Standard form of the Boston Naming Test: Normative data for the Latin American Spanish speaking adult population

2.1Participants

The sample consisted of 3,779 healthy individuals who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, 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. Socio-demographic and participant characteristics for each of the countries’ samples have been reported in 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.

2.2Instrument administration

The BNT requires examinees to denominate 60 pictures, which are presented in order of increasing difficulty. If the participant does not give a correct answer spontaneously, the examiner provides a semantic clue (in case of misrecognition error) or phonological clue (when the semantic clue is still not enough to generate a response, or during the spontaneous response there has been an error that is not a misrecognition error). The total score is the sum of correct spontaneous answers plus correct answers followed by a semantic clue (Goodglass, Kaplan, & Barresi, 2001). The administration of the BNT was rigorous. Thus, synonyms were not taken as correct responses.

2.3Statistical 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) t – tests for independent samples and effect sizes (r) were conducted to determine gender effects. If the effect size was larger than 0.3, gender was included in the model with gender dummy coded and female as the reference group (male = 1 and female = 0). b) A multivariable regression model was used to specify the predictive model including gender (if effect size was larger than 0.3), age as a continuous variable, and education as a dummy coded variable with 1 if the participant had >12 years of education and 0 if the participants had 1–12 years of education. If gender, age and/or education was not statistically significant in this multivariate model with an alpha of 0.05, the non-significant variables were removed and the model was re-run. Then a final regression model was conducted that included age (if statistically significant in the multivariate model), dichotomized education (if statistically significant in the multivariate model), and/or gender (if effect size was greater than 0.3) [yˆi=β0+(βAge· Agei) + (βEduc· Educi)  +   (β _Gender  ·  Gender _i )] c) residual scores were calculated based on this final model (ei=yi-yˆi) d) using the SD (residual) value provided by the regression model, residuals were standardized: z = e _i /SD _e , with SD _e (residual) = the standard deviation of the residuals in the normative sample; and e) standardized residuals were converted to percentile values (Strauss et al., 2006). Using each country’s dataset, these steps were applied to BNT score.

3.1Normative procedure

Norms (e.g., a percentile score) for the BNT score test 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 man, who is 59 years old and has 15 years of education. He has a score of 52 on the BNT. The steps to obtain the percentile for this score are: a) Check Table 1 to determine if the effect size of gender in the country of interest (Mexico) on this test and task (BNT) is greater than 0.3 by country. The column labelled r in Table 1 indicates the effect size. In this example, the effect size is 0.146, which is not greater than 0.3. For Mexicans on this test, gender does not influence scores to a sufficient degree to take it into account when determining the percentile. b) Find Mexico in Table 2, which provides the final regression models by country for the BNT. Use the B weights to create an equation that will allow you to obtain the predicted BNT score. The corresponding B weights are multiplied by the actual age and dichotomized education scores and added to a constant in order to calculate the predicted value. In this case, the predicted BNT score would be calculated using the equation [yˆi = 51.054 + (-0.081 . Agei) + (4.593 · Dichotomized Educational Leveli)] . (the values have been rounded for presentation in the formula). The subscript notation i indicates the person of interest. The person’s age is 59, but the education variable is not continuous in the model. Years of education is split into either 1 to 12 years (and assigned a 0) or more than 12 years (and assigned a 1) in the model. Since our hypothetical person in the example has 15 years of education, his educational level value is 1. Thus the predicted value is yˆi = 51.054 + (-0.081 · 59) + (4.593 · 1) = 51.054- 4.763 + 4.593 = 50.88) c) In order to calculate the residual value (indicated with an e in the equation), we subtract the actual value from the predicted value we just calculated ( ei = yi - yˆi) . In this case, it would be e _i   =  52  -  50.8  =  1.117. d) Next, consult the SD _e column in Table 2 to obtain the country-specific SD _e (residual) value. For Mexico it is 6.820. Using this value, we can transform the residual value to a standardized z score using the equation (e _i  / SD _e ). In this case, we have (1.117)  / 6.820  =  0.163. This is the standardized z score for a Mexican man aged 59 years with 15 years of education and a score of 52 on the BNT. e) The last step is to look-up the tables in the statistical reference books (e.g. Strauss et al., 2006) or use a trusted online calculator like the one available at http://www.measuringu.com/pcalcz.php. In the online calculator, you would enter the z score and choose a one-sided test and note the percent of area after hitting the submit button. In this case, the probability of 0.163 corresponds to the 56th percentile.

