 |
Research Article
Executive function and school performance among American children: Blacks’ diminished returns
1 Department of Family Medicine, School of Medicine, Charles R Drew University of Medicine and Science, Los Angeles, CA, USA
2 Department of Pediatrics, School of Medicine, Charles R Drew University of Medicine and Science, Los Angeles, CA, USA
3 Department of Family Medicine, School of Medicine, UCLA, Los Angeles, CA, USA
4 Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI 48104, USA
5 Center for Research on Ethnicity, Culture, and Health, University of Michigan School of Public Health, Ann Arbor, MI 48104, USA
Address correspondence to:
Shervin Assari
MD, MPH, Department of Family Medicine, School of Medicine, Charles R Drew University of Medicine and Science, Los Angeles, CA,
USA
Message to Corresponding Author
Article ID: 100008P05SA2020
Access full text article on other devices
Access PDF of article on other devices
How to cite this article
Assari S, Boyce S, Bazargan M, Caldwell CH. Executive function and school performance among American children: Blacks’ diminished returns. Edorium J Pediatr 2020;4:100008P05SA2020.ABSTRACT
Aims: In the United States, racial minorities experience weaker effects of economic and non-economic resources on tangible outcomes such as school performance, a pattern called Minorities′ Diminished Returns (MDRs). These MDRs are frequently documented for the effects of family economic resources on Black children’s school performance. However, the existing knowledge is limited regarding MDRs of non-economic assets, such as executive function on children’s school performance. We compared White and Black children for the association between executive function and children’s school performance.
Methods: This cross-sectional analysis included 4909 White or Black children from the Adolescent Brain Cognitive Development (ABCD) study. The predictor variable was executive function measured by the stop-signal task. The primary outcome was children’s school performance ranging from 1 to 6. Linear regression was used to perform data analysis.
Results: Overall, higher task-based executive function was associated with higher school performance. Race, however, showed a statistically significant interaction with executive function on children’s school performance. This interaction suggested that high executive function has a weaker positive association with Black children’s school performance than White children.
Conclusion: The positive association between executive function and school performance is weaker for Black than White children. To eliminate the racial gap in school performance, we need to address the diminished returns of Black families’ resources and assets. Not only should we equalize resources and assets but also increase their marginal returns for racial minorities, particularly Black families. Such efforts require public policies at multiple sectors and institutions. We need to empower Black families to better leverage their resources and assets and turn them into tangible outcomes. Simultaneously, we need to reduce discrimination at school and enhance schooling quality in urban areas. Finally, we need to address daily life stressors and barriers that Black families face in their daily lives.
Keywords: Cognition, Ethnicity, Executive function, Race, School performance, Socioeconomic status
INTRODUCTION
Compared to White children, racial minority children, particularly Blacks, are at an increased risk of poor school performance [1]. Black children are also at an increased risk of dropping out of school [2]. As poor school performance and dropping out are gateways to subsequent undesired economic and health problems [3],[4],[5],[6], closing racial inequalities in school is essential to creating more equitable outcomes later in life [7],[8],[9],[10],[11]. There is some hope that eliminating racial inequalities in school performances can considerably reduce if not closing subsequent disparities later in life [3],[4],[5],[6].
As race and socioeconomic status (SES) closely overlap [12], racial inequalities in school performance are commonly attributed to the racial gap in SES [13],[14],[15]. This traditional approach attributes lower school performance to lower resources and assets in racial minority families, such as Blacks [12],[16],[17],[18]. In statistical terms, resources and assets (e.g., family SES) are believed to mediate the association between racial minority status and children’s outcomes [19],[20],[21]. In this view, closing the racial differences in access to resources and assets (e.g., SES) would be a reasonable strategy for the elimination of racial gaps and inequalities [22],[23]. Some example policies include enhancing family SES through income redistribution policies, tax policies, and empowering racial minorities to secure gain income and accumulate wealth [22],[23].
A complementary explanation of the above explanation is Minorities’ Diminished Returns (MDRs) [24],[25]. The MDRs framework refers to weaker effects of resources and assets such as family SES on Blacks’ tangible children outcomes compared to Whites. This view suggests that as a result of social stratification, racism, segregation, and other structural inequalities, parental education [26], family income [27],[28], and marital status [29] all generate fewer tangible outcomes for Black than White children. Thus, there is a need to empower racial and ethnic minority populations to mobilize their own [30] and parental [31],[32],[33] resource and asset groups. Policy solutions would be helping Black families to take advantage of their opportunities, mobilize their available resources, navigate the systems, and secure tangible outcomes [25],[27],[32],[34],[35],[36]. In this view, policy solutions should go above and beyond merely providing access by addressing the barriers that hinder racial and ethnic minorities from utilizing the existing resources and translating them to outcomes [24],[25],[27],[28],[37].
