1-004 - Best Practices in Conceptualizing and Measuring Social Class with Secondary Data Sources

Session Type: Workshop

Abstract

Extensive multidisciplinary research has documented relations between social class and developmental outcomes, including physical and mental health, brain architecture, academic achievement, and educational attainment. (In secondary datasets, social class is generally indexed with socioeconomic status measures, such as education, occupational status, and/or household income.) Despite some inroads in understanding relations between social class and developmental phenomena, researchers continue to pay inadequate attention to or use simplistic conceptions of social class (e.g., simply controlling for social class or neglecting it entirely.) These varied and conflicting approaches to conceptualize and measure social class limit our understanding of how class affects key developmental outcomes – as well as how it intersects with other social identities (e.g., race/ethnicity, gender, sexual orientation). In short, there remains great ambiguity about how best to conceptualize and measure social class – particularly with secondary datasets. This workshop will clarify definitional and measurement issues related to the assessment of social class with secondary datasets, address their importance and relevance for developmental research, and review best practices with regard to social class measurement. Concrete guidance regarding social class measures in secondary datasets will be provided, using a social class measurement resource table designed for this workshop. Further, the (underutilized) capacity to leverage multiple social class measures (e.g., annual income vs. maternal education) in secondary datasets via sensitivity analyses will be illustrated. In sum, this workshop aims to improve the sophistication of how social class is considered in analyses of secondary datasets, informing developmental science.

Leaders: Matthew Diemer & Rashmita Mistry

Sub Unit

• Poverty

Leaders

• Matthew A Diemer, University of Michigan

• Rashmita S Mistry, UCLA