Statistical Horizons Blog


Teaching Stata in Bangladesh

When Paul Allison asked me if I wanted to teach a course in Bangladesh, my first reaction was confusion.  Other than knowing a few basic facts about the place, I had spent little time thinking about the country and none at all imagining myself going there. And here, suddenly, was an opportunity to spend a […]

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Linear vs. Logistic Probability Models: Which is Better, and When?

In his April 1 post, Paul Allison pointed out several attractive properties of the logistic regression model.  But he neglected to consider the merits of an older and simpler approach: just doing linear regression with a 1-0 dependent variable.  In both the social and health sciences, students are almost universally taught that when the outcome variable in […]

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Don’t Put Lagged Dependent Variables in Mixed Models

When estimating regression models for longitudinal panel data, many researchers include a lagged value of the dependent variable as a predictor. It’s easy to understand why. In most situations, one of the best predictors of what happens at time t is what happened at time t-1.  This can work well for some kinds of models, […]

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Maximum Likelihood is Better than Multiple Imputation: Part II

In my July 2012 post, I argued that maximum likelihood (ML) has several advantages over multiple imputation (MI) for handling missing data: ML is simpler to implement (if you have the right software). Unlike multiple imputation, ML has no potential incompatibility between an imputation model and an analysis model. ML produces a deterministic result rather than […]

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What’s So Special About Logit?

For the analysis of binary data, logistic regression dominates all other methods in both the social and biomedical sciences. It wasn’t always this way. In a 1934 article in Science, Charles Bliss proposed the probit function for analyzing binary data, and that method was later popularized in David Finney’s 1947 book Probit Analysis. For many […]

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Imputation by Predictive Mean Matching: Promise & Peril

Predictive mean matching (PMM) is an attractive way to do multiple imputation for missing data, especially for imputing quantitative variables that are not normally distributed. But, as I explain below, it’s also easy to do it the wrong way.  Compared with standard methods based on linear regression and the normal distribution, PMM produces imputed values […]

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Getting the Lags Right

In my November and December posts, I extolled the virtues of SEM for estimating dynamic panel models. By combining fixed effects with lagged values of the predictor variables, I argued that this approach offers the best option for making causal inferences with non-experimental panel data. It controls for all time-invariant variables, whether observed or not, […]

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More on Causal Inference With Panel Data

This is a follow-up to last month’s post, in which I considered the use of panel data to answer questions about causal ordering: does x cause y or does y cause x?  In the interim, I’ve done many more simulations to compare the two competing methods, Arellano-Bond and ML-SEM, and I’m going to report some […]

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Using Panel Data to Infer Causal Direction: ML vs. Arellano-Bond

Does x cause y or does y cause x? Virtually everyone agrees that cross-sectional data are of no use in answering this question. The ideal, of course, would be to do two randomized experiments, one examining the effect of x on y, and the other focused on the reverse effect. Absent this, most social scientists […]

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Sensitivity Analysis for Not Missing at Random

When I teach my seminar on Missing Data, the most common question I get is “What can I do if my data are not missing at random?” My usual answer is “Not much,” followed by “but you can do a sensitivity analysis.” Everyone agrees that a sensitivity analysis is essential for investigating possible violations of […]

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