Stroop Color-Word Interference Test: Normative data for the Latin American Spanish speaking adult population

2.1Participants

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. 62 participants were excluded from the analyses, with a final sample of 3915 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.

2.2Instrument administration

The Stroop Color and Word test consists of three pages, each with 100 components randomly organized into five columns. The subject has 45 seconds to read aloud, as quickly as possible, the columns from left to right. The sheet on words is formed by the words “Red,” “Green,” and “Blue” in black ink, and the score is the number of words read correctly. For the sheet on colors, there are groups of four X’s (“XXXX”) printed in blue, green, and red. The score is the number of elements properly named. Finally, the last list consists of the three words of the first printed page in the colors of the second, with words being incongruent with the color of the ink. The task is to name the ink color, inhibiting the reading of the word, and the score is the number of correctly named elements. Finally, an Interference Index is calculated with the formula: WC - [(WxC) / (P + W)], and indicates the degree to which the person has control over interference (Golden, 2007).

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 multivariate 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 participants had >12 years of education and 0 if 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)+(βGender.Genderi)]; 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 , 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 Stroop Word, Color, Word-Color, and Interference scores. In certain countries, there was no effect of gender, nor age, nor education on the scores. In these cases, the normative data were generated directly using the raw scores.

3.1Stroop Word

Regarding the effect of gender on Stroop Word, the t-tests showed significant differences between men and women in Guatemala, and Peru; however, none of these two countries had an effect size larger than 0.3. Table 1 shows the results of the gender analyses by country on Stroop Word. As shown in Table 1, the effect sizes for all countries were less than 0.3, and therefore gender was not taken into account to generate Stroop Word normative data for any of the countries in the study.

The final eleven Stroop Word multivariate linear regression models for each country are shown in Table 2. In all countries the Stroop Word score increased for those with more than 12 years of education (see Table 2) and, in all countries Stroop Word score decreased in a linear fashion as a function of age. The amount of variance explained in Stroop Word scores ranged from 14% (in Argentina) to 36% (in Chile).

3.2Stroop Color

Regarding the effect of gender on Stroop Color, the t-tests showed significant differences between men and women in the countries of Guatemala, Honduras, and Mexico; however, none of these three countries had an effect size larger than 0.3. Table 3 shows the results of the gender analysis by country on Stroop Color. As shown in Table 1, the effect sizes for all countries were less than 0.3, and therefore gender was not taken into account to generate Stroop Color normative data for any of the countries in the study.

The final eleven Stroop Color multivariate linear regression models for each country are shown in Table 4. In all countries, except Peru, the Stroop Color score increased for those with more than 12 years of education (see Table 4) in all countries Stroop Color score decreased in a linear fashion as a function of age. The amount of variance explained in Stroop Color scores ranged from 14% (in Guatemala) to 42% (in Chile).

3.3Stroop Word-Color

Regarding the effect of gender on Stroop Word-Color, the t-tests showed significant differences between men and women in the countries of Chile, Honduras, and Mexico, however, none of these countries had an effect size larger than 0.3. Table 5 shows the results of the gender analysis by country on Stroop Word-Color. As shown in Table 5, the effect sizes for all countries were less than 0.3, and therefore gender was not taken into account to generate Stroop Word-Color normative data for any of the countries in the study.

The final eleven Stroop Word-Color multivariate linear regression models for each country are shown in Table 6. In all countries the Stroop Word-Color score increased for those with more than 12 years of education (see Table 6) and decreased in a linear fashion as a function of age. The amount of variance explained in Stroop Word-Color scores ranged from 16% (in Guatemala) to 43% (in Chile).

3.4Stroop Interference

Regarding the effect of gender on Stroop Interference, the t-tests showed significant differences between men and women in Chile, Cuba, Mexico, and Puerto Rico, however, none of these four countries had an effect size larger than 0.3. Table 7 shows the results of the gender analysis by country on Stroop Interference. As shown in Table 7, the effect sizes for all countries were less than 0.3, and therefore gender was not taken into account to generate Stroop Interference normative data for any of the countries in the study.

The final seven Stroop Interference multivariate linear regression models for each country are shown in Table 8. El Salvador, Peru, and Puerto Rico, the Stroop Interference score increased for those with more than 12 years of education (see Table 8), and Argentina, Chile, Mexico, and Paraguay decreased in a linear fashion as a function of age. Cuba, Bolivia, Guatemala, and Honduras did not show effect to age, and education level. The amount of variance explained in Stroop Interference scores ranged from 4% (El Salvador) to 15% (Puerto Rico).

