{"id":353,"date":"2022-10-07T15:19:51","date_gmt":"2022-10-07T19:19:51","guid":{"rendered":"https:\/\/www.yorku.ca\/research\/scs\/?page_id=353"},"modified":"2024-10-04T12:02:18","modified_gmt":"2024-10-04T16:02:18","slug":"statistical-resources","status":"publish","type":"page","link":"https:\/\/www.yorku.ca\/research\/scs\/statistical-resources\/","title":{"rendered":"Statistical Resources"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"857\" height=\"247\" src=\"https:\/\/www.yorku.ca\/research\/scs\/wp-content\/uploads\/sites\/527\/2024\/01\/stats-ressources-banner.png\" alt=\"\" class=\"wp-image-399\" srcset=\"https:\/\/www.yorku.ca\/research\/scs\/wp-content\/uploads\/sites\/527\/2024\/01\/stats-ressources-banner.png 857w, https:\/\/www.yorku.ca\/research\/scs\/wp-content\/uploads\/sites\/527\/2024\/01\/stats-ressources-banner-400x115.png 400w\" sizes=\"auto, (max-width: 857px) 100vw, 857px\" \/><\/figure>\n\n\n\n<p>This page collects links to tutorials and other online resources we have found useful for SCS clients.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Graphical methods<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Royal Statistical Society <a href=\"https:\/\/royal-statistical-society.github.io\/datavisguide\/\">Best Practices for Data Visualisation<\/a>. Insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/robjhyndman.com\/hyndsight\/graphics\/\">Twenty rules for good graphics<\/a>. This post by Rob Hyndman, describes some <em>best practices<\/em> for producing graphs for <em>journal publication<\/em>.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.britishecologicalsociety.org\/wp-content\/uploads\/2017\/12\/guide-to-reproducible-code.pdf\">British Ecological Society's Guide to Reproducible Science<\/a>. The guide proposes a simple reproducible project workflow, and a guide to organizing projects for reproducibility. The Programming section provides concrete tips and traps to avoid (example: use relative, not absolute pathnames), and the Reproducible Reports section provides a step-by-step guide for generating reports with R Markdown.<\/li>\n\n\n\n<li><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Multiplicity Control<\/h2>\n\n\n\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=HpjlcEH4zuY\">https:\/\/www.youtube.com\/watch?v=HpjlcEH4zuY<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/journals.lww.com\/epidem\/Abstract\/1990\/01000\/No_Adjustments_Are_Needed_for_Multiple_Comparisons.10.aspx\">https:\/\/journals.lww.com\/epidem\/Abstract\/1990\/01000\/No_Adjustments_Are_Needed_for_Multiple_Comparisons.10.aspx<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.researchgate.net\/publication\/318326501_Multiplicity_Control_School_Uniforms_and_Other_Perplexing_Debates\">https:\/\/www.researchgate.net\/publication\/318326501_Multiplicity_Control_School_Uniforms_and_Other_Perplexing_Debates<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Negligible Effect (Equivalence) Testing<\/h2>\n\n\n\n<p><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/8316613\/\">Using significance tests to evaluate equivalence between two experimental groups<\/a>. Equivalency testing, a statistical method often used in biostatistics to determine the equivalence of 2 experimental drugs, is introduced to social scientists. Examples of equivalency testing are offered, and the usefulness of the method to the social scientist is discussed.<\/p>\n\n\n\n<p>Lakens et al.  <a href=\"https:\/\/osf.io\/qmgtn\/download\">Equiv<\/a><a href=\"https:\/\/doi.org\/10.1177\/2515245918770963\">alence Testing for Psychological Research: A Tutorial<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/cran.rstudio.com\/web\/packages\/TOSTER\/vignettes\/IntroductionToTOSTER.html\">Introduction to Equivalence Testing with TOSTER<\/a> describes the <code>TOSTER<\/code> package.<\/li>\n\n\n\n<li>The <a href=\"https:\/\/cran.r-project.org\/package=negligible\"><code>neglibible<\/code> package<\/a> provides functions that are useful for conducting negligible effect testing (also called equivalence testing),<br>including equivalence of means or the presence of a negligible association (correlation or regression)<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=AEpMHDXK8UI\">https:\/\/www.youtube.com\/watch?v=AEpMHDXK8UI<\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Linear models (ANOVA, Regression)<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/strengejacke.github.io\/regressionmodels\/\">Overview of R Modelling Packages<\/a>. An overview of R packages and functions for fitting different types of linear models, classified by the type<br>of outcome variable (continuous, binary, catgegorical). Contains links to examples and shows Bayesian equivalents of many frequentist approaches.