![]() You actually get better results by leaving the imputed values at impossible values, even though it’s counter-intuitive.Ģ. Recent research, however, has found that rounding off imputed values actually leads to biased parameter estimates in the analysis model. Suggestions for imputing categorical variables were to dummy code them, impute them, then round off imputed values to 0 or 1. Many common imputation techniques, like MCMC, require normally distributed variables. Don’t round off imputations for dummy variables. So here are a few updates that will help you achieve these goals.ġ. Remember that there are three goals of multiple imputation, or any missing data technique: Unbiased parameter estimates in the final analysis (regression coefficients, group means, odds ratios, etc.) accurate standard errors of those parameter estimates, and therefore, accurate p-values in the analysis and adequate power to find meaningful parameter values significant. The downside for researchers is that some of the recommendations missing data statisticians were making even five years ago have changed. Research is still ongoing, and each year new findings on best practices and new techniques in software appear. ![]() ![]() Missing Data, and multiple imputation specifically, is one area of statistics that is changing rapidly. ![]()
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