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Small sample size solutions : a guide for applied researchers and practitioners

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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.

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Detail Information

Series Title

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Call Number

519.542 SMA s

Publisher

London : Routledge., 2020

Collation

xiv, 269p. : ill.

Language

English

ISBN/ISSN

9780429273872

Classification

519.542

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Bayesian statistical decision theory

Specific Detail Info

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Statement of Responsibility

Edited by Rens van de Schoot and Milica Miočevic´

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