<\/li>\n\n\n\n<li><a href=\"https:\/\/modelsummary.com\/\">Data and Model Summaries in R<\/a>. <code>modelsummary<\/code> is an R a package to summarize data and statistical models in R. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. There is also a <a href=\"https:\/\/www.jstatsoft.org\/index.php\/jss\/article\/view\/v103i01\/4314\">JSS paper<\/a> that condenses many examples into a shorter format.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Meta Analysis<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Mathais Harrer <a href=\"https:\/\/bookdown.org\/MathiasHarrer\/Doing_Meta_Analysis_in_R\/\">Doing Meta Analysis in R<\/a>. An accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Structural Equation Models<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/psyarxiv.com\/4n3uk\/\">How to Conduct Power Analysis for Structural Equation Models: A Practical Primer<\/a><br>pwrSEM is a Shiny web app that researchers can use to conduct power analysis for structural equation models. Tutorial papers for pwrSEM can be found at: <a href=\"https:\/\/journals.sagepub.com\/doi\/full\/10.1177\/2515245920918253\">https:\/\/journals.sagepub.com\/doi\/full\/10.1177\/2515245920918253<\/a>;<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Effect size &amp; power analysis<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Matthew B Jane et al. <a href=\"https:\/\/matthewbjane.quarto.pub\/\">Guide to Effect Sizes and Confidence Intervals<\/a>. This guide aims to provide academics, students and researchers with hands-on, step-by-step instructions for calculating effect sizes and confidence intervals for common statistical tests used in the behavioral, cognitive and social sciences.<\/li>\n\n\n\n<li>Metin Bulus <a href=\"https:\/\/pwrss.shinyapps.io\/index\/\">Statistics Power Analysis and Sample Size Calculation Tools<\/a>. An online <a href=\"https:\/\/shiny.posit.co\/\">shiny<\/a> application with power \/ sample size calculators for a wide variety of statistical problems.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Latent Profile Analysis<\/h2>\n\n\n\n<p>Latent profile analysis (LPA) is a latent variable method that focuses on identifying latent sub-populations within a population based on observed variables.<br>LPA works best with continuous variables (and, in some cases, ordinal variables), but is not appropriate for dichotomous (binary) variables.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Spurk et al. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0001879120300701\">Latent profile analysis: A review and \u201chow to\u201d guide of its application within vocational behavior research<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/cran.r-project.org\/web\/packages\/tidyLPA\/vignettes\/Introduction_to_tidyLPA.html\">Introduction to tidyLPA<\/a>. The <code>tidyLPA<\/code> package provides an interface to the powerful and widely-used mclust package for Gaussian Mixture Modeling.<\/li>\n\n\n\n<li><a href=\"https:\/\/willhipson.netlify.app\/post\/latent-profile\/latent-profile\/\">Quick Example of Latent Profile Analysis in R<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This page collects links to tutorials and other online resources we have found useful for SCS clients. Graphical methods Multiplicity Control https:\/\/www.youtube.com\/watch?v=HpjlcEH4zuY https:\/\/journals.lww.com\/epidem\/Abstract\/1990\/01000\/No_Adjustments_Are_Needed_for_Multiple_Comparisons.10.aspx https:\/\/www.researchgate.net\/publication\/318326501_Multiplicity_Control_School_Uniforms_and_Other_Perplexing_Debates Negligible Effect (Equivalence) Testing Using significance tests to evaluate equivalence between two experimental groups. Equivalency testing, a statistical method often used in biostatistics to determine the equivalence of 2 experimental drugs, [&hellip;]<\/p>\n","protected":false},"author":708,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","footnotes":""},"tags":[],"class_list":["post-353","page","type-page","status-publish","hentry"],"taxonomy_info":[],"featured_image_src_large":false,"author_info":{"display_name":"cribbie","author_link":"https:\/\/www.yorku.ca\/research\/scs\/author\/cribbie\/"},"comment_info":"","_links":{"self":[{"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/pages\/353","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/users\/708"}],"replies":[{"embeddable":true,"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/comments?post=353"}],"version-history":[{"count":10,"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/pages\/353\/revisions"}],"predecessor-version":[{"id":481,"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/pages\/353\/revisions\/481"}],"wp:attachment":[{"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/media?parent=353"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.yorku.ca\/research\/scs\/wp-json\/wp\/v2\/tags?post=353"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}