Influences on Data Quality in Developmental Children Studies

Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11 studies with N = 727 infants and children (aged 5 months to 8 years). A random forest model suggests that the duration of the study, the children's age, and the age, gender, and experience of the experimenters are the most important predictors in explaining differences in children's data quality in this sample of children. Other researchers may consider shortening studies and ensuring extensive training for experimenters to help increase the probability of data retention.