What should you do with missing data in an instrumental variables analysis? For my dissertation, I’m using instrumental variables to examine the impact of family income on children’s cognitive skills. Having been trained as a psychologlist, there is an emphasis on handling missing data appropritately – Full Information Maximum Likelihood (FIML) and Multiple Imputation (MI) are popular techniques. However, in the causal inference courses I’ve taken, there seems to be less of a focus on how to handle missing data.

A Way to Look At Why Your SEM Model Has Poor Fit If you’ve done any Structural Equation Modeling, you’ve run into a bad fitting model. Embarassingly, aside from estimating covariances among correlated variables, I didn’t know much about how to inspect poor model fit until very recently. Enter the residual covariance matrix. It’s a super simple way to examine poor model fit. Here’s an example. You have 6 observed variables, and you want to create a latent factor.

R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. You can embed an R code chunk like this: summary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.

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