Abstract
Objective:
We aimed to investigate whether or not demographically-corrected test scores derived from the Neuropsychological Norms for the U.S.-Mexico Border Region in Spanish (NP-NUMBRS) would be less accurate if applied to Spanish-speakers with various degrees of English fluency.
Spanish-English Method:
One hundred and seventy primarily Spanish-speaking adults from the NP-NUMBRS project completed a comprehensive neuropsychological test battery. T-scores adjusted for age, education, and sex (but not degree of bilingualism), were derived for each test utilizing population-specific normative data. English fluency was assessed via the Controlled Oral Word Association Test in English (F-A-S), and Spanish fluency with “P-M-R,” and degree of relative English fluency was calculated as the ratio of English language words over total words produced in both languages. Effects of degree of bilingualism on the NUMBRS battery test scores (raw scores and T-scores) were examined via Pearson’s product moment correlation coefficients, and language groups (Spanish dominant vs. relative bilingual) were compared on demographically adjusted T-scores via independent samples t-tests.
Results:
Higher Spanish-English bilingualism was associated with higher education and SES, and was significantly associated with higher raw scores on all tests, but only associated with higher T-scores on a limited number of tests (i.e., WAIS-III Digit Symbol, Symbol Search, Letter-Number Sequencing and Trails B).
Conclusion:
Degree of Spanish-English bilingualism generally did not account for significant variance in the normed tests beyond the standard demographic adjustments on most tests. Overall, the normative adjustments provided by the NP-NUMBRS project appear applicable to native Spanish speakers from the U.S.-Mexico border region with various degrees of Spanish-English bilingualism.
Keywords: bilingualism, English fluency, Spanish-speakers, cultural neuropsychology
Introduction
Latinos/as are the fastest growing ethnic minority group in the United States. According to the U.S. Census Bureau (2017), the Latino/a population constitutes 17.8 percent of the nation’s total population, growing from 52 million in 2010 to almost 58 million in 2017. This same census reported that 40 million Latino/a U.S. residents spoke Spanish at home in 2016, representing a 133.4 percent increase since 1990. Of these, more than half also reported speaking English “very well” (Census Bearue, 2017). These numbers indicate that a substantial proportion of Spanish speakers in the U.S. are bilingual with varying degrees of proficiency in both Spanish and English. In their previous work, Suarez and colleagues (2014) examined whether or not learning English later in life impacted the performance of native Spanish speakers on the Stroop Test (Golden version in Spanish, Artiola i Fortuny et al., 1999). While there was a strong correlation between relative English fluency and years of education, regression-based analyses suggested that relative English fluency confers a unique benefit to inhibitory control beyond years of education alone. These findings were consistent with the literature suggesting that bilingualism confers a benefit on some aspects of executive functioning (Bialystok 1999, 2001; Carlson and Meltzoff, 2008; Bialystok, Craik, & Luk, 2008; Coderre et al., 2013). Other research suggests that monolinguals outperform bilinguals on tests of verbal abilities, such as confrontation naming (Gollan, Montoya, Bonanni, 2005) and verbal category fluency (Gollan, Montoya, & Werner, 2002) in their primary language. Despite the sizeable body of research that demonstrates both costs and benefits of a second language when performing cognitive tests, no study, to our knowledge, has attempted to examine the role of relative English fluency among primary Spanish-speakers on a comprehensive neuropsychological test battery. In addition, no study has examined whether or not norms derived for a native Spanish-speaking population in the U.S. are equally applicable to Spanish-speakers with various degrees of Spanish-English bilingualism.
In an attempt to operationalize degree of Spanish-English bilingualism in Spanish-speakers from the U.S.-Mexico Border Region who participated in this normative study, Suarez and colleagues (2014) used timed lexical retrieval to develop a language dominance index. The authors calculated the ratio of English words to total words produced in both languages (Spanish and English) using the well-known phonemic verbal fluency task, Controlled Oral Word Association Test (COWAT, Lezak, 2005; Artiola I Fortuny, Hermosillo, Heaton, & Pardee, 1999), with letters F-A-S in English and P-M-R in Spanish (FAS/FAS+PMR). In the current paper, we applied the language dominance index (FAS/FAS+PMR) as a predictor of test performance across all measures of the Neuropsychological Norms for the U.S.-Mexico Border Region in Spanish (NP-NUMBRS) project. This larger project presents demographically-corrected normative data on a comprehensive neuropsychological test battery in Spanish assessing 7 domains across 25 measures (see Methods section below).
