LEADER 05613 am 22007453u 450 001 9910377813603321 005 20230621140208.0 010 $a1-000-76101-0 010 $a1-000-76108-8 010 $a0-367-22222-1 010 $a0-429-27387-8 035 $a(CKB)4100000010265590 035 $a(OAPEN)1007799 035 $a(OCoLC)1142226472 035 $a(OCoLC-P)1142226472 035 $a(FlBoTFG)9780429273872 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/30625 035 $a(EXLCZ)994100000010265590 100 $a20200228d2020 uy 0 101 0 $aeng 135 $aurcnu|||unuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSmall sample size solutions $ea guide for applied researchers and practitioners /$fedited by Rens van de Schoot and Milica Mioc?evic? 210 $cTaylor & Francis$d2020 210 1$aLondon :$cRoutledge, Taylor & Francis Group,$d2020. 215 $a1 online resource (xiv, 269 pages) $cdigital, PDF file(s) 225 1 $aEuropean Association of Methodology series 311 08$aPrint version: 9780367221898 320 $aIncludes bibliographical references and index. 327 $aIntroduction (Van de Schootand Mioc?evic?) List of Symbols Part I: Bayesian solutions 1. Introduction to Bayesian statistics(Mioc?evic?, Levy,and van de Schoot) 2.The role of exchangeability in sequential updating of findings from small studies and the challenges of identifying exchangeable data sets (Mioc?evic?, Levy,and Savord) 3. A tutorial on using the WAMBS checklist to avoid the misuse of Bayesian statistics(van de Schoot, Veen, Smeets, Winter,and Depaoli) 4. The importance of collaboration in Bayesian analyses with small samples (Veenand Egberts) 5. A tutorial on Bayesian penalized regression with shrinkage priors for small sample sizes (van Erp) PartII: n=1 6. One by one: the designand analysis of replicated randomized single-case experiments(Onghena) 7. Single-case experimental designs in clinical intervention research (Maricand van der Werff) 8. How to improve the estimation of a specific examinee's (n=1)math ability when test data are limited(Lekand Arts) 9. Combining evidence over multiple individual analyses(Klaassen) 10. Going multivariate in clinical trial studies: a Bayesian framework for multiple binary outcomes(Kavelaars) PartIII: Complex hypotheses and models 11. An introduction to restriktor: evaluating informative hypotheses for linear models (VanbrabantandRosseel) 12. Testing replication with small samples: applications to ANOVA(Zondervan-Zwijnenburgand Rijshouwer) 13. Small sample meta-analyses: exploring heterogeneity using MetaForest (van Lissa) 14. Item parcels as indicators: why, when, and how to use them in small sample research(Rioux, Stickley, Odejimi,and Little) 15. Small samples in multilevel modeling(Hoxand McNeish) 16. Small sample solutions for structural equation modeling(Rosseel) 17. SEM with small samples: two-step modeling and factor score regression versus Bayesian estimation with informative priors (Smidand Rosseel) 18. Important yet unheeded: some small sample issues that are often overlooked(Hox) Index 330 $aResearchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This uniquebook provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapterillustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. Thisessential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect.The statistical models in the book rangefrom the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods.All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics. 410 0$aEuropean Association of Methodology series. 606 $aResearch$xMethodology 606 $aData sets 610 $astatistical methods 610 $aresearchers 610 $astatistical model 610 $aresearch 610 $asmall sample 610 $aestimation 610 $apopulation 610 $avariables 610 $aobservations 610 $asocial sciences 610 $abehavioral sciences 610 $amedical sciences 610 $aepidemiology 610 $apsychology 610 $amarketing 610 $aeconomics 610 $aanalysis 615 0$aResearch$xMethodology. 615 0$aData sets. 676 $a001.42 700 $avan de Schoot$b Rens$4auth$01366247 702 $aSchoot$b Rens van de 702 $aMioc?evic?$b Milica 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910377813603321 996 $aSmall sample size solutions$93388729 997 $aUNINA