Statistical Horizons Blog


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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Problems with the Hybrid Method

For several years now, I’ve been promoting something I called the “hybrid method” as a way of analyzing longitudinal and other forms of clustered data. My books Fixed Effects Regression Methods for Longitudinal Data Using SAS (2005) and Fixed Effects Regression Models (2009) both devoted quite a few pages to this methodology. However, recent research […]

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