Marginal Effects: A long-form document on using marginal effects (marginal means and marginal slopes) to improve interpretation of regression coefficients, especially in the presence of an interaction. Presents as a Rosetta stone for Stata’s `margins`

command and R. *Software*: Stata and R.

Interpreting log transformations in regression: Interpreting coefficients from a linear regression model (or linear mixed model) when the predictor and/or the outcome have been log transformed. *Software*: R, though concepts could transfer to other software.

Random intercepts in SPSS’s Mixed Model: There are two ways of specifying random intercepts in SPSS’s Mixed Model; this discusses their equivalence. *Software*: SPSS.

Moderation and Mediation via Regression: Although moderation and mediation typically arise in SEM/path analysis frameworks, moderation can be addressed in regression, and mediation can be conceptualized through regression.

IRR and ICC: Some notes on IRR versus ICC, as well as how to obtain the ICC. *Software*: R, though concepts could transfer to other software.

The issue of collinear predictors: A visualization to see the potential negative effect of including highly collinear variables in a model. *Software*: R, though concepts could transfer to other software.

Interpreting regression coefficients of different models: Very sparse notes on interpreting coefficients in linear, logistic, Poisson and negative binomial regression models.

Stata, `xt`

versus mixed model: Econometricians usually use the `xt`

framework to address repeated measures. This document shows how to fit the equivalent of fixed effects regression, between effects regression, and random effects regression using linear regression (`regress`

) and linear mixed models (`mixed`

). *Software*: Stata.

Nested versus crossed random effects: Multiple random effects in a mixed model are typically defined as either “nested” or “crossed”. This document shows that this is a false dichotomy (nested random effects aren’t real!), as well as showing some nice visualization. *Software*: R, though concepts could transfer to other software.

Linear splines versus interactions: Models such as interrupted time series or diff-in-diff are often considered special analyses. This document attempts to show that these are just regression models with particular interactions. *Software*: Stata, though concepts could transfer to other software.

Selecting a random subset of the data, potentially within subgroups: An easy way to generate a random sample of your data of arbitrary size, including a stratified approach. *Software*: Stata, though concepts could transfer to other software.

Mediation with Survey data: To fit a mediation model in Stata using complex survey data requires using `gsem`

. Unfortunately, the `gsem`

command does not support directly estimating direct, indirect and total effects. This documents how to compute them. *Software*: Stata.

The “Divide by 4” rule: An additional tool in interpreting logistic regression coefficients is the “Divide by 4” rule. *Software*: Stata, though concepts could transfer to other software.

Response Surface Model Plotting: An example of visualization after running a “response surface model”, aka just regression with an interaction. *Software*: R.

Modifying built-in-Stata commands: A fun story about hacking one of Stata’s shipped-with commands to make some code work. A story rather than a guide or instructions. *Software*: Stata.