Executive function, attention, and cognitive capacity are among major determinants of school performance [38],[39],[40]. Individuals with poor executive function, commonly seen in individuals diagnosed with attention-deficit/hyperactivity disorder (ADHD), report a lower school function [41],[42]. Although other psychological and cognitive assets have shown diminishing returns on health outcomes [43],[44],[45], and family SES has shown weaker effects for Blacks than Whites [13],[14],[15], we are not aware of many previous investigations on the differential effect of executive function on children’s school performance between Black than White children.
Aims
To extend the existing knowledge on the racial gap in school performance [11],[14],[15],[46], build on the MDRs literature [24],[25],[47], and to expand our past work on racial differences in correlates of school performance, we compared Black and White children for the association between executive function and children’s school performance. In line with the MDRs framework [24],[25],[47], we expected a weaker association between executive function and children’s school performance for Black than White children.
MATERIALS AND METHODS
Design
This is a secondary analysis of existing data. We used a cross-sectional design for our analysis. Data came from the Adolescent Brain Cognitive Development (ABCD) study [48],[49],[50],[51],[52]. Adolescent Brain Cognitive Development is one of the main brain development studies of children in the United States [48],[53].
Sampling
Participants of the ABCD study were 9–10-year-old children. The ABCD mainly recruited children from the US school systems of multiple cities across various states. Although ABCD sampling is fully described here [54], we provide a brief summary of the study sampling. Although the sampling was not a multi-stage sampling design, schools were carefully selected for the final sample to represent the United States in terms of race, ethnicity, sex, and socioeconomic status. A total number of 4909 children entered our analysis. Eligible children for enrollment to this analysis were Black or White children with valid data on race, task-based executive function, and school performance. Participants were eligible regardless of Hispanic ethnicity. Asian Americans and other racial groups, as well as children with mixed-race, were excluded.
Variables
Variables in our analysis included race, ethnicity, age, sex, family SES, task-based executive function, and school performance.
Independent variable
Children’s executive function
The stop-signal task (SST) was used to measure the executive function of the participating children. The SST in this study applied two runs each, including 180 trials. These trials showed images of a black arrow pointing either to the right or to the left. These arrows were displayed while the participant was in the scanner. Participants were instructed to click the appropriate button that would correspond to the arrow direction. They were asked to click as quickly as they could see the arrows. All participants were asked to use their dominant hand. From the overall number of the 180 trials, 30 displayed neither option. These cases signaled the participant to inhibit answering with either option. These trials were randomly dispersed between the runs. Executive function was measured as the mean response time number of correct “Go” trials in a run (variable tfmri_sst_all_beh_crgo_mrt in the ABCD study). This variable was continuous with a higher score indicating worse executive function. As such, we calculated the reverse ordered response time number of correct “Go” trials, which was reflective of higher executive function [55],[56],[57],[58].
Dependent variable
Parents were asked to report their child’s grades. They answered the question, “What kind of grades does your child get on average?” Responses included 1 = A’s/Excellent A/Excellente; 2 = B’s/Good B/Bien; 3 = C’s/Average C/Promedio; 4 = D’s/Below Average D/Pordebajo del promedio; 5 = F’s/Struggling a lot F. We reverse coded the responses so a higher score reflected better grades. This variable reflects average grades that the child is attaining regardless of they have been acquired in a specific timeframe. The variable was a continuous measure ranging from 1 to 6, with a higher score reflecting higher grades.
Moderator
Race
Race, self-identified, and a categorical variable, was coded 1 for Blacks and 0 for Whites (reference category).
Confounders
Ethnicity
Ethnicity, self-identified, and a categorical variable, was coded 1 for Hispanic and 0 for non-Hispanic.
Age
Parents were asked to report the age of their children. Age was a continuous measure in years.
Sex
Sex was a dichotomous variable: males = 1, females = 0.
Parental marital status
Parental marital status was a dichotomous variable. This variable was coded as married = 1 versus other = 0.
Parental employment
Parental employment was self-reported by the parent and was coded 1 for employed and 0 for unemployed.
