Data Analysis

  • Statistical tools and tutorials

Software

  • Annotated Output of different statistical techniques using different softwares (Stata, SAS, SPSS, Mplus, R)

  • Data analysis examples of different statistical techniques using different softwares (Stata, SAS, SPSS, Mplus, R)

R

  • The summarytools package (R). Provides summaries of variables in a data frame

  • Learning statistics with R

  • Learning statistics with R

  • Learning statistics with R

  • Library of R shiny apps for basic and advanced statistics

  • R Resources

  • Swirl: Package for learning R from inside R.

  • Slides and material for a SPSP “Introduction to R” workshop (Murphy)

  • French intro to tidyverse: Introduction à R et au tidyverse (Julien Barnier)

  • Datacamp: Great online courses.

  • Data skills for reproducible science (DeBruine)

  • Comprehensive book on R for data science

  • Facbook group “R Users psychology” with resources to learn R.

  • Using RMarkdown for integrating paper writing and data analysis (Michael C. Frank)

  • RMarkdown: the definitive guide.

  • Papaja: an add-on to RMarkdown that formats papers in line with APA style requirements. (Frederik Aust)

  • R Markdown Papaja Tutorial

  • citr: Rstudio addin for adding Markdown citations based on a library you specify. Supports Zotero (Frederik Aust)

  • Shiny Web Apps for designing experiments and analysing data

  • Resources for learning R by yourself (Sean Murphy)

Jasp

  • JASP: A free R based statistical software that also performs bayesian tests. Easy to use.

  • Collection of resources on using JASP to do Bayesian stats

  • Undergraduate statistics with JASP (Erin Buchanan)

jamovi

  • Jamovi: Similar to JASP but open source and with modular extensions

spss

  • SPSS & SAS Macros

other

Data Preparation

data wrangling

  • Data wrangling in R using tidyverse (Susan Baert)

codebooks

  • Codebook: an R package that creates a codebook based on an SPSS file

  • Resources for constructing codebooks

outliers / missing data

  • Rebutting misconceptions about multiple imputation

  • Detecting and handling outliers (Leys et al., 2019)

  • Argument against removing outliers (Bakker & Witchers, 2014)

Data Visualisation

General tips

  • R Data visualisation cheat sheet

  • Web generated data plots

  • Web generated box plots

  • When you don’t know how to present your data

  • Using graphs instead of tables in political science (Kastellec & Leoni)

  • Tools to enhance plots made by GGplots with results of statistical tests

  • A list of free tools for creating more transparent figures for small datasets

  • Examples of well formatted tables and graphs according to APA standards

  • Implementing Edward Tufte’s recommendations for cool looking graphs using R

  • Guide de démarrage pour GGPlot. French guide to ggplot

  • Tips on improving figures from a National Geographic science illustrator

  • Raincloud plots (alternative to bar plots) and resources for drawing them.

design-specific

  • Johnson-Neyman Technique for interactions in Mplus

  • SEM charts

  • Visual displays of interactions

  • Resources for plotting interactions optimally with R (McCabe et al., 2018)

  • Making it pretty: Plotting 2-way interactions with GGplot2

Statistical Approaches / Theory

  • Common statistical myths

  • Compendium of methods/stats resources for psychologists

  • Small effects with big implications

  • Analyzing ordinal data with metric models: what could go wrong?

Psychometrics

  • The dangers of averaging data (Morey)

probability theory

  • Seeing theory. Website using vizualizations to introduce to basic concepts in probability and stats.

  • Visual explanation of the central limit theorem

  • Visual introduction to probability and statistics

inference

  • Things I’ve learned so far (Jacob Cohen). Covers core issues psychologists should be attuned to when conducting statistical analyses.

  • Misunderstandings in statistical inference and their impact on scientific progress (Goodman)

  • Explanation of degrees of freedom (Ron Dotsch)

  • Beyond Statistics: Testing the null in mature sciences (Morey et al.)

  • The philosophy of multiple comparisons (Tukey)

  • Statistical thinking for the 21st century textbook (Russ Poldrack). Covers basic statistical concepts with an emphasis on reproducibility

  • Correlations across individuals do not match correlations among the same variables within individuals (Fischer et al.)

  • Learn Statistics: materials from Erin Buchanan’s class.

  • Open access stat introductory textbook with great animated vizualizations coded in R & ggplot (Crump et al.)

  • Rpsychologist: All kinds of vizualisations of common statistical procedures.

  • The source of common errors in the interpretation of cutoff criteria for widely used stats (e.g. Cronbach's Alpha; Butts et al.)

  • Improving your statistical inferences (free online course by Daniel Lakens)

frequentist

  • American Statistical Association’s statement on p values.

  • Understanding misconceptions about p values. (Lakens)

  • Fisher, Neymann-Pearson or NHST? A tutorial for teaching data testing (Perezgonzales et al.)

  • Justification for using parametric stats on likert scales when the sample size is low or the distribution far from normal (Geoff Norman)

  • p curve: Online tool and papers about evaluating publication bias or p-hacking (Simonsohn et al.)

