Theory-Based Data Analysis for the Social Sciences

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. She approaches this task by first providing an overview that explains the connection between data analysis, statistical technique and theory. This section includes a description of the elaboration model for analyzing the empirical association between two variables by adding a `third variable' to the analysis. The author then introduces a new concept into this model, the focal relationship. This concept is the one cause-and-effect type of relationship of primary significance that is indispensable to the entire theory. Building upon the focal relationship as the cornerstone for all subsequent analysis, two analytic strategies are developed to establish its internal validity: - An exclusionary strategy to eliminate alternative explanations for the focal relationship using control and other independent variables to rule out spuriousness and redundancy, respectively; and, - An inclusive strategy to demonstrate that the focal relationship fits within an interconnected set of relationships predicted by theory using antecedent, intervening and consequent variables. Using real examples of social research, the author demonstrates the use of this approach for two common forms of analysis, multiple linear regression and logistic regression. Whether learning data analysis for the first time or adding new techniques to your repertoire, this book provides an excellent basis for theory-based data analysis.

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 Summary Chapter 3: Associations and Relationships Association: The Basic Building Block Establishing Relatedness: The "Third Variable" Association and Causality Summary 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 Summary 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 Summary Chapter 6: Ruling Out Alternative Theoretical Explanations: Additional Independent Variables Redundancy: Alternative Theories Analytic Models For Redundancy Control Versus Independent Variable Summary 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 Summary 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 Summary Chapter 9: Synthesis and Commentary A Recap of Theory-Based Data Analysis Informative Comparisons Imperfect Knowledge