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  • Moderated Online Data-Collection for Developmental Research: Methods and Replications FRONTIERS IN PSYCHOLOGY Chuey, A., Asaba, M., Bridgers, S., Carrillo, B., Dietz, G., Garcia, T., Leonard, J. A., Liu, S., Merrick, M., Radwan, S., Stegall, J., Velez, N., Woo, B., Wu, Y., Zhou, X. J., Frank, M. C., Gweon, H. 2021; 12: 734398

    Abstract

    Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, many researchers still remain uncertain about the differences in available approaches as well as the validity of online methods more broadly. In this article, we aim to address both issues with a focus on moderated (synchronous) data collected using video-conferencing software (e.g., Zoom). First, we review existing approaches for designing and executing moderated online studies with young children. We also present concrete examples of studies that implemented choice and verbal measures (Studies 1 and 2) and looking time (Studies 3 and 4) across both in-person and online moderated data collection methods. Direct comparison of the two methods within each study as well as a meta-analysis of all studies suggest that the results from the two methods are comparable, providing empirical support for the validity of moderated online data collection. Finally, we discuss current limitations of online data collection and possible solutions, as well as its potential to increase the accessibility, diversity, and replicability of developmental science.

    View details for DOI 10.3389/fpsyg.2021.734398

    View details for Web of Science ID 000720000200001

    View details for PubMedID 34803813

    View details for PubMedCentralID PMC8595939

  • Learning Rust: How Experienced Programmers Leverage Resources to Learn a New Programming Language Abtahi, P., Dietz, G., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2020

    View details for DOI 10.1145/3334480.3383069

    View details for Web of Science ID 000626317803098

  • Effect of Confidence Indicators on Trust in AI-Generated Profiles Bruzzese, T., Ding, C., Gao, I., Romanos, A., Dietz, G., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2020

    View details for DOI 10.1145/3334480.3382842

    View details for Web of Science ID 000626317801109

  • Supporting Children's Math Learning with Feedback-Augmented Narrative Technology Ruan, S., He, J., Ying, R., Burkle, J., Hakim, D., Wang, A., Yin, Y., Zhou, L., Xu, Q., AbuHashem, A., Dietz, G., Murnane, E. L., Brunskill, E., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2020: 567-580

    View details for DOI 10.1145/3392063.3394400

    View details for Web of Science ID 000675620600050

  • Giggle Gauge: A Self-Report Instrument for Evaluating Children's Engagement with Technology Dietz, G., Pease, Z., McNally, B., Foss, E., ACM ASSOC COMPUTING MACHINERY. 2020: 614-623

    View details for DOI 10.1145/3392063.3394393

    View details for Web of Science ID 000675620600054