Handling Item Nonresponse in Youth Surveys: Insights from the VACS.

Background Although item nonresponse is typically minimal in surveys that use electronic data capture tools, the inclusion of “Don’t Know” or “Declined” response options can increase overall missingness. This study examines patterns of item nonresponse and evaluates how different missing data handling approaches affect prevalence estimates in the Violence Against Children Surveys (VACS). Methods We pooled data from ten VACS conducted between 2018 and 2022 and characterized missingness mechanisms and item nonresponse rates while identifying predictors of response propensity and missingness. Weighted prevalence estimates for 24 composite and single-item indicators were compared across four analytic approaches—listwise deletion (LDA), missing completely at random (MCAR), not missing completely at random (NOMCAR), and a sensitivity analysis—using an imputed dataset as the reference. Precision and accuracy were assessed using relative standard error (RSE) and relative bias. Results The overall survey response rate was 90% (n = 45,764). Item nonresponse averaged 0.56%, and 90% of respondents provided complete data for all 24 variables. Missingness was predominantly random (MCAR) and was associated with country income level, education, and survey year, but not with sex, perceived survey worthiness, response rates, or reported violence. Compared with the imputed reference (RSE = 4.16; relative bias = 0.0), listwise deletion produced the least precise estimates (RSE = 4.36). Sensitivity analyses yielded the largest relative bias (5.39%), whereas MCAR and NOMCAR approaches produced nearly identical estimates with comparable precision. Differences across methods were small and not statistically significant (p = 0.9996). Conclusions Given minimal and random missingness in key survey variables in the VACS, MCAR or NOMCAR approaches produce precise estimates, making routine imputation unnecessary. Further simulation studies might help determine thresholds at which imputation becomes beneficial.