What is Multiple Imputation

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What is Multiple Imputation?

Imputation is the practice of ‘filling in’ missing data with possible plausible values.  Therefore, it can be used when you are trying to analyse data that is incomplete or has “gaps”. This is an excellent way of solving missing-data problem at the start of your analytical process.

The issue of Missing Data is the subject of increasing debate in contemporary statistics. In any given study, missing data can have many causes. For instance, respondents may be unwilling to answer some questions (item non-response) or refuse to participate in a study (unit non-responses). In addition, transcription errors and dropouts in follow up studies and clinical trials can frequently occur.

IN 1987, Rubin and Little suggested that “naive” or unprincipled imputation methods may create more problems than they solve.

Major Advantages of Multiple Imputation:

• Better statistical validity than ad-hoc approaches
• Multiple Imputation is statistically efficient in that it uses the entire observed dataset in the statistical analysis, efficiency being the degree to which all information about the parameter of interest, available in the dataset, is used.
• Multiple Imputation saves money, since for the same statistical power, multiple imputation requires a smaller sample size than listwise deletion
• Once imputations have been generated by a knowledgeable user, researchers can use them for their own statistical analyses.

Multiple Imputation Explained

SOLAS for Missing Data Analysis, developed in conjunction with Prof. Donald B. Rubin, Harvard University, provides researchers with a range of imputation techniques in an easy to use, validated software application. SOLAS offers principled approaches to analyzing data with missing values, featuring 6 different imputation techniques and its own script language so that you can easily record your imputation choices.

Click here to download a free trial of SOLAS for Missing Data Analysis