The best way to handle missing data
Missing data is an inevitable part of the process. As data researchers, we pour a lot of resources, time and energy into making sure the data set is as accurate as possible. However, data inevitably goes missing. As someone who has been handling data analytics and overseen dozens of research projects for several years, missing data is just one of those “It sucks, but it’s no one’s fault” scenarios. Sometimes, data sets come up short, no matter how many times data scientists clean and prepare it. The best way to handle such situations is to develop contingency plans to minimise the damage.
Missing data – Why does it matter so much?
Missing data is a huge problem for data analysis because it distorts findings. It’s difficult to be fully confident in the insights when you know that some entries are missing values. Hence, why they must be addressed. According to data scientists, there are three types of missing data. These are Missing Completely at Random (MCAR) – when data is completely missing at random across the dataset with no discernable 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.
Best techniques to handle missing data
Use deletion methods to eliminate missing data
The deletion methods only work for certain datasets where participants have missing fields. There are several deleting methods – two common ones include Listwise Deletion and Pairwise Deletion. It means deleting any participants or data entries with missing values. This method is particularly advantageous to samples where there is a large volume of data because values can be deleted without significantly distorting readings. Alternatively, data scientists can fill out the missing values by contacting the participants in question. The problem with this method is that it may not be practical for large datasets. Furthermore, some corporations obtain their information from third-party sources, which only makes it unlikely that organisations can fill out the gaps manually. Pairwise deletion is the process of eliminating information when a particular data point, vital for testing, is missing. Pairwise deletion saves more data compared to likewise deletion because the former only deletes entries where variables were necessary for testing, while the latter deletes entire entries if any data is missing, regardless of its importance.
Use regression analysis to systematically eliminate data
Regression is useful for handling missing data because it can be used to predict the null value using other information from the dataset. There are several methods of regression analysis, like Stochastic regression. Regression methods can be successful in finding the missing data, but this largely depends on how well connected the remaining data is. Of course, the one drawback with regression analysis is that it requires significant computing power, which could be a problem if data scientists are dealing with a large dataset.
Data scientists can use data imputation techniques
Data scientists use two data imputation techniques to handle missing data: Average imputation and common-point imputation. Average imputation uses the average value of the responses from other data entries to fill out missing values. However, a word of caution when using this method – it can artificially reduce the variability of the dataset. Common-point imputation, on the other hand, is when the data scientists utilise the middle point or the most commonly chosen value. For example, on a five-point scale, the substitute value will be 3. Something to keep in mind when utilising this method is the three types of middle values: mean, median and mode, which is valid for numerical data (it should be noted that for non-numerical data only the median and mean are relevant).
Keeping things under control
Missing data is a sad fact of life when it comes to data analytics. We cannot avoid situations like these entirely because there are several remedial steps data scientists need to take to make sure it doesn’t adversely affect the analytics process. While these methods are helpful, they are not foolproof because they are contentious, meaning, their effectiveness depends heavily on circumstances. The best option available to data scientists is to work with powerful, processing tools that can make the data capturing and analysis process significantly easier. It is the best way to handle missing data.