https://rempsyc.remi-theriault.com/index.html
https://dereksonderegger.github.io/570L/16-rmarkdown-tricks.html
https://lukaswallrich.github.io/timesaveR/articles/getting_started.html
Making ANOVA Tables
Correlations
https://www.marsja.se/correlation-in-r-coefficients-visualizations-matrix/
https://www.marsja.se/report-correlation-in-apa-style-using-r-text-tables/
Intro to R
https://r-coder.com/r-tutorials/
https://posit.cloud/learn/recipes
https://posit.co/resources/cheatsheets/#ide
https://tinystats.github.io/teacups-giraffes-and-statistics/01_introToR.html
https://datacarpentry.org/r-socialsci/01-intro-to-r.html
https://swirlstats.com/
https://rc2e.com/
https://moderndive.com/index.html
https://r4ds.hadley.nz/
https://appforiarteam.shinyapps.io/PlayR/
https://www.datacamp.com/tutorial/category/r-programming
Stats with R
Some online stats books/courses that use the tidyverse.
https://solomonkurz.netlify.app/book/
https://statsthinking21.github.io/statsthinking21-R-site/index.html
https://psyteachr.github.io/stat-models-v1/index.html
https://michael-franke.github.io/intro-data-analysis/ordinary-least-squares-regression.html
https://benwhalley.github.io/just-enough-r/index.html
https://uoepsy.github.io/dapr3/2223/labs/01_regressionrefresh.html
https://r4ds.had.co.nz/model-basics.html
https://psych252.github.io/psych252book/simulation-1.html
https://rpruim.github.io/Kruschke-Notes/
https://experimentology.io/006-inference.html
And some useful blogs that make frequent use of tidyverse functions for statistics problems:
https://blog.djnavarro.net/
https://www.andrewheiss.com/blog/
https://solomonkurz.netlify.app/blog/
https://m-clark.github.io/R-models/
## Meta-Analysis
https://predictivehacks.com/meta-analysis-in-r/ https://psychmeta.com/ https://blog.revolutionanalytics.com/2014/07/r-and-meta-analysis.html https://mvuorre.github.io/posts/2016-09-29-bayesian-meta-analysis/ https://solomonkurz.netlify.app/blog/2020-10-16-bayesian-meta-analysis-in-brms-ii/ https://ourcodingclub.github.io/tutorials/mcmcglmm/ https://www.rdatagen.net/post/diagnosing-and-dealing-with-estimation-issues-in-the-bayesian-meta-analysis/ https://shinyapps.org/apps/metaExplorer/
Ordinal Regression
Some relevant R tutorials:
https://doingbayesiandataanalysis.blogspot.com/2014/11/ordinal-probit-regression-transforming.html
https://bookdown.org/content/3686/ordinal-predicted-variable.html
https://michael-franke.github.io/Bayesian-Regression/practice-sheets/03a-GLM-tutorial.html#ordinal-regression
https://solomonkurz.netlify.app/blog/2023-05-21-causal-inference-with-ordinal-regression/
https://ladal.edu.au/regression.html#Mixed-Effects_Ordinal_Regression
https://ecmerkle.github.io/cs/ord_ic.html
https://barumpark.com/blog/2019/Ologit-Predict/
Some relevant papers:
Bürkner, P.-C., & Vuorre, M. (2019). Ordinal Regression Models in Psychology: A Tutorial. Advances in Methods and Practices in Psychological Science, 2(1), 77–101. https://doi.org/10.1177/2515245918823199
Liddell, T. M., & Kruschke, J. K. (2018). Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology, 79, 328–348. https://doi.org/10.1016/j.jesp.2018.08.009
Schnuerch, M., Haaf, J. M., Sarafoglou, A., & Rouder, J. N. (2022). Meaningful comparisons with ordinal-scale items. Collabra: Psychology, 8(1), 38594. https://doi.org/10.1525/collabra.38594
Creating Virtual Reality Experiments
Might want to consider some of the existing task libraries/frameworks so that you don’t reinvent the wheel if you don’t need to.
