How to handle the missing data in the questionnaire?

Missing data in questionnaires necessitates methodological intervention to preserve analytical validity and reduce bias. Its management is feasible through various deletion or imputation techniques, contingent upon the nature and extent of missingness. Appropriate handling depends critically on diagnosing the missing data mechanism: Missing Completely At Random (MCAR), Missing At Random (MAR), or Missing Not At Random (MNAR), established via analysis such as Little’s MCAR test. Crucially, the proportion of missing data should be assessed, as excessive amounts may jeopardize results regardless of method. Selection involves weighing trade-offs: deletion methods induce information loss, while single imputation distorts variability and multiple imputation (MI), though statistically sound, demands computational sophistication. Rubin's taxonomy guides principled technique selection. Practical steps commence with exploring patterns and proportions of missingness. Apply deletion (listwise or pairwise) cautiously only if MCAR is plausible and data loss minimal. Prefer imputation for MAR data: employ mean/mode substitution for simplicity when handling limited univariate gaps, or regression imputation for patterned missingness. Utilize multiple imputation by chained equations (MICE) to generate several plausible datasets, accurately reflecting uncertainty, followed by pooled analysis. Finally, conduct sensitivity analyses to evaluate robustness under different MNAR assumptions. Validation against complete cases when feasible is advisable.