Financial stress
This study measured financial stress using the following seven items. The questions were “In the past 12 months, has there been a time when you and your immediate family experienced any of the following:” (1) “Needed food but couldn’t afford to buy it or couldn’t afford to go out to get it?,” (2) “Were without telephone service because you could not afford it?” (3) “Didn’t pay the full amount of the rent or mortgage because you could not afford it?,” (4) “Were evicted from your home for not paying the rent or mortgage?” (5) “Had services turned off by the gas or electric company, or the oil company wouldn’t deliver oil because payments were not made?” (6) “Had someone who needed to see a doctor or go to the hospital but didn’t go because you could not afford it?” and (7) “Had someone who needed a dentist but couldn’t go because you could not afford it?” With responses to each question being either 0 or 1, we calculated a mean score that was treated as a continuous measure. This variable ranged between 0 and 1, where 1 indicated the highest financial stress. Financial stress reflects some aspects of the SES, which are not captured by objective SES measures, such as education, income, and employment [59],[60],[61],[62],[63],[64],[65]. Financial stress, also called financial difficulties, economic stress, and economic difficulties, correlates with objective measures of SES such as education and income but predicts a wide range of health outcomes independent of them [59],[61],[62],[66],[67],[68].
Analysis and statistics
description of the study variables, depending on their type and level of measurement. We applied Pearson bivariate to rule out multicollinearity between the study variables. For multivariable analysis, linear regression models were used. Our first two regression models were performed in the overall sample. Our last two models were estimated in each race. Model 1 was performed without the executive function by race interaction term. Model 2 added the interaction term between race and executive function. Model 3 was run in Whites. Model 4 was tested in Blacks. Our models used sex, age, marital status, parental employment, and financial stress as covariates. Unstandardized regression coefficient (b) and p were reported.
Ethics
The ABCD study protocol was approved by multiple Institutional Review Boards (IRBs), including but not limited to that of the University of California, San Diego (UCSD). All children signed assent. Parents signed informed consent [53]. Our analysis was exempt from an IRB review.
RESULTS
Descriptives
As shown in Table 1, data of 4909, 9–10-year-old children were analyzed. Most were White (n = 3627; 73.9%) and the rest were Black (n = 1282; 26.1%). Table 1 shows the overall and race-specific summary of the variables. This table also compares Black and White children. White children were more likely to have married parents than Black children.
Similarly, White children were more likely to have employed parents than Black children. Finally, while White children had a higher grade point average (GPA) than Black children, Black children had higher financial stress levels than White children. White and Black children did not differ in age and sex.
Multivariate analysis (Pooled Sample)
As Table 2 shows, we performed two linear regression models in the overall sample. Model 1 (Main Effect Model) showed a boosting effect of executive function on school performance. Model 2 (Interaction Model) showed a statistically significant interaction term between race and executive function on school performance, suggesting that the boosting effect of high executive function on school performance is weaker for Black children than their White counterparts (Table 2).
Multivariate analysis in whites and blacks
As shown in Table 3, executive function only predicted White children’s school function but not Black children. As shown by Model 3, there was a boosting effect of high executive function on White children’s school performance. As shown by Model 4, we could not show any protective effect of executive function on school performance for Black children (Table 3).
DISCUSSION
Overall, high task-based executive function was associated with higher school performance. However, the positive association between executive function and school performance was diminished for Black than White children.
The observed diminished return of executive function on school performance for Black than White children is similar to previous research for other economic [30],[34],[69],[70] and non-economic [45],[71] resources. Minorities’ Diminished Returns are frequently established across SES resources, developmental phases, outcome variables, and sources of marginalization [24],[25]. These are shown for individual and household income [27], educational attainment of oneself and parents [30], occupation [72], and marital status [35]. Family SES results in more gain for White than Black children [27],[28],[37], adults [34], and older adults [73]. Also, MDRs not only apply to Black [28], Hispanic [30],[47],[74],[75], Asian American [76], or Native American [77] people, they also hold for lesbian, gay, bisexual, transgender, questioning (LGBTQ) [69], immigrant, and even poor White people.
There are several possible mechanisms and reasons why MDRs of resources and assets (e.g., executive function) emerge on securing tangible outcomes for Black families. Stress may reduce children’s ability to gain from their available resources and assets, such as executive function and family SES (parental education and income). It is shown that for Black families, high SES is associated with an increase in experience [78],[79],[80],[81],[82] and vulnerability [62] to discrimination. This is partly because high SES Black families are surrounded by White families, which means a higher level of exposure to discrimination [78],[79]. A high level of discrimination means reduced gains of SES [62],[81],[83].
