Show pageOld revisionsBacklinksBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== How to guides ====== ===== Before submission / reviews ===== * [[http://daniellakens.blogspot.com/2015/10/checking-your-stats-and-some-errors-we.html|Check your stats]] - [[http://statcheck.io/|statcheck app]] * [[https://www.grammarly.com/|Check your grammar - grammarly]] * [[http://reciteworks.com/|Check your citations - Reciteworks]] * [[http://www.p-curve.com/app4/|Check your p-curve]] * [[http://scienceandpublic.com/|De-Jargonizer: How accessible is your work]]? ([[http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181742|article]]) ===== Learning R and R scripts ===== ==== General R guides ==== * [[https://paulvanderlaken.com/2017/08/10/r-resources-cheatsheets-tutorials-books/|R resources (free courses, books, tutorials, & cheat sheets)]] * [[https://osf.io/a2x7j/|SIPS 2016 R Workshop]] * [[https://osf.io/69gub/|A gentle crash course in R using tidyverse - 2018 SIPS R workshop]] * [[http://compcogscisydney.org/psyr/|R for Psychological Science An introductory resource]] * [[https://blog.rstudio.com/2017/07/11/introducing-learnr/?utm_content=buffer03a56&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer|Introducing learnr]] * YaRrr! The Pirate’s Guide to R - [[https://bookdown.org/ndphillips/YaRrr/|book]] / [[https://www.youtube.com/playlist?list=PL9tt3I41HFS9gmeZFEuNrnu_7V_NFngfJ|videos]] * [[https://github.com/seanchrismurphy/A-Psychologists-Guide-to-R|A Psychologist's Guide to R]] * [[https://billpetti.github.io/Crash_course_in_R/|A Crash Course in the R Programming Language]] * [[https://www.datacamp.com/home|Datacamp]] is a good starting point * [[https://github.com/jalapic/learnR/blob/master/README.md|learnR - R Programming for Behavioral Scientists]] * [[http://r4ds.had.co.nz/|R for Data Science]]; Garrett Grolemund & Hadley Wickham * [[http://swirlstats.com/|Swirl Learn R, in R.]] * [[http://blog.revolutionanalytics.com/2012/12/coursera-videos.html|Videos from Coursera's four week course in R]] / [[https://www.youtube.com/user/rdpeng/playlists|more from the author]] * [[http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/|Learning Statistics with R - Computational Cognitive Science Lab]] * [[https://aupsychology.github.io/statsbook/|Statistics: The Story of Numbers - Statistics course with R]] * [[http://www.r-bloggers.com/how-to-learn-r-2/|How to Learn R]] (R bloggers) * [[http://seankross.com/rbootcamp/|Winter R Bootcamp]] - Sean Kross December 23, 2015 * [[http://tutorials.iq.harvard.edu/|Materials for the Research Technology Consulting statistical software workshops]] * [[http://ww2.coastal.edu/kingw/statistics/R-tutorials/|R tutorials]] * [[https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html|ggrepel Usage Examples]] * [[http://jaredknowles.com/r-bootcamp/|R Bootcamp]] - good slides * [[https://www.r-bloggers.com/my-3-video-presentations-on-essential-r/|My 3 video presentations on “Essential R”]] * [[https://github.com/jalapic/RPackage/blob/master/Writing%20an%20R%20Package.pdf|Writing an R Package]] * [[https://osf.io/fbj3z/|Robust statistical methods: a primer for clinical psychology and experimental psychopathology researchers]] (Andy Field Rand R. Wilcox, BRAT, 2017) * [[https://docs.google.com/spreadsheets/d/1XkvsNUG1Yrn66gPiAg7uu-0ew1swyMFmVKHDyu7_9nY/edit#gid=0|Twitter-sourced spreadsheet of resources for learning R]] * [[https://www.youtube.com/watch?v=pKwXOo4Kkiw|R Workflow using ProjectTemplate video]] | [[https://osf.io/jcg2t/|ProjectTemplate and R Workflow 2017]] * [[https://paulvanderlaken.com/2017/08/10/r-resources-cheatsheets-tutorials-books/|R Resources (Free Courses, Books, Tutorials, & Cheatsheets)]] * [[https://github.com/nmmichalak/novum_R_ganum/blob/master/learn_r_for_psychology_research_a_crash_course/learn_r_for_psychology_research_a_crash_course.md|learn R for psychology research: a crash course]] * [[https://www.kaggle.com/rtatman/kernels|Rachael Tatman's great R tutorials and more]] * [[https://r-posts.com/pipes-in-r-tutorial-for-beginners/|Pipes in R Tutorial For Beginners]] * [[https://bookdown.org/rdpeng/RProgDA/|Mastering Software Development in R]] * [[https://csgillespie.github.io/efficientR/index.html|Efficient R programming]] Courses: * [[http://datascience.tntlab.org/before-you-begin/|Data Science for Social Scientists]] * [[https://campus.datacamp.com/courses/sentiment-analysis-in-r/fast-dirty-polarity-scoring?ex=1|sentiment analysis in r]] | Datacamp Data cleaning * [[https://www.r-bloggers.com/21-online-courses-to-get-started-today-with-data-cleaning/|21+ Online Courses to Get Started Today with Data Cleaning]] * [[http://sfirke.github.io/janitor/index.html|janitor R package]] ==== Styling ==== * [[https://google.github.io/styleguide/Rguide.xml|Google's R Style Guide]] * [[http://style.tidyverse.org/|The tidyverse style guide]] * [[https://github.com/jimhester/lintr|Check your code style - lintr]] ==== Research design ==== * [[http://thomasleeper.com/designcourse/|Research Design in Political Science]] ==== Data prep ==== * [[https://github.com/underthecurve/r-data-cleaning-tricks|Tricks for cleaning your data in R]] * [[http://varianceexplained.org/r/teach-tidyverse/|Teach the tidyverse to beginners]] ==== Data check ==== * "Methods to Detect Low Quality Data and Its Implication for Psychological Research", preprint - https://osf.io/cv2bn/ ; video: https://www.youtube.com/watch?v=QE5_qktry5I ; Shinyapp: https://github.com/doomlab/shiny-server/tree/master/lq-screen ; package: https://osf.io/x6t8a/ ; https://github.com/doomlab/botbotbot * [[https://www.sciencedirect.com/science/article/pii/S0022103115000931?via%3Dihub|Methods for the detection of carelessly invalid responses in survey data]] (JESP, 2016) ==== Statistical tests ==== * [[http://moderndive.com/index.html|ModernDive An Introduction to Statistical and Data Sciences via R]] * [[http://rapport-package.info/|rapport an R templating system]] * [[https://www.youtube.com/watch?v=iFFW5sK3Bhk|Scoring Psychological Tests with R (and preliminary data analysis)]] * [[http://daniellakens.blogspot.com/2015/05/the-perfect-t-test.html?m=1|The perfect t-test]] * [[https://osf.io/6e5va/|ANOVAs in R]] * [[https://osf.io/sbp6k/|Why Psychologists Should by Default Use Welch's t-test Instead of Student's t-test]] (in press) * [[https://www.youtube.com/watch?v=rpFL5hI5qzw|EFA, CFA, Reliability, Correlation, Regression using R]] | [[https://www.youtube.com/watch?v=5fCXWHPcLIM|Exploratory Factor Analysis of Big 5 Personality using R and ProjectTemplate]] * [[https://psyarxiv.com/2y3w9/|Reliability from alpha to omega: a tutorial]] (preprint, 2018) * [[https://www.youtube.com/watch?v=a4_tpt4c2U8|4.1: Logistic Regression and Multilevel Models - Introduction to R Workshop]] * problems with cronbach and guide - [[https://www.researchgate.net/publication/313852796_Thanks_Coefficient_Alpha_We%27ll_Take_it_From_Here|Thanks Coefficient Alpha, We’ll Take it From Here]] (PsycMethods, 2017) * [[http://rpubs.com/seriousstats/vif|VIF and multicollinearity diagnostics]] ([[http://psychologicalstatistics.blogspot.com/2013/11/multicollinearity-and-collinearity-in.html|guide]]) * [[https://github.com/ropenscilabs/skimr|skimr]] frictionless approach to dealing with summary statistics * [[http://singmann.org/anova-in-r-afex-may-be-the-solution-you-are-looking-for/|ANOVA in R: afex]] * [[https://osf.io/preprints/psyarxiv/t3qub|Clarifying the Confusion Surrounding Correlations, Statistical Control, and Causation]] (preprint) * [[https://garstats.wordpress.com/2017/11/28/trimmed-means/|Trimmed means | Basic stats]] * [[Good practices in (cog-neuro) science and science communication|Good series on improving stats in neuro]] * [[http://www.pareonline.net/getvn.asp?v=19&n=17|A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists]] ==== Citations in R ==== * [[https://github.com/crsh/citr|citr: RStudio Addin to Insert Markdown Citations]] ==== Graphics / Plotting ==== * [[https://cran.r-project.org/web/packages/sjPlot/index.html|sjPlot: Data Visualization for Statistics in Social Science]] * [[https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html|summarytools - descriptives]] * [[https://github.com/IndrajeetPatil/ggstatsplot|ggstatsplot: ggplot2 Based Plots with Statistical