https://openmaze.duncanlab.org
https://expfactory.github.io/
https://www.psytoolkit.org/experiment-library/#exps
https://github.com/jspsych/jsPsych
https://dallinger.readthedocs.io/en/latest/index.html
https://psiturk.readthedocs.io/en/latest/index.html
https://debruine.github.io/experimentum/
https://pavlovia.org/explore?sort=DEFAULT
https://sweetpea-org.github.io/
https://osdoc.cogsci.nl/
Tutorials:
https://bradylab.ucsd.edu/ttt/
https://crumplab.com/programmingforpsych/web-experiments.html
https://www.youtube.com/watch?v=zH9LrcIHIIc
https://github.com/xiaozhi2/webgazertutorial
https://www.youtube.com/watch?v=Us-iznuY9wQ
https://www.youtube.com/watch?v=ARDtX8ggjlM&ab_channel=PsyToolkit
https://www.youtube.com/watch?v=BuhfsIFRFe8
Papers:
Almaatouq, A., Becker, J., Houghton, J. P., Paton, N., Watts, D. J., & Whiting, M. E. (2021). Empirica: A virtual lab for high-throughput macro-level experiments. Behavior Research Methods, 53(5), 2158–2171. https://doi.org/10.3758/s13428-020-01535-9
Cubillos, L. H., Augenstein, T. E., Ranganathan, R., & Krishnan, C. (2023). Breaking the barriers to designing online experiments: A novel open-source platform for supporting procedural skill learning experiments. Computers in Biology and Medicine, 106627. https://doi.org/10.1016/j.compbiomed.2023.106627
Haar, S., Sundar, G., & Faisal, A. A. (2021). Embodied virtual reality for the study of real-world motor learning. PLOS ONE, 16(1), e0245717. https://doi.org/10.1371/journal.pone.0245717
Hartshorne, J. K., de Leeuw, J. R., Goodman, N. D., Jennings, M., & O’Donnell, T. J. (2019). A thousand studies for the price of one: Accelerating psychological science with Pushkin. Behavior Research Methods, 51(4), 1782–1803. https://doi.org/10.3758/s13428-018-1155-z
Lindell, M. K., House, D. H., Gestring, J., & Wu, H.-C. (2019). A tutorial on DynaSearch: A Web-based system for collecting process-tracing data in dynamic decision tasks. Behavior Research Methods, 51(6), 2646–2660. https://doi.org/10.3758/s13428-018-1119-3
Santangelo, A. P., & Solovey, G. (2022). Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app. Journal of Cognition, 5(1), Article 1. https://doi.org/10.5334/joc.200
Strittmatter, Y., Spitzer, M. W. H., & Kiesel, A. (2022). A random-object-kinematogram plugin for web-based research: Implementing oriented objects enables varying coherence levels and stimulus congruency levels. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01767-3
Tsay, J. S., Lee, A. S., Ivry, R. B., & Avraham, G. (n.d.). Moving outside the lab: The viability of conducting sensorimotor learning studies online. arXiv Preprint arXiv:2107.13408., 17. https://github.com/alan-s-lee/OnPoint.
Watson, M. R., Voloh, B., Thomas, C., Hasan, A., & Womelsdorf, T. (2019). USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments for human, nonhuman, and artificially intelligent agents. Journal of Neuroscience Methods, 326, 108374. https://doi.org/10.1016/j.jneumeth.2019.108374
Commins, S., Duffin, J., Chaves, K., Leahy, D., Corcoran, K., Caffrey, M., Keenan, L., Finan, D., & Thornberry, C. (2020). NavWell: A simplified virtual-reality platform for spatial navigation and memory experiments. Behavior Research Methods, 52(3), 1189–1207. https://doi.org/10.3758/s13428-019-01310-5
Kristjansson, A., Draschkow, D., Kristjansson, T., Pálsson, Á., Jónsson, P. Ö., & Haraldsson, D. (2020). Moving foraging into 3D: Feature versus conjunction-based foraging in virtual reality [Preprint]. Open Science Framework. https://doi.org/10.31219/osf.io/fh9zt
PDF Processing tools
A few examples of open source pdf processing tools:
https://github.com/Layout-Parser/layout-parser
https://facebookresearch.github.io/nougat/
https://github.com/neuml/paperetl
https://github.com/ocrmypdf/OCRmyPDF
https://github.com/allenai/science-parse
https://github.com/pd3f/pd3f
PDF into R
Some relevant guides and other packages that might be useful:
https://blog.djnavarro.net/posts/2023-06-16_tabulizer/
https://ladal.edu.au/pdf2txt.html
https://debruine.github.io/post/data-from-images/
https://themockup.blog/posts/2021-01-18-reading-tables-from-images-with-magick/
https://pridiltal.github.io/staplr/
https://ropensci.org/blog/2018/12/14/pdftools-20/
https://github.com/TaylorRundell/rippR
https://github.com/Damian-Oswald/quickreadR/
Embeddng shiny
https://bookdown.org/yihui/rmarkdown/shiny-embedded.html
https://www.youtube.com/watch?v=qGyHZG6ZqwA
https://github.com/coatless-quarto/r-shinylive-demo
https://www.startupengineer.io/_repos/_transfer/data_science/14_rep_shiny/
https://datawookie.dev/blog/2021/06/shiny-inception-javascript-in-rendered-markdown/
Stats basics
Not as rigorous as a traditional textbook, but some of the interactive online stats books and apps/demos might be a helpful way to start building up your understanding of the core concepts.
