How is multiple imputation used in latent variable models?

How is multiple imputation used in latent variable models?

Mortaza Jamshidian, Matthew Mata, in Handbook of Latent Variable and Related Models, 2007 Multiple imputation consists of producing, say m, complete data sets from the incomplete data by imputing the missing data m times by some reasonable method.

Which is the best definition of multiple imputation?

Multiple imputation consists of producing, say m, complete data sets from the incomplete data by imputing the missing data m times by some reasonable method. Then each completed data set is analyzed using a complete data method and the resulting methods are combined to achieve inference. Multiple imputation is motivated by

How are single imputation methods used to estimate parameters?

These single imputation methods permit us to estimate the parameter’s values, but ignore the variability of the estimates, leading to underestimation of standard errors and confidence intervals for the estimated parameters. That is, the single value being imputed cannot reflect the sampling variability around the actual value.

How is multiple imputation motivated by the Bayesian framework?

Multiple imputation is motivated by the Bayesian framework and as such, the general methodology suggested for imputation is to impute using the posterior predictive distribution of the missing data given the observed data and some estimate of the parameters.

What is the purpose of multiple imputation in Stata?

Multiple Imputation in Stata. Introduction. Missing data is a common issue, and more often than not, we deal with the matter of missing data in an ad hoc fashion. The purpose of this seminar is to discuss commonly used techniques for handling missing data and common issues that could arise when these techniques are used.

How can I perform multiple imputation on longitudinal data?

Stata has a suite of multiple imputation (mi) commands to help users not only impute their data but also explore the patterns of missingness present in the data. In order to use these commands the dataset in memory must be declared or mi set as “mi” dataset.

What do you need to know about multiple imputation?

Multiple imputation and other modern methods such as direct maximum likelihood generally assumes that the data are at least MAR, meaning that this procedure can also be used on data that are missing completely at random.

How many imputations do you need to replicate a value?

That’s good replicability; despite three quarters of the values being imputed, the two point estimates differed by just 0.1%. But the SE estimate didn’t replicate as well; it was .023 the first time I imputed the data, and .026 the second time—a difference of 13%.

What should be true in multiply imputed data sets?

Certain things should always be true in multiply imputed data sets. For example, regular (unimputed) variables should have the same values in all imputations. The mi update command will check that this is so and fix any problems it finds (in this case, by setting the value in all imputations to the value in the observed data).

Why is a covariance matrix used in multiple imputation?

Meaning that a covariance (or correlation) matrix is computed where each element is based on the full set of cases with non-missing values for each pair of variables. This method became popular because the loss of power due to missing information is not as substantial as with complete case analysis.