Quantitative Methods for Psychology最新文献

英文中文

Conducting Simulation Studies in the R Programming Environment. 在R编程环境中进行仿真研究。

IF 2.2

Quantitative Methods for Psychology

Pub Date : 2013-10-12 DOI: 10.20982/tqmp.09.2.p043

Kevin A Hallgren

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.

模拟研究允许研究人员回答有关数据分析的具体问题，统计能力，以及在实证研究中获得准确结果的最佳实践。尽管仿真研究可以提供许多好处，但许多研究人员并不熟悉进行自己的仿真研究的可用工具。模拟研究的使用不必局限于具有统计和计算机编程高级技能的研究人员，并且这些方法可以由具有各种能力和兴趣的研究人员实施。本文介绍了使用R统计编程环境运行模拟研究的方法，是为运行模拟研究或使用R的经验最少的个人编写的。本文描述了使用模拟的基本原理和好处，并介绍了与许多模拟研究相关的R函数。三个例子说明了模拟研究的不同应用，包括(a)使用模拟来回答一个关于统计分析的新问题，(b)使用模拟来估计统计功率，(c)使用模拟来通过自举获得参数估计的置信区间。提供了这些示例的结果和完整注释的语法。

{"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}

引用次数: 36

Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. 计算观测数据间的可靠性:概述和教程。

IF 2.2

Quantitative Methods for Psychology

Pub Date : 2012-01-01 DOI: 10.20982/tqmp.08.1.p023

Kevin A Hallgren

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.

许多研究设计需要评估评分者间信度(IRR)，以证明多个编码器提供的观察评分之间的一致性。然而，许多研究使用了不正确的统计程序，未能充分报告解释其结果所需的信息，或者没有解决IRR如何影响其后续假设检验分析的能力。本文概述了与IRR评估相关的方法学问题，重点是研究设计，选择适当的统计数据，以及一些常用IRR统计数据的计算，解释和报告。计算示例包括SPSS和R语法，用于计算Cohen的kappa和类内相关性以评估IRR。

引用次数: 3199

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Quantitative Methods for Psychology

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