https://seeing-theory.brown.edu/index.html
https://artofstat.com/web-apps
https://tquant.eu/online-learning-contents/r-shiny-apps/miscellaneous-apps/
https://jkkweb.sitehost.iu.edu/KruschkeFreqAndBayesAppTutorial.html
https://rpsychologist.com/likelihood/
https://tinystats.github.io/teacups-giraffes-and-statistics/04_variance.html
http://mfviz.com/hierarchical-models/
https://lindeloev.github.io/tests-as-linear/
https://facweb.gvsu.edu/adriand1/happy_apps.html
https://statistics.calpoly.edu/shiny
https://shiny.abdn.ac.uk/Stats/apps/
Psych Stats
Psych Stats Books PsyTeachR courses
Modern Statistical Methods For Psychology
Russ Poldrack Statistics with R
Danielle Navarro statistics with R https://bookdown.org/ekothe/navarro26/
Andy Wills Statistics with R: https://ajwills72.github.io/rminr/
Glen Williams Statistics with R: https://glennwilliams.me/r4psych/
Martin Speekenbrink Statistics with R: https://mspeekenbrink.github.io/sdam-r-companion/index.html
Matt Crump Statistics with R: https://crumplab.github.io/statistics/
https://www.sas.upenn.edu/~baron/from_cattell/rpsych/rpsych.html
Matt Crump Programming for Psychologists https://crumplab.com/programmingforpsych/index.html
Using R for Psychology Research - Personality Project: http://personality-project.org/r/r.guide.html
Jamovi book: https://davidfoxcroft.github.io/lsj-book/
https://www.learnstatswithjamovi.com/
Other tutorials https://quantpsych.net/web-applications/
https://jkkweb.sitehost.iu.edu/KruschkeFreqAndBayesAppTutorial.html
https://rpsychologist.com/viz
https://www.sas.upenn.edu/~baron/from_cattell/rpsych/rpsych.html
https://ladal.edu.au/tutorials.html
https://bookdown.org/paul/computational_social_science/
https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists
https://github.com/seanchrismurphy/A-Psychologists-Guide-to-R
https://cu-psych-computing.github.io/cu-psych-comp-tutorial/tutorials/r-extra/accelerated-ggplot2/ggplot_summer2018_part2/#1-overview
https://www.andrewheiss.com/blog/2022/05/20/marginalia/
https://www.statisticshowto.com/
https://benwhalley.github.io/just-enough-r/
https://vasishth.github.io/Freq_CogSci/
https://uoepsy.github.io/
https://pittmethods.github.io/r4ss/
https://www.sas.upenn.edu/~baron/from_cattell/rpsych/rpsych.html
Shiny Apps
https://statistics.calpoly.edu/shiny
https://artofstat.com/web-apps
https://shiny.psy.lmu.de/felix/ShinyPHack/
https://r.tquant.eu/tquant/tquant/
https://rpsychologist.com/likelihood/
http://haines-lab.com/post/2020-06-13-on-curbing-your-measurement-error/2020-06-13-on-curbing-your-measurement-error/
https://setosa.io/conditional/
http://singmann.org/blog/
http://doingbayesiandataanalysis.blogspot.com[https://mvuorre.github.io]
(https://mvuorre.github.io)
https://psychology.fandom.com/wiki/Category:Statistics
https://jeremykun.com/
https://hausetutorials.netlify.app/posts/2019-02-18-fit-models-to-every-group/
https://mysocialbrain.org
Tutorials - mostly with R code
Cognitive Modelling Workingshop -https://github.com/jstbcs/CognitiveModelingWorkshop
Lee & Vandekerckhove Jasp Coursehttps://osf.io/qjxmc/
Bayesian Inference for Psychology: https://osf.io/ucmaz/
