Small Sample Size Solutions

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.

Small Sample Size Solutions is essential for social and behavioural science researchers aiming to test their hypotheses even when the required statistical model 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 by annotated syntax in R.

The methods described in this book will be useful for researchers across the social and behavioural sciences, including those in the medical sciences and epidemiology, as well as in psychology, marketing, and economics.