The quantitative methods for psychology最新文献

{"title":"Interpretation and Visualization of Moderation Effects and Random Slopes in Multilevel Models","authors":"Julie Lorah","doi":"10.20982/tqmp.18.1.p111","DOIUrl":"https://doi.org/10.20982/tqmp.18.1.p111","url":null,"abstract":"Interpretation of complex effects and models can be one of the most challenging and important aspects of quantitative data analysis. The present study tackles this issue for moderation effects, including random slope effects, for multilevel models. To demonstrate the generalization of these procedures beyond the basic multilevel model, the multilevel logistic regression model is used. Amoderation effect may be useful when a researcher would like to assess how a particular relationship differs for different groups or different levels of a moderator variable. When the moderator under consideration is a random effect, a random slope model arises. The random slope model has various applications; for example, when observations are nested within individuals comprising a longitudinal design, a random slopes model can be used to assess individual growth trajectories for the subjects in the study. However, these useful effects may be particularly difficult to interpret substantively. Therefore, the present study suggests a method combining the traditional aspects of plotting moderation effects with quantities of interest (QI) computation. Specific suggestions and examples, including R syntax, for associated data visualizations are provided.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44867270","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":"A note on the interpretation and simulation of reparameterized intercepts in constrained versions of the nominal response model","authors":"Carl F. Falk","doi":"10.20982/tqmp.17.4.p345","DOIUrl":"https://doi.org/10.20982/tqmp.17.4.p345","url":null,"abstract":"This is a brief expository paper on reparameterized intercepts under constrained variants of the nominal response model, including the generalized partial credit and partial credit models. Such parameterizations are commonly found in item response theory software packages such as flexMIRT®, IRTPRO, and OpenMx / rpf, and both these models are highly popular in educational and psychological testing. A heuristic graphical interpretation is provided. We give examples of how intercepts may be easily generated for Monte Carlo simulation studies, including a brief study to increase generalizability and explore limitations of a recently developed information matrix test to detect misspecification when collapsing adjacent response categories.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48195875","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":"{GRD} 2.1: An update to {GRD} for {SPSS} 27 and above","authors":"D. Cousineau, Bradley Harding","doi":"10.20982/tqmp.17.4.p391","DOIUrl":"https://doi.org/10.20982/tqmp.17.4.p391","url":null,"abstract":"GRD is a popular tool to genenrate random data on the fly. It is most useful in statistic classes where the students can generate with a single short syntax, or using a graphical interface, random data that differs on every run but yet can implement effect sizes, outliers, etc. With the new versions of SPSS (version 27 and above) which is now using a new version of Python, it was necessary to upgrade the extension. Here, GRD 2.1 is presented which works with SPSS 27 and above.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42506714","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":"L'analyse de variation monotone pour les moyennes et les proportions","authors":"L. Laurencelle","doi":"10.20982/tqmp.17.4.p374","DOIUrl":"https://doi.org/10.20982/tqmp.17.4.p374","url":null,"abstract":"Entre la variation statistique au hasard et la variation linéaire, toutes deux bien connues, il existe d’autres modèles de variation (polynomiale linéaire, périodique, etc.) et, notamment, la variation monotone. D’une condition à l’autre dans une série à contrôle croissant, d’un niveau d’intervention ou d’une dose médicinale à l’autre, la variable mesurée reflète-t-elle un effet consistant, croissant ou décroissant, cela sans qu’un modèle précis puisse lui être sous-tendu ? L’analyse de variation monotone, intégrée à l’analyse de variance classique, permet de décider si la variable observée répond de façon cohérente à une variable indépendante en escalier, celle-ci étant de type ordinal plutôt que linéaire. Nous présentons deux techniques adaptées à l’analyse de variance, celle de Barlow et collaborateurs (1972) et celle d’Abelson et Tukey (2013), permettant de rejeter l’hypothèse nulle en regard d’une hypothèse d’évolution consistance de la variable observée, l’application étant étendue à l’analyse des proportions. Des exemples, des tables de valeurs critiques et de coefficients et les formules de base sont aussi fournis. Between random statistical variation and simple linear change, both of which are well known, there are other models of variation (linear polynomials, periodic functions, etc.) and, particularly, that of monotonic variation. From one condition to the next in an increasing control series, from one intervention level or medicinal dose to the next, does the measured variable reflect a consistent, increasing or decreasing effect, without a definite mathematical pattern underlying it? Monotonic variation analysis, integrated with classical ANOVA, is used to decide whether the observed variable responds consistently to a staircase independent variable, which is ordinal rather than linear. We present here two techniques for ANOVA, that of Barlow and colleagues (1972) and that of Abelson and Tukey (2013), allowing the rejection of the null hypothesis against a hypothesis of consistent evolution of the observed variable; the application is extended to the analysis of proportions. Examples, tables of critical values and coefficients, and basic formulas are also provided.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49259449","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 3: Introduction to Functions and Libraries in Python","authors":"Matt Ross, Kinsey Church, Damien Rolon-M{'{e}}rette","doi":"10.20982/tqmp.17.4.s013","DOIUrl":"https://doi.org/10.20982/tqmp.17.4.s013","url":null,"abstract":"The third introductory tutorial of our series “Python for Researchers in Psychology” aims to teach researchers about the importance of functions and libraries. First, we introduce the concept of functions. We cover the advantages of functions and how to use them with the help of basic examples, including a paired-samples (dependent-samples) t-test. Then, libraries and their included functions are discussed, including how to import them and the functionality of some of the most popular libraries for researchers in psychology. Finally, a longer, more complex example shows how functions and libraries can help accelerate your research, statistical analyses, and data visualization.