Bernard Hart Stats Tutorials in Rmarkdown https://github.com/thartbm/RTutorials
https://github.com/Kucharssim/jaspLearnBayes
https://github.com/betanalpha/knitr_case_studies
https://github.com/lei-zhang/BayesCog_Wien
R Shiny apps - good for testing intuitions
P-hacking Simulations https://shiny.psy.lmu.de/felix/ShinyPHack/
Bayes Factor Design Analysishttp://shinyapps.org/apps/BFDA/
Shiny ROC probability density demohttps://maureen.shinyapps.io/SimpleROC/
Power Contourshttps://shiny.york.ac.uk/powercontours/
Distribution Zoohttps://ben18785.shinyapps.io/distribution-zoo/
Causal Inferencehttps://ksgr.shinyapps.io/CausalInference/ Build A Bayes:https://psyarxiv.com/wmf3r/
Relevant Youtube Channels/Videos
https://www.youtube.com/c/StatisticsofDOOM/videos
https://www.youtube.com/c/QuantPsych/videos
https://www.youtube.com/user/rockleeroy/videos
https://www.youtube.com/c/CrumpsComputationalCognitionLab/videos
https://www.youtube.com/watch?v=VfmisntqcqE&ab_channel=LancasterPsychology
Cognitive Psychology
https://experimentology.io/7-models
https://vasishth.github.io/bayescogsci/book/
https://ladal.edu.au/tutorials.html
http://haines-lab.com/#posts
https://www.compneuroprinciples.org/code-examples/all/all
https://kmckee90.github.io/
https://crumplab.com/programmingforpsych/
https://speekenbrink-lab.github.io/blog/
https://web.stanford.edu/group/pdplab/pdphandbook/
https://bookdown.org/paul/computational_social_science/
https://bookdown.org/amesoudi/ABMtutorial_bookdown/
https://lindeloev.github.io/utility-theory/
https://lindeloev.shinyapps.io/shiny-rt/
https://tensorflow.rstudio.com/tutorials/
Relevant Youtube Channels:
https://www.youtube.com/c/RandallOReilly/videos
https://www.youtube.com/channel/UChQucsNNsryDVOoawPZeZBw/videos
https://www.youtube.com/channel/UCZwtjjrWElSq1uvuygVRj6Q/videos
https://www.youtube.com/c/MITCBMM/videos
https://www.youtube.com/user/kendrickkay/videos
https://www.youtube.com/channel/UC2PfbaMZFzPrLMPJ-HfJ78A/videos
https://www.youtube.com/channel/UCNG3oPq_qJF7eWYNIxJycOQ/videos
Identifying outliers
Van Selst, M. & Jolicoeur, P. A solution to the effect of sample size on outlier elimination. Quarterly Journal of Experimental Psychology. A, Human Experimental Psychology 47A, 631–650 (1994).
Steyvers, M. & Benjamin, A. S. The joint contribution of participation and performance to learning functions: Exploring the effects of age in large-scale data sets. Behav Res 51, 1531–1543 (2019).
Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology 49, 764–766 (2013).
Jones, P. R. A note on detecting statistical outliers in psychophysical data. Atten Percept Psychophys 81, 1189–1196 (2019).
Leys, C., Delacre, M., Mora, Y. L., Lakens, D. & Ley, C. How to Classify, Detect, and Manage Univariate and Multivariate Outliers, With Emphasis on Pre-Registration. International Review of Social Psychology 32, 5 (2019).
Schaaf, J. V., Jepma, M., Visser, I. & Huizenga, H. M. A hierarchical Bayesian approach to assess learning and guessing strategies in reinforcement learning. Journal of Mathematical Psychology 93, 102276 (2019).