The purpose of the present study is to clarify whether degree of Spanish-English bilingualism, defined as the ratio of words produced in English to overall production in English and Spanish, has a significant influence on neuropsychological test scores adjusted for other demographics (age, education, gender) among primary Spanish-speaking adults from the NP-NUMBRS project. Such findings should help clarify whether relative degree of Spanish-English bilingualism ought to be considered when interpreting neuropsychological tests scores derived from the NP-NUMBRS norms when evaluating Spanish-speakers with varying degrees of bilingualism.
Methods
Participants
Participants were 170 native Spanish-speakers from the NP-NUMBRS project with data available on phonemic fluency (Controlled Oral Word Association Test) in both languages: Spanish with letters PMR (Artiola i Fortuny, et al., 1999) and English with letters FAS (Benton, Hamsher, & Sivan, 1994). Participants ranged in age from 20 to 55 years, had between 0 and 20 years of education, and 56% were women. All participants were recruited from the first wave of testing for the NP-NUMBRS project, which occurred between 1998 and 2000, with 53% of participants being tested in or near Tucson, Arizona and 47% in or near San Diego, California. Participants enrolled in the normative studies were carefully screened to ensure that they had no significant history of medical, psychiatric, developmental, or substance abuse disorders that could confound neuropsychological performance. Given the purpose of the original study, efforts were targeted at recruiting native Spanish-speakers who resided in the U.S. at least part of the time (see Cherner et al., on this issue for more details). We estimated degree of Spanish-English bilingualism as the ratio of FAS to total words in both languages [FAS/(FAS+PMR)] (Suárez et al., 2014). Participants in the upper tertile of this ratio (scores ≥ .67) were considered English-dominant and excluded from the NP-NUMBRS project. To reiterate, the primary purpose of the NP-NUMBRS project was to develop norms for native Spanish-speakers who had spent some time in the United States and English-dominance was an exclusion criteria.
Procedures
Degree of Spanish-English bilingualism.
As noted, we estimated relative degree of Spanish-English bilingualism as the ratio of FAS to total words in both languages [FAS/(FAS+PMR)] (Suárez et al., 2014). Original fluency norms developed by Artiola and colleagues (1998) in Spanish adopted the use of the letters P, M, R considering the equivalency of word frequency with the letters F, A, S in English. Cattie and Cherner (2011) compared levels of performance that would be obtained for the same raw phonemic fluency score using the available norms for FAS (Heaton et al, 1994) and PMR (Artiola et al,1998) by modeling every combination of age, education, gender, and fluency score (44,064 computer generated permutations). They found a correlation of .96 between T-scores computed with the FAS formula vs. the PMR formula for the same raw score and demographic characteristics, concluding that levels of performance in phonemic fluency are comparable in English and Spanish with the selected letters.
As noted above, participants with scores ≥ .67 were considered English-dominant and excluded from the normative sample. Participants with scores less than or equal to 0.33 were classified as Spanish-dominant, and those with scores greater than 0.33 to less than 0.67 were classified as relative bilingual. In the present sample, English fluency ratio scores ranged from 0 to 0.33 in the Spanish-dominant group (M=0.18, SD=0.10) and from 0.34 to 0.59 among those in the relative bilingual group (M=0.42, SD=0.05). Please note that nine participants were unable to produce any words in English that started with the letters F-A-S, which resulted in a score of zero in the relative English fluency ratio.
Language Use and Background.
Participants completed a self-report questionnaire about language background characteristics that included questions regarding first language (English, Spanish or both), and language usage in daily activities including radio and TV usage, reading, mathematical calculations, praying and time spent interacting with family in either language. Examinees were also asked to report whether they believed they spoke and understood (a) Spanish better than English; (b) English better than Spanish or (c) both equally. Examiners were asked a similar question regarding examinees and were also asked to judge whether examinees were bilingual or not.
