Abstract
OBJECTIVE:
To generate normative data for the Verbal Fluency Tests across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.
METHOD:
The sample consisted of 3,977 healthy adults who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and, Puerto Rico. Each subject was administered the Verbal Fluency Test as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.
RESULTS:
The final multiple linear regression models for the letter F explained 8−30% of the variance, 7−32% for letter A, 8−32% for the letter S, and 16−43% for the animal category in Verbal Fluency Test scores. Although t−tests showed significant differences between men and women on the Verbal Fluency Test, they did not have an effect size larger than 0.3. As a result, gender-adjusted norms were not generated.
CONCLUSIONS:
This is the first normative multicenter study conducted in Latin America aiming to create norms for the Verbal Fluency Test; this study will have important outcomes for the future of neuropsychology in the region.
Keywords
Introduction
Verbal fluency tests are used to assess complex cognitive functioning, including executive dysfunction. They are most commonly administered to individuals who have experienced neurological damage, including traumatic brain injury (TBI), brain lesions, multiple sclerosis, schizophrenia, and Parkinson’s (Henry & Crawford, 2004a; Strauss, Sherman, & Spreen, 2006). In healthy individuals, certain areas of the brain are activated when these tasks are attempted, including the left frontal cortex (associated with stimulation of Broca’s area), the dorsolateral prefrontal cortex, the premotor cortex, and the right cerebellum (Cabeza & Nyberg, 2000; Indefrey & Levelt, 2000; McGraw, Mathews, Wang, & Phillips, 2001).
Verbal fluency is defined as “the ability to form and express words in accordance with required criteria” and is essential for communication and functioning (Wysokiński et al., 2010, p. 438). Verbal fluency tasks require individuals to be flexible, organize information, provide effort, and exercise inhibition when necessary. An inability to complete these tasks is suggestive of frontal lobe dysfunction, specifically dysfunction in the left frontal cortex (Gouveia, Brucki, Malheiros, & Bueno, 2007). In phonological fluency tasks, individuals are asked to produce words corresponding with a specific letter of the alphabet (e.g., F), while semantic fluency tasks involve producing words corresponding with a target category of items (e.g., animals; Henry & Crawford, 2004a). They are often examined concurrently, although some studies have focused solely on one component of verbal fluency (Strauss et al., 2006). Additionally, these tasks can be combined to assess phonological and semantic fluency simultaneously in one operation (e.g., fruit that being with A; Heller & Dobbs, 1993).
In the phonological portion of the verbal fluency tests, participants are presented with a letter and asked to produce as many words that begin with that letter as possible. Participants are typically given the letters F, A, and S, one at a time, and then have one minute per letter to complete the task (Strauss et al., 2006). The letters C, F, and L are also occasionally used for this task, and research has shown that these two clusters of letters produce comparable results (Troyer, 2000). In other languages, different combinations of letters are used to assess phonological fluency in more culturally-appropriate ways. For example, one Arabic study tested the use of W, R, and G, and found that these letters are more appropriate for use within an Arabic population (Khalil, 2010). Similarly, a Greek study used X (Chi),
The semantic verbal fluency task requires requires participants to produce items that fall within a given category that is provided (e.g., animals), generally within one minute (Strauss et al., 2006). The most common category is “animals,” although other studies have used fruits, occupations, items from a grocery store, and furniture (da Silva, Petersson, Faisca, Ingvar, & Reis, 2005; Gocer March & Pattison, 2006; Price et al., 2012; Troyer, 2000). The result is the number of words produced for each category, and sometimes the total summed score of words produced across all semantic fluency tasks (Strauss et al., 2006; Wysokiński et al., 2010).
Verbal fluency tests have been administered in a variety of populations, including to patients with various neurological disorders (Strauss et al., 2006). Phonological and semantic fluency have been assessed in individuals with schizophrenia (Costafreda et al., 2011; Henry & Crawford, 2005), cognitive impairment (Price et al., 2012), TBI (Henry & Crawford, 2004a; Raskin & Rearick, 1996), brain lesions (Baldo & Shimamura, 1998), Alzheimer’s disease (Gocer March & Pattison, 2006; Pasquier, Lebert, Grymonprez, & Petit, 1995; Randolph, Braun, Goldberg, & Chase, 1993; Ting, Hameed, Earnest, & Tan, 2012), dementia (Moreno-Martinez & Montoro, 2010; Pasquier et al., 1995), Parkinson’s disease (Henry & Crawford, 2004b; Randolph et al., 1993), Huntington’s disease (Randolph et al., 1993; Weber, Koch, & Reilmann, 2012), clinically isolated syndrome (Anhoque, Biccas-Neto, Domingues, Teixeira, & Domingues, 2013; Till et al., 2013), and multiple sclerosis (Brissart et al., 2013; Till et al., 2013).