Some other mechanisms may be involved in explaining the weaker effect of executive function on the school performance of Black than White children. First diminishing stereotype threat [84]. It is known that stereotype threat reduces school performance, particularly the results of tests [84],[85],[86],[87]. Second, discrimination by teachers and principals may be associated with the worse performance [14]. A large body of research has documented Black children’s discrimination experiences within and beyond schools [11],[14],[80],[88]. The third explanation is low school quality for Black children across all SES and executive functions [89]. Considerable evidence exists in poor school quality and low education resources in urban areas [11],[90],[91]. A recent study showed that across all SES levels, Black children are more likely to attend schools with high-risk peers and environment [89].
LIMITATIONS
Given our use of cross-sectional data, we cannot draw causal inferences between executive function and school performance. Similarly, we only tested the MDRs of executive function. Other factors that shape school performance include discipline, parental involvement, resources, etc. Future research may test if similar MDRs can be found for other determinants of school performance (e.g., cognition, IQ, etc.). Future investigation is important to know why high SES and talented Black children still report suboptimal school outcomes. Finally, we only described MDRs of executive function on school performance. There is a need for studies that explore various contextual factors that may result in the observed MDRs.
CONCLUSION
Compared to White children, Black children show a weaker positive association between executive function and school performance. This may also explain why Black children from high SES families show worse than expected school performance. These findings are indicative of multiple layers of adversities for Black children. Not only are their executive functioning and school performance are lower, but their school performance also shows weaker effects of executive function in Blacks than Whites. These findings may indicate why some early childhood programs, such as head start, have shown less than expected effects. A real solution is to equalize Blacks and Whites’ living conditions, which needs to eliminate racism and social stratification. There is also a need to enhance school quality in urban areas.
REFERENCES
1.
Bumpus JP, Umeh Z, Harris AL. Social class and educational attainment: Do blacks benefit less from increases in parents’ social class status? Sociology of Race and Ethnicity 2020;6(2):223–41. [CrossRef]
2.
McFarland J, Cui J, Holmes J, Wang X. Trends in High School Dropout and Completion Rates in the United States: 2019 (NCES 2020–117). U.S. Department of Education. Washington, DC: National Center for Education Statistics; 2019. [Available at: https://nces.ed.gov/pubsearch]
3.
Cohen GL, Sherman DK. Stereotype threat and the social and scientific contexts of the race achievement gap. Am Psychol 2005;60(3):270–1. [CrossRef]
[Pubmed]
4.
Burchinal M, McCartney K, Steinberg L, et al. Examining the Black-White achievement gap among low-income children using the NICHD study of early child care and youth development. Child Dev 2011;82(5):1404–20. [CrossRef]
[Pubmed]
5.
Gorey KM. Comprehensive school reform: Meta-analytic evidence of black-white achievement gap narrowing. Educ Policy Anal Arch 2009;17(25):1–17.
[Pubmed]
6.
Hair NL, Hanson JL, Wolfe BL, Pollak SD. Association of child poverty, brain development, and academic achievement. JAMA Pediatr 2015;169(9):822–9. [CrossRef]
[Pubmed]
7.
Arcidiacono P, Beauchamp A, Hull M, Sanders S. Exploring the racial divide in education and the labor market through evidence from interracial families. J Hum Cap 2015;9(2):198–238. [CrossRef]
[Pubmed]
8.
Assari S, Boyce S, Caldwell CH, Bazargan M. Parental educational attainment and black-white adolescents’ achievement gap: Blacks’ diminished returns. Open J Soc Sci 2020;8(3):282–97. [CrossRef]
[Pubmed]
9.
Hahn RA, Truman BI. Education improves public health and promotes health equity. Int J Health Serv 2015;45(4):657–78. [CrossRef]
[Pubmed]
10.
Henry DA, Miller P, Votruba-Drzal E, Parr AK. Safe and sound? Exploring parents’ perceptions of neighborhood safety at the nexus of race and socioeconomic status. Adv Child Dev Behav 2019;57:281-313. [CrossRef]
[Pubmed]
11.
Chavous TM, Rivas-Drake D, Smalls C, Griffin T, Cogburn C. Gender matters, too: The influences of school racial discrimination and racial identity on academic engagement outcomes among African American adolescents. Dev Psychol 2008;44(3):637–54. [CrossRef]
[Pubmed]
12.
Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and health in blacks and whites: The problem of residual confounding and the resiliency of race. Epidemiology 1997;8(6):621–8.
[Pubmed]
13.
Assari S. Parental educational attainment and academic performance of American college students; Blacks’ diminished returns. J Health Econ Dev 2019;1(1):21–31.
[Pubmed]
14.
Assari S, Caldwell CH. Teacher discrimination reduces school performance of African American youth: Role of gender. Brain Sci 2018;8(10):183. [CrossRef]
[Pubmed]
15.