Educational and Social Background Characteristics.
Participants completed questionnaires about educational and social background characteristics, with questions regarding years of education in examinees’ country of origin and in the United States; details on the school attended in the country of origin, including size of the school (large, regular or small) and typical size of the classroom; and need to discontinue attending school in order to work. Social background characteristics included parental years of education, years lived in the country of origin and in the U.S., self-perceived socio-economic status (SES) growing up (i.e., very poor, poor, middle class, upper class), as well as history of employment during childhood, and current employment.
Neurocognitive Test Battery.
Participants completed a comprehensive battery of neuropsychological tests in Spanish covering eight ability domains (see Cherner, Marquine, et al, 2020, ) including verbal fluency (phonemic and semantic fluency; Marquine et al., 2020); processing speed (Trail Making Test-Part A [TMT-A]; Wechsler Adult Intelligence Scale-III [WAIS-III] Digit Symbol Coding and Symbol Search subtests; Rivera Mindt et al., 2020 and Suarez et al., 2020); attention/working memory (Paced Auditory Serial Addition Test [PASAT], Wechsler Adult Intelligence Scale-Third Edition [WAIS-III] Letter Number Sequencing Test [LNS], and Wechsler Adult Intelligence Scale-Revised [WAIS-R] Arithmetic subtest; Gooding et al., 2020; and Scott et al., 2020); executive function ([Halstead Category Test [HCT], Trail Making Test-Part B [TMT-B], Wisconsin Card Sorting Test-64 Item [WCST-64]; Marquine et al., 2020; Morlett Paredes et al., 2020; and Suarez et al., 2020); learning and memory (Brief Visuospatial Memory Test-Revised [BVMT-R] and the Hopkins Verbal Learning Test-Revised [HVLT-R]; Diaz-Santos et al., 2020); visuospatial construction (WAIS-R Block Design subtest; Scott et al., 2020); and fine motor skills (Finger Tapping Test and Grooved Pegboard Test; Heaton et al., 2020). We derived T-scores adjusted for age, education, and gender for each test using fractional polynomial regression equations based on a larger overall sample of 254 Spanish-speaking adults (170 of whom comprise the present study participants). See Cherner, Marquine and colleagues, 2020 for details on the NP-NUMBRS project methodology, including the statistical procedures used in the derivation of the demographically-corrected T-scores.
Statistical Analyses.
We compared demographic (age, education and gender), language use and background, and educational and social characteristics by language group (i.e., Spanish-dominant) [English fluency ratio ≤ 0.33] and relative bilinguals [0.33 < English fluency ratio < 0.67]) via a series of independent sample t-tests (for continuous variables) and Chi-square tests (for categorial variables). We then investigated the bivariate association between degree of Spanish-English bilingualism (based on the continuous English fluency ratio: FAS/[FAS+PMR]) and individual neurocognitive tests raw scores and T-scores through Pearson product moment correlation coefficients. We also compared language groups on individual neurocognitive tests’ T-scores, and computed Cohen’s d coefficients for tests with significant group differences. Given the large number of comparisons, significance level for associations with cognitive tests was set at p<=.01. For other analyses, p<.05 was considered statistically significant.
Results
Table 1 shows demographic characteristics of the overall study sample (n=170) and by language group (Spanish-dominant, n=102; relative bilingual, n=68). Language groups were similar in age and gender, but the relative bilingual group had, on average, significantly higher years of formal education. Regarding language background characteristics (Table 2), almost all participants in both the Spanish-dominant and relative bilingual groups reported Spanish as their first language, with 3% of participants in the relative bilingual group reporting their first language as both Spanish and English. Spanish-dominant participants reported significantly higher prevalence of Spanish language use in everyday life compared to those in the relative bilingual group. Nearly all Spanish-dominant participants and over half of relative bilingual participants reported that Spanish was the language that they understood and spoke best. The examiners’ appraisal of the examinees’ language abilities were generally congruent with self-report for both groups, with 94% of relative bilinguals identified as such by examiners (n=4), but about one third of participants (n=31) in the Spanish-dominant group judged to be bilingual by examiners. The English fluency ratio in this latter group ranged from 0.18 to 0.33 (M=0.28, SD=0.04) and was significantly higher than that of participants who were correctly classified as Spanish-dominant by examiners (range: 0–0.32 M=0.14, SD=0.10).