Previous studies have identified associations between demographic variables and verbal fluency, both phonological and semantic. Age is associated with phonological fluency, such that scores increase substantially between ages five and seven, continue to increase through early adulthood, and subsequently begin to decline in old age (Strauss et al., 2006). Semantic fluency abilities often level off around age 11 or 12 (Sauzeon, Lestage, Raboutet, N’Kaoua, & Claverie, 2004) and decline around age 20 (Mitrushina, 2005). Education level is related to verbal fluency, such that those with more years of education tend to achieve higher scores on both tasks (Strauss et al., 2006). Moreover, reading level and phonological fluency are moderately correlated, while a smaller correlation has been found between reading level and scores on semantic fluency (Johnson-Selfridge & Zalewski, 2001). Studies have shown differences in verbal fluency based on ethnicity, language, and geographic region. Specifically, Caucasians and non-Hispanics tend to produce more words on phonological and semantic tasks than individuals from other ethnic groups (Strauss et al., 2006). Higher level of intelligence is also related to better phonological and semantic fluency (Diaz-Asper, Schretlen, & Pearlson, 2004). Gender is another demographic variable that has been examined in the context of performance on phonological and semantic fluency tasks. Data on the impact of gender, however, are inconsistent. Gladsjo et al. (1999) conducted a review of reported verbal fluency norms and found that gender is inconsistently associated with phonological fluency and unrelated to semantic fluency.
To date, verbal fluency norms have been established for adults, specifically for the phonological and semantic tasks (Strauss et al., 2006). Metanorms and individual norms studies have been completed for the phonological component of verbal fluency using the cluster of letters F, A, and S, most commonly with English speaking adults in the United States and Canada (Acevedo et al., 2000; Gladsjo et al., 1999; Heaton, 2004; Loonstra, Tarlow, & Sellers, 2001; Mitrushina, 2005; Tombaugh, Kozak, & Rees, 1999). Additionally, norms have been established in the United States for phonological fluency using the letters C, F, and L (Ruff, Light, Parker, & Levin, 1996). Normative data are also available for elderly adults in these regions (Ivnik, Malec, Smith, Tangalos, & Peterson, 1996; Ravdin, Katzen, Agrawal, & Relkin, 2003; Steinberg, Bieliauskas, Smith, & Ivnik, 2005; Stricks, Pittman, Jacobs, Sano, & Stern, 1998). Stricks et al. (2008) collected normative data in Spanish from Spanish-speakers in the United States. Few norms studies on phonological fluency have been conducted in regions outside of the United States and Canada, including Sweden (Tallberg, Ivachova, Jones Tinghag, & Österberg, 2008), Saudi Arabia (Khalil, 2010), and the Netherlands (Van der Elst, Van Boxtel, Van Breukelen, & Jolles, 2006). Semantic fluency norms have also been established for animals, vegetables, fruits, foods, and clothing, primarily from the United States and Canada (Gladsjo et al., 1999; Lucas et al., 1998; Mitrushina, 2005; Tombaugh et al., 1999). Only a select few collected norms for Spanish-speaking individuals, none of which were established in Latin America (Acevedo et al., 2000; Lucas et al., 1998; Stricks et al., 1998).
Normative data are also available for verbal fluency for children and adolescents (Strauss et al., 2006), but they are less frequent. Several studies established norms for F, A, and S on the phonological verbal fluency task (Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Delis, Kaplan, & Kramer, 2001). In addition, normative data are available for the following clusters of letters: C, F, L and P, R, W, as well as the sound, “sh” (Barr, 2003; Halperin, Healey, Zeitchik, Ludman, & Weinstein, 1989; Schum, Sivan, & Benton, 1989). Semantic fluency norms have also been established for children and adolescents (Halperin et al., 1989).
