Statistical methods in social science research /

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Bibliographic Details
Author / Creator:Mukherjee, S. P., author.
Imprint:Singapore : Springer, [2018]
©2018
Description:1 online resource
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11718194
Hidden Bibliographic Details
Other authors / contributors:Sinha, Bikas K., author.
Chattopadhyay, Asis Kumar, author.
ISBN:9789811321467
9811321469
9789811321450
9811321450
9789811321474
9811321477
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (EBSCO, viewed October 10, 2018).
Summary:"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."--
Other form:Print version: Mukherjee, S.P. Statistical methods in social science research. Singapore : Springer, [2018] 9811321450 9789811321450
Standard no.:10.1007/978-981-13-2146-7
10.1007/978-981-13-2
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