Predictors of missing data when asking parents about their children's diet based on "Oral Health Situation of Iranian Children" Survey - Arash Shahravan; Hossein Hessari; Mohammad Reza Baneshi; Maryam Rad; Ali Akbar Haghdoost (Wolters Kluwer Medknow Publications, 2015)
Background: The predictors of missing data when parents fill out questionnaire about their children's diet are not defined. The aim of this study was to evaluate predictors which affect unresponsiveness to diet questions based on " Oral Health Situation of Iranian Children" Survey carried out in 1998. Materials and Methods: A dummy variable was created as dependent variable according to responding eight questions relating to diet. Then predictors of missing data were defined using multivariable logistic regression and classification tree method. To evaluate goodness of fit of logistic regression model, sensitivity and specificity were assessed. Classification tree analysis was done by Quest growing method. Significance level was set at 0.05 in logistic regression analysis. Observations and Results: "Missing data" variable was marked as missing in 616 (7.2%) of questionnaires. In logistic regression model revealed that, gender, mother's education level and father's education level didn't affect "missing data" variable (P > 0.05). But, dmf index (OR = 0.94), Area of living (OR = 0.49), number of children in the family (OR = 1.19), sibling order (OR = 0.85), brushing (OR = 0.84) and visiting dentist (OR = 0.59) had statistically significant association with dependent variable (P < 0.05). Classification tree analysis showed that questionnaires related to children in urban area whose dmf index is under 5 and haven't visited dentist are predicted to have missing data more than other groups. Conclusions: Area of living, dmf index, number of children in the family, sibling order, brushing and visiting dentist are significant variables for predicting the risk of missing data when asking parents about their children's diet.