{"title":"Conducting Simulation Studies in the R Programming Environment.","authors":"Kevin A Hallgren","doi":"10.20982/tqmp.09.2.p043","DOIUrl":"https://doi.org/10.20982/tqmp.09.2.p043","url":null,"abstract":"<p><p>Simulation studies allow researchers to answer specific questions about data analysis, statistical power, and best-practices for obtaining accurate results in empirical research. Despite the benefits that simulation research can provide, many researchers are unfamiliar with available tools for conducting their own simulation studies. The use of simulation studies need not be restricted to researchers with advanced skills in statistics and computer programming, and such methods can be implemented by researchers with a variety of abilities and interests. The present paper provides an introduction to methods used for running simulation studies using the R statistical programming environment and is written for individuals with minimal experience running simulation studies or using R. The paper describes the rationale and benefits of using simulations and introduces R functions relevant for many simulation studies. Three examples illustrate different applications for simulation studies, including (a) the use of simulations to answer a novel question about statistical analysis, (b) the use of simulations to estimate statistical power, and (c) the use of simulations to obtain confidence intervals of parameter estimates through bootstrapping. Results and fully annotated syntax from these examples are provided.</p>","PeriodicalId":45805,"journal":{"name":"Quantitative Methods for Psychology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4110976/pdf/nihms591919.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32538897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial.","authors":"Kevin A Hallgren","doi":"10.20982/tqmp.08.1.p023","DOIUrl":"https://doi.org/10.20982/tqmp.08.1.p023","url":null,"abstract":"<p><p>Many research designs require the assessment of inter-rater reliability (IRR) to demonstrate consistency among observational ratings provided by multiple coders. However, many studies use incorrect statistical procedures, fail to fully report the information necessary to interpret their results, or do not address how IRR affects the power of their subsequent analyses for hypothesis testing. This paper provides an overview of methodological issues related to the assessment of IRR with a focus on study design, selection of appropriate statistics, and the computation, interpretation, and reporting of some commonly-used IRR statistics. Computational examples include SPSS and R syntax for computing Cohen's kappa and intra-class correlations to assess IRR.</p>","PeriodicalId":45805,"journal":{"name":"Quantitative Methods for Psychology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402032/pdf/nihms372951.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30790856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}