Assari S, Caldwell CH. Parental educational attainment differentially boosts school performance of American adolescents: Minorities’ diminished returns. J Family Reprod Health 2019;13(1):7–13.
[Pubmed]
16.
Bell CN, Sacks TK, Thomas Tobin CS, Thorpe RJ Jr. Racial non-equivalence of socioeconomic status and self-rated health among African Americans and whites. SSM Popul Health 2020;10:100561. [CrossRef]
[Pubmed]
17.
Samuel LJ, Roth DL, Schwartz BS, Thorpe RJ, Glass TA. Socioeconomic status, race/ethnicity, and diurnal cortisol trajectories in middle-aged and older adults. J Gerontol B Psychol Sci Soc Sci 2018;73(3):468–76. [CrossRef]
[Pubmed]
18.
Fuentes M, Hart-Johnson T, Green CR. The association among neighborhood socioeconomic status, race and chronic pain in Black and white older adults. J Natl Med Assoc 2007;99(10):1160–9.
[Pubmed]
19.
Assari S, Khoshpouri P, Chalian H. Combined effects of race and socioeconomic status on cancer beliefs, cognitions, and emotions. Healthcare (Basel) 2019;7(1):17. [CrossRef]
[Pubmed]
20.
Assari S. Number of chronic medical conditions fully mediates the effects of race on mortality; 25-year follow-up of a nationally representative sample of americans. J Racial Ethn Health Disparities 2017;4(4):623–31. [CrossRef]
[Pubmed]
21.
Assari S. Distal, intermediate, and proximal mediators of racial disparities in renal disease mortality in the United States. J Nephropathol 2016;5(1):51–9. [CrossRef]
[Pubmed]
22.
Williams DR, Costa MV, Odunlami AO, Mohammed SA. Moving upstream: How interventions that address the social determinants of health can improve health and reduce disparities. J Public Health Manag Pract 2008;14(Suppl):S8–17. [CrossRef]
[Pubmed]
23.
Williams DR. Race, socioeconomic status, and health. The added effects of racism and discrimination. Ann N Y Acad Sci 1999;896:173–88. [CrossRef]
[Pubmed]
24.
Assari S. Health disparities due to diminished return among black Americans: Public policy solutions. Social Issues and Policy Review 2018;12(1):112–45. [CrossRef]
25.
Assari S. Unequal gain of equal resources across racial groups. Int J Health Policy Manag 2018;7(1):1–9. [CrossRef]
[Pubmed]
26.
Assari S, Caldwell CH, Bazargan M. Association between parental educational attainment and youth outcomes and role of race/ethnicity. JAMA Netw Open 2019;2(11):e1916018. [CrossRef]
[Pubmed]
27.
Assari S, Caldwell CH, Mincy R. Family socioeconomic status at birth and youth impulsivity at age 15; Blacks’ diminished return. Children (Basel) 2018;5(5):58. [CrossRef]
[Pubmed]
28.
Assari S, Thomas A, Caldwell CH, Mincy RB. Blacks’ diminished health return of family structure and socioeconomic status; 15 years of follow-up of a National Urban sample of youth. J Urban Health 2018;95(1):21–35. [CrossRef]
[Pubmed]
29.
Assari S, Bazargan M. Being married increases life expectancy of white but not black Americans. J Family Reprod Health 2019;13(3):132–40.
[Pubmed]
30.
Assari S, Farokhnia M, Mistry R. Education attainment and alcohol binge drinking: Diminished returns of hispanics in Los Angeles. Behav Sci (Basel) 2019;9(1):9. [CrossRef]
[Pubmed]
31.
Assari S. Parental education attainment and educational upward mobility; Role of race and gender. Behav Sci (Basel) 2018;8(11):107. [CrossRef]
[Pubmed]
32.
Assari S. Parental educational attainment and mental well-being of college students; Diminished returns of blacks. Brain Sci 2018;8(11):193. [CrossRef]
[Pubmed]
33.
Assari S. Parental education better helps white than black families escape poverty: National survey of children’s health. Economies 2018;6(2):30. [CrossRef]
34.
Assari S. Blacks’ diminished return of education attainment on subjective health; Mediating effect of income. Brain Sci 2018;8(9):176. [CrossRef]
[Pubmed]
35.
Assari S, Caldwell CH, Zimmerman MA. Family structure and subsequent anxiety symptoms; Minorities’ diminished return. Brain Sci 2018;8(6):97. [CrossRef]
[Pubmed]
36.
