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 papers or websites that I use regularly when planning or analyzing research studies. I hope you will find it useful as well.

Olivier Klein.


List of the topics covered in this page:

Research Design

Methodological Issues with Online Surveys

Measurement

Power estimation and effect sizes

Qualitative Methods

Preregistration

Displaying data

Data Preparation

Testing

Inference

Linear Regression

SEM

Bayesian

Mixed Models

Statististical Software

SPSS

R

Social networks (sharing resources & knowledge)

Ethics

Other stuff

 

Research design

  • Bill McGuire’s important annual review chapter about creative hypothesis generation in psychology.
  • 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

Measurement

Power estimation & Effect sizes

Qualitative methods

Preregistration

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.

Testing

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 and the paper now in SPPS.
  • 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.
  • Ever wondered about the meaning of degrees of freedom? Check out this excellent tutorial by Ron Dotsch(thanks, Rui Costa Lopes for the suggestion!) .

Regression

  • Partial Least Square Regression: I am not very knowledgeable on this one but colleagues recommended that I include this method for testing causal models on this page. So here are two links: one recommended by Davide Del Cason and the other recommended by Carole Fantini.

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

SPSS

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

Jamovi: Even better. Open source.

R

 

Ethics

Other stuff

  • Shiny Web Apps for designing experiments and analysing data
  • 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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