On Obtaining Estimates of the Fraction of Missing Information From Full Information Maximum Likelihood

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New Confidence Intervals and Bias Comparisons Show That Maximum Likelihood Can Beat Multiple Imputation in Small Samples (2013, methodology): “The estimate is calculated directly from the incomplete data using ML, and is more precise than the estimate of that is commonly used under MI (Savalei & Rhemtulla, 2012). is the degrees of freedom that would apply if the data were complete—e.g., 2 for a simple regression.”
Planned missing designs to optimize the efficiency of latent growth parameter estimates (2014, background): “By the same logic, to have the same power as the complete data design, we would need to increase the sample size to n ¼ 100=:6 ¼ 167 (Savalei & Rhemtulla, 2012).”