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ABSTRACT Given the foundational nature of infant visual attention and potential cascading effects on later development, studies of individual variability in developmental trajectories in a normative sample are needed. We longitudinally tested newborns (N= 77) at 1–2 and 3–4 weeks, then again at 2, 4, 6, 8 and 14 months of age, assessing individual differences in their attention. Newborns viewed live stimuli (facial gesturing, rotating disk), one at a time, for 3 min each. Older infants viewed a 10‐s side‐by‐side social–nonsocial video (people talking, rotating disk). We found short‐term developmental stability of interindividual differences in infants’ overall, social, and nonsocial attention, within the newborn period (1–4 weeks), and within the later infancy period (2–14 months). Additionally, we found that overall attention, but not social and nonsocial attention, was developmentally stable long term (newborn through 14 months). This novel finding that newborn overall attention predicts later overall attention through the first year suggests a robust individual difference. This study is a first step toward developing individual difference measures of social and nonsocial attention. Future studies need to understand why newborns vary in their attention and to identify the potential impact of this variability on later social and cognitive development. 

A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications.