Online data collection for developmental research

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018 === Cataloged from PDF version of thesis. Page 140 blank. === Includes bibliographical references (pages 134-139). === The strategies infants and young children use to understand the world around...

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Main Author: Scott, Kimberly M.,Ph. D.Massachusetts Institute of Technology.
Other Authors: Laura Schulz.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2020
Subjects:
Online Access:https://hdl.handle.net/1721.1/127709
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-1277092020-09-29T05:09:45Z Online data collection for developmental research Scott, Kimberly M.,Ph. D.Massachusetts Institute of Technology. Laura Schulz. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brain and Cognitive Sciences. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018 Cataloged from PDF version of thesis. Page 140 blank. Includes bibliographical references (pages 134-139). 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. by Kimberly M. Scott. Ph. D. Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences 2020-09-25T20:03:39Z 2020-09-25T20:03:39Z 2018 2018 Thesis https://hdl.handle.net/1721.1/127709 1196081343 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 140 pages application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Brain and Cognitive Sciences.
spellingShingle Brain and Cognitive Sciences.
Scott, Kimberly M.,Ph. D.Massachusetts Institute of Technology.
Online data collection for developmental research
description Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018 === Cataloged from PDF version of thesis. Page 140 blank. === Includes bibliographical references (pages 134-139). === 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. === by Kimberly M. Scott. === Ph. D. === Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
author2 Laura Schulz.
author_facet Laura Schulz.
Scott, Kimberly M.,Ph. D.Massachusetts Institute of Technology.
author Scott, Kimberly M.,Ph. D.Massachusetts Institute of Technology.
author_sort Scott, Kimberly M.,Ph. D.Massachusetts Institute of Technology.
title Online data collection for developmental research
title_short Online data collection for developmental research
title_full Online data collection for developmental research
title_fullStr Online data collection for developmental research
title_full_unstemmed Online data collection for developmental research
title_sort online data collection for developmental research
publisher Massachusetts Institute of Technology
publishDate 2020
url https://hdl.handle.net/1721.1/127709
work_keys_str_mv AT scottkimberlymphdmassachusettsinstituteoftechnology onlinedatacollectionfordevelopmentalresearch
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