000			02342 a2200373 4500
005			20250526161934.0
008			250430042020GB 152 eng
020			_a9780367222222 _qBC
037			_bTaylor & Francis _cGBP 37.99 _fBB
040			_a01
041			_aeng
072	7		_aJMB _2thema
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072	7		_aPSY000000 _2bisac
072	7		_aPSY030000 _2bisac
072	7		_a001.42 _2bisac
100	1		_aRens van de Schoot _91093
245	1	0	_aSmall Sample Size Solutions _bA Guide for Applied Researchers and Practitioners
250			_a1
260			_aOxford _bRoutledge _c20200225
300			_a270 p
520			_bResearchers 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.
700	1		_aMilica Miočević _4B01 _91094
999			_c10663 _d10663