# Interpreting regression models: a reading list

Last semester I taught a class for PhD students and collaborators that focused on how the output of regression models is to be interpreted. Most participants had at least some experience with fitting regression models, but I had noticed that they were often unsure about the precise *statistical* interpretation of the output of these models (e.g., *What does this parameter estimate of 1.2 correspond to in the data?*). Moreover, they were usually a bit too eager to move from the model output to a *subject-matter* interpretation (e.g., *What does this parameter estimate of 1.2 tell me about language learning?*). I suspect that the same goes for many applied linguists, and social scientists more generally, so below I provide an overview of the course contents as well as the reading list.

## Course content

### Week 1: Uses of statistical models: Description vs. prediction vs. inference

Regression models have three main uses. The first is to describe the data at hand. The difficulty here mostly consists in figuring out what aspects of the data the parameter estimates reflect. Weeks 6, 7, 8 and 11 are devoted to statistical interpretation of model parameters and how variables can be recoded so that the model output aligns more closely with the research questions.

However, the main use of regression models in the social sciences is to draw inferences, usually causal ones. Moving from a descriptive to causal interpretation of a statistical model requires making additional assumptions. Weeks 2 through 5 are devoted to a tool (directed acyclic graphs) that allows you to make explicit the assumptions you’re willing to make about the causal relationships between your variables and that allows you to derive from these assumptions any further permissible causal claims. Another type of inference is the move from observable quantities (e.g., test scores) to unobervables (e.g., language skills). Weeks 10 and 11 are devoted to this topic.

The third use is to use the model to make predictions about new data. This week’s text (Shmueli 2010) explains why a model that has been optimised for making predictions about new data may be all but worthless for inference, and why a model that has been optimised for inference may not yield the best possible predictions. The take-home points are that when planning a research project, you need to be crystal-clear what its main goal is (e.g., causal inference or prediction) and that you should be careful not to assume that a model selected for its predictive power is best-suited for drawing causal conclusions.

- Text: Shmueli (2010).
- Further reading: Breiman (2001); Yarkoni and Westfall (2017).

### Week 2: Causal inferences from observational data (I)

The texts for weeks 2 through 5 introduce directed acyclic graphs (DAGs) and go through numerous examples for them. DAGs are useful for identifying the variables that you should control for and the ones you should *not* control for if you want to estimate some causal relationship in your data. (Some researchers seem to assume that the more variables you control for, the better, but controlling for the wrong variables can mess up your inferences entirely.) This, of course, is most useful when you’re still planning your research project, because otherwise you may find that you need to control for a variable that you didn’t collect, or that you controlled (on purpose or by accident) for a variable you shouldn’t have controlled for.

- Text: McElreath (2020), Chapter 5.

### Week 3: Causal inferences from observational data (II)

- Text: McElreath (2020), Chapter 6.

### Week 4: Causal inferences from observational data (III)

- Text: Rohrer (2018).

### Week 5: Causal inferences from observational data (IV)

- No obligatory reading.
- Further reading: Elwert (2013).

### Week 6: Understanding parameter estimates (I)

Leaving causal interpretations aside, what do all those numbers in the output of a regression model actually express? DeBruine and Barr (2019) explain how you can analyse simulated datasets to learn which parameter estimates in the simulation correspond to which parameter settings in the simulation set-up.

A related point that I highlighted in class was that the random effect estimates as well as the BLUPs in mixed-effects models should always be interpreted conditionally on the fixed effects in the model. This is true of all estimates in regression models, but people tend to have more difficulties in interpreting random effects and BLUPS. Another point was that you can also gain a better understanding of what the model parameters express by *first* fitting the model on your data and *then* having this model predict new data. By figuring out how the model came up with these predictions, you learn what each parameter estimate literally means.

- Text: DeBruine and Barr (2019).

### Week 7: Understanding parameter estimates (II)

Weeks 7 and 8 were devoted to contrast coding, i.e., how you can recode non-numeric predictors such that the model’s output aligns more closely with what you want to know. I’ve recently blogged about contrast coding, and I was surprised I didn’t learn about this useful technique until 2020 (of all years).

- Text: Schad et al. (2020), up to and including the section
*What makes a good set of contrasts?*

### Week 8: Understanding parameter estimates (III)

- No obligatory reading.
- Further reading: Schad et al. (2020), from the section
*A closer look at hypothesis and contrast matrices*.

### Week 9: Consequences of measurement error (I)

The measured variables included in a model are often but approximations of what is actually of interest. For instance, you may be interested in the learners’ L2 skills, but what you’ve measured is their performance on an L2 test. The test results will only approximately reflect the learners’ true skills. Interpreting the output of a model, which may be valid at the level of the observed variables, in terms of such unobserved but inferred constructs is fraught with difficulties that researchers and consumers of research need to be aware of.