Table 1.
Descriptive Characteristics of the study sample by English fluency ratio
| Overall sample (N=170) | Spanish-dominant (n=102) | Relative Bilingual (n=68) | pa | |
|---|---|---|---|---|
| Age, M(SD) | 37.2 (9.5) | 37.6 (9.2) | 36.8 (10.2) | .60 |
| [range] | [20–55] | [20–55] | [20–55] | |
| Education, M(SD) | 10.1 (4.2) | 8.3 (3.8) | 12.9 (3.1) | <.001 |
| [range] | [0–20] | [0–16] | [6–20] | |
| Female, n (%) | 95 (55.8%) | 53.9% | 58.8% | .53 |
Note.
Represents results from independent samples t-tests and Chi-Square tests between the monolingual and bilingual group.
Table 2.
Language background characteristics of the study sample by English fluency ratio group
| Spanish-dominant (n=102) | Relative Bilingual (n=68) | pa | |
|---|---|---|---|
| Self-report | |||
| First Language | -- | -- | .16 |
| Spanish | 100% | 97% | -- |
| English | 0% | 0% | -- |
| Both | 0% | 3% | -- |
| Current Language Use Rating, M(SD)b | -- | -- | -- |
| Radio or TV | 2.0 (0.8) | 3.0 (0.9) | <.001 |
| Reading | 1.7 (0.9) | 2.9 (1.0) | <.001 |
| Math | 1.2 (0.5) | 1.8 (1.3) | <.001 |
| Praying | 1.0 (0.2) | 1.4 (0.8) | <.001 |
| With family | 1.3 (0.6) | 1.6 (0.9) | .05 |
| Current language comprehension and fluency | -- | -- | <.001 |
| Spanish better than English | 99% | 68.7% | -- |
| Similar | 1% | 26.9% | |
| English better than Spanish | 0% | 4.5% | |
| Examiner report | |||
| Examinee’s current language comprehension and fluency | -- | -- | <.001 |
| Spanish better than English | 95.8% | 66.1% | -- |
| Similar | 3.1% | 32.3% | -- |
| English better than Spanish | 1.0% | 1.6% | -- |
| Examinee bilingual (Yes) | 33.7% | 93.6% | <.001 |
Note.
Represents results from independent samples t-tests and Chi-Square tests between the Lower and Higher English fluency groups.
Ratings for each activity ranged from 1 “Always in Spanish” to 5 “Always in English”, with 3 being “similarly in English and Spanish”).
Table 3 shows educational and social background characteristics by language group. Participants in the relative bilingual group had more years of formal education in the U.S. and were more likely to have attended a large school in their country of origin than those in the Spanish-dominant group. They were also less likely to have stopped attending school to work than the Spanish-dominant group. Compared to Spanish-dominant individuals, relative bilingual participants had higher years of parental education, had lived longer in the U.S. than in their country of origin, had higher childhood SES, and were less likely to have worked as children. Among those who worked, relative bilingual participants were more likely to have done so for their own benefit rather than to help their family financially, and they were older at the time they started working as a child than those in the Spanish-dominant group.
Table 3.