Details]] * [[http://slowkow.com/ggrepel/#1|tool for labelling in ggplot! “Introduction to ggrepel”]] * [[https://cran.r-project.org/web/packages/dlookr/|dlookr]] - collection of tools that support data diagnosis, exploration, & transformation / [[https://cran.r-project.org/web/packages/dlookr/vignettes/|vignettes]] * [[https://cran.r-project.org/web/packages/yarrr/vignettes/pirateplot.html|pirateplot]] * [[http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html|Top 50 ggplot2 Visualizations - The Master List]] (With Full R Code) * [[http://www.graphicaldescriptives.org/|Graphical Descriptives website]] ([[http://journals.sagepub.com/doi/pdf/10.1177/1745691616663875|Graphical Descriptives: A Way to Improve Data Transparency and Methodological Rigor in Psychology]] (PPS)) * [[http://bbarrowm.tumblr.com/|R for data analysis and visualization]] and the [[https://github.com/bbarrowm?tab=repositories|Github page]] * [[https://www.datacamp.com/courses/data-visualization-with-ggplot2-2|Data Visualization with ggplot2]] * [[https://www.r-bloggers.com/7-visualizations-you-should-learn-in-r/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29|7 Visualizations You Should Learn in R]] * [[https://github.com/ecsalomon/summaryPlotting|Summary Plotting]] - Erica Solomon * [[https://www.r-statistics.com/2016/11/ggedit-interactive-ggplot-aesthetic-and-theme-editor/|ggedit – interactive ggplot aesthetic and theme editor]] * [[http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html|The Complete GGplot2 Tutorial - Part1 | Introduction To Ggplot]] (Full R code) * P[[https://nickmichalak.blogspot.com/2017/01/plotting-relationships-between-two.html|lotting relationships between two variables (with 95% confidence bands), holding other variables constant]] * [[https://nickmichalak.blogspot.com/2016/10/basic-plotting-in-r-and-spss-for.html|Basic Plotting in R and SPSS for Psychologists]] * [[http://www.win-vector.com/blog/2015/06/wanted-a-perfect-scatterplot-with-marginals/|Wanted: A Perfect Scatterplot (with Marginals)]] * [[https://inattentionalcoffee.wordpress.com/2017/02/14/data-in-the-raw-violin-plots/|Violin plots]] * [[http://eamoncaddigan.net/dataviz/r/psych/2015/09/26/violin-plots/|Violin plots with box plots]] * [[https://www.r-bloggers.com/sigr-simple-significance-reporting/|sigr: Simple Significance Reporting]] * [[http://www.nicebread.de/visually-weighted-watercolor-plots-new-variants-please-vote/|Visually weighted/ Watercolor Plots, new variants]] * [[https://mcaule.github.io/d3_exploding_boxplot/?utm_content=buffera0ac8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer|D3 exploding boxplot]] * [[http://janhove.github.io/reporting/2017/04/23/visualising-models-1|Tutorial: Plotting regression models]] * Create Diagrams - [[http://rich-iannone.github.io/DiagrammeR/|DiagrammeR]] * [[http://janhove.github.io/analysis/2017/06/26/continuous-interactions|Interactions between continuous variables]] * [[https://bhaskarvk.github.io/user2017.geodataviz/|GeoSpatial Data Visualization in R]] * [[http://shiny.chemgrid.org/boxplotr/|BoxPlotR: a web-tool for generation of box plots]] * [[https://drsimonj.svbtle.com/visualising-residuals?utm_content=buffer877fa|Visualising Residuals]] * [[https://osf.io/preprints/psyarxiv/fzh6c/|Diamond Plots: a tutorial to introduce a visualisation tool that facilitates interpretation and comparison of multiple sample estimates while respecting their inaccuracy]] (preprint, 2017) * [[http://motioninsocial.com/tufte/|Tufte in R]] * Fundamentals of Data Visualization ([[https://github.com/clauswilke/dataviz|R markdown]] / [[http://serialmentor.com/dataviz/|Online HTML version]]/ [[http://serialmentor.com/blog/2018/1/23/fundamentals-of-data-visualization|intro]]) * [[https://sakaluk.wordpress.com/2015/08/27/6-make-it-pretty-plotting-2-way-interactions-with-ggplot2/#APA|Make It Pretty: Plotting 2-way Interactions with ggplot2]] * [[https://github.com/connorjmccabe/InterActive|InterActive: analyze, create graphics for, and ultimately better understand interaction effects]] * [[https://cran.r-project.org/web/packages/ggstatsplot/index.html|ggstatsplot: 'ggplot2' Based Plots with Statistical Details]] * [[https://cran.r-project.org/web/packages/BoutrosLab.plotting.general/vignettes/PlottingGuide.pdf|A guide to data visualization using BoutrosLab.plotting.genera]] ([[https://cran.r-project.org/web/packages/BoutrosLab.plotting.general/index.html|package]]) * [[https://cran.r-project.org/web/packages/factoextra/index.html|factoextra]] - [[http://www.sthda.com/english/rpkgs/factoextra/|Guide to extract and visualize the output of the most common (exploratory) multivariate data analyses (PCA, MFA, etc.)]] * [[https://alison.rbind.io/talk/ohsu-biodatavis/|Take a Sad Plot & Make It Better]] * [[http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization|Be Awesome in ggplot2: A Practical Guide to be Highly Effective - R software and data visualization]] * [[https://prelights.biologists.com/highlights/raincloud-plots-multi-platform-tool-robust-data-visualization/|Raincloud plots: a multi-platform tool for robust data visualization]] Visualization galleries: * [[http://oa-eurovis.jamesscottbrown.com/|Open Access EuroVis]] * [[http://oavis.steveharoz.com/|Open Access VIS]] * [[https://github.com/GRousselet/rogme/tree/v0.2.0|Robust Graphical Methods For Group Comparisons]] ==== Reporting ==== * [[https://github.com/Pakillo/grateful|grateful]] - very easy to cite the R packages used in any report or publication * [[https://cran.r-project.org/web/packages/tangram/vignettes/example.html|Table Grammar Examples]] ==== Reproducibility==== * [[https://www.youtube.com/watch?v=MxCnnqpnN5Y&feature=youtu.be|Writing Reproducible Scientific Papers in R]] | Downloading our Toolkit (video series) * [[https://speakerdeck.com/jdblischak/the-workflowr-r-package-a-framework-for-reproducible-and-collaborative-data-science|The workflowr R package: a framework for reproducible and collaborative data science]] * [[https://psyarxiv.com/rtygm/|A practical guide for transparency in psychological science]] (preprint, 2018) * [[https://libscie.github.io/rmarkdown-workshop/handout.html|RMarkdown for writing reproducible scientific papers]] * [[https://nceas.github.io/sasap-training/materials/reproducible-analysis-in-r/|Reproducible Analysis With R]] * [[https://vickysteeves.gitlab.io/repro-papers/|Writing reprocible geoscience papers using R Markdown, Docker, and GitLab]] * [[https://legacy.gitbook.com/book/open-science-training-handbook/book/details|The Open Science Training Handbook]] * [[https://felixhenninger.gitbooks.io/open-science-knowledge-base/content/?q|Open science knowledge base]] * [[http://datacolada.org/69|Eight things I do to make my open research more findable and understandable]] (Uri Sim, Data Colada, 2018) * [[https://osf.io/gupxv/ |Advanced Course on Methods for Reproducible Science now available on OSF]] https://osf.io/gupxv/ * [[https://www.youtube.com/watch?v=CH6CYI6NheI|4.2: RMarkdown and knitr - Introduction to R Workshop]] * [[http://www.njtierney.com/r/rbloggers/2017/01/11/reprex-magic/|Magic reprex - Making reproducible examples]] * [[http://www.the100.ci/2017/02/19/reproducible-websites-for-fun-and-profit/|Reproducible websites for fun and profit]] * [[https://github.com/crsh/papaja|papaja: Create APA manuscripts with R Markdown]] ([[https://raw.githubusercontent.com/crsh/papaja/master/example/example.pdf|example]]) * Example: [[https://osf.io/wm6vc/|Searching for Dumbfounding]] * Videos: [[https://www.youtube.com/watch?v=I_HW5Rraqg8|R - Markdown with Papaja]] / [[https://www.youtube.com/watch?v=dmB6sHDbs7Q|Markdown with Papaja 2]] * [[http://haozhu233.github.io/kableExtra/|kableExtra Construct APA complex tables easily]] * e[[http://gdemin.github.io/expss/|xpss: Tables with Labels in R]] * [[https://www.youtube.com/watch?v=GKtjr-lxHYM|Using R and OSF for Open Science in Psychology]] * [[https://www.youtube.com/watch?v=2o64wx_lEHQ|Example 3: Debugging RMarkdown and ProjectTemplate]] * [[https://github.com/BITSS/IMEBESS2017|IMEBESS Reproducibility Training]] * [[https://ropensci.org/packages/|rOpenSci packages]] * [[http://sites.northwestern.edu/stattag/|StatTag]] is a free, open-source software plug-in for conducting reproducible research ([[https://www.youtube.com/watch?v=7Ds260TL8s8|intro