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Archive of posts filed under the Multilevel Modeling category.

How do you interpret standard errors from a regression fit to the entire population?

James Keirstead writes: I’m working on some regressions for UK cities and have a question about how to interpret regression coefficients. . . . In a typical regression, one would be working with data from a sample and so the standard errors on the coefficients can be interpreted as reflecting the uncertainty in the choice […]

Yummy Mr. P!

Chris Skovron writes: A colleague sent the attached image from Indonesia. For whatever reason, it seems appropriate that Mr. P is a delicious salty snack with the tagline “good times.” Indeed. MRP has made the New York Times and Indonesian snack food. What more can we ask for?

A linguist has a question about sampling when the goal is causal inference from observational data

Nate Delaney-Busch writes: I’m a PhD student of cognitive neuroscience at Tufts, and a question came recently with my colleagues about the difficulty of random sampling in cases of highly controlled stimulus sets, and I thought I would drop a line to see if you had any reading suggestions for us. Let’s say I wanted […]

Differences between econometrics and statistics: From varying treatment effects to utilities, economists seem to like models that are fixed in stone, while statisticians tend to be more comfortable with variation

I had an interesting discussion with Peter Dorman (whose work on assessing the value of a life we discussed in this space a few years ago). The conversation started when Peter wrote me about his recent success using hierarchical modeling for risk analysis. He wrote, “Where have they [hierarchical models] been all my life? In […]

Stan World Cup update

The other day I fit a simple model to estimate team abilities from World Cup outcomes. I fit the model to the signed square roots of the score differentials, using the square root on the theory that when the game is less close, it becomes more variable. 0. Background As you might recall, the estimated […]

Stan goes to the World Cup

I thought it would be fun to fit a simple model in Stan to estimate the abilities of the teams in the World Cup, then I could post everything here on the blog, the whole story of the analysis from beginning to end, showing the results of spending a couple hours on a data analysis. […]

Chicago alert: Mister P and Stan to be interviewed on WBEZ today (Fri) 3:15pm

Niala Boodho on the Afternoon Shift will be interviewing Yair and me about our age-period-cohort extravaganza which became widely-known after being featured in this cool interactive graph by Amanda Cox in the New York Times. And here’s the interview. The actual paper is called The Great Society, Reagan’s revolution, and generations of presidential voting and […]

“P.S. Is anyone working on hierarchical survival models?”

Someone who wishes to remain anonymous writes: I’m working on building a predictive model (not causal) of the onset of diabetes mellitus using electronic medical records from a semi-panel of HMO patients. The dependent variable is blood glucose level. The unit of analysis is the patient visit to a network doctor or hospitalization in a […]

Comment of the week

This one, from DominikM: Really great, the simple random intercept – random slope mixed model I did yesterday now runs at least an order of magnitude faster after installing RStan 2.3 this morning. You are doing an awesome job, thanks a lot!

Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems

Andreas Graefe sends along this paper (with Helmut Kuchenhoff, Veronika Stierle, and Bernhard Riedl) and writes: We summarize prior evidence from the field of economic forecasting and find that the simple average was more accurate than Bayesian model averaging in three of four studies; on average, the error of BMA was 6% higher than the […]