Educational and social background characteristics of the study sample by English fluency ratio group
| Characteristics | Spanish-dominant (n=102) | Relative Bilingual (n=68) | pa |
|---|---|---|---|
| Educational Background | |||
| Years of education in country of origin, M(SD) | 8.0 (3.8) | 9.0 (5.2) | .19 |
| Years of education in the U.S., M(SD) | 0.3 (1.1) | 3.9 (4.5) | <.001 |
| Proportion of education by country | -- | -- | <.001 |
| More years of education in country of origin | 97.1% | 73.5% | -- |
| More years of education in the U.S. | 2.0% | 25.0 | -- |
| Equal number of years of education in both countries | .98% | 1.47 | -- |
| Type of school attendedb | -- | -- | .01 |
| Large | 48.5% | 58.8% | -- |
| Regular | 43.6% | 41.2% | -- |
| Small | 7.9% | 0.00% | -- |
| Number of students in the class | -- | -- | .10 |
| Less than 21 | 6.9% | 8.8% | -- |
| 21 to 30 | 32.7% | 50.0% | -- |
| 31 to 40 | 30.7% | 22.1% | -- |
| 40+ | 29.7% | 19.1% | -- |
| Had to stop attending school to work (Yes) | 41.2% | 17.6% | <.001 |
| Social Background | |||
| Mother’s years of education, M(SD) | 4.7 (3.5) | 7.4 (3.3) | <.001 |
| Father’s years of education, M(SD) | 5.4 (4.6) | 9.2 (4.9) | <.001 |
| Years lived in country of origin, M(SD) | 29.5 (9.9) | 25.3 (14.1) | .03 |
| Years living in the U.S., M(SD) | 7.9 (8.5) | 11.4 (10.3) | .02 |
| Childhood SES | -- | -- | <.01 |
| Very poor | 7.8% | 2.9% | -- |
| Poor | 34.3% | 14.7% | -- |
| Middle class | 52.9% | 64.7% | -- |
| Upper class | 4.9% | 17.6% | -- |
| Worked as a child (Yes) | 64.7% | 46.3% | .02 |
| Reason to work | -- | -- | .001 |
| Help family financially | 45.2% | 15.4% | -- |
| Own benefit | 54.8% | 84.6% | -- |
| Age started working as a child, M(SD) | 12.7 (3.2) | 14.6 (1.9) | <.001 |
| Currently Gainfully Employed (Yes) | 65.3% | 61.2% | .58 |
Note. M: mean; SD: standard deviation.
Represents results from independent samples t-tests and Chi-Square tests between the Lower and Higher English fluency group
Type of school attended: ‘large’ refers to large school that had many classrooms and room to play); “regular’ refers to a school of regular size that had at least one classroom per grade and room to play; and small school refers to a small school with less than one classroom per grade.
Results from Pearson product moment correlation coefficients (Table 4) showed that higher relative degree of Spanish-English bilingualism was significantly associated with better neurocognitive test performance, as reflected in raw scores across tests in all domains, except most measures of fine motor skills. In contrast, similar analyses using T-scores (adjusted for age, education, and gender) showed that higher degree of English bilingualism was only associated with better performance on the WAIS-III Digit Symbol, Symbol Search, and Letter -Number Sequencing, and the Trail Making Test Part B.
Table 4.
Association between degree of English fluencya and individual tests raw scores and T-scores
| Pearson’s rb | ||||
|---|---|---|---|---|
| Domain | Test | N | Raw Scores | T-Scores |
| Verbal Fluency | Letter Fluency | 170 | 0.21** | −0.04 |
| Animal Fluency | 170 | 0.31*** | 0.12 | |
| Speed of Information Processing | Trail Making Test A | 169 | −0.34*** | 0.12 |
| WAIS-III Digit Symbol | 127 | 0.58*** | 0.23** | |
| WAIS-III Symbol Search | 127 | 0.57*** | 0.23** | |
| Attention/Working Memory | PASAT-200 | 169 | 0.33*** | 0.12 |
| PASAT-50 | 169 | 0.28*** | 0.04 | |
| WAIS-III L-N Sequencing | 127 | 0.56*** | 0.21* | |
| WAIS-R Arithmetic | 168 | 0.35*** | 0.10 | |
| Executive Function | Trail Making Test B | 163 | −0.51*** | 0.17* |
| WCST-64 Total Errors | 118 | −0.45*** | 0.14 | |
| WCST-64 Perseverative Responses | 118 | −0.28** | 0.06 | |
| WCST-64 Perseverative Errors | 115 | −0.30** | 0.11 | |
| WCST-64 Categories | 118 | 0.43*** | 0.11 | |
| Halstead Category Test Total Score | 170 | −0.36*** | 0.11 | |
| Learning | Hopkins Verbal Learning Test-Revised: Learning | 126 | 0.27** | 0.11 |
| Brief Visuospatial Memory Test – Revised: Learning | 128 | 0.40*** | 0.12 | |
| Memory | Hopkins Verbal Learning Test-Revised: Delayed Recall | 126 | 0.35*** | 0.15 |
| Brief Visuospatial Memory Test – Revised: Delayed Recall | 128 | 0.41*** | 0.16 | |
| Visual-spatial skills | WAIS-R Block Design | 168 | 0.33*** | 0.05 |
| Fine motor skills | Grooved Pegboard (Dominant hand) | 170 | −0.09 | −0.05 |
| Grooved Pegboard (Non-Dominant hand) | 170 | −0.18* | 0.03 | |
| Finger Tapping (Dominant hand) | 170 | 0.26** | 0.10 | |
| Finger Tapping (Non-Dominant hand) | 170 | 0.20* | 0.08 | |
p<.05
p<.01
p<.001
Note.
