Question

You are given a data set consisting of variables having more than 30% missing values? Let's say, out of 50 variables, 8 variables have missing values higher than 30%. How will you deal with them?

Answer 1

When faced with variables that have more than 30% missing values, consider data imputation, excluding the variables, or using algorithmic approaches that handle missing data.

Step-by-step explanation:

To address the issue of variables in a data set with more than 30% missing values, one could employ several strategies. First, consider the possibility of data imputation where the missing entries are filled in using statistical methods such as mean, median, or mode substitution, or more complex techniques like multiple imputation or model-based methods, depending on the context and the nature of the data. If the variables with missing values are not critical to the analysis or the missing data is not random, it might be appropriate to exclude the variables altogether. Another option could be to use algorithmic approaches that can handle missing data, such as certain machine learning models. The choice of method should be guided by the relevance of the variables to the research question and the assumed mechanism behind the missing data.