Managing Missing Data

Managing missing data is an important step in the data analysis process. Missing data almost always happens; people drop out of studies or skip a few questions on a survey. It’s crucial to determine if data is missing randomly or follows a systematic pattern affecting results. Therefore, researchers categorize missing data in three ways: MCAR (missing completely at random), MAR (missing at random, ignorable), and MNAR (missing not at random, unignorable). Although no set standard exists for acceptable missing data, many experts suggest tolerating less than 5%.

After identifying unignorable missing data, the next step is deciding how to address it. The most common approach is to remove cases with missing data from the analysis. Programs such as SPSS and SAS will delete these missing values automatically. One downside to this option, however, is the potential for a large loss of data. Especially if missing cases spread randomly throughout the data. Therefore, a researcher can choose to estimate the values of the missing data in a variety of ways. One way could be to use prior knowledge of the literature to make an educated guess on what the value should be.

This method can be biased by researchers’ beliefs about the study. It assumes that values remain unchanged over time. Some researchers prefer mathematical approaches to estimate missing values. These methods include mean substitution or using predicted values from regression. Regression-based estimates are more objective and may align better with existing data than real scores. Some researchers run analyses with and without missing data to check for differences in results. Experts highly recommend this option when a data set is small and contains a large amount of missing data.

Overall, there are many ways to deal with missing data, all accompanied with several pros and cons to consider. It is up to the researcher to determine the pattern of missing data and the appropriate solution to deal with it based on the study in question.