English Fluency=FAS/(FAS+PMR)
based on Pearson product moment correlation coefficients.
A series of independent sample t-tests on T-scores by language group (Table 5) showed relative bilinguals had significantly (p value ≤ .01) higher T-scores than Spanish-dominant participants on the WAIS-III Digit Symbol (Cohen’s d = 0.55) and Symbol Search (Cohen’s d = 0.47), and Trail Making Test Part B (Cohen’s d = 0.43), with marginally significant differences (p value ≤ .05) on the following tests: Trail Making Test Part A (Cohen’s d = 0.32), WAIS-III Letter Number Sequencing (Cohen’s d = 0.45), WAIS-R Arithmetic (Cohen’s d = 0.30), and Halstead Category Test (Cohen’s d = 0.32). The maximum difference between the higher and lower English fluency groups was 5 T-scores, with most differences between 3 and 4 T-scores.
Table 5.
Comparisons of individual tests T-scores by English fluency ratio group
| Domain | Test | Spanish-dominant M(SD) (n=102) | Relative Bilingual M(SD) (n=68) | pa |
|---|---|---|---|---|
| Verbal Fluency | Letter Fluency in Spanish | 49.8 (9.9) | 50.1 (9.9) | .85 |
| Animal Fluency in Spanish | 49.8 (10.1) | 52.3(10.9) | .13 | |
| Speed of Information Processing | Trail Making Test A | 48.9 (9.7) | 52.1 (10.2) | .04 |
| WAIS-III Digit Symbol | 48.7 (8.8) | 54.0 (11.0) | <.01 | |
| WAIS-III Symbol Search | 48.7 (9.9) | 53.2 (9.8) | .01 | |
| Attention/Working Memory | PASAT-200 | 49.6 (10.7) | 52.0 (9.1) | .13 |
| PASAT-50 | 50.3 (9.9) | 51.5 (9.7) | .45 | |
| WAIS-III L-N Sequencing | 50.0 (8.5) | 54.0 (9.3) | .02 | |
| WAIS-R Arithmetic | 48.9 (10.3) | 51.9 (9.3) | .05 | |
| Executive Function | Trail Making Test B | 48.7 (10.0) | 53.0 (10.3) | <.01 |
| WCST-64 Total Errors | 49.9 (9.6) | 52.1 (11.0) | .27 | |
| WCST-64 Perseverative Responses | 50.6 (10.0) | 50.91 (10.0) | .88 | |
| WCST-64 Perseverative Errors | 50.2 (9.96) | 51.6 (10.0) | .47 | |
| WCST-64 Categories | 49.2 (9.04) | 51.7 (19.5) | .19 | |
| Halstead Category Test Total Score | 48.9 (10.01) | 52.2 (10.8) | .05 | |
| Learning | Hopkins Verbal Learning Test-Revised: Learning | 50.9 (10.5) | 51.6 (9.8) | .71 |
| Brief Visuospatial Memory Test – Revised: Learning | 49.7 (10.0) | 50.5 (10.5) | .66 | |
| Memory | Hopkins Verbal Learning Test-Revised: Delayed Recall | 49.5 (10.6) | 51.9 (9.7) | .19 |
| Brief Visuospatial Memory Test – Revised: Delayed Recall | 50.1 (10.1) | 51.2 (9.0) | .50 | |
| Visual-spatial skills | WAIS-R Block Design | 49.0 (10.2) | 51.4 (9.9) | .14 |
| Fine motor skills | Grooved Pegboard (Dominant hand) | 50.1 (9.9) | 49.4 (9.6) | .66 |
| Grooved Pegboard (Non-dominant hand) | 49.8 (10.8) | 50.3 (10.9) | .76 | |
| Finger Tapping (Dominant hand) | 49.2 (10.1) | 51.2 (9.8) | .18 | |
| Finger Tapping (Non-Dominant hand) | 48.9 (10.3) | 51.3 (9.8) | .12 | |
Note.