Zeigenfuse, M. D. & Lee, M. D. A general latent assignment approach for modeling psychological contaminants. Journal of Mathematical Psychology 54, 352–362 (2010).
Ulitzsch, E., Pohl, S., Khorramdel, L., Kroehne, U. & von Davier, M. A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data. Psychometrika (2021) doi:10.1007/s11336-021-09817-7.
Dougherty, M. R., Thomas, R. P., Brown, R. P., Chrabaszcz, J. S. & Tidwell, J. W. An Introduction to the General Monotone Model with Application to Two Problematic Data Sets. Sociological Methodology 45, 223–271 (2015).
Morís Fernández, L. & Vadillo, M. A. Flexibility in reaction time analysis: many roads to a false positive? Royal Society Open Science 7, 190831.
Ngiam, W. X. Q., Foster, J. J., Adam, K. C. S. & Awh, E. Distinguishing guesses from fuzzy memories: Further evidence for item limits in visual working memory. https://osf.io/bkdya (2022) doi:10.31234/osf.io/bkdya.
Piray, P., Dezfouli, A., Heskes, T., Frank, M. J. & Daw, N. D. Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput Biol 15, e1007043 (2019).
Stefan, A. & Schönbrodt, F. D. Big Little Lies: A Compendium and Simulation of p-Hacking Strategies. https://osf.io/xy2dk (2022) doi:10.31234/osf.io/xy2dk. https://shiny.psy.lmu.de/felix/ShinyPHack/
Ordinal Regression
http://doingbayesiandataanalysis.blogspot.com/2018/09/analyzing-ordinal-data-with-metric.html
http://doingbayesiandataanalysis.blogspot.com/2017/12/which-movie-is-rated-better-dont-treat.html
Bürkner, P.-C. & Vuorre, M. Ordinal Regression Models in Psychology: A Tutorial. Advances in Methods and Practices in Psychological Science 2, 77–101 (2019).
Liddell, T. M. & Kruschke, J. K. Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology 79, 328–348 (2018). https://www.sciencedirect.com/science/article/pii/S0022103117307746
Collaborating RMarkdown or Quarto to Word
https://github.com/ClaudioZandonella/trackdown https://www.gerkelab.com/blog/2021/04/netlifycms-rmd-ghpages/ https://jksserver.shinyapps.io/shiny_markdown_organiser/ https://danovando.github.io/publications-with-rmarkdown/presentations/pubs-with-rmarkdown#46 https://info201.github.io/git-collaboration.html
Computational and Models in Psychology
https://m-clark.github.io/mixed-models-with-R/introduction.html
http://haines-lab.com/post/2019-05-29-thinking-generatively-why-do-we-use-atheoretical-statistical-models-to-test-substantive-psychological-theories/thinking-generatively-why-do-we-use-atheoretical-statistical-models-to-test-substantive-psychological-theories/
Miller, J., & Ulrich, R. (2016). Optimizing Research Payoff. Perspectives on Psychological Science, 11(5), 664–691. https://doi.org/10.1177/1745691616649170
Stefan, A., & Schönbrodt, F. D. (2022). Big Little Lies: A Compendium and Simulation of p-Hacking Strategies (https://shiny.psy.lmu.de/felix/ShinyPHack/) [Preprint]. PsyArXiv; https://github.com/astefan1/phacking_compendium. https://doi.org/10.31234/osf.io/xy2dk
Watts, A. L., Lane, S. P., Bonifay, W., Steinley, D., & Meyer, F. A. C. (2020). Building Theories on Top of, and Not Independent of, Statistical Models: The Case of the p-factor. Psychological Inquiry, 31(4), 310–320. https://doi.org/10.1080/1047840X.2020.1853476
Ballard, T., Palada, H., Griffin, M., & Neal, A. (2019). An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data. Organizational Research Methods, 24, 109442811988120. https://osf.io/4euhr/. https://doi.org/10.1177/1094428119881209
Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71(3), 425–440. https://doi.org/10.1007/s11336-006-1447-6
Flake, J. K., & Fried, E. I. (2020). Measurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them. Advances in Methods and Practices in Psychological Science, 3(4), 456–465. https://doi.org/10.1177/2515245920952393
Guest, O., & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. Perspectives on Psychological Science, 16(4), 789–802. https://doi.org/10.1177/1745691620970585
Haines, N., Kvam, P. D., Irving, L. H., Smith, C., Beauchaine, T. P., Pitt, M. A., Ahn, W.-Y., & Turner, B. (2020). Theoretically Informed Generative Models Can Advance the Psychological and Brain Sciences: Lessons from the Reliability Paradox [Preprint]. PsyArXiv; https://github.com/Nathaniel-Haines/Reliability_2020. https://doi.org/10.31234/osf.io/xr7y3
