Small Sample Size Solutions
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 n...
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| Формат: | Online |
|---|---|
| Язык: | английский |
| Опубликовано: |
Taylor & Francis
2025
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| Предметы: | |
| Online-ссылка: | ONIX_20250530T083217_9781000760941_98 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869527623216922624 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-161011 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1610112025-05-31T06:32:38Z Small Sample Size Solutions van de Schoot, Rens Miočević, Milica Van De Schoot small sample size problems MCMC Sample latent variables MCMC Algorithm exchangeable data sets Smaller Prior Variance Bayesian penalized regression Vice Versa Bayesian methods Data Set Posterior Probability Posterior Distributions Frequentist Estimation Methods NHST. Bayesian Estimation Prior Distribution Informative Hypotheses Open Science Framework BF MCMC FSR Shiny App Bayesian Conditions Trace Plots Shrinkage Priors Single Case Experiments Interim Analyses Constraint Syntax thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine::MBNS Epidemiology and Medical statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCH Econometrics and economic statistics thema EDItEUR::J Society and Social Sciences::JP Politics and government 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. 2025-05-31T06:32:37Z 2025-05-31T06:32:37Z 2025-05-30T06:47:05Z 2020 book ONIX_20250530T083217_9781000760941_98 https://library.oapen.org/handle/20.500.12657/103145 9781000760941 9780429273872 9781000761085 9780367222222 9780367221898 https://directory.doabooks.org/handle/20.500.12854/161011 eng European Association of Methodology Series open access image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://library.oapen.org/bitstream/20.500.12657/103145/1/9781000760941.pdf Taylor & Francis Routledge 10.4324/9780429273872 10.4324/9780429273872 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 da087c60-8432-4f58-b2dd-747fc1a60025 e0bd4373-4073-4641-9c13-774e2b3e6588 9781000760941 9780429273872 9781000761085 9780367222222 9780367221898 Dutch Research Council (NWO) Routledge 284 Oxford [...] Nederlandse Organisatie voor Wetenschappelijk Onderzoek Netherlands Organisation for Scientific Research 10.13039/501100003246 open access |
| spellingShingle | Van De Schoot small sample size problems MCMC Sample latent variables MCMC Algorithm exchangeable data sets Smaller Prior Variance Bayesian penalized regression Vice Versa Bayesian methods Data Set Posterior Probability Posterior Distributions Frequentist Estimation Methods NHST. Bayesian Estimation Prior Distribution Informative Hypotheses Open Science Framework BF MCMC FSR Shiny App Bayesian Conditions Trace Plots Shrinkage Priors Single Case Experiments Interim Analyses Constraint Syntax thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine::MBNS Epidemiology and Medical statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCH Econometrics and economic statistics thema EDItEUR::J Society and Social Sciences::JP Politics and government Small Sample Size Solutions |
| title | Small Sample Size Solutions |
| title_full | Small Sample Size Solutions |
| title_fullStr | Small Sample Size Solutions |
| title_full_unstemmed | Small Sample Size Solutions |
| title_short | Small Sample Size Solutions |
| title_sort | small sample size solutions |
| topic | Van De Schoot small sample size problems MCMC Sample latent variables MCMC Algorithm exchangeable data sets Smaller Prior Variance Bayesian penalized regression Vice Versa Bayesian methods Data Set Posterior Probability Posterior Distributions Frequentist Estimation Methods NHST. Bayesian Estimation Prior Distribution Informative Hypotheses Open Science Framework BF MCMC FSR Shiny App Bayesian Conditions Trace Plots Shrinkage Priors Single Case Experiments Interim Analyses Constraint Syntax thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine::MBNS Epidemiology and Medical statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCH Econometrics and economic statistics thema EDItEUR::J Society and Social Sciences::JP Politics and government |
| topic_facet | Van De Schoot small sample size problems MCMC Sample latent variables MCMC Algorithm exchangeable data sets Smaller Prior Variance Bayesian penalized regression Vice Versa Bayesian methods Data Set Posterior Probability Posterior Distributions Frequentist Estimation Methods NHST. Bayesian Estimation Prior Distribution Informative Hypotheses Open Science Framework BF MCMC FSR Shiny App Bayesian Conditions Trace Plots Shrinkage Priors Single Case Experiments Interim Analyses Constraint Syntax thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine::MBNS Epidemiology and Medical statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCH Econometrics and economic statistics thema EDItEUR::J Society and Social Sciences::JP Politics and government |
| url | ONIX_20250530T083217_9781000760941_98 |