Sample interview questions: How do you handle missing or incomplete data in your research?
Sample answer:
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Assess the Impact of Missing Data: Evaluate the extent and pattern of missing data to determine its potential impact on the research findings. Consider whether the missing data is random, systematic, or clustered, as this can influence the validity of the results.
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Explore Data Imputation Techniques: Depending on the nature of the missing data, various imputation techniques can be employed to fill in the missing values. These techniques include:
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Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the observed data for that variable.
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Multiple Imputation: Generate multiple plausible datasets by imputing missing values multiple times using different imputation methods. The results from each imputed dataset are then combined to obtain overall estimates.
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Regression Imputation: Use a regression model to predict missing values based on other observed variables that are correlated with the missing variable.
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Machine Learning Imputation: Apply machine learning algorithms, such as k-nearest neighbors or random forests, to impute missing values based on the patterns and relationships learned from the observed … Read full answer