Assari S, Hani N. Household income and children’s unmet dental care need; Blacks’ diminished return. Dent J (Basel) 2018;6(2):17. [CrossRef]
[Pubmed]
37.
Assari S, Caldwell CH, Mincy RB. Maternal educational attainment at birth promotes future self-rated health of white but not black youth: A 15-year cohort of a national sample. J Clin Med 2018;7(5):93. [CrossRef]
[Pubmed]
38.
Barry TD, Lyman RD, Klinger LG. Academic underachievement and attention-deficit/hyperactivity disorder: The negative impact of symptom severity on school performance. Journal of School Psychology 2002;40(3):259–83. [CrossRef]
39.
Diamantopoulou S, Rydell AM, Thorell LB, Bohlin G. Impact of executive functioning and symptoms of attention deficit hyperactivity disorder on children’s peer relations and school performance. Dev Neuropsychol 2007;32(1):521–42. [CrossRef]
[Pubmed]
40.
Gormley MJ, DuPaul GJ, Weyandt LL, Anastopoulos AD. First-year GPA and academic service use among college students with and without ADHD. J Atten Disord 2019;23(14):1766–79. [CrossRef]
[Pubmed]
41.
Gormley MJ, Pinho T, Pollack B, et al. Impact of study skills and parent education on first-year GPA among college students with and without ADHD: A moderated mediation model. J Atten Disord 2018;22(4):334–48. [CrossRef]
[Pubmed]
42.
Langberg JM, Becker SP, Dvorsky MR. The association between sluggish cognitive tempo and academic functioning in youth with attention-deficit/hyperactivity disorder (ADHD). J Abnorm Child Psychol 2014;42(1):91–103. [CrossRef]
[Pubmed]
43.
Assari S, Lankarani MM. Reciprocal associations between depressive symptoms and mastery among older adults; Black-white differences. Front Aging Neurosci 2017;8:279. [CrossRef]
[Pubmed]
44.
Assari S. Race, sense of control over life, and short-term risk of mortality among older adults in the United States. Arch Med Sci 2017;13(5):1233–40. [CrossRef]
[Pubmed]
45.
Assari S. General self-efficacy and mortality in the USA; Racial differences. J Racial Ethn Health Disparities 2017;4(4):746–57. [CrossRef]
[Pubmed]
46.
Assari S. Parental educational attainment and academic performance of American college students; Blacks’ diminished returns. J Health Econ Dev 2019;1(1):21–31.
[Pubmed]
47.
Assari S. Socioeconomic determinants of systolic blood pressure; Minorities’ diminished returns. J Health Econ Dev 2019;1(1):1–11.
[Pubmed]
48.
Alcohol Research: Current Reviews Editorial Staff. NIH’s Adolescent Brain Cognitive Development (ABCD) study. Alcohol Res 2018;39(1):97.
[Pubmed]
49.
Casey BJ, Cannonier T, Conley MI, et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci 2018;32:43–54. [CrossRef]
[Pubmed]
50.
Karcher NR, O’Brien KJ, Kandala S, Barch DM. Resting-state functional connectivity and psychotic-like experiences in childhood: Results from the adolescent brain Cognitive development study. Biol Psychiatry 2019;86(1):7–15. [CrossRef]
[Pubmed]
51.
Lisdahl KM, Sher KJ, Conway KP, et al. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods. Dev Cogn Neurosci 2018;32:80–96. [CrossRef]
[Pubmed]
52.
Luciana M, Bjork JM, Nagel BJ, et al. Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev Cogn Neurosci 2018;32:67–79. [CrossRef]
[Pubmed]
53.
Auchter AM, Hernandez Mejia M, et al. A description of the ABCD organizational structure and communication framework. Dev Cogn Neurosci 2018;32:8–15. [CrossRef]
[Pubmed]
54.
Garavan H, Bartsch H, Conway K, et al. Recruiting the ABCD sample: Design considerations and procedures. Dev Cogn Neurosci 2018;32:16–22. [CrossRef]
[Pubmed]
55.
Clark SV, King TZ, Turner JA. Cerebellar contributions to proactive and reactive control in the stop signal task: A systematic review and meta-analysis of functional magnetic resonance imaging studies. Neuropsychol Rev 2020;30(3):362–85. [CrossRef]
[Pubmed]
56.
Dupuis A, Indralingam M, Chevrier A, et al. Response time adjustment in the stop signal task: Development in children and adolescents. Child Dev 2019;90(2):e263–72. [CrossRef]
[Pubmed]
57.
