ABSTRACT

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.

part I|84 pages

Bayesian solutions

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part II|70 pages

n = 1

chapter 6|15 pages

One by One

The design and analysis of replicated randomized single-case experiments
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chapter 10|16 pages

Going Multivariate In Clinical Trial Studies

A Bayesian framework for multiple binary outcomes
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part III|111 pages

Complex hypotheses and models

chapter 11|16 pages

An Introduction to Restriktor

Evaluating informative hypotheses for linear models
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chapter 13|17 pages

Small Sample Meta-Analyses

Exploring heterogeneity using MetaForest
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chapter 14|12 pages

Item Parcels as Indicators

Why, when, and how to use them in small sample research
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chapter 17|16 pages

Sem with Small Samples

Two-step modeling and factor score regression versus Bayesian estimation with informative priors
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chapter 18|11 pages

Important Yet Unheeded

Some small sample issues that are often overlooked
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