video]]) * [[https://github.com/ropenscilabs/gramr|gramr]] - checking a RMarkdown document for grammatical errors * [[https://github.com/matloff/revisit|revisit: a "Statistical Audit" for Statistical Reproducibility and Alternate Analysis]] * [[http://slidify.org/|Slidify - Stunning presentations from markdown]] * [[https://psychstatsworkshop.wordpress.com/2016/04/24/write-apa-style-manuscripts-directly-in-rstudio/|Write APA-Style Manuscripts Directly in RStudio]] * [[http://rpubs.com/ndphillips/rpackagescience|Creating an R package for Research documentation]] * [[http://www.martin-elff.net/knitr/memisc/codebook.html|Generate a Codebook of a Data Set]] * [[https://osf.io/c5n6y/|Addressing threats to reproducibility through research transparency]] * [[http://www.britishecologicalsociety.org/wp-content/uploads/2017/12/guide-to-reproducible-code.pdf|A Guide to Reproducible Code in Ecology and Evolution]] ==== SEM in R ==== * [[http://github.com/jknowles/SEMtutorialsR|SEM using R]] * [[http://personality-project.org/r/r.sem.html|Structural Equation Modeling in R]] * [[https://www.udemy.com/structural-equation-modeling-sem-with-lavaan/|Structural equation modeling (SEM) with lavaan on Udemy]] (not free) * Plot it with the wonderful [[https://github.com/brandmaier/onyxR|OnyxR]] * SEM and causality - [[http://bayes.cs.ucla.edu/BOOK-09/ch11-5-3-final.pdf|Defending the Causal Interpretation of SEM (or, SEM Survival Kit)]] * Introduction to lavaan: [[https://osf.io/rny3k/|Tutorial]] and [[https://www.youtube.com/watch?v=kCXN7CRYKVo|Video]] (Jeromy Anglim, 2017) * [[http://users.ugent.be/~yrosseel/lavaan/zurich2017/MULTILEVEL/lavaan_multilevel_zurich2017.pdf|Multilevel Structural Equation Modeling with lavaan]] ==== Pre-registering SEM analyses ==== * [[https://osf.io/v4wkf/|Why social psychologists using Structural Equation Modelling need to pre-register their studies]] (presentation Matt Williams, Massey University) * [[https://docs.google.com/document/d/1QIlGwpLHqNeLtoOwVJSf1qJcmVz5ciyvlZh6S6TUGDE/edit#|SEM preregistration template]] ==== R Studio ==== * [[https://www.rstudio.com/rviews/2016/11/11/easy-tricks-you-mightve-missed/|RStudio IDE Easy Tricks You Might’ve Missed]] * [[http://enhancedatascience.com/2017/07/10/the-packages-you-need-for-your-r-shiny-application/?utm_content=buffer7217a|The R Shiny packages you need for your web apps!]] ==== Text mining ==== * [[http://tidytextmining.com/|Tidy Text Mining with R]] ==== Advanced R uses ==== * [[http://thinktostart.com/analyze-face-emotions-r/|Analyze Face Emotions with R]] * [[https://bigdataenthusiast.wordpress.com/2016/10/02/microsoft-cognitive-services-text-analytics-api-in-r/|Microsoft Cognitive Services (Text Analytics API) in R]] * [[https://bigdataenthusiast.wordpress.com/|Face API in R – Microsoft Cognitive Services]] * [[https://github.com/odeleongt/flexdashboard-poster|preparing a conference poster using rmarkdown]] * [[https://mvuorre.github.io/psychLit/articles/tracking-topics.html|Tracking topics with Keywords in Psychological Literature]] * [[https://www.r-bloggers.com/course-on-multiple-correspondence-analysis-with-factominer/|Course on Multiple Correspondence Analysis with FactoMineR]] * [[https://cran.r-project.org/web/packages/FFTrees/vignettes/guide.html|FFTrees: Fast-and-frugal decision trees]] * [[https://github.com/jalapic/RPackage/blob/master/Writing%20an%20R%20Package.pdf|Writing an R Package]] * [[https://tvpollet.github.io/PY0782/|Advanced Quantitative Research Methods R course]] * [[https://www.r-bloggers.com/survival-analysis-with-r-3/|Survival Analysis with R]] * [[https://www.r-bloggers.com/time-series-analysis-in-r-part-2-time-series-transformations/|Time Series Analysis in R Part 2: Time Series Transformations]] * [[http://seankross.com/2017/09/25/Create-Videos-from-R-Markdown-Documents-with-Ari.html|Create Videos from R Markdown Documents with Ari]] * [[https://github.com/daniel1noble/metaDigitise|metaDigitise]] ([[https://www.biorxiv.org/content/early/2018/01/15/247775|paper explaining the package]]) * [[http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software|Create and format PowerPoint documents from R software]] Developing packages: * [[http://www.masalmon.eu/2017/12/11/goodrpackages/|How to develop good R packages (for open science)]] * [[http://r-pkgs.had.co.nz/|R packages book]], bit updated (Oreilly, 2015) ==== Cheat sheets ==== * [[http://zevross.com/blog/2016/04/19/r-powered-web-applications-with-shiny-a-tutorial-and-cheat-sheet-with-40-example-apps/?utm_content=buffer940fd|R powered web applications with Shiny]] (a tutorial and cheat sheet with 40 example apps) * [[https://www.rstudio.com/resources/cheatsheets/|RStudio Cheat Sheets]] ===== General stats courses ===== * [[https://www.youtube.com/watch?v=WFv2vS8ESkk&list=PLDcUM9US4XdMdZOhJWJJD4mDBMnbTWw_z|Statistical Rethinking course videos]] ([[http://xcelab.net/rm/statistical-rethinking/|book and sample code]]) * [[https://osf.io/qe5ym/|Statistics for Psychologists: An Online Textbook]] * [[https://bookdown.org/roback/bookdown-bysh/|Broadening Your Statistical Horizons]] * [[https://aupsychology.github.io/statsbook/|Statistics: The Story of Numbers]] (with R) * [[http://compcogscisydney.org/learning-statistics-with-r/|Learning Statistics with R]] * [[http://cescup.ulb.be/a-compendium-of-useful-stats-pages-for-social-psychologists/|A compendium of methods and stats resources for (social) psychologists]] * [[https://crumplab.github.io/statistics/|Answering questions with data]] / [[https://crumplab.github.io/statistics/gifs.html|Important animated graphs explaining statistics with code]] ===== T-test and Equivalence testing ===== * [[http://daniellakens.blogspot.com/2015/05/the-perfect-t-test.html|The perfect t-test]] * [[http://rpsychologist.com/d3/equivalence/|Equivalence, non-inferiority and superiority testing]] * [[http://daniellakens.blogspot.com/2017/01/examining-non-significant-results-with.html|Examining Non-Significant Results with Bayes Factors and Equivalence Tests]] * [[https://psyarxiv.com/v3zkt/|Equivalence Testing for Psychological Research: A Tutorial]] (preprint, 2017) * [[https://medium.com/@dsquintana/using-summary-statistics-to-determine-whether-a-non-significant-result-supports-the-absence-of-an-1ff61e97f7cf|Using summary statistics to determine whether a non-significant result supports the absence of an effect]] ===== Basic correlations ===== * [[http://janhove.github.io/teaching/2016/11/21/what-correlations-look-like|What data patterns can lie behind a correlation coefficient?]] ===== Regressions ===== * [[http://www.utexas.edu/courses/schwab/sw388r7/SolvingProblems/MultipleRegression_BasicRelationships.ppt|Regressions]] * [[http://twu.seanho.com/09fall/cpsy501/lectures/09/09-Fac_ANOVA.pdf|Repeated measures MANCOVA]] (change in performance, etc.) / [[http://www.indiana.edu/~iscc/files/WIM_Fall_2012/GLM%20workshop%20slides%20Part%202_%202011-10-03.pdf|Repeated measures - 3 methods with SPSS]] * [[https://www.r-bloggers.com/stepwise-regression-whats-not-to-like/|Stepwise Regression – What’s not to like ?]] * comparing different predictors in a regression: [[http://link.springer.com/article/10.1007/s10869-014-9351-z|Relative Weight Analysis]] with [[http://relativeimportance.davidson.edu/|this tool]] * [[https://www.kaggle.com/rtatman/the-5-day-regression-challenge|Rachael Tatman: The 5-Day Regression Challenge]] * [[https://www.mathsisfun.com/data/least-squares-calculator.html|Least Squares Calculator: Least Squares Regression is a way of finding a straight line that best fits the data, called the "Line of Best Fit".]] ===== Mediation ===== * [[http://www.sciencedirect.com/science/article/pii/S0022103117300628|Unwarranted inferences from statistical mediation tests – An analysis of articles published in 2015]] (JESP, 2018) * [[https://academic.oup.com/jcr/article/doi/10.1093/jcr/ucx081/3978091/Meaningful-Mediation-Analysis-Plausible-Causal|Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication]] (JCP, 2017) * [[http://afhayes.com/spss-sas-and-mplus-macros-and-code.html|Mediation - Bootstrapping and Sobel test on SPSS (macro)]] ([[http://www.afhayes.com/introduction-to-mediation-moderation-and-conditional-process-analysis.html|intro]] / [[https://www.youtube.com/watch?v=nOKHtYDhmDI|Bootstrapping