Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.
Multiple Imputation
Member Training: Multiple Imputation for Missing Data
There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each of these simplistic methods introduces bias into our model results.
Multiple imputation is considered to be the superior method of working with missing data. It eliminates the bias introduced by the simplistic methods in many missing data situations.
[Read more…] about Member Training: Multiple Imputation for Missing Data
Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables
A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to which variable type it is.
For example, a categorical variable like marital status could be coded in the data set as a single variable with 5 values: [Read more…] about Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables
How to Diagnose the Missing Data Mechanism
One important consideration in choosing a missing data approach is the missing data mechanism—different approaches have different assumptions about the mechanism.
Each of the three mechanisms describes one possible relationship between the propensity of data to be missing and values of the data, both missing and observed.
The Missing Data Mechanisms
Missing Completely at Random, MCAR, means there is no relationship between [Read more…] about How to Diagnose the Missing Data Mechanism
Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood
Two methods for dealing with missing data, vast improvements over traditional approaches, have become available in mainstream statistical software in the last few years.
Both of the methods discussed here require that the data are missing at random–not related to the missing values. If this assumption holds, resulting estimates (i.e., regression coefficients and standard errors) will be unbiased with no loss of power.
The first method is Multiple Imputation (MI). Just like the old-fashioned imputation [Read more…] about Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood
Is Multiple Imputation Possible in the Context of Survival Analysis?
Sure. One of the big advantages of multiple imputation is that you can use it for any analysis.
It’s one of the reasons big data libraries use it–no matter how researchers are using the data, the missing data is handled the same, and handled well.
I say this with two caveats. [Read more…] about Is Multiple Imputation Possible in the Context of Survival Analysis?