Maresch, J., Werner, S., & Donchin, O. (2021). Methods matter: Your measures of explicit and implicit processes in visuomotor adaptation affect your results. European Journal of Neuroscience, 53(2), 504–518. https://osf.io/6yj3u/. https://doi.org/10.1111/ejn.14945
Navarro, D. J. (2021). If Mathematical Psychology Did Not Exist We Might Need to Invent It: A Comment on Theory Building in Psychology. Perspectives on Psychological Science, 1745691620974769. https://github.com/djnavarro/shepard-theory. https://doi.org/10.1177/1745691620974769
Oberauer, K. (2016). Parameters, Not Processes, Explain General Intelligence. Psychological Inquiry, 27(3), 231–235. https://doi.org/10.1080/1047840X.2016.1181999
Shanks, D. R., Malejka, S., & Vadillo, M. A. (20210902). The challenge of inferring unconscious mental processes. Experimental Psychology, 68(3), 113. https://osf.io/hm4ta/. https://doi.org/10.1027/1618-3169/a000517
Smith, P. L., & Little, D. R. (2018). Small is beautiful: In defense of the small-N design. Psychonomic Bulletin & Review, 25(6), 2083–2101. https://doi.org/10.3758/s13423-018-1451-8
Vanpaemel, W. (2020). Strong theory testing using the prior predictive and the data prior. Psychological Review, 127(1), 136–145. https://doi.org/10.1037/rev0000167
Watts, A. L., Lane, S. P., Bonifay, W., Steinley, D., & Meyer, F. A. C. (2020). Building Theories on Top of, and Not Independent of, Statistical Models: The Case of the p-factor. Psychological Inquiry, 31(4), 310–320. https://doi.org/10.1080/1047840X.2020.1853476
Tauber, S., Navarro, D. J., Perfors, A., & Steyvers, M. (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124(4), 410–441. https://doi.org/10.1037/rev0000052
Variance and Heterogeneity
https://www.nature.com/articles/s41398-020-00986-0
https://www.mdpi.com/2227-7390/6/7/119/htm
https://asmedigitalcollection.asme.org/mechanicaldesign/article/143/6/061702/1088248/Design-Variety-Measurement-Using-Sharma-Mittal
https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12613
https://psycnet.apa.org/doiLanding?doi=10.1037%2Fa0027129
Multidimensional Scaling
https://www.tandfonline.com/doi/pdf/10.1080/09298215.2010.523470
https://www.sciencedirect.com/science/article/pii/S0022249699913007
https://www.sciencedirect.com/science/article/pii/S0022249604000884
https://link.springer.com/article/10.1007/s11192-022-04331-8
https://link.springer.com/content/pdf/10.1155/2007/24602.pdf
https://www.researchgate.net/profile/Nitin-Pise/publication/290797421_Clustering_Techniques_and_the_Similarity_Measures_used_in_Clustering_A_Survey/links/5fca30f945851568d13a9b51/Clustering-Techniques-and-the-Similarity-Measures-used-in-Clustering-A-Survey.pdf
Github
https://github.com/dicarlolab
https://github.com/learning-memory-and-decision-lab
https://github.com/TheGoldLab
https://github.com/flowersteam
https://github.com/schapirolab
https://github.com/summerfieldlab
https://github.com/cogtoolslab
https://github.com/kel-github
https://github.com/compmem
https://github.com/SDN-lab
https://github.com/PerceptionCognitionLab
https://github.com/doomlab
https://github.com/CCS-Lab
https://github.com/thartbm
https://github.com/SFU-Cognitive-Science-Lab
https://github.com/cskemp
https://github.com/mdlee
https://github.com/CogWorks
https://github.com/NYUCCL
https://github.com/FelixHenninger
https://github.com/hauselin
https://github.com/ubcspin
https://github.com/MbCN-lab
https://github.com/danheck
Games in Research
1.Frey, S. & Goldstone, R. L. Cyclic Game Dynamics Driven by Iterated Reasoning. PLoS ONE 8, e56416 (2013). 2.Hawkins, R. D., Frank, M. C. & Goodman, N. D. Characterizing the dynamics of learning in repeated reference games. arXiv:1912.07199 [cs] (2020). 3.Hardy, J. H., Day, E. A., Hughes, M. G., Wang, X. & Schuelke, M. J. Exploratory behavior in active learning: A between- and within-person examination. Organizational Behavior and Human Decision Processes 125, 98–112 (2014). 4.Spiliopoulos, L. & Hertwig, R. A map of ecologically rational heuristics for uncertain strategic worlds. Psychological Review 127, 245–280 (2020). 1.Weihs, L. et al. Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game. arXiv:1912.08195 [cs] (2019). 2.Chabris, C. F. Six Suggestions for Research on Games in Cognitive Science. Topics in Cognitive Science 9, 497–509 (2017). 1.Zhang, Y. & Goh, W. B. The influence of peer accountability on attention during gameplay. Computers in Human Behavior 84, 18–28 (2018).
