The quantitative methods for psychology最新文献

{"title":"Variability of Bayes Factor estimates in Bayesian Analysis of Variance","authors":"R. Pfister","doi":"10.20982/tqmp.17.1.p040","DOIUrl":"https://doi.org/10.20982/tqmp.17.1.p040","url":null,"abstract":"Bayes Factor estimation for Bayesian Analysis of Variance (ANOVA) typically relies on iterative algorithms that, by design, yield slightly different results on every run of the analysis. The variability of these estimates is surprisingly large, however: The present simulations indicate that repeating one and the same Bayesian ANOVA on a constant dataset often results in Bayes Factors that differ by a factor of 2 or more within only a few runs when using common analysis procedures. Results may at times even suggest evidence for the null hypothesis of no effect on one run while supporting the alternative hypothesis on another run. These observations call for a cautious approach to the results of Bayesian ANOVAs at present, and I outline three possibilities to circumvent or minimize this limitation.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68079962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compte rendu du colloque sur l'enseignement des statistiques 2020","authors":"Marcel Goulet","doi":"10.20982/tqmp.16.4.p308","DOIUrl":"https://doi.org/10.20982/tqmp.16.4.p308","url":null,"abstract":"La troisième édition du colloque sur l’enseignement des statistiques s’est déroulée le 27 mars 2020 par visioconférence. Le compte rendu du colloque inclut un résumé 1) des présentations de trois conférenciers; 2) des discussions auxquelles les présentations ont mené; et 3) des projets de l’Open Statistics Teaching Initiative (OSTI) pour les prochaines années. Le premier conférencier fut Alexandre Williot, qui proposa de considérer le profil d’apprenant des étudiantes afin d’adapter l’enseignement des statistiques aux besoins de chacun. Le deuxième conférencier fut Bernard Fournier, qui démontra l’utilisation d’un outil de génération de données aléatoires dans le logiciel R. Cet outil permet aux étudiantes de simuler des situations statistiques personnalisées. Enfin, le troisième conférencier fut Alexandre Gellen-Kamel, qui proposa une manière alternative d’enseigner les statistiques en s’inspirant de la pédagogie comportementale, spécifiquement de l’enseignement programmé. Les projets de l’OSTI pour les prochaines années incluent la validation d’un questionnaire sur les profils d’apprenant des étudiantes, la création d’un générateur de données aléatoires dans le logiciel Excel, l’élaboration de vignettes d’enseignement sur des concepts statistiques qui seraient disponibles pour les enseignantes et la réalisation de courtes capsules vidéo destinées aux étudiantes.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45606046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction and purpose of the tutorial series Python for Research in Psychology","authors":"Damien Rolon-Mérette, Kinsey Church, Matt Ross, Thadde Rolon-Merette","doi":"10.20982/tqmp.16.5.s001","DOIUrl":"https://doi.org/10.20982/tqmp.16.5.s001","url":null,"abstract":"In the era of advanced computers and state-of-the-art software, there have been great strides in the domains of research and engineering. These advancements have been made possible through the popularity, accessibility, and integration of computer programming. This has made au-tomating tasks, analysing results through complex statistical analysis, and the development of more advanced artificial intelligence possible. However, in psychology, computer programming is still be-ing underutilized. A major factor contributing to this is the steep learning curve for programming that is compounded by a lack of tutorials and knowledge specifically geared towards psychology. Thus, the purpose of this series is to bridge this gap and lower the learning barrier for researchers. This series invites anyone to send their own tutorials and contribute towards building a compre-hensive guide to programming for psychological research in one of the most versatile languages: Python! For submission details, please see the guidelines below.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48004360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Esprit et enjeux de l'analyse factorielle exploratoire","authors":"A. Achim","doi":"10.20982/tqmp.16.4.p213","DOIUrl":"https://doi.org/10.20982/tqmp.16.4.p213","url":null,"abstract":"L’analyse factorielle exploratoire est présentée en considérant les facteurs comme des sources communes de variance exprimées dans les données et responsables des corrélations observées. Les variables ayant généralement chacune une part de variance propre, il s’y trouve ainsi plus de sources d’information qu’il n’y a de variables observées. Alors que l’analyse en composantes principales inclut nécessairement une part des variances propres (souvent de l’erreur de mesure) dans chaque composante, l’analyse factorielle exploratoire vise à expurger les variances propres pour n’expliquer que les corrélations, c’est-à-dire l’information partagée provenant de facteurs communs. Il existe maintenant une méthode de modélisation pour identifier par un test formel combien de dimensions sont nécessaires et suffisantes pour rendre compte des corrélations dans les données. L’interprétation factorielle de ces dimensions demande généralement des rotations. Les enjeux de ces rotations et de l’obtention de scores des individus sur les facteurs retenus sont également discutés. Enfin, on propose une méthode simple pour l’analyse factorielle de mesures répétées. Exploratory factor analysis is presented by viewing factors as sources of variance expressed in the data that are completely responsible for the correlations observed among the variables. Since each variable also expresses some unique variance besides that from common factors, the data express more sources of information than there are variables. While principal component analysis necessarily includes some of the unique variances (often measurement error) in each component, exploratory common factor analysis concentrates on explaining the correlations, which entirely depend on shared information from common factors, ignoring as much as possible the unique sources of variance. There is now a modelling method to identify by formal testing how many dimensions are necessary and sufficient to account for the data correlations. The factorial interpretation of these dimensions generally requires rotations. The issues involved in rotations and in obtaining factor scores for the retained factor solution are also discussed. Finally, a simple method is proposed to factor analyse repeated measurements.