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.
在这本书中描述的方法将是有用的for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.
TABLE OF CONTENTS
partI
|
84pages
Bayesian solutions
chapter3
|
20pages
A Tutorial on Using The Wambs Checklist to Avoid The Misuse of Bayesian Statistics
chapter5
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14pages
A Tutorial on Bayesian Penalized Regression with Shrinkage Priors for Small Sample Sizes
partII
|
70pages
n = 1
chapter6
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15pages
One by One
chapter8
|
14pages
How to Improve the Estimation of A Specific Examinee’s (n = 1) Math Ability When Test Data are Limited
chapter10
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16pages
Going Multivariate In Clinical Trial Studies
partIII
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111pages
Complex hypotheses and models