Trends in verbal fluency scores and demographic variables have been identified, as have normative data on the phonological and semantic components of verbal fluency (Strauss et al., 2006). Norms from Latin America, however, are non-existent, and as a result, there is a need to establish normative data on verbal fluency in Latin America given the paucity of research in this diverse region.
Method
Participants
The sample consisted of 3,977 healthy individuals who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and, Puerto Rico. The participants were selected according to the following criteria: a) were between 18 to 95 years of age, b) were born and currently lived in the country where the protocol was conducted, c) spoke Spanish as their native language, d) had completed at least one year of formal education, e) were able to read and write at the time of evaluation, f) scored ≥23 on the Mini-Mental State Examination (MMSE, Folstein, Folstein, & McHugh, 1975), g) scored ≤4 on the Patient Health Questionnaire–9 (PHQ-9, Kroenke, Spitzer, & Williams, 2001), and h) scored ≥90 on the Barthel Index (Mahoney & Barthel, 1965).
Participants with self-reported neurologic or psychiatric disorders were excluded due to a potential effect on cognitive performance. Participants were volunteers from the community and signed an informed consent. Nine participants were excluded from the analyses, with a final sample of 3,961 participants. Socio-demographic and participant characteristics for each of the countries’ samples have been reported elsewhere Guárdia-Olmos, Peró-Cebollero, Rivera, & Arango-Lasprilla, (2015). The multi-center study was approved by the Ethics Committee of the coordinating site, the University of Deusto, Spain.
Instrument administration
For the present study, phonological and semantic verbal fluency tests were applied. The aim of each test is to create, within 60 seconds, as many words as participants can that begin with certain letter (F, A, S) or that belong to a particular category (animals). Participants were told to avoid proper names, augmentatives, and diminutives. At the same time, the examiner provided prompts if the participant gave no responses within 10 seconds during each trial. The total score consisted of the total correct answers for each letter or category.
Statistical analyses
The detailed statistical analyses used to generate the normative data for this test are described in Guárdia-Olmos et al. (2015). In summary, the data manipulation process for each country-specific dataset involved five-steps: a)
Results
Phonological fluency - letter F
Regarding the effect of gender on letter F, the
The final eleven letter F multivariate linear regression models for each country are shown in Table 2. In all countries, except Argentina, Guatemala, Honduras, and, Paraguay, the letter F score increased for those with more than 12 years of education (see Table 2) and, in all countries except Argentina, Guatemala, Honduras, and, Paraguay, letter F score decreased in a linear fashion as a function of age. The amount of variance explained in letter F scores ranged from 8% (in Cuba) to 30% (in Chile).
Phonological fluency - letter A
Regarding the effect of gender on letter A, the
The final eleven letter A multivariate linear regression models for each country are shown in Table 4. In all countries, the letter A score increased for those with more than 12 years of education (see Table 4) and decreased in a linear fashion as a function of age except in Argentina, Guatemala and, Paraguay. The amount of variance explained in letter A scores ranged from 7% (in Paraguay) to 32% (in Chile).
Phonological fluency - letter S
Regarding the effect of gender on letter S, the
The final eleven letter S multivariate linear regression models for each country are shown in Table 6. In all countries, the letter S score increased for those with more than 12 years of education (see Table 6) and decreased in linear fashion as a function of age except in Argentina, Guatemala and, Paraguay. The amount of variance explained in letter S scores ranged from 8% (in Cuba) to 32% (in Chile).
Semantic fluency - animals
Regarding the effect of gender on animals, the
The final eleven animals multivariate linear regression models for each country are shown in Table 8. In all countries, the animals score increased for those with more than 12 years of education (see Table 2) and decreased in a linear fashion as a function of age. The amount of variance explained in animals scores ranged from 16% (in Cuba) to 43% (in Paraguay).
