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Publicado por Taylor & Francis Ltd, London, 2020
ISBN 10: 0367222221 ISBN 13: 9780367222222
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Añadir al carritoPaperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Publicado por Taylor & Francis Ltd, London, 2020
ISBN 10: 0367222221 ISBN 13: 9780367222222
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Añadir al carritoPaperback. Condición: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Añadir al carritoKartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 h.
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Añadir al carritoEinband - fest (Hardcover). Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 h.
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.