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How does missing data handling make selection bias worse?
In several study fields, such as statistics, epidemiology, and machine learning, missing data is a major problem. Numerous factors, such as survey nonresponse, measurement problems, or incorrect data entry, might cause it. While imputation and maximum likelihood estimation are alternate approaches for handling missing data, they could introduce bias into the study. Selection bias, in particular, can be made worse by poor data management. This blog post will discuss the idea of selection bias, how missing data can introduce bias, and strategies for dealing with missing data that can minimize selection bias's impact.
What is selection bias?
Selection bias is a sort of bias that develops when the population of interest is not adequately represented in the sample of people or observations being researched. Self-selection, nonresponse, and measurement mistakes are only a few of the causes of selection bias. Selection bias can alter the generalizability of the findings and result in erroneous or misleading estimations of demographic characteristics. For instance, if research only includes individuals who satisfy specific requirements, the findings could not apply to the entire community. It can also happen if a sample contains groups that are either overrepresented or underrepresented, which might result in findings that are not representative of the population as a whole.
How does missing data handling make selection bias worse?
The handling of missing data can aggravate selection bias in a variety of ways.
If missing data is not random, it is linked to the variable of interest or another variable in the dataset (MNAR). Due to the missing data, the population parameter estimates in this scenario can be skewed. For instance, if a sample is skewed and does not accurately represent the population because persons with certain qualities are more likely to have missing data.
"Full case analysis" is a method for dealing with missing data that involves deleting observations with inadequate data; nevertheless, it may introduce bias by excluding persons or observations that differ from those included in the research. As a result, an unrepresentative sample of the population might be formed, potentially leading to incorrect findings.
If the imputed values are inaccurate or if the imputation method is inappropriate for the dataset, imputation approaches, which replace missing data with estimates based on the observed data, can potentially cause bias.
If the model is inappropriate for the dataset, bias could also be introduced via maximum likelihood estimation, which bases missing data estimates on a probabilistic model.
In general, it's crucial to take into account how missing data could affect selection bias and to employ techniques that lessen this effect. Using the weighting approach, for instance, the weights of the observations are modified to account for missing data. Although it is more difficult to adopt, it can lessen prejudice.
Methods for handling missing data
Missing data can be handled in a variety of ways, including −
Full Case Analysis − All observations with missing data are eliminated from the study using this technique. If the missing data is not random, this might induce bias.
Imputation − This approach substitutes missing data with estimations derived from the observed data. The many imputation techniques include mean imputation, median imputation, and multiple imputations. Despite the fact that imputation can minimize bias, if the imputed values are inaccurate or the imputation method is not suitable for the dataset, bias may still be introduced.
Estimation of Maximum Likelihood − According to a probabilistic model, this strategy uses the seen data to estimate the missing data. If the model is inappropriate for the dataset, this technique may add bias even though it could be more accurate than imputation.
Weighting − In order to account for missing data, this strategy includes changing the weights of the observations. This can lessen prejudice, but it can also be trickier to put into practice.
The particular dataset and research objective will determine which missing data management technique is best. In general, it's critical to take into account the possible effects of missing data on selection bias and to employ techniques that lessen these effects.
Conclusion
In conclusion, Missing data is a frequent issue in many different types of study. Although there are other approaches to addressing missing data, including maximum likelihood estimate and imputation, these approaches can potentially bring bias into the research. For example, addressing missing data might exacerbate selection bias. It is crucial to take into account the potential effects of missing data, the kind of missing data, and the suitable approach for processing missing data in order to reduce the effect of selection bias.
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