We Need to Talk about Money: How Ignoring Socioeconomic Status Hurts Research
By Meagan Sweeney, MA (Graduate Intern, APA Office of Socioeconomic Status)
There is a social convention to not talk about money. We consider it rude to discuss differences in income, education, or spending ability, even among our close friends. While that rule of thumb may work at the family dinner table or office water cooler, researchers who ignore socioeconomic status (SES) risk producing misleading results in their work. And when research influences policy, omitting SES can be especially harmful.
According to the Census Bureau, in 2013, 14.5% of the population were living at or below the poverty line. When we focus on children, the National Center for Children in Poverty states that 22% of all children in the United States live in families below the federal poverty level. This amount, $23,550 for a family of four, covers about half of basic living expenses on average. If we use double that amount as a more reasonable standard, 44% of children in the U.S. live in low-income families.
Despite mounting research documenting the detrimental effects of poverty on physical health and psychological well-being, researchers often neglect the measurement and reporting of SES in studies, even as a control variable. For instance, in the United States, race and SES often correlate. Yet, as is mentioned in the book chapter, A Decade of Measuring SES: What It Tells Us and Where to Go From Here, review articles show that researchers of racial issues often fail to control for SES in their analyses. In an examination of 40 years of research on African American children, 23% of the studies confounded race and SES. “Researchers tended to compare low-income African Americans to middle-income European Americans without controlling for the effect of SES,” stated Ensminger and Fothergill (p. 16).
Given what we know about the correlation between race and income, research that does not control for SES is in jeopardy of producing misleading findings. SES also correlates with gender, sexual orientation, gender identity, and disability status. Research about these groups should prioritize the inclusion of SES in their models.
Some researchers do include SES in their work. However, it is oftentimes relegated to a control variable. “This practice is problematic because whereas controlling for social class may yield less biased estimates, it does not address whether the nature of the relationships or mechanisms among the study variables are mediated or moderated by social class,” says psychologist, Matthew Diemer, author of a work he refers to as “social class for dummies.”
When researchers include SES, even as a control variable, they often say, “We controlled for SES” without discussing how they determined SES and how that decision could affect their study’s outcomes. There are nuances to be considered in their choice of measurement which can cause differences in results.
Research has shown that a study’s conclusions can vary depending on whether it used family income, mother’s education, father’s education, mother’s and father’s occupation, or a combination of income and expenses as a proxy for SES. These are all valid methods of measurement, but researchers should be explicit about how they decided on those measurements and how their value affects the results.
For instance, many immigrant families send a significant portion of their income home. If we only measure their income, it gives a skewed view of the money they have available to spend. One measure, the Federal Poverty Threshold, “does not account for variation in job-related expenses, such as child care and transportation costs, or for geographic differences in cost-of-living, despite evidence of wide variation in the costs of meeting basic family needs,” according to Diemer.
What steps can a researcher take to address these issues?
First, researchers can simply report on SES characteristics to better understand “who” the sample is. This is routinely done for gender and race, and should be done more often for SES. Having that information published would provide a wealth of information.
Second, researchers can delve into analyzing the way SES is affecting outcomes in a more careful and nuanced way. Instead of simply controlling for SES, it should be looked at as a moderator, mediator or main effect more often.
The campaign has three main goals:
to encourage researchers to measure and report SES characteristics of research participants,
to encourage journal editors to make that information an expectation, and
to provide researchers with the resources necessary to accomplish this.
What if you are not a researcher? How does this information affect members of the general public? Well, policies are often crafted based on research. If that research does not consider SES, the conclusions may not represent the entire situation. This has the potential to have a real and significant impact on the daily lives of all people. The public should look critically at science in the news and question whether a new proposal might be based on flawed research.
American Psychological Association. (2006). Report of the APA task force on socioeconomic status. Washington, D.C.: APA. Retrieved from: http://www.apa.org/pi/ses/resources/publications/task-force-2006.pdf
Diemer, M. A., Mistry, R. S., Wadsworth, M. E., López, I. and Reimers, F. (2013), Best Practices in Conceptualizing and Measuring Social Class in Psychological Research. Analyses of Social Issues and Public Policy, 13: 77–113. doi: 10.1111/asap.12001
Ensminger, M. E., Fothergill, K. E., Bornstein, M. H., & Bradley, R. H. (2003). A decade of measuring SES: What it tells us and where to go from here. Socioeconomic Status, Parenting, and Child Development, 13-27.
Hackman, D. A., Gallop, R., Evans, G. W. and Farah, M. J. (2015), Socioeconomic status and executive function: developmental trajectories and mediation. Developmental Science. doi: 10.1111/desc.12246
Lawson, G. M., Duda, J.T., Avants, B.B., Wu, J., Farah, M.J. (2014). Associations between Children’s Socioeconomic Status and Prefrontal Cortical Thickness. Developmental Science. 16(5), 641-652.
Marks, G.N. (2011). Issues in the conceptualization and measurement of socioeconomic background: Do different measures generate different conclusions? Social Indicators Research, 104(2), 225-251.
U.S. Census Bureau (2015). State and county quick facts. Washington, DC: Author. Retrieved from: http://quickfacts.census.gov/qfd/states/11000.html
Wight, V.R., Chau, M., Aratani, Y. (2010). Who are America’s poor children? The official story. New York, NY: National Center for Children in Poverty. Retrieved from: http://www.nccp.org/publications/pub_912.html