Skip to main content Skip to local navigation
Home » Statistical Resources

Statistical Resources

This page collects links to tutorials and other online resources we have found useful for SCS clients.

Graphical methods

  • British Ecological Society's Guide to Reproducible Science. The guide proposes a simple reproducible project workflow, and a guide to organizing projects for reproducibility. The Programming section provides concrete tips and traps to avoid (example: use relative, not absolute pathnames), and the Reproducible Reports section provides a step-by-step guide for generating reports with R Markdown.

Hypothesis testing

Multiplicity Control

https://www.youtube.com/watch?v=HpjlcEH4zuY

https://journals.lww.com/epidem/Abstract/1990/01000/No_Adjustments_Are_Needed_for_Multiple_Comparisons.10.aspx

https://www.researchgate.net/publication/318326501_Multiplicity_Control_School_Uniforms_and_Other_Perplexing_Debates

Negligible Effect (Equivalence) Testing

Using significance tests to evaluate equivalence between two experimental groups. Equivalency testing, a statistical method often used in biostatistics to determine the equivalence of 2 experimental drugs, is introduced to social scientists. Examples of equivalency testing are offered, and the usefulness of the method to the social scientist is discussed.

Lakens et al. Equivalence Testing for Psychological Research: A Tutorial

https://www.youtube.com/watch?v=AEpMHDXK8UI

Linear models (ANOVA, Regression)

  • Overview of R Modelling Packages. An overview of R packages and functions for fitting different types of linear models, classified by the type
    of outcome variable (continuous, binary, catgegorical). Contains links to examples and shows Bayesian equivalents of many frequentist approaches.
  • Data and Model Summaries in R. modelsummary is an R a package to summarize data and statistical models in R. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. There is also a JSS paper that condenses many examples into a shorter format.

Meta Analysis

  • Mathais Harrer Doing Meta Analysis in R. An accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.

Structural Equation Models

Effect size & power analysis

Latent Profile Analysis

Latent profile analysis (LPA) is a latent variable method that focuses on identifying latent sub-populations within a population based on observed variables.
LPA works best with continuous variables (and, in some cases, ordinal variables), but is not appropriate for dichotomous (binary) variables.