video]] | [[http://www.youtube.com/watch?v=PoKVnkd3HlM|Regression mediation video]]) * [[http://www.nrhpsych.com/mediation/logmed.html|Mediation with Dichotomous Outcomes]] * [[http://pages.bangor.ac.uk/~pes004/resmeth/mediation/mediation.htm|The more classic multi-step regression mediation]] * [[http://www.afhayes.com/public/hpcatx.pdf|Statistical mediation analysis with a multicategorical independent variable]] (basically create dummies, insert one, and control for the other) * [[https://nickmichalak.blogspot.com/2016/07/reproducing-hayess-process-models.html|Reproducing Hayes’s PROCESS Models' results in R]] * [[https://nickmichalak.blogspot.com/2016/11/im-posting-supplement-to-my-earlier.html?view=sidebar|Reproducing Hayes's PROCESS Model 1 with Dichotomous Moderator (in R)]] * [[https://osf.io/s48e2/|Within-subject mediation analysis]] (OSF preprint) ===== Interactions ===== * [[http://journals.sagepub.com/doi/full/10.1177/2515245917746792|Improving Present Practices in the Visual Display of Interactions]] (w/ R📦). See also: -[[https://cran.r-project.org/web/packages/interplot/vignettes/interplot-vignette.html|@fredericksolt's interplot R]], [[http://yiqingxu.org/software.html|interflex R]] ([[https://twitter.com/mjbsp/status/1012690901653643266?s=03|through Twitter]]) * [[http://jeremydawson.com/Moderation-PDW-slides.pdf|Everything You Wanted to Know about Moderation]] (slides) * [[https://cdn.auckland.ac.nz/assets/psych/about/our-research/nzavs/Misc/utilities-for-examining-interactions.xlsx|The best Excel I found to do plots]] / {{::utilities-for-examining-interactions_-_gilad_version.xlsx|My version}} (unprotected, more help) * Do/plot an interaction with [[http://www.danielsoper.com/Interaction/|Interaction! software]] / [[http://www.jeremydawson.co.uk/slopes.htm|Useful excels]] * Interaction between a categorical and continuous variable - [[http://www.psychwiki.com/wiki/Interaction_between_categorical_and_continuous_variables|1]] / [[http://www.ats.ucla.edu/stat/spss/library/hetreg.htm|2]] / [[http://www.stat-help.com/spreadsheets/Graph%20of%20interaction%20between%20a%20categorical%20IV%20and%20a%20continuous%20IV%202010-05-30.xls|used this]] ([[https://www.researchgate.net/profile/Manuela_Barreto/publication/6194031_Group_Virtue_The_Importance_of_Morality_vs_Competence_and_Sociability_in_the_Positive_Evaluation_of_In-Groups/links/540dcd5f0cf2df04e7566f4c.pdf|sample paper using this]]) * [[http://www.johannjacoby.de/stattools/SiSSy1.12.5.html|Simple slopes using SPSS macros]] / [[http://my.ilstu.edu/~jhkahn/medmod.html|reporting simple slopes]] * [[http://stats.stackexchange.com/a/55505|Compare two slopes using Z]] , and [[http://www.socscistatistics.com/pvalues/normaldistribution.aspx|check Z significance]] * [[http://www.quantpsy.org/interact/index.htm|Probing interactions in multiple linear regression, latent curve analysis, and hierarchical linear modeling: Interactive calculation tools for establishing simple intercepts, simple slopes, and regions of significance]] ANOVA two way interaction (with constrasts: * [[https://www.youtube.com/watch?v=cOmbrK-TOqo|VIDEO: ANOVA two-way interactions explain right]] (with the way to do contrasts through SPSS syntax) Basic code: <code> UNIANOVA DV BY IV1 IV2 /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=IV1(TUKEY) /PLOT=PROFILE(IV1*IV2) /EMMEANS=TABLES(IV2) COMPARE ADJ(LSD) /EMMEANS=TABLES(IV1) COMPARE ADJ(LSD) /EMMEANS=TABLES(IV1*IV2) COMPARE(IV2) /CONTRAST(IV1)=Simple /CONTRAST(IV2)=Simple /PRINT=OPOWER ETASQ HOMOGENEITY DESCRIPTIVE /CRITERIA=ALPHA(.05) /DESIGN=IV1 IV2 IV1*IV2. </code> (might want to change the "EMMEANS=TABLES(IV1*IV2) COMPARE(IV2)" to "EMMEANS=TABLES(IV1*IV2) COMPARE(IV1)" to see if more convinient) ===== U-Curve / quadratic regressions ===== * [[https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3021690|Two-Lines: The First Valid Test of U-Shaped Relationships]] * [[http://datacolada.org/62|Two-lines: The First Valid Test of U-Shaped Relationships]] ===== Excel Magic ===== * [[http://www.xltoolbox.net/annotate.html#|Daniel's XL toolbox]] - Annotate Chart function to show significant differences * [[http://www.appspro.com/Utilities/ChartLabeler.htm|XY Chart label]] * [[https://bert-toolkit.com/|run R functions from Excel's formula bar]] ([[http://blog.revolutionanalytics.com/2018/08/how-to-use-r-with-excel.html|blog post]]) ===== General SPSS magic ===== * [[http://www2.jura.uni-hamburg.de/instkrim/kriminologie/Mitarbeiter/Enzmann/Software/Enzmann_Software.html|Centering variables]] and other useful SPSS macros Generally highly recommended - [[http://rt.uits.iu.edu/visualization/analytics/docs/index.php|UITS Tutorials and Working Papers]]. * [[http://www.glennlthompson.com/?p=92|SPSS macro program for automatically transforming categorical variables into dummy variables]] * [[https://shotgunapproach.wordpress.com/2011/01/26/ceiling-and-floor-computation-in-spss/|Ceiling and Floor Computation in SPSS]] ===== Effect size ===== General * [[https://osf.io/ixgcd/|Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-tests and ANOVAs]] (Daniel Lakens on Frontiers and OSF) * [[http://daniellakens.blogspot.com/2014/06/calculating-confidence-intervals-for.html|Calculating confidence intervals for Cohen’s d and eta-squared using SPSS, R, and Stata]] * Reporting effect sizes in original psychological research: A discussion and tutorial. (Psychological Methods, 2018) * [[https://www.researchgate.net/publication/313398162_Reporting_effect_sizes_in_original_psychological_research_A_discussion_and_tutorial|Reporting Effect Sizes in Original Psychological Research: A Discussion and Tutorial]] (psychological methods, 2016) Tools: * [[http://journals.sagepub.com/doi/abs/10.1177/0956797617723724|Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty]] (PsycSci 2017) / [[https://cran.r-project.org/web/packages/BUCSS/index.html|BUCSS: Bias and Uncertainty Corrected Sample Size]] * [[http://vassarstats.net/rho.html?|Pearson correlation confidence intervals tools]] (why doesn't SPSS report these?!) * [[https://dl.dropboxusercontent.com/u/30399959/effectSizeCalculator.html|Effect Size Calculators]] (an increasingly important factor [[https://www.psychologicalscience.org/index.php/publications/journals/psychological_science/ps-submissions|to report in psychological science]]) * [[http://www.aggieerin.com/stats/mote.php|Mote - calculates various effect sizes and their confidence intervals]] * [[https://www.usablestats.com/calcs/2samplet&summary=1|2 Sample t-test Calculator with effect size]] * [[http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00863/full|Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs]] (Frontiers, 2015) * [[http://jakewestfall.org/blog/index.php/2016/03/25/five-different-cohens-d-statistics-for-within-subject-designs/|Five different “Cohen’s d” statistics for within-subject designs]] * [[http://rpsychologist.com/d3/cohend/|Interpreting Cohen's d effect size an interactive visualization]] and other funky tools - [[http://rpsychologist.com/|R psychologist]] * [[http://www.tandfonline.com/doi/abs/10.1207/S15328007SEM0904_8?journalCode=hsem20|How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power]] (Muthén & Muthén, 2009) * [[http://www.lyonsmorris.com/ma1/|The Meta Analysis Calculator]] - convert between effect sizes Interpreting effect sizes, Andy Field summarizes in his methods book: <blockquote>Cohen (1992, 1988) has made some widely accepted suggestions about what constitutes a large or small effect: •r = 0.10 (small effect): in this case, the effect explains 1% of the total variance. •r = 0.30 (medium effect): the effect accounts for 9% of the total variance. •r = 0.50 (large effect): the effect accounts for 25% of the variance. </blockquote> Table: {{::effect-size.png?linkonly|}} Readings about effect size estimates in psychology: * [[http://neuron4.psych.ubc.ca/~schaller/Psyc591Readings/RichardBondStokes-Zoota2003.pdf|One Hundred Years of Social Psychology Quantitatively Described]] (Richard et al., 2003) - effect size is psychology is about .21 * [[http://www.sciencedirect.com/science/article/pii/S0191886916308194|Effect size guidelines for individual differences researchers (PID, 2016) paper]] : guidelines for effect size in psyc ===== Power analyses ===== * [[https://pigee.wordpress.com/2016/09/13/the-power-dialogues/|The Power Dialogues]] (basic conversational explanation of power with links - Brent Roberts UIUC) * [[http://www.ats.ucla.edu/stat/gpower/|Power Analysis Using G*Power]] * [[https://www.rips-irsp.com/article/10.5334/irsp.181/|A Practical Primer To Power Analysis for Simple Experimental Designs]] * [[http://journals.sagepub.com/doi/abs/10.1177/1948550617715068?journalCode=sppa|Determining Power and Sample Size for Simple and Complex Mediation Models]] (SPPS, 2017) * [[http://disjointedthinking.jeffhughes.ca/2017/09/power-simulations-r/|Running Power Simulations with the Power of R!]] * [[https://jakewestfall.shinyapps.io/pangea/|PANGEA (v0.2): Power ANalysis for GEneral Anova designs]] * [[http://www.danielsoper.com/statcalc3/category.aspx?id=19|Sample size calculators]] and [[http://www.danielsoper.com/statcalc3/category.aspx?id=20|posthoc power analysis]] * [[http://www.bristol.ac.uk/cmm/software/mlpowsim/|MLPowSim]] ==== Moderation ==== * [[https://approachingblog.wordpress.com/2018/01/24/powering-your-interaction-2/|Powering Your Interaction]] ==== Mediation ==== * [[http://marlab.org/power_mediation/|Power Analysis with Mediation Models]] * [[http://journals.sagepub.com/doi/abs/10.1111/j.1467-9280.2007.01882.x|Required Sample Size to Detect the Mediated Effect]] (Psychological Science, 2007) * [[https://www.researchgate.net/post/How_do_I_run_a_power_analysis_on_a_moderated_mediation_model_Hayes_Model_7_Is_this_possible_with_GPower_settings_does_anyone_know|Power analysis on a moderated mediation model]] ([[http://inst-mat.utalca.cl/jornadasbioestadistica2011/doc/CursoCasella/%20UseR-SC-10-B-Part1.pdf|Monte Carlo r guide]]) ==== Multi level power analysis calculations ==== * [[http://rpsychologist.com/do-you-need-multilevel-powerlmm-0-4-0|Do you really need a multilevel model? A preview of powerlmm 0.4.0]] * [[https://www.r-bloggers.com/introducing-powerlmm-an-r-package-for-power-calculations-for-longitudinal-multilevel-models/|‘powerlmm’ an R package for power calculations for longitudinal multilevel models]] * [[https://aguinis.shinyapps.io/ml_power/|Multilevel Power Tool]] (Calculations are based on the article written by [[https://www.researchgate.net/profile/Herman_Aguinis/publication/224955627_Understanding_and_Estimating_the_Power_to_Detect_Cross-Level_Interaction_Effects_in_Multilevel_Modeling/links/02bfe5102fa9d04b9e000000.pdf|Mathieu, Aguinis, Culpepper, & Chen (2012) in the Journal of Applied Psychology]]) * [[https://aguinis.shinyapps.io/jomr/|Cross-Level Interaction Effect Calculator]], is an R Shiny for this article: [[chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/http://hermanaguinis.com/JOMcrosslevel.pdf|Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling (JOM, 2013)]] ([[http://hermanaguinis.com/JOMR.html|page for sample data file and R code]]) * [[https://aguinis.shinyapps.io/ml_power/ |Interactive online program for power estimates to detect cross-level interactions using multilevel modeling]] * [[https://www.journalofcognition.org/articles/10.5334/joc.10/|Power Analysis and Effect Size in Mixed Effects Models: A Tutorial]] (Journal of Cognition, 2018) ===== Multi-level / HLM ===== * [[https://www.cmm.bris.ac.uk/lemma/course/view.php?id=13|Best course - LEMMA]] * [[How to do multilevel HLM analysis with SPSS]] * [[http://rt.uits.iu.edu/visualization/analytics/docs/hlm-docs/hlm5.php|HLM in SPSS]] / [[http://epm.sagepub.com/content/65/5/717.full.pdf+html|Guide journal article]] * [[http://www.sagepub.com/upm-data/47528_ch_1.pdf|HLM in general]] / [[http://www.ats.ucla.edu/stat/hlm/|resources]] * [[https://cdn.auckland.ac.nz/assets/psych/about/our-research/nzavs/Misc/utilities-for-examining-interactions.xlsx|Plotting HLM 2-way]] * Robert van Doorn's materials - {{::mla_tutorial_2015-2016.pdf|MLA guide}} / {{::three_datasets.xls|Data to practice}} / {{::2-way_unstandardised.xls|Interaction plots}} * [[http://www.page-gould.com/r/indirectmlm/|Bootstrapping Accurate Indirect Effects in Multilevel Models]] * [[http://tutorials.iq.harvard.edu/R/Rstatistics/Rstatistics.html#orgheadline36|Harvard's easy guide to multi-level modelling]] * [[https://njrockwood.com/mlmed/|MLMED - Multilevel Mediation in SPSS]] * [[http://www.bristol.ac.uk/cmm/software/r2mlwin/|R2MLwiN: Running MLwiN from within R]] ([[http://www.bristol.ac.uk/cmm/software/mlwin/|free for UK academics]]) * [[https://www.rips-irsp.com/articles/10.5334/irsp.90/|Multilevel Logistic Modeling]] (IRSP, 2017) * [[http://psycnet.apa.org/doiLanding?doi=10.1037%2Fmet0000184|quantifying explained variance in multilevel models, including an integrative solution, graphical representations, and an R function]] ([[https://my.vanderbilt.edu/jasonrights/software/r2mlm/|r2MLM]]) ===== CFA ===== * Run a CFA with Amos to see whether two scales are separate : construct two models, one with the two separate scales, one with a combined scale. Look at the chisquare for each of those models, calculate the difference and check the chisquare table to see whether this indicates a significant difference. see this [[http://zencaroline.blogspot.com/2007/05/chi-square-difference-test-for-nested.html|blog post]] for further explanation. * [[http://statwiki.kolobkreations.com/index.php?title=Confirmatory_Factor_Analysis|Great WIKI guide on CFA using SEM]] * [[https://www.youtube.com/watch?v=JkZGWUUjdLg|Confirmatory factor analysis using AMOS]] video on Youtube * [[http://www.psych.umass.edu/uploads/people/79/Fit_Indices.pdf|Interpreting fit indices]] (RMSEA, SRMR, CFI, chi-square) * [[http://statwiki.kolobkreations.com/wiki/Confirmatory_Factor_Analysis|What are good CFA fit indices]] * [[https://www.academia.edu/2968546/Confirmatory_Factor_Analysis_Using_AMOS|Confirmatory Factor Analysis Using AMOS]] on Academia.edu ===== Meta analysis ===== * [[https://github.com/kylehamilton/JamoviMeta|Jamovi Meta Analysis Module]] ([[https://www.youtube.com/watch?v=wilIbKJwzdE|video example]]) * [[http://blogs.plos.org/absolutely-maybe/2017/07/03/5-tips-for-understanding-data-in-meta-analyses/|5 Tips for Understanding Data in Meta-Analyses]] * [[http://www.internationalgme.org/Resources/IGMELinks2.htm#MA|Meta analysis convert and calculator (XLS)]] * [[http://onlinelibrary.wiley.com/doi/10.1348/000711010X502733/pdf|How to do a meta-analysis Andy P. Field and Raphael Gillett]] * [[https://sakaluk.wordpress.com/2016/02/16/7-make-it-pretty-plots-for-meta-analysis/|Make It Pretty: Plots for Meta-Analysis]] * [[http://onlinelibrary.wiley.com/doi/10.1111/spc3.12267/abstract;jsessionid=6959616E2E7C583C905ED141A5CE32F0.f03t03|Mini Meta-Analysis of Your Own Studies: Some Arguments on Why and a Primer on How]] (SPPC, 2016) * [[http://www.erim.eur.nl/research-support/meta-essentials/|Meta-Essentials: Workbooks for meta-analysis]] * [[http://adampegler.blogspot.com/2017/02/sarahs-pearsons-r-mini-meta-analysis.html|Pearson's r mini-meta analysis]] * [[http://www.human.cornell.edu/hd/qml/software.cfm|Continuously Cumulative Meta Analysis]] ([[http://pps.sagepub.com/content/9/3/333.full|paper]] / [[http://www.human.cornell.edu/hd/qml/upload/ccmatemplate.zip|R code]] / [[http://www.human.cornell.edu/hd/qml/upload/ccmatemplate.xlsx|Excel]]) * [[https://link.springer.com/article/10.3758/s13428-013-0386-2|Meta-analyzing dependent correlations: An SPSS macro and an R script]] (BRM, 2014) * [[https://cran.r-project.org/web/packages/psychmeta/index.html|psychmeta: Psychometric Meta-Analysis Toolkit]] * [[http://www.deeplytrivial.com/2018/07/statistics-sunday-mixed-effects-meta.html?m=1|Statistics Sunday: Mixed Effects Meta-Analysis]] Forest plots: * Meta analysis - [[http://www.metafor-project.org/doku.php/metafor|metafor]] * Forest plots - [[https://github.com/FredHasselman/ManyLabs1|Manylabs1]] or [[http://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups|Forest Plot with Subgroups]] * [[https://psyarxiv.com/myg6s/|MetaForest: Exploring heterogeneity in meta-analysis using random forests]] * [[https://github.com/RobbievanAert/puniform|puniform]] (meta-analysis methods that correct for publication bias) * information-rich plots of meta-analytic data in `ggplot2` (variants of forest and funnel plots)? [[https://cran.r-project.org/web/packages/metaviz/vignettes/metaviz.html|R package `metaviz`]] ([[https://twitter.com/patilindrajeets/status/1035156563261104130|twitter post]]) Combining two metas (SEM): * [[https://www.researchgate.net/publication/258447296_Meta-Analytical_Structural_Equation_Modeling_An_Easy_Introduction_to_the_Two-Step_Approach|Meta-Analytical Structural Equation Modeling: An Easy Introduction to the Two-Step Approach]] (Holger Steinmetz & Isidor, unpublished) * [[http://onlinelibrary.wiley.com.sci-hub.cc/doi/10.1002/jrsm.1166/abstract|Random-effects models for meta-analytic structural equation modeling: review, issues, and illustrations]] (Research Synethsis, 2014) * [[https://www.researchgate.net/publication/7925278_Meta-Analytic_Structural_Equation_Modeling_A_Two-Stage_Approach|Meta-Analytic Structural Equation Modeling: A Two-Stage Approach]] (Psyhcological methods, 2005) * [[http://link.springer.com.sci-hub.cc/article/10.3758/s13428-013-0386-2|Meta-analyzing dependent correlations: An SPSS macroand an R script]] (Behavioral Research, 2014) Best way to assess publication bias: * [[https://osf.io/preprints/psyarxiv/9h3nu|Correcting for bias in psychology: A comparison of meta-analytic methods]] (preprint) - [[https://osf.io/rf3ys/|OSF code]] / [[http://crystalprisonzone.blogspot.com/2017/05/trim-and-fill-just-doesnt-work.html|blog post]] * Correcting bias in meta-analyses: What not to do (meta-showdown [[http://www.nicebread.de/meta-showdown-1/|Part 1]]) * [[http://bmjopen.bmj.com/content/8/3/e019703|Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review]] (BMJ Open, 2018) Pooling means and SD/variance: * [[https://home.ubalt.edu/ntsbarsh/business-stat/otherapplets/Pooled.htm|Pooling the Means, and Variances]] * [[http://atozmath.com/CONM/Ch2_CombinedSD.aspx|Find Combined Mean and Standard deviation]] New directions: * [[https://osf.io/pa4ur/|Beyond Overall Effects: A Bayesian Approach to Finding Constraints Across A Collection Of Studies In Meta-Analysis]] (preprint) Power: * [[https://towardsdatascience.com/how-to-calculate-statistical-power-for-your-meta-analysis-e108ee586ae8|How to calculate statistical power for your meta-analysis]] (Dan Quintana, blog post) Promising packages: * metavis * [[https://cran.r-project.org/web/packages/psychmeta/psychmeta.pdf|psycmeta]] ===== Reporting ===== * [[https://statistics.laerd.com/spss-tutorials/two-way-anova-using-spss-statistics-2.php|A two way ANOVA main-effects & interaction]] ([[http://www.statisticshell.com/docs/twoway.pdf|another resource]]) ===== Writing ===== * [[http://faculty.washington.edu/heagerty/Courses/b572/public/StrunkWhite.pdf|STrunk and White The Elements of Style]] * [[A step-by-step guide to writing a research paper, from idea to full manuscript|A step-by-step guide to writing a research paper, from idea to full manuscript]] ===== Pre-registration ===== * [[https://aspredicted.org/|As predicted]] (explained [[http://datacolada.org/2015/12/01/44_aspredicted/|here]]) * [[https://cran.r-project.org/web/packages/prereg/index.html|prereg: R Markdown Template to Preregister Scientific Studies]] * [[https://github.com/crsh/prereg|prereg: R Markdown Templates for Preregistrations of Scientific Studies]] * [[https://docs.google.com/document/d/1I3hXca9nQHnYX2iAwP7ILyUazd-zxIIZ8oYBR7iOjRU/edit#heading=h.s44548ln3mw|How to Preregister: A Practical Guide]] (OSF) * [[https://osf.io/5y8w7/|Evaluating Registered Reports: A Naturalistic Comparative Study of Article Impact]] * [[https://osf.io/bpuw3/|Preregistration of Secondary Data Analysis Template]] ===== Versioning/Github ===== * [[https://github.com/CoAxLab/DataSciencePsychNeuro_CMU85732/blob/master/Tutorials/Version%20control%20-%20%20Git%20and%20GitHub.ipynb|Version control - Git and GitHub]] * [[http://neuroplausible.com/github|Git (and GitHub) Cheat Sheet]] ===== Data Transformations ===== * [[https://www.hitpages.com/doc/5243018244259840/35#pageTop|Data transformations]] * [[http://wweb.uta.edu/faculty/ricard/Classes/KINE-5305/Log%20Tranformations%20Guidelines.pdf|Normalizing Data Transformations]] ===== Multi choice data bank ===== * Use software like [[https://www.respondus.com/products/respondus/single.shtml|Respondus]] * [[http://vasishth-statistics.blogspot.com/2016/01/automating-r-exercises-and-exams-using.html|Automating R exercises and exams using the exams package]] * [[http://www.r-exams.org/|R/Exams]] ===== Detecting cheating ===== * [[http://jd-mathbio.blogspot.com/2015/02/finding-cheaters-using-multiple-choice.html|Finding cheaters using multiple-choice comparisons]] * [[http://rynesherman.com/blog/adventures-in-cheater-detection/|Adventures in Cheater Detection]] ===== Common method bias using AMOS ===== * [[http://statwiki.kolobkreations.com/wiki/Confirmatory_Factor_Analysis#Common_Method_Bias_.28CMB.29|Addressing common method bias]] (StatsWiki) * [[http://www.kolobkreations.com/CLF.vb|Use the AMOS plugin]]! [[https://www.youtube.com/watch?v=etPciNEgWGk|Youtube video]] ===== Bayesian ===== * Course: [[https://www.datacamp.com/courses/fundamentals-of-bayesian-data-analysis-in-r|Fundamentals of Bayesian Data Analysis in R]] * [[https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-018-1761-4|Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP]] (BMC Psychiatry, 2018) * Introduction to Bayesian data analysis - [[https://www.youtube.com/watch?v=3OJEae7Qb_o|Part 1]] / [[https://www.youtube.com/watch?v=mAUwjSo5TJE|Part 2]] / [[https://www.youtube.com/watch?v=Ie-6H_r7I5A|Part 3]] using [[http://mc-stan.org/interfaces/|Stan]] * [[http://xcelab.net/rm/statistical-rethinking/|Statistical Rethinking: A Bayesian Course with Examples in R and Stan]] * [[https://osf.io/preprints/psyarxiv/q46q3|Introduction to Bayesian Inference for Psychology]] (preprint by Alex Etz) * [[http://rd.springer.com/article/10.3758%2Fs13423-016-1221-4|The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective]] (Psychonomic Bulletin & Review, 2017) * [[BAYESIAN STATISTICS: WHY AND HOW|BAYESIAN STATISTICS: WHY AND HOW]] (JEPS) * [[http://www.frontiersin.org/books/Improving_Bayesian_Reasoning_What_Works_and_Why_/792|Improving Bayesian Reasoning: What Works and Why?]] Ebook * [[http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1167&context=jps|What Are the Odds? A Practical Guide to Computing and Reporting Bayes Factors]] * ANOVA with Bayes - [[https://osf.io/ahhdr/|Bayesian Inference for Psychology. Part II: ExampleApplications with JASP]] * [[https://www.coursera.org/learn/bayesian-statistics/|Bayesian Statistics: From Concept to Data Analysis - Coursera course]] * [[http://journals.sagepub.com/doi/abs/10.1177/0149206313501200?journalCode=joma|Bayesian Estimation and Inference: A User’s Guide]] | JOM * [[http://journals.sagepub.com/doi/10.1177/2515245918779348|Bayesian Reanalyses From Summary Statistics: A Guide for Academic Consumers]] (AMPPS, 2018) If you still want to do NHST of the null hypothesis: * [[http://daniellakens.blogspot.com/2016/05/absence-of-evidence-is-not-evidence-of.html|Absence of evidence is not evidence of absence: Testing for equivalence]] (Daniel Lakens) ===== Absence of an effect ===== * [[http://www.psychologicabelgica.com/articles/abstract/10.5334/pb-51-2-109/|How to Statistically Show the Absence of an Effect]] * [[http://daniellakens.blogspot.com/2016/12/tost-equivalence-testing-r-package.html|TOST equivalence testing R package (TOSTER) and spreadsheet]] ===== Analyzing change ===== * [[http://stats.stackexchange.com/questions/15713/is-it-valid-to-include-a-baseline-measure-as-control-variable-when-testing-the-e/15748#15748|Is it valid to include a baseline measure as control variable when testing the effect of an independent variable on change scores?]] * [[http://www.uccs.edu/lbecker/gainscore.html|Analysis of Pretest and Posttest Scores with Gain Scores and Repeated Measures]] - UCCS Dr. Lee Becker * [[http://homes.ori.org/~keiths/Tips/Stats_GainScores.html|Gain Score Analysis]] - Keith Smolkowski * [[http://www.ats.ucla.edu/stat/spss/seminars/Repeated_Measures/|Repeated Measures Analysis with SPSS]] In experiments: * [[http://www.jerrydallal.com/lhsp/prepost.htm|The Analysis of Pre-test/Post-test Experiments]] - Gerard E. Dallal, Ph.D. * [[http://pareonline.net/getvn.asp?v=14&n=6|From Gain Score t to ANCOVA F (and vice versa)]] (Knapp and Schafer, 2009) * [[http://www.theanalysisfactor.com/pre-post-data-repeated-measures/|Analyzing