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by S P Mukherjee (Author), Bikas K Sinha (Author), Asis Kumar Chattopadhyay (Author)
About the Author
Shyama Prasad Mukherjee retired as the Centenary Professor of Statistics at Calcutta University, where he was involved in teaching, research and promotional work in the areas of statistics and operational research for more than 35 years. He was the Founder Secretary of the Indian Association for Productivity, Quality and Reliability and is now its Mentor. He received the Eminent Teacher Award [2006] from Calcutta University, P.C. Mahalanobis Birth Centenary Award from the Indian Science Congress Association [2000] and Sukhatme Memorial Award for Senior Statisticians from the Government of India [2013]. A Fellow of the National Academy of Sciences of India, Professor Mukherjee has about 80 research papers to his credit. He was a Vice-President of the International Federation of Operational Research Societies (IFORS ).
Bikas Kumar Sinha was affiliated to the Indian Statistical Institute (ISI), Kolkata, India for more than 30 years until his retirement in 2011. He has travelled extensively within USA and Europe for collaborative research and teaching assignments. He has more than 140 research articles published in peer-reviewed journals and almost 100 research collaborators worldwide. His research interests cover a wide range of theoretical and applied statistics. He has co-authored three volumes on Optimal Designs in Springer’s Lecture Notes Series in Statistics (Vol. 54 in 1989, Vol. 163 in 2002 and Vol. 1028 in 2014) and another volume on theory and applications of Optimal Covariate Designs, also published by Springer (2015).
Asis Kumar Chattopadhyay is a Professor of Statistics at Calcutta University, Kolkata, India, from where he also obtained his PhD in Statistics. With over 50 papers in respected international journals, proceedings and edited volumes, he has published three books on statistics including two published by Springer – ‘Statistical Methods for Astronomical Data Analysis’ (Springer Series in Astrostatistics, 2014) and ‘Statistics and its Applications: Platinum Jubilee Conference, Kolkata, India, December 2016’ (Springer Proceedings in Mathematics & Statistics, 2018). His main interests are in stochastic modeling, demography, operations research and astrostatistics.
About this book
This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method – as distinct from a ‘science’ related to any one type of phenomena – is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.
Table of contents
1 Introduction 1
1. 1 The Domain of Social Sciences 1
1. 2 Problems in Social Science Research 2
1. 3 Role of Statistics 4
1. 4 Preview of this Book 6
References and Suggested Readings 11
2 Randomized Response Techniques 13
2. 1 Introduction 13
2. 2 Warner’s Randomized Response Technique [RRT] 14
2. 3 Generalizations of RRMs 17
2. 4 Not-at-Homes: Source of Non-response 18
2. 5 RRMs—Further Generalizations 18
2. 6 RRMs for Two Independent Stigmatizing Features 19
2. 7 Toward Perception of Increased Protection of Confidentiality 20
2. 8 Confidentiality Protection in the Study of Quantitative Features 22
2. 9 Concluding Remarks 26
References and Suggested Readings 26
3 Content Analysis 29
3. 1 Introduction 29
3. 2 Uses of Content Analysis 30
3. 3 Steps in Content Analysis 30
3. 4 Reliability of Coded Data 31
3. 5 Limitations of Content Analysis 36
3. 6 Concluding Remarks 36
References and Suggested Readings 37
4 Scaling Techniques 39
4. 1 Introduction 39
4. 2 Scaling of Test Scores 40
4. 2. 1 Percentile Scaling 41
4.2.2 Z-Scaling or a-Scaling 41
4. 2. 3 T-Scaling 41
4. 2. 4 Method of Equivalent Scores 42
4. 3 Scaling of Categorical Responses 45
4. 3. 1 Estimation of Boundaries 45
4. 3. 2 Finding Scale Values 46
4. 4 Use of U-Shaped Distributions 46
4. 5 Product Scaling 47
4. 6 Other Unidimensional Scaling Methods 50
4. 7 Concluding Remarks 52
References and Suggested Readings 52
5 Data Integration Techniques 53
5. 1 Introduction 53
5. 2 Elementary Methods for Data Integration 53
5. 3 Topsis Method: Computational Algorithm in a Theoretical Framework and Related Issues 55
5. 4 Topsis Method: Computational Details in an Illustrative Example 56
References and Suggested Readings 60
6 Statistical Assessment of Agreement 61
6. 1 General Introduction to Agreement 61
6. 2 Cohen’s Kappa Coefficient and Its Generalizations: An Exemplary Use 62
6. 3 Assessment of Agreement in Case of Quantitative Responses 66
References and Suggested Readings 67
7 Meta-Analysis 69
7. 1 Introduction 69
7. 2 Estimation of Common Bernoulli Parameter “p” 70
7. 3 Estimation of Common Mean of Several Normal Populations 70
7. 4 Meta-Analysis in Regression Models 72
References and Suggested Readings 74
8 Cluster and Discriminant Analysis 75
8. 1 Introduction 75
8. 2 Hierarchical Clustering Technique 76
8. 2. 1 Agglomerative Methods 76
8. 2. 2 Similarity for Any Type of Data 77
8. 2. 3 Linkage Measures 78
8. 2. 4 Optimum Number of Clusters 79
8. 2. 5 Clustering of Variables 79
8. 3 Partitioning Clustering-k-Means Method 80
8. 4 Classification and Discrimination 81
8. 5 Data 83
References and Suggested Readings 94
9 Principal Component Analysis 95
9. 1 Introduction 95
9. 1. 1 Method 96
9. 1. 2 The Correlation Vector Diagram (Biplot, Gabriel 1971) 99
9. 2 Properties of Principal Components 101
References and Suggested Readings 102
10 Factor Analysis 103
10. 1 Factor Analysis 103
10. 1. 1 Method of Estimation 106
10. 1. 2 Factor Rotation 108
10. 1. 3 Varimax Rotation 108
10. 2 Quartimax Rotation 109
10. 3 Promax Rotation 109
References and Suggested Readings 111
11 Multidimensional Scaling 113
11. 1 Introduction 113
11. 2 Types of MDS 114
11. 2. 1 Non-metric MDS 115
11. 2. 2 Replicated MDS 115
11. 2. 3 Weighted MDS 115
11. 2. 4 Sammon Mapping 116
11. 3 MDS and Factor Analysis 116
11. 4 Distance Matrix 117
11. 5 Goodness of Fit 119
11. 6 An Illustration 120
11. 7 Metric CMDS 121
References and Suggested Readings 122
12 Social and Occupational Mobility 123
12. 1 Introduction 123
12. 2 Model 1 126
12. 2. 1 Some Perfect Situations 127
12. 2. 2 Possible Measures of Career Pattern 127
12. 2. 3 Measure of Career Pattern Based on Mahalanobis Distance 128
12. 2. 4 Measure of Career Pattern Based on Entropy 129
12. 2. 5 An Example 130
12. 3 Model 2 131
References and Suggested Readings 132
13 Social Network Analysis 135
13. 1 Introduction 135
13. 2 Sampling and Inference in a SN 137
13. 3 Data Structure in a Random Sample of Units 138
13. 4 Inference Procedure 140
13. 5 Estimation of Average Out-Degree Based on Data Type – 1/ 2 140
13. 6 Inference Formulae for Data Type 3 Using Sample Size n 142
13. 7 Computations for the Hypothetical Example 144
13. 8 Estimation of Average Reciprocity 145
References and Suggested Readings 152
Length: 158 pages
Publisher: Springer; 1st ed. edition (Oct 11, 2018)
Language: English
ISBN-13: 978-981-13-2145-0
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