A compendium of methods and stats resources for (social) psychologists

A compendium of methods and stats resources for (social) psychologists

This page helps me recover useful paper or websites that I use regularly when planning or analyzing research studies. I hope you will find it useful as well.

Olivier Klein.

Research design

  • Paper by Pirlott & McKinnon on how to design studies with mediation in mind
  • The classic paper by Spencer, Zanna & Fong on why designing experiments is preferable to mediational analysis in examining causal processes.
  • Document destined to Ph.D. students at the University of Guelph explaining how to avoid questionable research practices in planning and reporting experiments.

Methodological issues with online surveys



Power estimation & Effect sizes

Qualitative methods


Displaying data

Data preparation

  • Our paper “Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median”
  • Bakker & Witchers’s paper arguing against outlier removal.


Statistical Inference (Fisher/Neymann-Pearson)

  • Jacob Cohen’s paper, Things I’ve learned so far. Essential reading that covers many of the core issues psychologists should be attuned to when conducting (inferential) statistical analyses.
  • Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Paper by Greenland et al. See also American Statistical Association’s statement on p values.
  • One simple effect is significant, the other not but no interaction. Paper by Gelman on this.
  • Is it a problem to use parametric stats on likert scales when the sample size is low or the distribution far from normal? Usually not according to this paper by Geoff Norman.
  • Using covariates when testing for interactions. Paper by Yzerbyt et al.
  • p curve
  • Testing that the null is true without Bayes. Blog post by Daniël Lakens on Equivalence testing
  • A post by Heino Matti on false expectations about the relation between p values and sample size. Includes great vizualisations.
  • Aligning scientific reasoning and statistical inference: Short “Science” paper by Steven Goodman on misunderstandings in statistical inference and their impact on scientific progress.
  • Great paper by Miller and Chapman on misunderstandings surrounding the interpretation of ANCOVA.

Mixed Models

  • Mixed Models: Introduction to treating stimuli as random factors and code for common statistical software by Westfall et al.
  • Follow-up on mixed models: Annual review chapter by the same authors addressing various research designs
  • Significance testing in lme4
  • Should you fit the “maximal model”? Parsimony in model construction. Paper by Bates et al.
  •  Centering predictors in mixed models. Paper by Enders & Tofighi.

Mediation & Moderation

Bayesian approaches

  • Zoltan Dienes’ very useful webpage on Bayesian stats for beginners (including online calculators).
  • How to get the most of nonsignificant results? Paper by Zoltan Dienes based on a a Bayesian approach.
  • Is there a free lunch in inference? Forceful advocacy of the Bayesian approach by Rouder et al. Very clear for nonspecialists.
  • Short intro to Bayesian stats with R examples by Fabian Dablander.

Structural Equation modeling

  • Paper by Goodboy & Kline: Statistical and practical concerns with research featuring structural equation modeling. Good primer on some of the errors you want to avoid!

Social Networks

Statistical Software


JASP: a free statistical software that also performs bayesian tests.


Other stuff

  • Rpsychologist: All kinds of vizualisations of common statistical procedures. Splendid for pedagogical purposes especially.
  • Nice post by Chris Holdgraf on designing and interpreting funnel plots.
  • Paper  by Butts et al. on the source of common errors in the interpretation of cutoff criteria for widely used stats (including .70 for Cronbach’s alpha)
  • Blog post by Dick Morey on the dangers of averaging data.
  • On a similar topic, paper by Colin Leach on how to integrate the person with macro-level processes (and look differently at regression plots).
  • Paper by LeBel & al summarizing criticisms of open science and advocacy of high powered research movement in social psych. and addressing them. Useful to find all the important recent references.
  • Statcheck: Online tool to check whether there any errors in your paper. Just upload a PDF.

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