How can you handle missing data in your research?

1 Types and mechanisms of missing data

The first step to handle missing data is to understand the types and mechanisms of missing data. There are three main types of missing data: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). MCAR means that the probability of missing data is the same for all observations and does not depend on any observed or unobserved variables. MAR means that the probability of missing data depends on some observed variables, but not on the missing values themselves. MNAR means that the probability of missing data depends on the missing values or some unobserved variables. The type of missing data determines the impact and the assumptions of the methods to handle missing data.

2 Methods to handle missing data

There are many methods to handle missing data, but they can be broadly classified into three categories: deletion, imputation, and model-based methods. Deletion methods remove the observations or variables that have missing data, such as listwise deletion, pairwise deletion, or available case analysis. Imputation methods replace the missing values with estimated values, such as mean imputation, regression imputation, or multiple imputation. Model-based methods use statistical models to account for the missing data mechanism and the uncertainty of the missing values, such as maximum likelihood estimation, Bayesian estimation, or weighted generalized estimating equations.

3 Choosing appropriate methods to handle missing data

The choice of the methods to handle missing data depends on several factors, such as the type and mechanism of missing data, the amount and pattern of missing data, the research design and objectives, and the availability of software and resources. There is no one-size-fits-all solution for handling missing data, and each method has its advantages and limitations. Some general guidelines are: avoid deletion methods if the amount of missing data is large or the pattern is complex, use imputation methods with caution and check the plausibility and variability of the imputed values, and prefer model-based methods if they are feasible and compatible with your research questions and data structure.

4 Reporting and interpreting findings with missing data

The last step to handle missing data is to report and interpret your findings with transparency and accuracy. You should describe the amount and pattern of missing data, the reasons and assumptions for missing data, the methods and criteria for handling missing data, and the sensitivity and robustness of your results to different methods and scenarios. You should also acknowledge the limitations and uncertainties of your findings due to missing data, and provide recommendations for future research or practice to address the gaps or challenges caused by missing data.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?