Hiraoka K, Kinoshita A, Kunimura H, Matsuoka M. Effect of variability of sequence length of go trials preceding a stop trial on ability of response inhibition in stop-signal task. Somatosens Mot Res 2018;35(2):95–102. [CrossRef]
[Pubmed]
58.
Carver AC, Livesey DJ, Charles M. Age related changes in inhibitory control as measured by stop signal task performance. Int J Neurosci 2001;107(1–2):43–61. [CrossRef]
[Pubmed]
59.
Assari S, Smith J, Mistry R, Farokhnia M, Bazargan M. Substance use among economically disadvantaged African American Older Adults; Objective and Subjective Socioeconomic Status. Int J Environ Res Public Health 2019;16(10):1826. [CrossRef]
[Pubmed]
60.
Chen E, Paterson LQ. Neighborhood, family, and subjective socioeconomic status: How do they relate to adolescent health? Health Psychol 2006;25(6):704–14. [CrossRef]
[Pubmed]
61.
Moon C. Subjective economic status, sex role attitudes, fertility, and mother’s work. Ingu Pogon Nonjip 1987;7(1):177–96.
[Pubmed]
62.
Assari S, Preiser B, Lankarani MM, Caldwell CH. Subjective socioeconomic status moderates the association between discrimination and depression in African American youth. Brain Sci 2018;8(4):71. [CrossRef]
[Pubmed]
63.
Bøe T, Petrie KJ, Sivertsen B, Hysing M. Interplay of subjective and objective economic well-being on the mental health of Norwegian adolescents. SSM Popul Health 2019;9:100471. [CrossRef]
[Pubmed]
64.
Wright CE, Steptoe A. Subjective socioeconomic position, gender and cortisol responses to waking in an elderly population. Psychoneuroendocrinology 2005;30(6):582–90. [CrossRef]
[Pubmed]
65.
Ye Z, Wen M, Wang W, Lin D. Subjective family socio-economic status, school social capital, and positive youth development among young adolescents in China: A multiple mediation model. Int J Psychol 2020;55(2):173–81. [CrossRef]
[Pubmed]
66.
Ursache A, Noble KG, Blair C. Socioeconomic status, subjective social status, and perceived stress: Associations with stress physiology and executive functioning. Behav Med 2015;41(3):145–54. [CrossRef]
[Pubmed]
67.
Senn TE, Walsh JL, Carey MP. The mediating roles of perceived stress and health behaviors in the relation between objective, subjective, and neighborhood socioeconomic status and perceived health. Ann Behav Med 2014;48(2):215–24. [CrossRef]
[Pubmed]
68.
Manuck SB, Phillips JE, Gianaros PJ, Flory JD, Muldoon MF. Subjective socioeconomic status and presence of the metabolic syndrome in midlife community volunteers. Psychosom Med 2010;72(1):35–45. [CrossRef]
[Pubmed]
69.
Assari S. Education attainment and obesity: Differential returns based on sexual orientation. Behav Sci (Basel) 2019;9(2):16. [CrossRef]
[Pubmed]
70.
Assari S. Family income reduces risk of obesity for white but not black children. Children (Basel) 2018;5(6):73. [CrossRef]
[Pubmed]
71.
Assari S. Perceived neighborhood safety better predicts risk of mortality for whites than blacks. J Racial Ethn Health Disparities 2016. [CrossRef]
[Pubmed]
72.
Assari S. Life expectancy gain due to employment status depends on race, gender, education, and their intersections. J Racial Ethn Health Disparities 2018;5(2):375–86. [CrossRef]
[Pubmed]
73.
Assari S, Lankarani MM. Education and alcohol consumption among older Americans; Black-white differences. Front Public Health 2016;4:67. [CrossRef]
[Pubmed]
74.
Shervin A, Mistry R. Diminished return of employment on ever smoking among hispanic whites in Los Angeles. Health Equity 2019;3(1):138–44. [CrossRef]
[Pubmed]
75.
Assari S. Socioeconomic status and self-rated oral health; Diminished return among hispanic whites. Dent J (Basel) 2018;6(2):11. [CrossRef]
[Pubmed]
76.
Assari S, Boyce S, Bazargan M, Caldwell CH. Mathematical performance of American youth: Diminished returns of educational attainment of Asian-American parents. Educ Sci (Basel) 2020;10(2):32.
[Pubmed]
77.
Assari S, Bazargan M. Protective effects of educational attainment against cigarette smoking; Diminished returns of American Indians and alaska natives in the national health interview survey. Int J Travel Med Glob Health 2019;7(3):105–10. [CrossRef]
[Pubmed]
78.
