Statistical analysis
’ father’s age, years of schooling of caregiver
Data entry was done by using Microsoft Access and data analysis was done by STATA (version 14). Dietary diversity score was determined by adding the number of food categories consumed by each participant over the previous 24 hours. The questionnaire contained 16 food groups. The 16 food groups were—cereals; white roots and tubers; vitamin A-rich vegetables and tubers; dark green leafy vegetables; other vegetables; vitamin A-rich fruits, other fruits; organ meat; flesh meats; eggs; fish and seafood; legumes; nuts and seeds; milk and milk products; oils and fats; sweets, spices, condiments, and beverages. We combined certain food groups into a single food group for analytical purposes. We analyzed 9 food groups for measuring individual dietary diversity score. Dietary diversity score ranged from 0 to 9. According to the guideline, when we aggregated two food groups into one, any of the food group’s “Yes” answer is considered as “Yes” for that aggregated group and numbered as “1” for the respected group. According to the guidelines, there is no established cut-off point in terms of food groups to indicate adequate or inadequate dietary diversity [Categorical variables were presented as frequency and percentage. Continuous variables were presented as mean with standard deviation. To see the relationship with the study group, we performed t-test for normally distributed data and Mann-Whitney test for skewed data. As the dietary diversity score was incomparable between the control and intervention arm at baseline, difference-in-difference (DID) analysis was performed to assess the effect of the intervention. During performing the DID we adjusted for adolescent’s age, adolescent girls’ father’s age, years of schooling of caregiver, adolescents’ father’s years of schooling, household’s monthly income, household head’s occupation and asset index.