Court Justices	p. 83
Order-Constrained Ultrametrics	p. 84
The M-file ultrafnd_confit.m	p. 85
The M-file ultrafnd.confnd.m	p. 87
Representing an (Order-Constrained) Ultrametric	p. 88
Alternative (and Generalizable) Graphical Representation for an Ultrametric	p. 91
Alternative View of Ultrametric Matrix Decomposition	p. 93
Ultrametric Extensions by Fitting Partitions Containing Contiguous Subsets	p. 95
Ordered Partition Generalizations	p. 104
Extensions to Additive Trees: Incorporating Centroid Metrics	p. 106
References	p. 111
Multiobjective Multidimensional (City-Block) Scaling	p. 113
Introduction	p. 113
City-Block MDS	p. 115
Multiobjective City-Block MDS	p. 116
The Metric Multiobjective City-Block MDS Model	p. 116
The Nonmetric Multiobjective City-Block MDS Model	p. 118
Combinatorial Heuristic	p. 119
Numerical Examples	p. 121
Example 1	p. 121
Example 2	p. 124
Summary and Conclusions	p. 128
References	p. 130
Critical Differences in Bayesian and Non-Bayesian Inference	p. 135
Introduction	p. 135
The Mechanics of Bayesian Inference	p. 137
Example with Count Data	p. 139
Comments on Prior Distributions	p. 141
Specific Differences Between Bayesians and non-Bayesians	p. 142
Paradigms For Testing	p. 143
Change-point Analysis of Thermonuclear Testing Data	p. 148
Conclusion	p. 151
References	p. 153
Bootstrap Test of Shape Invariance Across Distributions	p. 159
Lack of Robustness of a Parametric Test	p. 161
Development of a Nonparametric Shape Test	p. 163
Example	p. 166
Extension of the Shape Test	p. 166
Characteristics of the Bootstrap Shape Test	p. 167
Application	p. 169
Conclusion	p. 171
	p. 172
Statistical Software for the Social Sciences	p. 175
Social Science Research: Primary Capabilities	p. 176
Statistical Social Science Statistical applications	p. 178
R	p. 178
SAS	p. 179
SPSS	p. 180
Stata	p. 181
Statistica	p. 183
StatXact/LogXact	p. 184
Statistical Application Utilities	p. 186
Stat/Transfer	p. 186
ePrint Professional	p. 186
nQuery Advisor	p. 187
Summary Comments	p. 188
References	p. 189
Conclusion: Roundtable Discussion	p. 191
Index	p. 195
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Statistics in the Social Sciences Current Methodological Developments

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