No Participant Left Behind: Conducting Science During COVID-19

Lourenco, Stella F.; Tasimi, Arber

doi:10.1016/j.tics.2020.05.003

Cited by 102 publications

(96 citation statements)

References 11 publications

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“…Because unmoderated remote research -as we have implemented it -requires a computer with a webcam and reliable internet, participation is easiest for those who have access to this equipment in their homes. Families can participate from computers in other locations as well (e.g., schools, libraries, YMCAs, and so on), but these requirements do create a barrier for families without easy access to this technology (Lourenco & Tasimi, 2020). We hope to explore solutions to this challenge in our future work (for instance, by creating ways for families to participate via a broader range of technology), but for now this remains a challenge.…”

Section: Sample Recruitment and Demographicsmentioning

confidence: 99%

Advancing Developmental Science via Unmoderated Remote Research with Children

Rhodes

Rizzo

Foster‐Hanson

et al. 2020

Journal of Cognition and Development

126

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This article introduces an accessible approach to implementing unmoderated remote research in developmental science-research in which children and families participate in studies remotely and independently, without directly interacting with researchers. Unmoderated remote research has the potential to strengthen developmental science by: (1) facilitating the implementation of studies that are easily replicable, (2) allowing for new approaches to longitudinal studies and studies of parent-child interaction, and (3) including families from more diverse backgrounds and children growing up in more diverse environments in research. We describe an approach we have used to design and implement unmoderated remote research that is accessible to researchers with limited programming expertise, and we describe the resources we have made available on a new website (discoveriesonline.org) to help researchers get started with implementing this approach. We discuss the potential of this method for developmental science and highlight some challenges still to be overcome to harness the power of unmoderated remote research for advancing the field. The field of cognitive development was founded upon remarkable insights gleaned from everyday interactions with children. Piaget's theory of cognitive development (1954) began with his observations of his own children playing on their mats, dropping things from their highchairs, and playing with marbles. Carey (1985) revolutionized our understanding of how concepts originate and change by analyzing conversations with her own child about birth, the nature of life, and death. The field is full of stories of great theoretical insights made by researchers closely watching children as they crawl around near the sides of high beds (Adolph, Kretch, & LoBue, 2014), negotiate the rules of games among themselves on a playground (Borman, 1981), and try to sit down on way-too-tiny toy tractors (DeLoache, 1987). Of course, the field has never relied on the observations of individual researchers alone. We use these observations to design experiments that recreate the situations in which the behaviors were first observed, which can then be replicated by labs around the world. But still, individual interactions between researchers and their participants have always been central to the field of cognitive development. The notion that we can learn from watching and interacting with children, along with the intriguing challenges of thinking about how to recreate the conditions of everyday life in CONTACT Marjorie Rhodes

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Section: Sample Recruitment and Demographicsmentioning

confidence: 99%

Advancing Developmental Science via Unmoderated Remote Research with Children

Rhodes

Rizzo

Foster‐Hanson

et al. 2020

Journal of Cognition and Development

126

View full text Add to dashboard Cite

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“…This may promote the inclusion of a more diverse and representative group of participants (Leshin et al, 2020;Rhodes et al, 2020). However, there is also concern that online testing may exacerbate existing barriers to research inclusion (Lourenco & Tasimi, 2020). We examined distributions of participant race and ethnicity within our online sample and compared them to a recent in-lab study (Potter et al, 2017), as well as national and New York City demographics ( Figure 1A).…”

Section: Participants Participantsmentioning

confidence: 99%

Moving Developmental Research Online: Comparing In-Lab and Web-Based Studies of Model-Based Reinforcement Learning

Nussenbaum

Scheuplein

Phaneuf

et al. 2020

Collabra: Psychology

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For years, adult psychological research has benefitted from web-based data collection. There is growing interest in harnessing this approach to facilitate data collection from children and adolescents to address foundational questions about cognitive development. To date, however, few studies have directly tested whether findings from in-lab developmental psychology tasks can be replicated online, particularly in the domain of value-based learning and decision-making. To address this question, we set up a pipeline for online data collection with children, adolescents, and adults, and conducted a replication of Decker et al. (2016). The original in-lab study employed a sequential decision-making paradigm to examine shifts in value-learning strategies from childhood to adulthood. Here, we used the same paradigm in a sample of 151 children (N = 50; ages 8 - 12 years), adolescents (N = 50; ages 13 - 17 years), and adults (N = 51; ages 18 - 25 years) and replicated the main finding that the use of a “model-based” learning strategy increases with age. In addition, we adapted a new index of abstract reasoning (MaRs-IB; Chierchia et al. 2019) for use online, and replicated a key result from Potter et al. (2017), which found that abstract reasoning ability mediated the relation between age and model-based learning. Our re-analyses of two previous in-lab datasets alongside our analysis of our online dataset revealed few qualitative differences across task administrations. These findings suggest that with appropriate precautions, researchers can effectively examine developmental differences in learning computations through unmoderated, online experiments.

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“…One limitation of using MTurk, however, is that crowdsourced samples tend to over-represent women and younger individuals. (Mortensen and Hughes 2018) Some estimates suggest lowerincome workers are likely accessing MTurk more than the general population (Lourenco and Tasimi 2020). Numerous studies are relying on MTurk for high-quality data during the pandemic (French, Mortensen, and Timming 2020;Park et al 2020;Clements 2020;Tull et al 2020).…”

Section: Mturk Surveysmentioning

confidence: 99%