Theory-Based Data Analysis for the Social Sciences / Edition 1

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The advent of complex and powerful computer-generated statistical models has greatly eroded the former prominence of social theory in data analysis, replacing it with an emphasis on statistical technique. To correct this trend, Carol S. Aneshensel presents a method for bringing data analysis and statistical technique into line with theory.

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Editorial Reviews

Robert Bickel
“In clear and accessible prose, this book presents a compelling logic for the analysis of non-experimental data. In the process, it clarifies the concepts of statistical association, confounding, and statistical control, and it provides really compelling elaboration schemes to make substantive, rather than purely statistical, sense of it all. It also provides a means for making causal assertions when the criteria of association, causal priority, non-spuriousness, and theoretical rationale are met.”
Eun Young Mun
“This text does a superb job of explaining critical concepts such as mediation, moderation, spuriousness, and control variable, using graphic illustrations as well as published articles as examples. I think the illustrated examples are really helpful for illuminating these concepts. Furthermore, the majority of empirical research in the social behavioral sciences utilizes regression, logistic regression, and path analysis. The book discusses many empirical articles utilizing these analytic approaches as examples. Thus, the contents of this book are highly relevant and understandable for those in early stages of research training.”
Maria Karepova
I think this is a very much awaited book. I think it is very well suited for PG level, particularly for those MA students who are writing their dissertations (and Ph D students as well). As strange as it may seem, students struggle to understand what theory is and how it relates to data. For me this book engages comprehensively in these questions. There is a good discussion of how to work with variables, about the construction of associations and causalities. I wish I could teach more of this book in my MA course, but due to time constraints I would definitely recommend Chapter 1-4 (where the focus is on conceptual discussion). The book is very helpful for those constructing and designing their research. The book is not the ‘beginner level’. It would be helpful if students already have some background in quantitative analysis, therefore I would not, however, recommend for BA level.
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Product Details

Meet the Author

Carol S. Aneshensel is a sociologist (Ph.D., Cornell University) and Professor at the University of California, Los Angeles. She specializes in the fields of the sociology of mental health and medical sociology, with an emphasis on the social origins of stress and its impact on depression. She has been Principal Investigator for numerous studies funded by the National Institute on Aging and the National Institute of Mental Health. She has published more than 75 peer-review journal articles and several books, including her work as lead editor of the Handbook of the Sociology of Mental Health, Second Edition (Springer, 2012). She has received awards for distinguished contributions from the Sociology of Mental Health and Medical Sociology sections of the American Sociological Association (ASA). Theory-Based Data Analysis for the Social Sciences received Honorable Mention for Best Publication in 2003 from the Sociology of Mental Health section, ASA.

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Table of Contents

Chapter 1: Introduction to Theory-Based Data Analysis
The Connection Between Analysis, Theory and Statistics
Elements of Theory-Based Analysis
The Inherent Subjectivity of Analysis
Looking Ahead
Chapter 2: The Logic of Theory-Based Data Analysis
Inductive and Deductive Processes
Operationalization and The Assessment of Fit
The Roundabout Route of Failing to Reject
Chapter 3: Associations and Relationships
Association: The Basic Building Block
Establishing Relatedness: The "Third Variable "
Association and Causality
Chapter 4: The Focal Relationship: Demonstrating Internal Validity
Coincident Associations: The Exclusionary "Third Variable "
Causal Connections: The Inclusive "Third Variable "
An Example of Exclusionary and Inclusive "Third Variables "
Explaining Y Versus the Focal Relationship
Chapter 5: Ruling Out Alternative Explanations: Spuriousness and Control Variables
Spuriousness: The Illusion of Relationship
The Analysis of Simple Spuriousness
Complex Sources of Spuriousness
The Analysis of Complex Spuriousness
Death Looms on the Horizon: An Example of Partial Spuriousness
Chapter 6: Ruling Out Alternative Theoretical Explanations: Additional Independent Variables
Redundancy: Alternative Theories
Analytic Models For Redundancy
Control Versus Independent Variable
Chapter 7: Elaborating an Explanation: Antecedent, Intervening, and Consequent Variables
Intervening Variables: The Causal Mechanism
The Analysis of Intervening Variables
Mediation Illustrated: Explaining the Intergenerational Transmission of Divorce
Antecedent and Consequent Variables
Antecedent and Consequent Variables Illustrated: Divorce and Intergenerational Family Relations
Chapter 8: Specifying Conditions of Influence: Effect Modification and Subgroup Variation
Conditional Relationships
Conditional Relationships as Interactions
Subgroup Analysis of Conditional Relationships
Subgroup Versus Interaction Analysis
Considerations in the Selection of Moderating Variables
Chapter 9: Synthesis and Commentary
A Recap of Theory-Based Data Analysis
Informative Comparisons
Imperfect Knowledge

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