# Teaching resources

## Quantitative methodology: An introduction

The script for the course on quantitative methodology I teach is available here.

Contents:

1. Association and causality.
2. Constructing a control group.
3. Alternative explanations.
4. Inferential statistics 101. (The course is not a statistics course, but there’s no avoiding talking about p-values given their omnipresence.)
5. Increasing precision.
6. Pedagogical interventions.
7. Within-subjects experiments.
8. Quasi-experiments and correlational studies.
9. Constructs and indicators.
10. Questionable research practices.

I’ve also included two appendices:

• Reporting research transparently.

## Introduction to the general linear model

These are the lecture notes for a summer school module I taught. They are available from GitHub.

Contents:

1. Nuts and bolts: General linear model equation; optimisation criteria (least absolute deviations, least squares, maximum likelihood); estimating uncertainty (bootstrapping, i.i.d. normality assumption).
2. Adding a predictor: Interpretation of parameter estimates and regression lines; confidence bands.
3. Group differences: Dummy variables (treatment coding, sum coding); bootstrapping without homoskedasticity.
4. Interactions.
5. Multiple predictors: Confounding variables; control variables; posttreatment variables.
6. The basic of logistic regression: Linear probability model; odds, odds ratios, log-odds.

## Einführung in die quantitative Datenanalyse

(In German only.) Das Skript Einführung in die quantitative Datenanalyse ist eine Einführung in die Analyse quantitativer Daten, die sich an erster Stelle an Forscherinnen und Forscher im Bereich der angewandten Linguistik richtet. Das Skript und die verwendeten Datensätze können Sie kostenlos auf GitHub herunterladen.

## Working with datasets and visualising data in R: Two primers

I wrote a primer on working with datasets in R and another on visualising data in R. You can find the datasets used in the primers on GitHub.

## Visualising statistical uncertainty using model-based graphs

I wrote a tutorial about visualising the statistical uncertainty in statistical models for the BICLCE 2019 conference in Bamberg. You can find the tutorial here: Visualising statistical uncertainty using model-based graphs.

Contents:

1. Why plot models, and why visualise uncertainty?
2. The principle: An example with simple linear regression
• Step 1: Fit the model
• Step 2: Compute the conditional means and confidence intervals
• Step 3: Plot!
3. Predictions about individual cases vs. conditional means
4. More examples
• Several continuous predictors
• Dealing with categorical predictors
• t-tests are models, too
• Dealing with interactions
• Ordinary logistic regression
• Mixed-effects models
• Logistic mixed effects models
5. Caveats
• Other things may not be equal
• Your model may be misspecified
• Other models may yield different pictures

## Miscellaneous tutorials

The blog archive contains a number of tutorials.

## cannonball (R package)

The `cannonball` package bundles a couple of functions that I use when teaching introductory courses in quantitative methodology and statistics. These include

• `plot_r()` for drawing different scatterplots with the same correlation coefficient,
• `walkthrough_p()` and `walkthrough_blocking()`, which both aim to help students see the connection between an experiment’s design and its analysis,
• `clustered_data()` for simulating data from cluster-randomised experiments,
• `parade()` and associated functions for helping researchers check the assumptions of their statistical models.