Assari S, Gibbons FX, Simons R. Depression among black youth; Interaction of class and place. Brain Sci 2018;8(6):108. [CrossRef]
[Pubmed]
79.
Assari S, Gibbons FX, Simons RL. Perceived discrimination among black youth: An 18-year longitudinal study. Behav Sci (Basel) 2018;8(5):44. [CrossRef]
[Pubmed]
80.
Assari S. Does school racial composition explain why high income black youth perceive more discrimination? A gender analysis. Brain Sci 2018;8(8):140. [CrossRef]
[Pubmed]
81.
Assari S, Lankarani MM, Caldwell CH. Does discrimination explain high risk of depression among high-income African American men? Behav Sci (Basel) 2018;8(4):40. [CrossRef]
[Pubmed]
82.
Assari S, Moghani Lankarani M. Workplace racial composition explains high perceived discrimination of high socioeconomic status African American men. Brain Sci 2018;8(8):139. [CrossRef]
[Pubmed]
83.
Assari S, Caldwell CH. Social determinants of perceived discrimination among black youth: Intersection of ethnicity and gender. Children (Basel) 2018;5(2):24. [CrossRef]
[Pubmed]
84.
Spencer SJ, Logel C, Davies PG. Stereotype threat. Annu Rev Psychol 2016;67:415–37. [CrossRef]
[Pubmed]
85.
Spencer SJ, Steele CM, Quinn DM. Stereotype threat and women’s math performance. Journal of Experimental Social Psychology 1999;35(1):4–28. [CrossRef]
86.
Steele CM, Aronson J. Stereotype threat and the intellectual test performance of African Americans. J Pers Soc Psychol 1995;69(5):797–811. [CrossRef]
[Pubmed]
87.
Schmader T, Johns M. Converging evidence that stereotype threat reduces working memory capacity. J Pers Soc Psychol 2003;85(3):440–52. [CrossRef]
[Pubmed]
88.
Assari S, Moghani Lankarani M, Caldwell CH. Discrimination increases suicidal ideation in black adolescents regardless of ethnicity and gender. Behav Sci (Basel) 2017;7(4):75. [CrossRef]
[Pubmed]
89.
Boyce S, Bazargan M, Caldwell CH, Zimmerman MA, Assari S. Parental educational attainment and social environment of Urban public schools in the U.S.: Blacks’ diminished returns. Children (Basel) 2020;7(5):44. [CrossRef]
[Pubmed]
90.
Backhouse EV, McHutchison CA, Cvoro V, Shenkin SD, Wardlaw JM. Cognitive ability, education and socioeconomic status in childhood and risk of post-stroke depression in later life: A systematic review and meta-analysis. PLoS One 2018;13(7):e0200525. [CrossRef]
[Pubmed]
91.
Margioti E, Kosmidis MH, Yannakoulia M, et al. Exploring the association between subjective cognitive decline and frailty: The Hellenic Longitudinal Investigation of Aging and Diet Study (HELIAD). Aging Ment Health 2020;24(1):137–47. [CrossRef]
[Pubmed]
SUPPORTING INFORMATION
Author Contributions
Shervin Assari - Conception of the work, Design of the work, Acquisition of data, Drafting the work, Revising the work critically for important intellectual content, Final approval of the version to be published, Agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Shanika Boyce - Conception of the work, Design of the work, Drafting the work, Revising the work critically for important intellectual content, Final approval of the version to be published, Agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Mohsen Bazargan - Conception of the work, Design of the work, Drafting the work, Revising the work critically for important intellectual content, Final approval of the version to be published, Agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Cleopatra H Caldwell - Conception of the work, Design of the work, Acquisition of data, Analysis of data, Revising the work critically for important intellectual content, Final approval of the version to be published, Agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
AcknowledgmentsData used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health Grants [U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147]. A full list of supporters is available at https://abcdstudy.org/nih-collaborators. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from DOI: 10.15154/1504041 which can be found at: https://nda.nih.gov/study.html?id=721.
Guaranter of SubmissionThe corresponding author is the guarantor of submission.
Source of SupportNone
Consent StatementAll children signed assent. Written informed consent was taken from the parents.
Data AvailabilityAll relevant data are within the paper and its Supporting Information files.
Conflict of InterestAuthors declare no conflict of interest.
Copyright© 2020 Shervin Assari et al. This article is distributed under the terms of Creative Commons Attribution License which permits unrestricted use, distribution and reproduction in any medium provided the original author(s) and original publisher are properly credited. Please see the copyright policy on the journal website for more information.
