Psychology statistics group

An informal and multidisciplinary group of psychology researchers who are interested in statistics

PsychStats is an informal and multidisciplinary group of psychology researchers (broadly interpreted) who are interested in statistics. Areas of research represented include education, emotion, intelligence, linguistics, personality, reasoning. Members have experience with a range of methods requiring overlapping knowledge (e.g., ANOVA, multiple regression, multilevel/mixed effects modelling, factor analysis, structural equation modelling), using a range of different software packages (e.g., SPSS, R, MPlus, SAS, Mx).

I have yet to see any problem, however complicated, which, when you looked at it the right way, did not become still more complicated.

Paul AndersonNew Scientist, 25–Sept–1969


  • Meet to discuss research from the applied statistics literature;
  • Help each other using and learning statistical methods and...
  • Merrily ignore discipline boundaries.

Mailing list

If you would like to be added to the list, please email Tom Booth. This is a low-volume list on which people discuss statistics, and on which PsychStatsBanter gatherings (see below) are organised and advertised.

Tom Booth

Helpful resources

The local R user's code wiki

our CiteULike group

Quick-R - Straightforward examples of doing almost anything you want in R.

PsychStatsBanter Gatherings

We meet on an occasional basis, usually in S38, Psychology Building, 7 George Square, with meetings lasting around an hour. Each meeting is focussed on issues in statistics that people find particularly troubling, socially and morally.

A list of things that trouble people...

  • More on mixed effects models... e.g. reporting models, analogues of Tukey's HSD, etc
  • Simplifying terms in models, e.g. by merging levels
  • Assumptions made when using covariates to "control" for something
  • Signal detection theory
  • Relationship between SEM, e.g. using them for latent growth models, and multilevel models
  • Loglinear models and other models for categorical data
  • Survival Analysis (and complex variants)
  • Running simulations
  • Causal inference