Twitter is a goldmine of relational info. Who follows whom, who retweets whom, who replies to whom, and so on. And a lot of this data is available for analysis (though some of it is on a drip-feed). I decided to try to understand my twitter identity by looking at who the people who follow me follow.
I had the great pleasure of acting as opponent for Aleksi Karhula’s successful PhD defence in Turku last Friday. Two of the papers presented in his PhD use sequence analysis to compare siblings’ lifecourses (Karhula, Erola, Raab and Fasang, and Raab, Fasang, Karhula and Erola, the latter already published in Demography), and I naturally found these particularly interesting. I have never done dyadic sequence analysis in the course of research, but I have written some code to deal with it using SADI, for teaching purposes. This note arises out of that experience, in reaction to Aleksi and colleagues’ approach.
Simulating and modelling binary outcomes
When we have a binary outcome and want to fit a regression model,
fitting a linear regression with the binary outcome (the so called Linear
Probability Model) is deprecated, and logistic and probit regression are
the standard practice.
But how well or poorly does the linear probability model function
relative to logistic or probit regression?