Missing Data

Most of us did not anticipate the problem of missing data. In most cases, there will be missing data. Therefore, Missing data should be managed and analyse. There is a YouTube video, http://www.youtube.com/watch?v=XlUdRVdT7iU, that explain about how to deal with missing data.

According to the presenter, researcher should plan for missing data – for example design the questionnaire in such a way to avoid missing data ie not applicable box. The video list these possibilities for missing data, not applicable, not available, unknown, refusal to answer and true missing (maybe accidentally)

Type of missing data

1.      Missing completely at random – probability of missing data on variable Y is unrelated to the true value of Y or other variables in the dataset

2.      Missing at random - probability of missing data on Y is unrelated to Y only after adjusting for one or more other variables

3.      Not missing at random – probability of missing data on Y is dependent on value of Y

Benefits of documenting missing data

 1.      Informs quality control reporting

2.      Allows for full disclosure in publication or presentation of data

3.      Some statistical analysis methods are dependent on missing completely at random or missing at random

4.      Useful for methodological researcher