  • Equivalence testing: Testing that the null is true without Bayes (Lakens)

  • Statistically Showing the Absence of an Effect: covers equivalence testing, power analysis & use of confidence intervals (Quertemont)

  • False expectations about the relation between p values and sample size with visualizations (Heino Matti)

bayesian

  • Bayesian stats for beginners, including online calculators (Zoltan Dienes)

  • How to get the most of nonsignificant results? Bayesian approach (Zoltan Dienes)

  • Reasons for using a Bayesian approach (Rouder et al.)

  • Short intro to Bayesian stats with R examples (Fabian Dablander)

  • Tutorial for performing Bayesian t tests and ANOVAs (Richard Morey)

  • Statistical Rethinking: textbook on statistics from a Bayesian perspective.

  • Free resources including recorded lectures and slides (Richard McElreath)

  • BRMS: an R package for linear mixed models using a bayesian approach (Paul Buerkner)

estimation approach

  • Evaluating effect sizes in psychological research

Design Specific Resources

Regression / GLM

  • One simple effect is significant, the other not but no interaction (Gelman)

  • Using covariates when testing for interactions. (Yzerbyt et al.)

  • Misunderstandings surrounding the interpretation of ANCOVA (Miller and Chapman)

  • Guide to partial Least Square Regression

  • Why Welch t-test is better than Student's t test (Delacre et al.)

  • The perfect t-test. R program that reports the results of a t-test completely formatted, with graphs, tests of assumptions, etc. (Lakens)

  • Simple linear regression lessons

  • Categorical variables coding for linear regression in R.

  • Contrast coding for testing interactions (Abelson)

  • Ordinal regression tutorial with BRMS (Paul Bruekner)

  • Tutorial on logistic regression.

Mixed models

  • Mixed Models: Introduction to treating stimuli as random factors and code for common statistical software (Westfall et al.)

  • Review chapter on mixed models chapter by the same authors addressing various research designs

  • Significance testing in lme4

  • Should you fit the “maximal model”? Parsimony in model construction. (Bates et al.)

  • Centering predictors in mixed models. (Enders & Tofighi)

  • Multilevel logistic regression with scripts in R, Stata, MPlus and SPSS (Sommet and Morselli)

  • Variance measures for multilevel models. (LaHuis et al.)

  • Mixed models using R (Winters)

  • Code for testing repeated measures designs in R

Mediation / moderation

  • Multilevel mediation macro

  • Mplus syntax for single- and multi-level mediation

  • Mplus syntax for mediation, moderation, & moderated mediation

  • Testing indirect effects in mediation models (Yzerbyt et al., 2018)

  • Simple overview of mediation (Kenny)

  • Broad overview of mediation and moderation (Judd et al., 2014).

  • Interactions do not tell us when but also tell us how. (Jacoby & Sassenberg, 2011)

  • When is mediation analysis is warranted? (Pieters DATE)

  • Why testing reverse mediation to check for directionality is a terrible idea (Gollwitzer et al.)

  • Why testing reverse mediation to check for directionality is a terrible idea (Thoemmes)

  • Twitter discussion on why mediation analyses shouldn’t be reported as process evidence

Logistic

SEM

  • APIM app

  • Two-level dynamic SEM primer

  • Partial Least Square Regression guide

  • Statistical and practical concerns with research featuring structural equation modeling (Goodboy & Kline) 

Meta analysis

  • Best practices for reviews & meta-analyses

  • Meta-analysis common problems

  • Metafun: excel spreadsheet that allows you to implement meta-analysis in R using the “Metafor” package

  • Meta-analysis on SPSS. See Andy Field’s paper.

  • Computing the meta-analytic effect size manually. (Goh et al., 2016)

  • Designing and interpreting funnel plots

  • Video on how to conduct a mixed effect meta-analysis in R

Network analysis

Longitudinal

  • Multilevel vs. SEM for longitudinal modeling

  • Multilevel modeling for intensive longitudinal data

Multilevel

  • Multilevel modeling beginner tutorials & resources

  • Multilevel textbook recommendations

Qualitative

  • Introduction to using thematic analysis in psychology. (Braun & Clarke)

  • Three approaches to qualitative Content Analysis. (Hsieh & Shannon)

  • Discourse analysis resources recommended by Theofilos Gkinopoulos

  • Using interpretative repertoires for discursive psychology (Reynolds & Wetherell, 2003)

  • Automated text analysis in psychology (Iliev et al., 2014).

  • Methods for diagnostic agreement

Cross-lagged models

  • Critical takes on cross-lagged models

  • More critical takes on cross-lagged models

  • Cross-lagged panel resources

Latent variable analysis

  • Latent class/mixture models in Mplus

  • Latent variable analysis resources

Other

  • Multiple imputation for three-level & cross-classified data

Big Data

General Tools

Effect size calculators

  • Critical value calculations

  • Effect size calculator

  • Effect size converter

  • Effect sizes in education research

  • Effect size primer for t-tests & ANOVAs

  • Introduction to estimating and reporting effect size (Lakens)

  • User friendly effect size calculator (Wood)

  • Comprehensive online app for computing effect sizes based on a variety of designs and measurement levels (Buchanan et al.)

  • Converting effect sizes (e.g., from d to eta square, etc)

Organization

GitHub

  • Learning to use GitHub. Module 5 of an Open Science MOOC.

  • Githbub for R users (Jenny Brian)