Normative procedure
Norms (e.g., a percentile score) for the verbal fluency different scores were established using the five-step procedure described above. To facilitate the understanding of the procedure to obtain the percentile associated with a score on this test, an example will be given. Suppose you need to find the percentile score for a Mexican woman, who is 50 years old and has 8 years of education. She has a score of 20 on animals. The steps to obtain the percentile for this score are: a) Check Table 7 to determine if the effect size of gender in the country of interest (Mexico) on this test and time point (animals) is greater than 0.3 by country. The column labelled
User-friendly normative data
The five-step normative procedures explained above can provide more individualized norms. However, this method can be prone to human error due to the number of required computations. To enhance user-friendliness, the authors have completed these steps for a range of raw scores based on small age range groupings (see Guárdia-Olmos et al., 2015) and created tables so that clinicians can more easily use to obtain a percentile range associated with a given raw score on this test. These tables are available by country and type of test in the Appendix. In order to obtain an approximate percentile for the above example (converting a raw score of 20 in animals for a Mexican women who is 50 years old and has 8 years of education using the simplified normative tables provided, the following steps are recommended. (1) First, identify the appropriate table ensuring the specific country and test. In this case, the table for animals scores for Mexico can be found in Table A41. (2) Note if the title of the table indicates that it is only to be used for one specific gender. In this case, gender is not specified. Thus Table A41 is used for both males and females. (3) Next, the table is divided based on educational level (1 to 12 vs. 12 years of education). Since this woman has 8 years of education, she falls into 1–12 years of education category. These data can be found in the top section of the table. (4) Determine the age range most appropriate for the individual. In this case, 50 falls into the column 48–52 years of age. (5) Read down the age range column to find the approximate location of the raw score the person obtained on the test. Reading down the 48–52 column, the score of 20 obtained by this Mexican woman corresponds to an approximate percentile of 70.
The percentile obtained via this user-friendly table method (70th) is slightly different than the more exact one (72th) obtained following the individual conversion steps above because the table method is based on an age range (e.g., individuals aged 48–52) instead of the exact age (individuals aged 50). If the exact score is not listed in the column, you must estimate the percentile value from the listed raw scores.
Discussion
The purpose of the current study was to generate normative data on the verbal fluency tests across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate. The final multiple linear regression models explained between 8–30% of the variance in letter F scores, 7–32% of the variance in letter A scores, 8–32% of the variance in letter S scores, and 16–43% of the variance in animals category scores. There were a number of gender differences across all four verbal fluency test scores in several different countries, but the all effect sizes were small, so gender-adjusted norms were not generated for any country. These findings were in line with the previous literature, which has shown that the impact of gender on phonological fluency and semantic fluency performance is inconsistent or non-existent (Gladsjo et al., 1999). When considering the previous research, the current results suggest that gender-adjustments should not be made when obtaining percentiles for the verbal fluency tests in Latin America.
Verbal fluency test scores increased linearly as a function of education in all countries and tests. This robust pattern is consistent with the previous literature which has found participants with more years of education to score higher on verbal fluency tasks (Strauss et al., 2006). When considering the previous research, it is suggested that neuropsychologists in Latin America use education-adjusted norms for each country when administering the verbal fluency tests in that country. As there are likely large differences in education throughout Latin America, this study’s education adjustments will be important when administering the verbal fluency tests across many different Latin American countries.
Verbal fluency test scores worsened with increasing age in most countries. However, Argentina, Paraguay, Honduras, and Guatemala did not show an effect of age on letter F scores; Argentina, Paraguay, and Guatemala did not show an effect of age on letter A scores; and Argentina, Paraguay, and Guatemala did not show an effect of age on letter S scores. Previous studies have found that higher age is associated with reduced phonological fluency (Strauss et al., 2006) and semantic fluency (Mitrushina, 2005). In consideration of the previous findings, the current study suggests that verbal fluency test corrections for age in Latin America should be made in all countries and tests except for those that showed no age effect in the current study.
Limitations and future directions
The current study has several limitations and directions for future research. First, participants in this study spoke Spanish as a primary language, and no data were collected on bilingualism. Verbal fluency test performance could likely differ among individuals who speak secondary languages, so future research should examine effects of bilingualism on verbal fluency test performance. Participants were recruited in specific cities and regions of the countries in this study as opposed to nationally. Although the current study is the largest verbal fluency test normative study to date in Latin America, or in any global region, it should be seen as a first step toward larger, nationally representative normative studies. Although many participants in this study had fewer than 12 years of education, illiterate individuals did not participate, and the current norms cannot generalize well to illiterate adults. Similarly, participants with a history of neurological conditions and children were excluded, so future normative research should be conducted with these groups.