Based on independent sample t-tests
Given differences across language groups on educational and social background characteristics (Table 3), in secondary analyses we investigated whether T-Score differences by language group might be accounted for by group differences in educational and social background. To do so, we ran separate backward stepwise regression models on T-scores for each of the tests that differed significantly by language group (i.e., WAIS-III Digit Symbol and Symbol Search, and Trail Making Test Part B), including as predictors language group (Spanish-dominant/relative bilingual), and educational and social background characteristics that differed by group (i.e., years of education in the US, years living in the U.S., type of school attended, childhood SES, stopped attending school to work, worked as a child, and parental education). Results from these models showed an independent effect of relative degree of English-Spanish fluency on T-scores after adjusting for these variables, such that relative bilinguals obtained significantly higher T-scores than Spanish-dominant participants on Digit Symbol (Estimate=4.55, SE=1.74, p=.01), Symbol Search (Estimate=4.13, SE=1.86, p=.03) and Trails B (Estimate=3.95, SE=1.63, p=.02).
Discussion
The purpose of this study was to understand the degree to which second language ability influences neuropsychological test performance in the native language, above and beyond normative demographic adjustments for age, sex, and years of education. In the context of much research supporting bilingual advantages and disadvantages in different cognitive processes (Bialystok 1999, 2001; Carlson and Meltzoff, 2008; Bialystok, Craik, & Luk, 2008; Coderre et al., 2013; Gollan, Montoya, & Werner, 2002; Gollan, Montoya, Bonanni, 2005; Suarez et al., 2014), the current study is unique in that it examines whether or not learning a second language later in life ought to be accounted for when using demographically corrected population-based norms. In particular, because higher degrees of bilingualism were associated with higher education and other important socioeconomic advantages in our sample of U.S.-dwelling, primary Spanish speakers, we wanted to understand whether adjusting scores for years of formal education would sufficiently account for these differences. Overall, we found that years of education is a good proxy for these background differences across most of the battery, with some exceptions.
It is important to emphasize that our analyses of Spanish-English bilingualism used an index of relative fluency in English (compared to a ratio of words produced in each language), and not absolute level of English fluency, as a way to control for overall cognitive ability. Nevertheless, greater degree of relative bilingualism in this population of primary Spanish-speakers (i.e., with higher relative English fluency) was associated with better raw scores on almost all of the tests in the NP-NUMBRS battery. On the other hand, when demographically corrected T-scores were applied (instead of raw scores), higher relative bilingualism was associated with better performance on only four tests: WAIS-III Digit Symbol, WAIS-III Symbol Search, WAIS-III Letter Number Sequencing, and the Trail Making Test-Part B. Group comparisons showed that participants with higher degrees of relative bilingualism had significantly higher T-scores on three of those four tests (WAIS-III Digit Symbol, Symbol Search, and the Trail Making Test B) compared to those with lower relative bilingualism (i.e., low English fluency scores). These findings suggest that norms adjusted for education, age and gender correct for much of the observed effect of Spanish-English bilingualism on neuropsychological test performance in this primarily Spanish-speaking sample. The results of the current study might also suggest that equal T-scores for Spanish-dominant vs. relative bilingual invididuals represent differences in abilities given that mean T-score differences varied between 4.3 and 5.3 for the 3 tests where significant differences were found. Without further replication, however, we are hesitant to overinterpret the meaning of the relationships and group differences that reached statistical significance as indicating cognitive domain-specific advantages, but in our sample tests where these were observed involved aspects of processing speed, working memory, and cognitive set-shifting, and also visual scanning, familiarity with abstract symbols, and familiarity with the alphabet.
