A unified approach to demographic data collection for research with young children across diverse cultures.

Abstract

Culture is a key determinant of children’s development both in its own right and as a measure of generalizability of developmental phenomena. Studying the role of culture in development requires information about participants’ demographic backgrounds. However, both reporting and treatment of demographic data are limited and inconsistent in child development research. A barrier to reporting demographic data in a consistent fashion is that no standardized tool currently exists to collect these data. Variation in cultural expectations, family structures, and life circumstances across communities make the creation of a unifying instrument challenging. Here, we present a framework to standardize demographic reporting for early child development (birth to 3 years of age), focusing on six core sociodemographic construct categories: biological information, gestational status, health status, community of descent, caregiving environment, and socioeconomic status. For each category, we discuss potential constructs and measurement items and provide guidance for their use and adaptation to diverse contexts. These items are stored in an open repository of context-adapted questionnaires that provide a consistent approach to obtaining and reporting demographic information so that these data can be archived and shared in a more standardized format.

The Infant and Toddler Curiosity Questionnaire: A validated caregiver-report measure of curiosity in children from 5 to 24 months

“…Additionally, respondents were asked to provide demographic information (prematurity, developmental concerns, country, languages spoken, birth order, and socio-economic status (SES) via their educational level; Singh et al, 2023), and could optionally contribute qualitative responses.…”

Effects of environmental diversity on exploration and learning: The case of bilingualism.

“…As a result of this underreporting, sample diversity—even across multilab studies—is often not well defined. One step in this direction is a recent ManyBabies initiative, ManyBabies Demographics, which provides developmental researchers with a standardized framework to capture participant demographic characteristics as a measure of different facets of sample diversity, including language experience (Singh et al, 2024). Greater recording of demographic information would allow for analysis of how infants’ experiences influence the allocation of attention.…”

Greater Attention to Socioeconomic Status in Developmental Research Can Improve the External Validity, Generalizability, and Replicability of Developmental Science

“…For studies conducted within a region, we acknowledge that the method of categorization of SES groups we use here reflect the authors' within-region descriptors of a sample's socio-economic profile. Within-region descriptors, particularly those that are decontextualized, may not be easily interpreted in a global context unless the author(s) used anchoring information to standardize data across world regions (e.g., Purchasing Power Parity; the International Wealth Index [see Singh, Barakova, Lopera, et al, 2024 for a description of these tools in infant sociodemographic reporting]). The second limitation of using author-reported data is arguably a categorical approach to both reporting and characterizing SES.…”

“…For this reason, we advocate for clear reporting of how SES data were collected and an accompanying justification for these data to avoid making presumptions of samples independent of evidence. In To that end, a recent publication provides a framework for developmental researchers to collect and deposit sociodemographic data in culturally variable settings (Singh, Barakova, Lopera, et al, 2024). Singh, Barakova, Lopera, et al (2024) also provide guidance on how socioeconomic data, and other sources of personal data, can be de-identified to protect participant privacy.…”

“…Being able to do so relies crucially on clearly defined measures of SES within research reports. This relates to broader concerns of how rarely sociodemographic variables are reported (Arnett, 2008;Henrich et al, 2010;Moriguchi, 2021;Nielsen et al, 2017;Singh, Barakova, Lopera, et al, 2024;Singh, Cristia, Karasik, et al, 2023). Major epistemic costs of not reporting sociodemographic data are that claims of generalizability become difficult to determine and replication efforts become more challenging (Singh, Cristia, Karasik, et al, 2023).…”