Pre-Post Data with Repeated Measures or ANCOVA]] ===== APA style ===== * [[http://www.learningscientists.org/blog/2016/3/10-1|Teaching APA Style: An APA Template Paper]] ===== Helping others with their MTurk ===== <blockquote> In the last few lab meetings some of you mentioned that you are interested in collecting data with Amazon Mechanical Turk but that you do not have an account and asked if I might be able to help. I have an account, and have had some experience with this, so I’d be happy to help you run your studies and to help you adjust them to MTurk to increase the chances of getting reliable high-quality data. The email below is to give you an idea of how we can work on that. First, a quick background. Although MTurk does have some limitations, many of the issues that came up in the various lab meetings can definitely be addressed with MTurk data collection – a replication attempt, scale validation, a pre-test to determine power, quick access to working people and/or underprivileged sectors, etc. I personally believe that MTurk is also good enough for running an independent study complementary to other collected data – if done correctly. In my experience, MTurk data collection is comparable if not better than student participant pool data. I also use a tool called TurkPrime that rides on top on MTurk and intended for academics, that completely automates academic data collection and increases data reliability (preventing duplicates and lots of potential problems, etc.). My experience is summarized in the following two posts: * Generally about MTurk - http://mgto.org/running-experiments-with-amazon-mechanical-turk/ * About working with Turkprime - http://mgto.org/turkprime-easy-and-powerful-mturk-data-collection/ If you’d like my help to run something with MTurk, what you’ll need to do is: * Have a working Qualtrics survey that you’ve tested. I can share a Qualtrics demo with you designed for MTurk that includes a consent form, funneling section, demographics, and debriefing. * Make sure you can get reimbursed and have the money available. Once MTurk runs it automatically pays from my account, so I would ask that you transfer the funds to me before I start running this, so that I don’t have to bear the financial costs of waiting for everyone’s reimbursements. If you can’t, we can talk about that. * Plan how many participants you’ll need (N), the intended pay (minimum for IRB in some places is 0.05US$ a minute, most do a minimum of 0.10US$ a minute), sample characteristics (location, qualifications, etc.). Costs you’ll need to take into account : (N + 10+ participants for pretest run) * pay + 20% Amazon MTurk commission [Note: The default Amazon commission is actually 40%, but TurkPrime uses a feature called Micro-Batch that reduces that to 20%. In some study designs this feature cannot be used, and I’ll alert you if that is the case] The procedure would usually be: * You pretest your study with one or two other persons to make sure it’s working well (preferably a number of times to test all conditions, if you have manipulations). * We run a pretest of 10 participants on MTurk to see it all goes well (possibly more if you have many conditions). * We run a full sample. Depending on the kind of survey you’re running and your intended sample size, data collection could take anything from half an hour to a few days. Please let me know if you have any other questions. </blockquote> Also: <blockquote> You’ll need to work out your sample size (+small pretest) and payment for each participant with the formula before, confirm your reimbursement, and then transfer that money to me in advance. In the meanwhile, make a copy of your survey on Qualtrics and share the copy with me, and I’ll make sure it’s all set. Also, it’s not a must, ofcourse, but I would strongly encourage you : * Calculate needed sample size based on power analysis using G*Power and an expected effect size. * Take 15-30min to pre-register your survey layout and your main predictions on OSF or “As predicted”. To me, personally, as a reviewer, these two things significantly increase my confidence in the findings (and, in a way, in the researcher). </blockquote> ===== Control variables ===== * [[http://onlinelibrary.wiley.com/doi/10.1002/job.2053/abstract|Statistical control in correlational studies: 10 essential recommendations for organizational researchers]] (JOB, 2016) * [[http://orm.sagepub.com/content/14/2/287.short|Methodological Urban Legends: The Misuse of Statistical Control Variables]] (ORM, 2011) ===== IRB (Maastricht) ===== * {{::format_aanmeldingsformulier_ercpn_en_march_2016.docx|Forms}} * [[http://www.maastrichtuniversity.nl/web/Faculteiten/FPN/Thema/OverDeFaculteit/Organisatiestructuur/EthischeCommissiePsychologie.htm|ECP]] ===== Build a scholar website ===== * Use [[https://wordpress.org/|Wordpress]] * Some WordPress themes - [[https://www.competethemes.com/author/|Author]] * Using R - [[https://twitter.com/dsquintana/status/993410504570888192|Dan Quintana guide on Twitter]] ===== Twitter research ===== * [[http://tacit.usc.edu/|TACIT - Text Analysis,Crawling and Interpretation Tool]] (e.g., used in "[[https://www.ncbi.nlm.nih.gov/pubmed/26726910|Purity homophily in social networks]]", JEP:G, 2016) * [[https://www.r-bloggers.com/text-mining-in-r-and-python-8-tips-to-get-started/|Text Mining in R and Python: 8 Tips To Get Started]] * [[https://www.researchgate.net/publication/315665721_A_hands-on_guide_to_conducting_psychological_research_on_Twitter|A hands-on guide to conducting psychological research on Twitter]] (SPPS, 2017) * [[https://shiring.github.io/text_analysis/2017/06/28/twitter_post|Characterizing Twitter followers with tidytext]] * [[https://medium.com/@sqrendk/how-you-can-use-facebook-to-track-your-friends-sleeping-habits-505ace7fffb6|How you can use Facebook to track your friends’ sleeping habits]] (Medium, 2017) | [[https://github.com/sqren/fb-sleep-stats|Sourcecode]] | [[http://sci-hub.cc/10.1016/j.cub.2017.08.005|paper]] * [[https://github.com/dwiwad/mini-data-projects-analysis-and-viz/blob/master/sips-tweets/SIPS-twitter.pdf|Scraping tweets guide]] ===== Games in surveys ===== Negotiations: * [[http://dornsife.usc.edu/labs/cssl/software/|Objects Negotiation Task]] Public goods: * [[http://gaips.inesc-id.pt/invite/?page_id=71|INVITE AI project]] ===== Dealing with outliers or invalid repsonses ===== * [[http://www.sciencedirect.com/science/article/pii/S0022103113000668|Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median]] (JESP, 2013) [with R/SPSS code] * [[http://www.sciencedirect.com/science/article/pii/S0022103117302123|Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance]] (JESP, 2018) * [[https://psyarxiv.com/n3wbc/|Methods for the detection of carelessly invalid responses in survey data]] (preprint, 2018) ===== Country level analyses ===== * [[https://www.researchgate.net/profile/Ian_Rickard/publication/264502355_What_Can_Cross-Cultural_Correlations_Teach_Us_about_Human_Nature/links/54229e600cf26120b7a1fe50.pdf|What Can Cross-Cultural Correlations TeachUs about Human Nature?]] (Human Nature, 2014) ===== Spatial dependence analyses ===== * [[http://www.ats.ucla.edu/stat/mult_pkg/faq/general/spatial_autocorr.htm|FAQ: How can I detect/address spatial autocorrelation in my data?]] * [[http://www.people.fas.harvard.edu/~zhukov/spatial.html|Workshop: Applied Spatial Statistics in R]] * [[https://ideas.repec.org/c/boc/bocode/s500501.html|SPATDWM: Stata module for US state and county spatial distance matrices]] (not only for STATA, has useful CSV for states centroids) * [[http://www.ats.ucla.edu/stat/r/faq/morans_i.htm|How can I calculate Moran's I in R?]] * [[http://www.csiss.org/gispopsci/workshops/2011/PSU/readings/W15_Anselin2007.pdf|Spatial Regression Analysis in RA Workbook]] * [[https://github.com/hrbrmstr/pewpew/blob/master/country_latlon.csv|Coordinates of countries]] (to be used with world values survey and data archive research) (file: [[http://dev.maxmind.com/geoip/legacy/codes/country_latlon/|country_latlon.csv]]) ===== MPlus ===== * [[http://offbeat.group.shef.ac.uk/FIO/mplusmedmod.htm|Mplus code for mediation, moderation, and moderated mediation models]] ===== Lab participant management ===== * [[https://www.sona-systems.com/default.aspx|SONA]] (commercial) * [[http://www.orsee.org/web/index.php|Online Recruitment System for Economic Experiments (ORSEE)]] - [[http://www.orsee.org/wiki/doku.php?id=start|Wiki]] ===== Mobile data collection ===== * [[http://www.cost.eu/module/download/Mobile_Research_Methods|Mobile Research Methods Opportunities and challenges of mobile research methodologies]] (Ebook, 2015) * [[http://www.psychologicalscience.org/index.php/publications/observer/2015/march-15/measurement-on-the-move.html|Measurement on the Move Advancing Science Through Mobile Technology]] * [[http://experiencesampler.com/|Experience Sampler]] * [[http://www.surveysignal.com/|Survey Signal]] * [[http://www.kobotoolbox.org/|Kobo Toolbox]] * [[https://www.youtube.com/watch?v=nQBBVp9vBIQ&feature=youtu.be|Online seminar video]] * [[http://pps.sagepub.com/content/11/6/838?etoc|Using Smartphones to Collect Behavioral Data in Psychological Science Opportunities, Practical Considerations, and Challenges]] (PPos, 2016) * [[http://www.otago.ac.nz/psychology/otago047475.pdf|Experience Sampling and Ecological Momentary Assessment with Mobile Phones]] ==== Create a mobile app ==== * [[https://github.com/google/paco/wiki|PACO]] (mobile platform for behavioral science) / [[https://www.pacoapp.com/|info]] ==== Machine learning ==== * Analyzing emotions - [[https://blog.exploratory.io/analyzing-emotions-using-facial-expressions-in-video-with-microsoft-ai-and-r-8f7585dd0780#.1a6mfm4pz|Analyzing Emotions using Facial Expressions in Video with Microsoft AI and R]] * [[https://osf.io/preprints/psyarxiv/pvjac/|PredPsych: A toolbox for predictive machine learning based approach in experimental psychology research]] * [[https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/|Code for Workshop: Introduction to Machine Learning with R]] ==== Collaborations ==== * [[http://www.apa.org/science/leadership/students/authorship-determination-scorecard.pdf|Determine authorship]] ==== Share presentations and data ==== * [[https://figshare.com/|Figshare]] * [[https://zenodo.org/|Zenodo]] ==== Simulations ==== * [[https://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html|Simulating study data with simstudy r package]] * [[https://www.rips-irsp.com/articles/10.5334/irsp.115/|Simulate this! An Introduction to Agent-Based Models and their Power to Improve your Research Practice]] (IRSP, 2017) ==== Social network analysis ==== * [[http://journals.sagepub.com/doi/abs/10.1177/1948550617709114|An Introduction to Social Network Analysis for Personality and Social Psychologists]] (Clifton & Webster, 2017, SPPS) ==== Network analysis ==== * [[http://journals.sagepub.com/doi/abs/10.1177/1948550617709827|Network Analysis on Attitudes]] (SPPS, 2017) ==== Machine learning ==== * [[https://osf.io/b6m3n/|Crashkurs Predictive Modeling/Machine Learning - DPPD 2017]] ==== Conducting replications ==== * [[http://www.sciencedirect.com/science/article/pii/S0022103113001819|The Replication Recipe: What makes for a convincing replication?]] ===== Various ===== See all kinds of great resources from the SIPS workshop: * [[https://osf.io/xwsyt/|How to Promote Transparency and Replicability as a Reviewer]] (Stephen Lindsay and Roger Giner-Sorolla) * [[https://osf.io/693ad/|Writing papers to be transparent, reproducible, and fabulous]] (Bobbie Spellman & Simine Vazire) * [[https://osf.io/nmdtq/|Intro to Single Paper Meta-Analysis]] (Courtney Soderberg) * [[https://github.com/libscie/rmarkdown-workshop|Fundamentals of Rmarkdown]] (Chris Hartgerink and Mike Frank) * [[https://osf.io/9d5hr/|IRBs and Best Practices for Data Sharing]] (Rick Gilmore & and Gustav Nilsonne) * [[https://osf.io/p7zv3|Preparing and Curating Data for Sharing]] (Simon Podhajsky and David Condon) * [[https://osf.io/a2x7j/|Fundamentals of R]] (Elizabeth Page-Gould and Alex Danvers) * [[https://osf.io/ha4q8/|Sample Size and Effect Size Workshop]] (Daniel Lakens and Jeremy Biesanz) * [[https://osf.io/fkt8e/|Getting Started with Preregistration]] (Charlie Ebersole, Hans IJzerman, Mike Wagner, & Randy McCarthy) * [[https://docs.google.com/document/d/11jyoXtO0m2lUywpC04KjLvI5QcBUY4YtwEvw6cg2cMs/edit|Measurement mattes Google Doc for reading list on measurement]] * [[https://twitter.com/dsquintana/status/992307288571502593|Extracting data from scatterplots]] * [[https://psyarxiv.com/afmbg|Are manipulation checks necessary?]] (preprint, 2018) * [[https://mfr.osf.io/render?url=https://osf.io/w85jn/?action=download%26mode=render|SO, YOU’RE GOING TO TALK TO A JOURNALIST ABOUT YOUR RESEARCH]] * [[https://robchavez.github.io/datascience_gallery/|UO Psychology Data Science Gallery]] * [[http://www.p-curve.com/app/|p-curve web app]] * Recovering data from summary statistics: Sample Parameter Reconstruction via Iterative TEchniques ([[https://peerj.com/preprints/26968v1/|SPRITE]]) (preprint, 2018) * [[https://osf.io/zj68b/|multiverse analysis]] * [[https://medium.com/advice-and-help-in-authoring-a-phd-or-non-fiction/how-to-write-a-blogpost-from-your-journal-article-6511a3837caa#.kk4x7zx69|How to write a blogpost from your journal article]] * Address sample selection bias using [[https://en.wikipedia.org/wiki/Heckman_correction|Heckman method]] ([[http://faculty.smu.edu/millimet/classes/eco7321/papers/heckman02.pdf|Heckman, 1979]]) ([[https://www.researchgate.net/profile/Deborah_Kistler/publication/289532129_World_Values_Survey_Response_and_Behavior_Emancipative_and_Secular_Values_Predict_Cooperation_Pro-_tection_of_Property_and_Pro-Social_Behavior/links/568f7e9f08aead3f42f19fb9.pdf?origin=publication_detail&ev=pub_int_prw_xdl&msrp=My9G7r6QBP6soNC4I_OkCX7YUHmfkL3GTO0I54S8Qp7KnvsbythV8uPLLmnxzhI_y5yg_PPVV98wv6t023WJGg.h1Pafky6gmEOxgX2O6FvnOFV8CJAwTw1uKUdTthhd3HXP6DnxwpNWk26K8jCzxb5i5d2IGiK6uGBNb2WDsOIuw.WnQHvUDXd1EY5kwZOapHgP6Mi-_dO1rKkSZ5xzuXZ-nTL3xitayqx1Th18rSPOEg61YNHVsNlrcRX6_v3rF34g|example for use in a followup on the WVS]]) / [[http://www-01.ibm.com/support/docview.wss?uid=swg21475054|SPSS]] * [[https://hbr.org/2016/02/a-refresher-on-statistical-significance|A Refresher on Statistical Significance]] (HBR, 2016) - great for students * [[http://daniellakens.blogspot.com/2016/03/one-sided-tests-efficient-and-underused.html|One-sided tests: Efficient and Underused]] * Understanding type 1 errors - [[https://onedrive.live.com/view.aspx?resid=DF3F7227F3844BE2!114185&ithint=file%2cdocx&app=Word&authkey=!AI5_jfYU5mVGbpM|The positive predictive value by Daniel Lakens]] * [[http://graphpad.com/quickcalcs/|Quickcalcs]] - for calculating [[http://graphpad.com/quickcalcs/catMenu/|Chisquare]] and such * [[https://www.theorymaps.org/|Theory mapping]] * [[https://aushsi.shinyapps.io/orcid/|Reducing the administrative burden on researchers generate automatic CV from ORCID]] Content mining * Academic papers - [[https://github.com/ContentMine|ContentMine]] ===== Open Science ===== * [[https://docs.google.com/viewer?a=v&pid=forums&srcid=MTAxOTg4Mjg3MDExMDc2NDA2MDEBMTE4NDE0MzQzMjE5Njk4MDA1NjgBMktrdzZoY1lCQUFKATAuMQEBdjI|OPEN SCIENCE MADE EASY]] * [[https://www.slideshare.net/secret/Dwt7tir9Ai08ot|The Art of Collaboration (by June Gruber)]] ===== Research workflow ===== * [[https://osf.io/mv8pj/|Checklists for Research Workflow]] * [[https://github.com/acoppock/Green-Lab-SOP|Standard Operating Procedures for Don Green's Lab at Columbia]] ===== Lab manuals ===== * [[https://github.com/LucyMcGowan/dissertation-toolkit|Dissertation Toolkit]] Some examples: * [[https://github.com/jpeelle/peellelab_manual|Peelle Lab Manual]] * [[https://github.com/acoppock/Green-Lab-SOP|Standard Operating Procedures for Don Green's Lab at Columbia]] * [[https://docs.google.com/document/d/1oMkTCEFtOq_DB0eoNiyk-B5QCgL6sVSF5pVvD1ONZDc/edit#heading=h.yu06me3nd7sj|Open Science Manual]] ===== Remove line breaks in office (when copying from PDFs) ===== Do this macro: <code> Sub RemoveParaOrLineBreaks() Dim oRng As Range Dim oFind As Range Set oRng = Selection.Range Set oFind = Selection.Range With oFind.Find .Replacement.Text = "\1" Do While .Execute(FindText:="[^13^l]{1,}([a-z])", _ MatchWildcards:=True, _ Replace:=wdReplaceAll) Loop End With lbl_Exit: Set oRng = Nothing Set oFind = Nothing Exit Sub End Sub </code> Add a keyboard shortcut from options->ribbbon->keyboard->macros->RemoveParaOrLineBreaks how_to_guides.txt Last modified: 2019/04/25 19:10by filination