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Table of Contents
Intro; Preface; Contents; About the Authors; 1 Introduction; 1.1 The Domain of Social Sciences; 1.2 Problems in Social Science Research; 1.3 Role of Statistics; 1.4 Preview of this Book; References and Suggested Readings; 2 Randomized Response Techniques; 2.1 Introduction; 2.2 Warner's Randomized Response Technique [RRT]; 2.3 Generalizations of RRMs; 2.4 Not-at-Homes: Source of Non-response; 2.5 RRMs-Further Generalizations; 2.6 RRMs for Two Independent Stigmatizing Features; 2.7 Toward Perception of Increased Protection of Confidentiality
4.3.2 Finding Scale Values4.4 Use of U-Shaped Distributions; 4.5 Product Scaling; 4.6 Other Unidimensional Scaling Methods; 4.7 Concluding Remarks; References and Suggested Readings; 5 Data Integration Techniques; 5.1 Introduction; 5.2 Elementary Methods for Data Integration; 5.3 Topsis Method: Computational Algorithm in a Theoretical Framework and Related Issues; 5.4 Topsis Method: Computational Details in an Illustrative Example; References and Suggested Readings; 6 Statistical Assessment of Agreement; 6.1 General Introduction to Agreement
6.2 Cohen's Kappa Coefficient and Its Generalizations: An Exemplary Use6.3 Assessment of Agreement in Case of Quantitative Responses; References and Suggested Readings; 7 Meta-Analysis; 7.1 Introduction; 7.2 Estimation of Common Bernoulli Parameter ``p''; 7.3 Estimation of Common Mean of Several Normal Populations; 7.4 Meta-Analysis in Regression Models; References and Suggested Readings; 8 Cluster and Discriminant Analysis; 8.1 Introduction; 8.2 Hierarchical Clustering Technique; 8.2.1 Agglomerative Methods; 8.2.2 Similarity for Any Type of Data; 8.2.3 Linkage Measures
8.2.4 Optimum Number of Clusters8.2.5 Clustering of Variables; 8.3 Partitioning Clustering-k-Means Method; 8.4 Classification and Discrimination; 8.5 Data; References and Suggested Readings; 9 Principal Component Analysis; 9.1 Introduction; 9.1.1 Method; 9.1.2 The Correlation Vector Diagram (Biplot, Gabriel71); 9.2 Properties of Principal Components; References and Suggested Readings; 10 Factor Analysis; 10.1 Factor Analysis; 10.1.1 Method of Estimation; 10.1.2 Factor Rotation; 10.1.3 Varimax Rotation; 10.2 Quartimax Rotation; 10.3 Promax Rotation; References and Suggested Readings
4.3.2 Finding Scale Values4.4 Use of U-Shaped Distributions; 4.5 Product Scaling; 4.6 Other Unidimensional Scaling Methods; 4.7 Concluding Remarks; References and Suggested Readings; 5 Data Integration Techniques; 5.1 Introduction; 5.2 Elementary Methods for Data Integration; 5.3 Topsis Method: Computational Algorithm in a Theoretical Framework and Related Issues; 5.4 Topsis Method: Computational Details in an Illustrative Example; References and Suggested Readings; 6 Statistical Assessment of Agreement; 6.1 General Introduction to Agreement
6.2 Cohen's Kappa Coefficient and Its Generalizations: An Exemplary Use6.3 Assessment of Agreement in Case of Quantitative Responses; References and Suggested Readings; 7 Meta-Analysis; 7.1 Introduction; 7.2 Estimation of Common Bernoulli Parameter ``p''; 7.3 Estimation of Common Mean of Several Normal Populations; 7.4 Meta-Analysis in Regression Models; References and Suggested Readings; 8 Cluster and Discriminant Analysis; 8.1 Introduction; 8.2 Hierarchical Clustering Technique; 8.2.1 Agglomerative Methods; 8.2.2 Similarity for Any Type of Data; 8.2.3 Linkage Measures
8.2.4 Optimum Number of Clusters8.2.5 Clustering of Variables; 8.3 Partitioning Clustering-k-Means Method; 8.4 Classification and Discrimination; 8.5 Data; References and Suggested Readings; 9 Principal Component Analysis; 9.1 Introduction; 9.1.1 Method; 9.1.2 The Correlation Vector Diagram (Biplot, Gabriel71); 9.2 Properties of Principal Components; References and Suggested Readings; 10 Factor Analysis; 10.1 Factor Analysis; 10.1.1 Method of Estimation; 10.1.2 Factor Rotation; 10.1.3 Varimax Rotation; 10.2 Quartimax Rotation; 10.3 Promax Rotation; References and Suggested Readings