3 Methods for Handling Missing Data

The best possible method of handling the missing data. Is preventing the problem by well-planning the study and collecting the data carefully.

In the last article we talk about missing data. And the different types of missing data. These are Missing Completely at Random (MCAR) . When data is completely missing at random across the dataset with no discernible pattern. There is also Missing At Random (MAR) . When data is not missing randomly, but only within sub-samples of data. Finally, there is Not Missing at Random (NMAR), when there is a noticeable trend in the way data is missing.

Today article we will talk about it the different methods to handle missing data.

Our Techniques to Handle Missing Data

1-Ignore It

It may sound a bit stupid. And one of the valid solutions is ignoring the missing data. But it only valid in three circumstances:

First Case: If the potential impact of the missing data is small. Then the missing data may be ignored in the further analysis.

Second Case: If only the dependent variable has missing values and auxiliary variables. Auxiliary variables not included in the regression analysis. but correlated with a variable with missing values (and/or) related to its missingness.