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Theory-Based Data Analysis for the Social Sciences

Theory-Based Data Analysis for the Social Sciences

Second Edition

December 2012 | 472 pages | SAGE Publications, Inc
This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.

About the Author
Part I. Conceptual Foundations of the Elaboration Model
Chapter 1. Introduction to Theory-Based Data Analysis
Chapter 2. The Logic of Theory-Based Data Analysis
Chapter 3. Relationships as Associations
Chapter 4. The Focal Relationship: Causal Inference
Part II. Regression with Simple Random Samples and Complex Samples
Chapter 5. The Elaboration Model With Multiple Linear Regression
Chapter 6. Regression With Survey Data From Complex Samples
Part III. The Elaboration Model With Multiple Linear Regression
Chapter 7. Ruling Out Alternative Explanations: Spuriousness and Control Variables
Chapter 8. Ruling Out Alternative Theoretical Explanations: Rival Independent Variables
Chapter 9. Elaborating the Focal Relationship: Mediation and Intervening Variables
Chapter 10. Elaborating the Focal Relationship: Antecedent and Consequent Variables
Chapter 11. Specifying Conditions of Influence: Moderating Variables
Part IV. The Elaboration Model With Logistic Regression
Chapter 12. The Elaboration Model With Logistic Regression
Part V. Conclusion
Chapter 13. Synthesis and Comments

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 PhD students as well). Students often 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 project. The book is not a ‘beginner level’ though. It would be helpful for students who already have some background in quantitative analysis, therefore I would not, however, recommend for BA level.

Dr Maria Adamson
Business School, University of East London
January 22, 2014

The prose is too dense for the typical master's degree student. Probably suitable for doctoral students.

Dr Melvin Musick, EdD
Education Dept, Pepperdine University Graduate School of Education/ Psychology - West Los Angeles
September 10, 2013

This will be my first year using the second edition. I have used tthe first edition for several years. I am a bit concerned that the second edition will overlap with DeVellis. This book has been ordered for Fall 2013

Cynthia Gross
Pharmacy Dept, University of Minnesota - Twin Cities
August 8, 2013

Wonderful Text... Dr. Aneshensel makes multivariate analysis sensible and connected to theoretical propositions in a way I have not seen before.

Professor Chris Francovich
Other, Gonzaga University
March 7, 2013
Key features


  • This title's Elaboration Model with Multiple Linear Regression provides a logical plan of analysis for testing a social science theory with the most frequently used multivariate statistical technique.
  • The Elaboration Model with Logistic Regression addresses a critical emergent issue in the use of logistic regression in social science research, namely that coefficients cannot be compared across nested models as variables are added to the model. To date, this material has appeared only in journal articles aimed primarily at statistical audiences, but growing awareness within substantive areas of the social sciences makes this material essential to graduate students and researchers using logistic regression. Chapter 12 addresses this emergent issue in detail.
  • Aneshensel provides a logical plan of analysis for observational data in addition to the standard statistical basis for multiple linear regression and logistic regression.



  • Twelve new case studies, taken from recent research literature, exemplify the use of the elaboration model. These case studies make the application of the elaboration model come alive for students, clearly demonstrating the link between theory, statistical technique, and conclusions.
  • Two new Analysis Journals are included: the Northridge Earthquake Study and the Health and Retirement Study. They provide a step-by-step blueprint for testing social science theory with observational data. The use of real data instead of contrived data provides a more realistic picture of actual data analysis.
  • A brand new Chapter 6 describes the major components of complex sample designs, explains why these data should not be analyzed using statistical methods that assume a simple random sample, describes the use of sample weights and adjustments to standard errors, and ends by emphasizing that these statistical adjustments do not change the interpretation of results.

Sample Materials & Chapters


CH 1

CH 6

For instructors

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