The reading for week 9 deals with some consequences of measurement error on a predictor variable. The reading of week 10 doesn’t strictly deal with measurement error but with the mapping of the observed outcome variable on the unobserved construct of interest and how it affects the interpretation of interactions.

- Text: Westfall and Yarkoni (2016).
- Further reading: Berthele and Vanhove (2020).

### Week 10: Consequences of measurement error (II)

- Text: Wagenmakers et al. (2012).
- Further reading: Wagenmakers (2015).

### Week 11: Understanding parameter estimates (IV)

Logistic regression models can be difficult to understand, and the linear probability model (i.e., ordinary linear regression) isn’t to be dismissed out of hand when working with binary data. A related blog post is *Interactions in logistic regression models*.

- Text: Huang (2019).

### Week 12: Translating verbal research questions into quantitative hypotheses

In week 12 I went through some examples of verbal research questions or hypotheses that at first blush seem pretty well delineated. On closer inspection, however, it becomes clear that radically different patterns in the data would yield the same answer to these questions, and that the research questions or hypotheses were, in fact, underspecified. Drawing several possible data patterns and interpreting them in light of your literal research question or hypothesis can help you rephrase that question or hypothesis less ambiguously.

No texts.

### Week 13: Ascension (no class)

### Week 14: Take-home points + working reproducibly

For the last week, I stressed the following take-home points from this course:

- Be crystal-clear about the main aim of your statistical model: Describing the data, predicting new data, or drawing inferences about causality or unobserved phenomena? Plan accordingly by identifying the factors that must be controlled for and those that mustn’t be controlled for.
- Anticipate the consequences of measurement error. If measurement error could mess up the interpretation of the results, try to collect several indictators of the constructs of interest and adopt a latent variable approach.
- Outline
*precisely*how you’d interpret the possible patterns in the data in terms of your research question. - If a regression model is necessary, recode your predictors so that you can interpret the parameter estimates directly in terms of your research question.
- Analyse simulated data if you’re unsure what the model’s parameter estimates correspond to.
- Keep in mind that parameter estimates are always to be interpreted conditionally on the other predictors in the model. I suspect that lots of counterintuitive findings stem from researchers interpreting their parameter estimates unconditionally.

I also showed how you can make your analyses reproducible by working with RStudio projects, the here package, and R Markdown.

No texts.

## References

Berthele, Raphael and Jan Vanhove. 2020. What would disprove interdependence? Lessons learned from a study on biliteracy in Portuguese heritage language speakers in Switzerland. *International Journal of Bilingual Education and Bilingualism* 23(5). 550-566.

Breiman, Leo. 2001. Statistical modeling: The two cultures. *Statistical Science* 16. 199-215.

DeBruine, Lisa M. & Dale J. Barr. 2019. Understanding mixed effects models through data simulation. PsyArXiv.

Elwert, Felix. 2013. Graphical causal models. In S. L. Morgan (ed.), *Handbook of Causal Analysis for Social Research*, pp. 245-273. Dordrecht, The Netherlands: Springer.

Huang, Francis L. 2019. Alternatives to logistic regression models in experimental studies. *Journal of Experimental Education*.

McElreath, Richard. 2020. *Statistical rethinking: A Bayesian course with examples in R and Stan*, 2nd edn. Boca Raton, FL: CRC Press.

Rohrer, Julia. 2018. Thinking clearly about correlations and causation: Graphical causal models for observational data. *Advances in Methods and Practices in Psychological Science* 1(1). 27-42.

Schad, Daniel J., Shravan Vasishth, Sven Hohenstein and Reinhold Kliegl. 2020. How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. *Journal of Memory and Language* 110.

Shmueli, Galit. 2010. To explain or to predict? *Statistical Science* 25. 289-310.

Wagenmakers, Eric-Jan. 2015. A quartet of interactions. *Cortex* 73. 334-335.

Wagenmakers, Eric-Jan, Angelos-Miltiadis Krypotos, Amy H. Criss and Geoff Iverson. 2012. On the interpretation of removable interactions: A survey of the field 33 years after Lotus. *Memory & Cognition* 40. 145-160.

Westfall, Jacob and Tal Yarkoni. 2016. Statistically controlling for confounding constructs is harder than you think. *PLoS ONE* 11(3). e0152719.

Yarkoni, Tal and Jacob Westfall. 2017. Choosing prediction over explanation in psychology: Lessons from machine learning. *Perspectives on Psychological Science* 12. 1100-1122.