Sample interview questions: How do you handle missing data or incomplete responses in the scoring process?
Sample answer:
Handling missing data or incomplete responses in the scoring process is crucial to ensure the accuracy and reliability of psychometric assessments. Here are several methods commonly employed:
1. Listwise Deletion:
- Excludes cases with missing data from the analysis, leading to a reduced sample size.
- Appropriate when data are missing completely at random (MCAR).
- Preserves the original relationships among observed variables but may bias estimates if the missing data pattern is non-random.
2. Pairwise Deletion:
- Uses all available data for each variable, even if other variables have missing values.
- Increases the sample size but may introduce bias if the missing data pattern is related to the observed variables.
3. Imputation Techniques:
- Replaces missing values with plausible values based on statistical models or auxiliary information.
- Mean imputation: Replaces missing values with the mean of the observed values.
- Multiple imputation: Generates multiple plausible values for missing data and pools the results to reduce bias.
- Model-based imputation: Uses statistical models, such as regression or multiple imputations, to estimate missing values.
4. Sensitivity Analysis:
- Runs the analysis multiple times under different assumptions about t… Read full answer