Weakliem D.L. Hypothesis Testing and Model Selection in the Social Sciences

New York: The Guilford Press, 2016. — 202 p. — (Methodology in the Social Sciences). — ISBN: 9781462525652.Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website (www.guilford.com/weakliem-materials) supplies data and syntax files for the book's examples.Hypothesis Testing and Model SelectionStandard Procedure of Hypothesis TestingModel Selection
Purpose and Plan of the Book
Hypothesis Testing: Criticisms and Alternatives
Hypothesis Testing and Its Discontents
Uses of Hypothesis Tests
Criticisms of Conventional Hypothesis Testing
Implications of the Criticisms
Alternatives to Conventional Tests
Examples
Summary and Conclusions
Recommended Reading
The Classical Approach
Random Sampling and Classical Tests
Two Approaches to Hypothesis Tests
Confidence Intervals
Choosing a Significance Level
Comparison to Conventional Practice
Implications of Choosing an α Level
Other Kinds of Errors
Example of Choosing an α Level
Evaluation of Criticisms
Summary and Conclusions
Recommended Reading
Bayesian Hypothesis Tests
Bayes’s Theorem
Bayesian Estimation
Bayes Factors
Bayesian Confidence Intervals and Bayes Factors
Approaches to Bayesian Hypothesis Testing
The Unit Information Prior
Limits on Bayes Factors
Bayes Factors for Multiple Parameters
Summary and Conclusions
Recommended Reading
The Akaike Information Criterion
Information
Prediction and Model Selection
The AIC
Consistency and Efficiency
Cross-Validation and the AIC
A Classical Perspective on the AIC
A Bayesian Perspective on the AIC
A General Class of Model Selection Criteria
Summary and Conclusions
Recommended Reading
Three-Way Decisions
Substantive and Statistical Hypotheses
Bayes Factors for Directional Hypotheses
Bayes Factors for Three-Way Decisions
Summary and Conclusions
Recommended Reading
Model SelectionBayesian Model Selection
The Value of Model Selection
The Risks of Model Selection
Examples of Model Selection
Summary and Conclusions
Recommended Reading
Hypothesis Tests
Hypothesis Tests and the Strength of Evidence
When Should Hypotheses Be Tested?
The Role of Hypothesis Tests
Overfitting
Hypothesis Tests and the Development of Theory
Summary and Conclusions
Recommended Reading