The NP-NUMBRS project was not originally designed to study effects of bilingualism. To this end, our data are not directly comparable with much of the bilingualism literature in that our sample comprises adult native Spanish speakers who were tested in Spanish as their preferred language, most of whom acquired their varying degrees of English ability as adults, and had similar levels of education-corrected Spanish language ability regardless of their level of English fluency. Thus, it is not clear whether our limited results suggest an advantage in learning a second language for certain aspects of cognitive functioning, even when cognitively engaging in the native language. Alternatively, or in addition, the results may reflect that the constellation of sociodemographic advantages associated with bilingualism in this population, are largely, but not fully, captured by our demographic adjustments (especially education adjustments) on most tests in the NUMBRS battery. Participants with greater relative English fluency had more years of education, as well as more years of education in the U.S., higher parental educational attainment, longer time living in the U.S., reported higher SES, and were less likely to have worked as children. Secondary analyses in the present study showed that the bilingual advantage on three out of seven measures was not explained by these factors. These findings would be consistent with a recent study by Naeem and colleagues (2018) examined socioeconomic status as a modulator of the “bilingual advantage” and found that bilingualism might improve processing speed for those of lower SES, but not necessarily for people who were, at the outset, of higher SES. These findings might, at least partially, explain the overall effect of better processing speed not previously found in other studies in that fewer than 18% of our sample considered themselves to be of upper SES. To this end, ongoing work by our group is examining the impact of these nuanced, complex, and interrelated set of language, educational, and social factors in the NP-NUMBRS cohort. Findings could shed some light on the independent or potentially interacting effects of these variables on test performance, but with the limitation that this is an observational study with only imperfect, retrospective reports of background factors that may be relevant to bilingualism and other aspects of cognitive functioning in adults. From a practical perspective, it is worth considering that regardless of the factors driving the link between degree of bilingualism and cognition in the present study, the measure we used provides an objective way to capture a variable that is not fully accounted for in the standard demographic adjustments.
To reiterate, the current study is limited in that the data was not originally collected with the intent of examining the effects of bilingualism on cognitive testing. Had this been the case, an English-dominant group would not have been excluded to allow for comparison between this group and the relative balanced bilingual group. This comparison would have been valuable presuming these two groups were comparable with regard to SES and associated educational capital given that the balanced bilingual group demonstrated SES advantages early in life when compared to the Spanish-dominant group. To this end, a paper in preparation by our group will directly address the effects of quality of education on overall cognitive abilities and will further examine if the effects of relative bilingualism remain after controlling for educational background, childhood socioeconomic environment, and language usage (Kamalayan, et al., in preparation). In addition, future research should examine whether or not an acculturation measures should be considered after disentangling the effects of quality of education and bilingualism. Ultimately, this research could aid clinicians in clinical-decision making when tasked with the challenge of selecting the appropriate demographically derived norms for use with Spanish-speaking adults living in the United States who have learned English as a second language.
Overall, these findings suggest that the demographically-adjusted norms generated with adult, U.S.-dwelling native Spanish-speakers from the NP-NUMBRS project are generally appropriate for primary Spanish-speakers with varying degrees of Spanish-English bilingualism and dramatically correct differences in raw test scores between those with relative lower and higher English fluency. Yet, degree of Spanish-English bilingualism may need to be considered when interpreting results of specific tests in the NP-NUMBRS battery. Ignoring a patient’s bilingual capacity when tested in their native language could result in reduced sensitivity of tests where an effect of second language fluency was found. Current findings underscore the importance of gathering thorough information about a patient’s level of second language ability in the course of neuropsychological assessments, as further research may confirm the need to take into account Spanish-English bilingualism as part of interpretation standards. A relatively easy to administer English fluency index of the type used in the current study could serve to guide a clinician and could prove to be a sufficient proxy in the absence of more detailed sociodemograhic and acculturation information.
Acknowledgements
This work was supported by grants from the National Institutes of Health (P30MH62512, R01MH57266, K23MH105297, P30AG059299, U01AG052564-01) and the UCSD Hispanic Center of Excellence (HRSA D34HP31027).
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