Small Sample Size Solutions : A Guide for Applied Researchers and Practitioners
Language: English
Published by Routledge, 2020
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 322.40
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
Seller Inventory # 9780367221898
- Title
- Small Sample Size Solutions : A Guide for Applied Researchers and Practitioners
- Author
- Milica Mio¿evi¿
- Publisher
- Routledge
- Publication year
- 2020
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0367221896
- ISBN 13
- 9780367221898
- Item weight
- 596 grams
- Dimensions
- 240x161x20 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
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 Miočević 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. Miočević’s research evaluates optimal ways to use Bayesian methods in the social sciences, particularly for mediation analysis.
"About the title" may belong to another edition of this title.
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