Online data collection for developmental research

Abstract

The strategies infants and young children use to understand the world around them provide unique insight into the structure of human cognition. However, developmental research is subject to heavy pragmatic constraints on recruiting large numbers of participants, bringing families back for repeat sessions, and working with special populations or diverse samples. These constraints limit the types of questions that can be addressed in the lab as well as the quality of evidence that can be obtained. In this dissertation, I present a new platform, "Lookit," that allows researchers to conduct developmental experiments online via asynchronous webcam-recorded sessions, with the aim of expanding the set of questions that we can effectively answer. I first present the results of a series of empirical studies conducted in the laboratory to assess difficulty faced by infants in integrating information across visual hemifields (Chapter 2), as an illustration of the creative workarounds in study design necessary to accommodate the difficulty of participant recruitment. The rest of this work concerns the development of the online platform, from designing the prototype (Chapter 3) and initial proof-of-concept studies (Chapter 4) to the demonstration of an interface for researchers to specify and manage their studies on a collaborative platform (Chapter 5). I show that we are able to reliably collect and code dependent measures including looking times, preferential looking, and verbal responses on Lookit; to work with more representative samples than in the lab; and to flexibly implement a wide variety of study designs of interest to developmental researchers.