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42624823","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":"A brief guide to sampling in educational settings","authors":"A. George","doi":"10.20982/tqmp.17.3.p286","DOIUrl":"https://doi.org/10.20982/tqmp.17.3.p286","url":null,"abstract":"This tutorial gives an overview of sampling techniques commonly used in the field of education such as simple randomized sampling, stratification methods and (multistage) cluster randomized sampling. Advantages and disadvantages of these techniques are describedwithout diving too deep into sampling theory. Instead, each technique is exemplified with data and program code in R. Finally, all presented techniques are combined to show the complexity of samples in famous educational large-scale studies such as PISA. Again an example with R code illustrates the theoretical descriptions.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42957832","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 Corsi Blocks Task: Variations and coding with jsPsych","authors":"Rose-Marie Gibeau","doi":"10.20982/tqmp.17.3.p299","DOIUrl":"https://doi.org/10.20982/tqmp.17.3.p299","url":null,"abstract":"The Corsi Blocks Task is a widely used task in both clinical and experimental work. However, many versions exist, and it can be difficult to, firstly, choose between the variations of each parameter and, secondly, to program it in a computer software. No article has yet been published on the different versions of the computerized Corsi Blocks Task. Herein, we summarize possible variations of this task. We also provide an implementation of the task using jsPsych.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48552615","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":"Who Belongs in School? Using Statistical Learning Techniques to Identify Linear, Nonlinear and Interactive Effects","authors":"Rafael Quintana","doi":"10.20982/tqmp.17.3.p312","DOIUrl":"https://doi.org/10.20982/tqmp.17.3.p312","url":null,"abstract":"The sense of school belonging refers to students’ feelings of being accepted and connected to their particular school. School belonging has been considered an important determinant of a range of academic and socioemotional outcomes. Yet despite an extensive literature on the topic, it is not clear what factors are more strongly related to the students’ sense of school belonging. Using a nationally representative dataset, we investigated the extent to which school belonging in fifth grade can be predicted by a wide range of individual and contextual-level factors using two statistical learning techniques (Lasso andMARS). The strongest predictor of school belonging across all models was students’ feelings of peer social support, followed by students’ feelings of loneliness at school. These results suggest that peer social relationships are a key component of students feeling of being connected to their school.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47296792","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":"Le traitement statistique des proportions incluant l'analyse de variance, avec des exemples // The statistical handling of proportions including analysis of variance, with worked out examples","authors":"L. Laurencelle","doi":"10.20982/tqmp.17.3.p272","DOIUrl":"https://doi.org/10.20982/tqmp.17.3.p272","url":null,"abstract":"The simple proportion, p = x/n , a pervasive statistical tool equally used in public and academic research areas, is in the literature still short of the necessary analytical implements needed for full-scale statistical treatments. This is partly ascribable to its discrete numerical character, but it proceeds mostly from its other distributional properties: its variance is tied up with its expectation, and its density shows a strong U-shaped asymmetry reactive to its π parameter, the habitual normal-based analytical procedures thus being contra-indicated. We here revive the Fisher-Yates angular transformation of the proportion, y ( x, n ) = sin − 1 √ x , heralded for its π -independent variance and smoothed-out non-normality, and put to trial three of its improved descendants (Ans-combe 1948, Tukey & Freeman 1950, Chanter 1975). Following a would-be thorough study of the three y functions retained (bias, variance, precision, test accuracy, power), largely documented in Laurencelle’s (2021a) investigation, we develop and illustrate the z test of significance on one proportion, the z test on the difference of two independent proportions, the analysis of variance of k ≥ 2 independent proportions and finally the test of two and anova of k correlated proportions . A critical appraisal of the McNemar test for the difference of two correlated proportions is also essayed.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42352920","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":"La modélisation par équations structurelles -- Un guide d'accompagnement pour l'interface R","authors":"Karianne Dion, Alexandra Maria Bodnaruc, Geneviève Trudel, J. Lamarche, Valérie Ranger, Sophie Fobert, Kinsey Antonina Church, Joana Ntumba Mukunzi, Jean-Louis René","doi":"10.20982/tqmp.17.3.p198","DOIUrl":"https://doi.org/10.20982/tqmp.17.3.p198","url":null,"abstract":"Abstract La modélisation par équations structurelles avec MPlus (Caron, 2019) est un ouvrage de référence qui, divisé en trois parties, aborde les fonctions de base de Mplus (Partie 1 – Les rudiments), le traitement de données avec Mplus (Partie 2 – Traitement de données), ainsi que l’exécution d’un ensemble d’analyses statistiques impliquant de la modélisation par équations structurelles en utilisant Mplus (Partie 3 – Les analyses). Reconnaissant l’utilisation croissante de la modélisation par équations structurelles dans le domaine des sciences sociales, ainsi que le nombre limité de ressources éducatives disponibles à ce sujet, nous proposons un guide d’accompagnement pour la troisième partie du livre de Caron (2019), qui étendra son application à l’interface de programmation R. R est une interface de programmation libre d’accès et très versatile qui, similairement à Mplus, permet la réalisation d’analyses statistiques impliquant de la modélisation par équations structurelles. L’objectif de cet article est de présenter la traduction en langage de programmation R des syntaxes de la troisième partie du livre de Caron (2019) ainsi que les résultats des sorties associées. Puisque cet article vise à servir de guide complémentaire au livre de Caron (2019), les bases théoriques sous-tendant les analyses statistiques qui y sont couvertes ainsi que l’interprétation des résultats issus de celles-ci ne font pas l’objet du présent article. Cet article couvre les analyses suivantes : la régression logistique, l’analyse de trajectoire, l’analyse factorielle exploratoire et confirmatoire, la médiation, la modération, la médiation modérée, l’analyse de classes latentes, l’analyse de modèles autorégressifs et autorégressifs croisés, l’analyse de trajectoire latente, et l’analyse de groupes multiples.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41703512","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}