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48793260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to {A}ppendix C of \"A review of effect sizes and their confidence intervals, Part I: The Cohen's d family\"","authors":"Jean-Christophe Goulet-Pelletier, D. Cousineau","doi":"10.20982/tqmp.16.4.p422","DOIUrl":"https://doi.org/10.20982/tqmp.16.4.p422","url":null,"abstract":"","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45998496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to Anaconda and Python: Installation and setup","authors":"Damien Rolon-Mérette, Matt Ross, Thadde Rolon-Merette, Kinsey Church","doi":"10.20982/tqmp.16.5.s003","DOIUrl":"https://doi.org/10.20982/tqmp.16.5.s003","url":null,"abstract":"Python has become one of the most popular programming languages for research in the past decade. Its free, open-source nature and vast online community are some of the reasons behind its success. Countless examples of increased research productivity due to Python can be found across a plethora of domains online, including data science, artificial intelligence and scientific research. This tutorial’s goal is to help users get started with Python through the installation and setup of the Anaconda software. The goal is to set users on the path toward using the Python language by preparing them to write their first script. This tutorial is divided in the following fashion: a small introduction to Python, how to download the Anaconda software, the different content that comes with the installation, and a simple example related to implementing a Python script.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46577696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does preregistration improve the credibility of research findings?","authors":"Mark Rubin","doi":"10.20982/tqmp.16.4.p376","DOIUrl":"https://doi.org/10.20982/tqmp.16.4.p376","url":null,"abstract":"Preregistration entails researchers registering their planned research hypotheses, methods, and analyses in a time-stamped document before they undertake their data collection and analyses. This document is then made available with the published research report to allow readers to identify discrepancies between what the researchers originally planned to do and what they actually ended up doing. This historical transparency is supposed to facilitate judgments about the credibility of the research findings. The present article provides a critical review of 17 of the reasons behind this argument. The article covers issues such as HARKing, multiple testing, p-hacking, forking paths, optional stopping, researchers’ biases, selective reporting, test severity, publication bias, and replication rates. It is concluded that preregistration’s historical transparency does not facilitate judgments about the credibility of research findings when researchers provide contemporary transparency in the form of (a) clear rationales for current hypotheses and analytical approaches, (b) public access to research data, materials, and code, and (c) demonstrations of the robustness of research conclusions to alternative interpretations and analytical approaches.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46501558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tutorial on Heteroskedasticity using HeteroskedasticityV3 SPSS macro","authors":"A. Daryanto","doi":"10.20982/tqmp.16.5.v008","DOIUrl":"https://doi.org/10.20982/tqmp.16.5.v008","url":null,"abstract":"In this paper, I demonstrate how to assess the heteroskedasticity problems in cross-sectional studies that use linear regression models using my HeteroskedasticityV3 SPSS macro. I present two illustrative examples inspired from real research. This paper also provides the annotations of the macro outputs. In my classroom demonstrations, students were asked to analyse data sets used in this paper and discuss their regression results with and without implementing robust standard errors. The merits of checking for the presence of heteroskedasticity prior to adjusting robust standard errors were also discussed in class.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48631522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Une étude statistique des séries de deux ou plusieurs types d'éléments The statistical study of series of two or many types of elements: a digest","authors":"L. Laurencelle","doi":"10.20982/tqmp.16.4.p391","DOIUrl":"https://doi.org/10.20982/tqmp.16.4.p391","url":null,"abstract":"aUniversité du Québec à Trois-Rivières Abstract Series of outcomes, of conditions, of events regularly occur in our lives or they are encountered in our workplace, and sometimes they ought to be scrutinized. For example, we find at the factory that machine #2 produces every day from 3 to 5 defective artifacts out of 20, or that early in the morning at the hospital emergency clinic, out of 112 patients examined, 28 had severe symptoms of enteritis. The systematic mathematical study of series was, as we know, first addressed to games of chance, at the same time as was probability theory. For example, throwing a coinN times, what are the chances that we observe n Face, or that the series ofN has r runs (or sequences with results of the same side), or that the longest run has L Face? Here we present a summary of the main results of the study of statistical series of N items falling into 2 or k (>2) categories, whether these items are provided from the outset or whether they emanate from a parameterized random process (binomial or multinomial). Formulas, examples and tables of critical values are provided.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43221711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The EZ Diffusion Model: An overview with derivation, software, and an application to the Same-Different task","authors":"Julien T. Groulx, Bradley Harding, D. Cousineau","doi":"10.20982/tqmp.16.2.p154","DOIUrl":"https://doi.org/10.20982/tqmp.16.2.p154","url":null,"abstract":"The diffusion model is useful for analyzing data from decision making experiments as it gives information about a dataset that regular statistical tests cannot, including: the rate of processing, the encoding and motor response times, and decision thresholds. The EZ diffusion model is a restricted version of the diffusion model with some parameter variability set to zero, allowing for quicker analyses. Here we describe the EZ diffusion model –including how it was derived mathematically– the measurement units of the parameters, and how it can be generalized to starting points other than the mid-point. We also show how its parameters can be estimated using computer software (the model is available with many software programs such as R and Excel, to which we add SPSS and a Mathematica code). Finally, an EZ analysis was run on one dataset obtained from a “Same”-“Different” experiment.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44012900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}