Sample interview questions: How do you address issues of missingness in social network analysis and imputation of missing tie data in sociological research?
Sample answer:
Addressing Missingness in Social Network Analysis
Missing tie data is a common challenge in social network analysis, as individuals may not respond to all survey questions or may not provide complete information about their social ties. To address this issue, researchers can employ several strategies:
- Multiple Imputation: Multiple imputation involves generating multiple plausible values for missing data using statistical techniques. By imputing missing values multiple times, researchers can account for uncertainty in the imputed data and produce more robust results.
- Sensitivity Analysis: Sensitivity analysis evaluates the impact of missingness on the results of social network analysis. Researchers can vary the number and location of missing values in the data and examine how the results change. This helps determine the sensitivity of the analysis to missingness and provides insights into the potential bias introduced.
- Exclusion of Incomplete Cases: In some cases, researchers may choose to exclude cases with missing tie data from the analysis. This approach is justified if missingness is non-random and may represent a specific subgroup within the population. However, it can also lead to a reduction in sample size and potential bias.
Imputation of Missing Tie Data
When missing tie data cannot be excluded, imputation methods can be used to fill in the missing values. Common imputation methods include: