11.7 Data Quality and Ethical Considerations

Regardless of data type, data quality is crucial. Poor data—be it incomplete, biased, or improperly measured—can lead to incorrect conclusions. Researchers should:

• Ensure Validity and Reliability: Use well-designed instruments and consistent measurement techniques.

• Address Missing Data: Apply appropriate imputation methods if feasible.

• Manage Attrition (in Panel Data): Consider weighting or sensitivity analyses to deal with dropouts.

• Check Representativeness: Especially in cross-sectional and repeated cross-sectional surveys, ensure sampling frames match the target population.

• Protect Confidentiality and Privacy: Particularly in panel studies with repeated contact, store data securely and follow ethical guidelines.

• Obtain Proper Consent: Inform participants about study details, usage of data, and rights to withdraw.