Reaction time
1.Hawkins, R. X. D. Conducting real-time multiplayer experiments on the web. Behav Res 47, 966–976 (2015).
2.Ratcliff, R. & Hendrickson, A. T. Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results? Behav Res (2021) doi:10.3758/s13428-021-01573-x.
3.Anwyl-Irvine, A. L., Armstrong, T. & Dalmaijer, E. S. MouseView.js: Reliable and valid attention tracking in web-based experiments using a cursor-directed aperture. Behav Res (2021) doi:10.3758/s13428-021-01703-5.
4.Krüger, A. et al. TVA in the wild: Applying the theory of visual attention to game-like and less controlled experiments. Open Psychology 3, 1–46 (2021).
5.Bridges, D., Pitiot, A., MacAskill, M. R. & Peirce, J. W. The timing mega-study: comparing a range of experiment generators, both lab-based and online. PeerJ 8, e9414 (2020).
1.Anglada-Tort, M., Harrison, P. M. C. & Jacoby, N. REPP: A robust cross-platform solution for online sensorimotor synchronization experiments. http://biorxiv.org/lookup/doi/10.1101/2021.01.15.426897 (2021) doi:10.1101/2021.01.15.426897.
2.Hartshorne, J. K., de Leeuw, J. R., Goodman, N. D., Jennings, M. & O’Donnell, T. J. A thousand studies for the price of one: Accelerating psychological science with Pushkin. Behav Res 51, 1782–1803 (2019).
3.Tsay, J. S., Lee, A. S., Ivry, R. B. & Avraham, G. Moving outside the lab: The viability of conducting sensorimotor learning studies online. arXiv preprint arXiv:2107.13408. 17.
4.Almaatouq, A. et al. Empirica: a virtual lab for high-throughput macro-level experiments. Behav Res 53, 2158–2171 (2021).
Beyesian Stats
https://iupbsapps.shinyapps.io/KruschkeFreqAndBayesApp/
https://seeing-theory.brown.edu/bayesian-inference/index.html#section2
https://cidlab.shinyapps.io/Build-A-Bayes/
\[ SS_{\text{total}} = \sum_{i=1}^{I} \sum_{j=1}^{J} (X_{ij} - \bar{X}_{\cdot\cdot})^2 \]
Principal component analysis vs. Factor analysis
Principal component analysis involves extracting linear composites of observed variables.
Factor analysis is based on a formal model predicting observed variables from theoretical latent factors.
In psychology these two techniques are often applied in the construction of multi-scale tests to determine which items load on which scales. They typically yield similar substantive conclusions (for a discussion see Comrey (1988) Factor-Analytic Methods of Scale Development in Personality and Clinical Psychology). This helps to explain why some statistics packages seem to bundle them together. I have also seen situations where “principal component analysis” is incorrectly labelled “factor analysis”.
In terms of a simple rule of thumb, I’d suggest that you:
Run factor analysis if you assume or wish to test a theoretical model of latent factors causing observed variables.
Run principal component analysis If you want to simply reduce your correlated observed variables to a smaller set of important independent composite variables.