Further learning and methodological references

Beginner R resources

R for Data Science, 2e

Excellent for the import-transform-visualize workflow and for learning R through complete data-analysis tasks.

Data Carpentry: R for Social Scientists

Research-oriented and designed for learners with little or no programming experience.

ModernDive

Useful for connecting visualization, regression, confidence intervals, and statistical inference.

Posit RStudio guide

Official orientation to the RStudio working environment.

Posit cheatsheets

Useful quick references during practical work.

Method references used in the redesigned lessons

t-tests

• R documentation: t.test()

PCA, EFA, and CFA

• UCLA: Principal Components and Exploratory Factor Analysis
• Penn State: Principal Components Analysis
• lavaan CFA tutorial
• Fabrigar, Wegener, MacCallum & Strahan (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272

Multilevel models

• Bates, Mächler, Bolker & Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01
• Schonfeld & Rindskopf (2007). Hierarchical Linear Modeling in Organizational Research. Organizational Research Methods. https://doi.org/10.1177/1094428107300229
• lme4 project and vignettes

Scenario prediction

• R documentation: predict.lm()

A standing methodological rule

R can calculate a result even when the research design does not justify the interpretation.

For every analysis ask:

1. What is the management/research question?
2. What is the unit of observation?
3. What are the variable types and measurement assumptions?
4. Are observations independent, nested, or repeated?
5. What assumptions does the method make?
6. Is the evidence descriptive, associational, predictive, or causal?
7. What uncertainty and limitations should be reported?