This unique resource provides guidelines and tools for implementing solutions to issues that arise in small sample research. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology, to psychology, marketing, and economics.
This unique resource provides guidelines and tools for implementing solutions to issues that arise in small sample research. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology, to psychology, marketing, and economics.
Prof. Dr. Rens van de Schoot works as a Full Professor teaching Statistics for Small Data Sets at Utrecht University in the Netherlands and as Extra-ordinary professor North-West University in South Africa. He obtained his PhD cum laude on the topic of applying Bayesian statistics to empirical data. Dr. Milica Miöevi¿ is an Assistant Professor in the Department of Psychology at McGill University. She received her PhD in Quantitative Psychology from Arizona State University in 2017. Dr. Miöevi¿¿s research evaluates optimal ways to use Bayesian methods in the social sciences, particularly for mediation analysis.
Inhaltsangabe
Introduction (Van de Schoot and Miöevi ) List of Symbols Part I: Bayesian solutions 1. Introduction to Bayesian statistics (Miöevi , Levy, and van de Schoot) 2. The role of exchangeability in sequential updating of findings from small studies and the challenges of identifying exchangeable data sets (Miöevi , Levy, and Savord) 3. A tutorial on using the WAMBS checklist to avoid the misuse of Bayesian statistics (van de Schoot, Veen, Smeets, Winter, and Depaoli) 4. The importance of collaboration in Bayesian analyses with small samples (Veen and Egberts) 5. A tutorial on Bayesian penalized regression with shrinkage priors for small sample sizes (van Erp) Part II: n=1 6. One by one: the design and analysis of replicated randomized single-case experiments (Onghena) 7. Single-case experimental designs in clinical intervention research (Maric and van der Werff) 8. How to improve the estimation of a specific examinee's (n=1) math ability when test data are limited (Lek and Arts) 9. Combining evidence over multiple individual analyses (Klaassen) 10. Going multivariate in clinical trial studies: a Bayesian framework for multiple binary outcomes (Kavelaars) Part III: Complex hypotheses and models 11. An introduction to restriktor: evaluating informative hypotheses for linear models (Vanbrabant and Rosseel) 12. Testing replication with small samples: applications to ANOVA (Zondervan-Zwijnenburg and Rijshouwer) 13. Small sample meta-analyses: exploring heterogeneity using MetaForest (van Lissa) 14. Item parcels as indicators: why, when, and how to use them in small sample research (Rioux, Stickley, Odejimi, and Little) 15. Small samples in multilevel modeling (Hox and McNeish) 16. Small sample solutions for structural equation modeling (Rosseel) 17. SEM with small samples: two-step modeling and factor score regression versus Bayesian estimation with informative priors (Smid and Rosseel) 18. Important yet unheeded: some small sample issues that are often overlooked (Hox) Index
Introduction (Van de Schoot and Miöevi ) List of Symbols Part I: Bayesian solutions 1. Introduction to Bayesian statistics (Miöevi , Levy, and van de Schoot) 2. The role of exchangeability in sequential updating of findings from small studies and the challenges of identifying exchangeable data sets (Miöevi , Levy, and Savord) 3. A tutorial on using the WAMBS checklist to avoid the misuse of Bayesian statistics (van de Schoot, Veen, Smeets, Winter, and Depaoli) 4. The importance of collaboration in Bayesian analyses with small samples (Veen and Egberts) 5. A tutorial on Bayesian penalized regression with shrinkage priors for small sample sizes (van Erp) Part II: n=1 6. One by one: the design and analysis of replicated randomized single-case experiments (Onghena) 7. Single-case experimental designs in clinical intervention research (Maric and van der Werff) 8. How to improve the estimation of a specific examinee's (n=1) math ability when test data are limited (Lek and Arts) 9. Combining evidence over multiple individual analyses (Klaassen) 10. Going multivariate in clinical trial studies: a Bayesian framework for multiple binary outcomes (Kavelaars) Part III: Complex hypotheses and models 11. An introduction to restriktor: evaluating informative hypotheses for linear models (Vanbrabant and Rosseel) 12. Testing replication with small samples: applications to ANOVA (Zondervan-Zwijnenburg and Rijshouwer) 13. Small sample meta-analyses: exploring heterogeneity using MetaForest (van Lissa) 14. Item parcels as indicators: why, when, and how to use them in small sample research (Rioux, Stickley, Odejimi, and Little) 15. Small samples in multilevel modeling (Hox and McNeish) 16. Small sample solutions for structural equation modeling (Rosseel) 17. SEM with small samples: two-step modeling and factor score regression versus Bayesian estimation with informative priors (Smid and Rosseel) 18. Important yet unheeded: some small sample issues that are often overlooked (Hox) Index
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