3.2User-friendly normative data tables

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 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 in the Appendix. In order to obtain an approximate percentile for the above example (converting a raw score of 52 for a Mexican man who is 59 years old and has 15 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 BNT scores for Mexico can be found in Table A7. (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 A8 is used for both males and females. (3) Next, the table is divided based on educational level (1 to 12 vs. more than 12 years of education). Since this man has 15 years of education, he falls into the more than 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, 59 falls into the column 58–62 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 58–62 column, the score of 52 obtained by this Mexican man corresponds to an approximate percentile of 60.

The percentile obtained via this user-friendly table method (60th) is slightly different than the more exact one (56th) obtained following the individual conversion steps above because the table method is based on an age range (e.g., individuals aged 58–62) instead of the exact age (individuals aged 59). If the exact score is not listed in the column, you must estimate the percentile value from the listed raw scores.

4.1Limitations and future directions

This study has several limitations that should be taken into account and that can also be seen as directions for future research. First, neuropsychologists should exercise caution when using the BNT norms from this study for people in countries other than the 10 countries from which data were collected. It is important that future studies create BNT norms in other Latin American countries such as Ecuador, Uruguay, Venezuela, and Panama. Nonetheless, the current BNT norms could be more accurate in those Latin American countries than other norms currently in use, such as those from Spain. Examining this generalizability is a crucial area for future research.

Second, the BNT is a very common neuropsychological measure in Latin America, but many other assessments need to be normed in the same manner to improve their use in this region. Future studies should investigate the psychometric properties and ecological validity of the BNT and other typical instruments in Latin America. If shown to be low, researchers need to create instruments within Latin American cultures that have strong ecological validity. For example, the Modified Boston Naming Test-Spanish (MBNT-S; Ponton et al., 1996) is less sensitive to dementia-related naming impairment than a naming test developed for Spanish-speakers (Texas Naming Test [TNT]; de la Plata et al., 2008). Much more of this line of research needs to be conducted, as the BNT was created in a Western culture that may differ from the diverse cultures in Latin America. Future studies would benefit from creating assessments within local cultures, not simply translate and norm tests from other cultures and countries.

Third, the current data should be interpreted and used in light of several sampling limitations. Participants in the current study spoke Spanish as their primary language. This study did not collect data on whether participants spoke additional local languages or English, and BNT performance could differ among people who speak secondary languages. Future research should examine the influence of bilingualism on BNT performance. It should also be noted that data collection occurred in specific cities or regions of the countries in in this study as opposed to nationally. Although the current study is the largest neuropsychological normative study in the history of Latin America for the BNT, or in any global region, it should be seen as a first step toward larger, nationally representative normative studies. For example, many participants had fewer than 12 years of education, but illiterate individuals were excluded. Therefore the current norms cannot generalize well to illiterate adults. Similarly, participants were excluded who had a history of neurological conditions; future studies should be conducted with illiterate adults, with neurological populations, as well as children.

Although these limitations are present, only limited studies have produced BNT norms in Spanish-speaking populations (Allegri et al., 1997; Peña-Casanova et al., 2009; Rami et al., 2008). This study was the first to generate BNT norms across ten countries in Latin America with nearly 4,000 participants. Therefore this was the largest, most comprehensive BNT normative study to date in any global region, and its norms will likely affect the standard of neuropsychological assessment with the BNT in Latin America unlike any study before it.