Disruption Leads to Methodological and Analytic Innovation in Developmental Sciences: Recommendations for Remote Administration and Dealing With Messy Data - Krogh-Jespersen, Sheila; MacNeill, Leigha A.; Anderson, Erica L.; Stroup, Hannah E.; Harriott, Emily M.; Gut, Ewa; Blum, Abigail; Fareedi, Elveena; Fredian, Kaitlyn M.; Wert, Stephanie L.; Wakschlag, Lauren S.; Norton, Elizabeth S. (Frontiers in Psychology, 2022)
The COVID-19 pandemic has impacted data collection for longitudinal studies in developmental sciences to an immeasurable extent. Restrictions on conducting in-person standardized assessments have led to disruptive innovation, in which novel methods are applied to increase participant engagement. Here, we focus on remote administration of behavioral assessment. We argue that these innovations in remote assessment should become part of the new standard protocol in developmental sciences to facilitate data collection in populations that may be hard to reach or engage due to burdensome requirements (e.g., multiple in-person assessments). We present a series of adaptations to developmental assessments (e.g., Mullen) and a detailed discussion of data analytic approaches to be applied in the less-than-ideal circumstances encountered during the pandemic-related shutdown (i.e., missing or messy data). Ultimately, these remote approaches actually strengthen the ability to gain insight into developmental populations and foster pragmatic innovation that should result in enduring change.