Rens van de Schoot

Milica Mioceviƒá

Researchers often have difficulties collecting enough data to test their hypotheses,  either because target groups are small or hard to access, or because data collection  entails prohibitive costs. Such obstacles may result in data sets that are too small for  the complexity of the statistical model needed to answer the research question. This  unique book provides guidelines and tools for implementing solutions to issues  that arise in small sample research. Each chapter illustrates statistical methods that  allow researchers to apply the optimal statistical model for their research question  when the sample is too small.  This essential book will enable social and behavioral science researchers to test  their hypotheses even when the statistical model required for answering their  research question is too complex for the sample sizes they can collect. The statistical  models in the book range from the estimation of a population mean to models with  latent variables and nested observations, and solutions include both classical and  Bayesian methods. All proposed solutions are described in steps researchers can  implement with their own data and are accompanied with annotated syntax in R.  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.

Small Sample Size Solutions

Rens van de Schoot, Milica Mioceviƒá

Information

About This Book