Second, neuropsychologists need to exercise caution in using the verbal fluency test norms from this study for people in countries outside of those from which data were collected. Researchers need to create verbal fluency test norms in Latin American countries such as Ecuador, Uruguay, Venezuela, and Panama. Despite this limitation, these verbal fluency test norms may be actually more accurate in Latin American countries not a part of this study than current norms in use, but this generalizability needs to be investigated in future studies.
Third, the verbal fluency tests are common neuropsychological instruments in Latin America, but other measures need to be normed using a similar approach to improve their accuracy in this region. Future research should also examine the psychometrics and ecological validity of the verbal fluency tests, and that of other common assessments in Latin America. Researchers need to create instruments within Latin American cultures with good ecological validity, as the verbal fluency tests were created in a Western culture perhaps very different from the diverse cultures in Latin America. Future research can develop assessments within local cultures, not simply translate and norm tests from other countries.
Despite these limitations, limited studies have produced verbal fluency test norms in Spanish-speakers in the United States (Lucas et al., 1998; Stricks et al., 1998; Acevedo et al., 2000). The current study was the first to generate verbal fluency test norms across 11 countries in Latin America with nearly 4,000 participants. As a result, it was the largest, most comprehensive verbal fluency test normative study to date in any global region, and its norms will influence the standard of neuropsychological assessment with the verbal fluency tests in Latin America unlike any study before it.
Appendix:
Normative data for the Letter F stratified by education levels for ARGENTINA
Normative data for the Letter F stratified by age and education levels for BOLIVIA
Normative data for the Letter F stratified by age and education levels for CHILE
Normative data for the Letter F stratified by age and education levels for CUBA
Normative data for the Letter F stratified by age and education levels for EL SALVADOR
Normative data for the Letter F stratified by education levels for GUATEMALA
Normative data for the Letter F stratified by education levels for HONDURAS
Normative data for the Letter F stratified by age and education levels for MEXICO
Normative data for the Letter F stratified by education levels for PARAGUAY
Normative data for the Letter F stratified by age and education levels for PERU
Normative data for the Letter F stratified by age and education levels for PUERTO RICO
Normative data for the Letter A stratified by education levels for ARGENTINA
Normative data for the Letter A stratified by age and education levels for BOLIVIA
Normative data for the Letter A stratified by age and education levels for CHILE
Normative data for the Letter A stratified by age and education levels for CUBA
Normative data for the Letter A stratified by age and education levels for EL SALVADOR
Normative data for the Letter A stratified by education levels for GUATEMALA
Normative data for the Letter A stratified by age and education levels for HONDURAS
Normative data for the Letter A stratified by age and education levels for MEXICO
Normative data for the Letter A stratified by education levels for PARAGUAY
Normative data for the Letter A stratified by age and education levels for PERU
Normative data for the Letter A stratified by age and education levels for PUERTO RICO
Normative data for the Letter S stratified by education levels for ARGENTINA
Normative data for the Letter S stratified by age and education levels for BOLIVIA
Normative data for the Letter S stratified by age and education levels for CHILE
Normative data for the Letter S stratified by age and education levels for CUBA
Normative data for the Letter S stratified by age and education levels for EL SALVADOR
Normative data for the Letter S stratified by education levels for GUATEMALA
Normative data for the Letter S stratified by age and education levels for HONDURAS
Normative data for the Letter S stratified by age and education levels for MEXICO
Normative data for the Letter S stratified by education levels for PARAGUAY
Normative data for the Letter S stratified by age and education levels for PERU
Normative data for the Letter S stratified by age and education levels for PUERTO RICO
Normative data for the Animals category stratified by age and education levels for ARGENTINA
Normative data for the Animals category stratified by age and education levels for BOLIVIA
Normative data for the Animals category stratified by age and education levels for CHILE
Normative data for the Animals category stratified by age and education levels for CUBA
Normative data for the Animals category stratified by age and education levels for EL SALVADOR
Normative data for the Animals category stratified by age and education levels for GUATEMALA
Normative data for the Animals category stratified by age and education levels for HONDURAS
Normative data for the Animals category stratified by age and education levels for MEXICO
Normative data for the Animals category stratified by age and education levels for PARAGUAY
Normative data for the Animals category stratified by age and education levels for PERU
Normative data for the Animals category stratified by age and education levels for PUERTO RICO