3.5Normative procedure

Norms (e.g., a percentile score) for the Stroop Word-Color test 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 Peruvian man, who is 50 years old and has 14 years of education. He has a score of 80 on Stroop Word. 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 (Peru) on this test and time point (Stroop Word) 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 0.144, which is not greater than 0.3. For Peruvian on this test, gender does not influence scores to a sufficient degree to take it into account when determining the percentile. b) Find Peru in Table 2, which provides the final regression models by country for Stroop Word scores. Use the B weights to create an equation that will allow you to obtain the predicted Stroop Word 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 Stroop Word score would be calculated using the equation [yˆi=104.045+(-0.409.Agei)+(10.152DichotomizedEducationalLeveli)] (the values have been rounded for presentation in the formula). The subscript notation i indicate the person of interest. The person’s age is 50, 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 14 years of education, his educational level value is 1. Thus the predicted value is yˆi=104.045+(-0.409·50)+(10.152·1)=104.045-20.463+10.152=93.733. 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  = 80 - 93.201 = -13.733. d) Next, consult the SD _e column in Table 2 to obtain the country-specific SD _e (residual) value. For Mexico it is 17.477. 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 (–13.733)/1 + 17.447 = -0.787. This is the standardized z score for a Peruvian man aged 50 and 14 years of education and a score of 80 on Stroop Word. 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 -0.787 corresponds to the 22th percentile. Please remember to use the appropriate tables that correspond to each test (Stroop Word, Color, Word-Color, and Interference) when performing these calculations. If the percentile for the Stroop Color, Word-Color, and Interference scores is desired, Tables 3–8 must be used.

3.6User-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 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 80 for a Peruvian man who is 50 years old and has 14 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 Stroop Word scores for Peru can be found in Table A10. (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 A10 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 14 years of education, he falls into the “>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 fall 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 80 obtained by this Peruvian man corresponds to an approximate percentile of 20.

The percentile obtained via this user-friendly table method (20th) is slightly different than the more exact one (22nd) 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.

4.1Limitations and future directions

This study has several limitations and directions for future research. First, neuropsychologists need to exercise caution in using the Stroop norms from the current study when performing assessments in countries other than those in which data were collected. An extremely important direction for future research involves creating Stroop norms in other countries in Latin America that were not included in the current study, such as Ecuador, Uruguay, Venezuela, and Panama. Although this is an important limitation, the Stroop norms from this study may be more accurate in other Latin American countries than the norms that neuropsychologists in those countries are currently using. This generalizability is extremely important to examine in future research.

Second, participants in this study spoke Spanish as their primary language, but it is unknown whether they spoke additional secondary languages. Stroop performance could differ in bilingual individuals from Latin America, and future research should study potential effects of bilingualism on performance. Participants were recruited from specific cities or regions of countries as opposed to nationally within those countries. This study is the largest neuropsychological normative study conducted to date in Latin America for the Stroop, or in any global region, so it should be considered a first step in the direction of larger, nationally representative normative studies. The sample was limited also in that although a good number of participants had fewer than 12 years of education, those who were unable to read were excluded, and as a result, these norms may not apply to illiterate adults. Similarly, participants with a history of neurological conditions and children did not participate in the study; future studies should collect data from these distinct populations.

Third, administration of the Stroop is extremely common in Latin America for neuropsychological assessment, but other common instruments need to be normed in the same manner to improve their use in this region as well. In the same vein, future research should examine the ecological validity and psychometric properties of the Stroop and other common neuropsychological instruments in Latin America. Researchers need to create instruments within Latin American cultures that have high ecological validity, as the Stroop was created in a Western culture that potentially differs in important ways from the diverse cultures in Latin America. Future studies would benefit from developing assessments within local cultures, instead of simply translating and norming instruments developed in other cultural contexts.

Although these limitations are present, only limited studies have produced Stroop norms in Spanish-speaking populations such as Spanish-speakers from the US-Mexico border (Artiola et al., 1999), Spain (Llinás-Reglá et al., 2013; Peña-Casanova et al., 2009), or Florida (Rosselli et al., 2002). As a result, this study was the first to create Stroop norms across 11 countries in Latin America with nearly 4,000 participants. It was the largest, most comprehensive Stroop normative study in any global region conducted to date, and its norms have the potential to have lasting effects on the standard of neuropsychological assessment with the Stroop in Latin America unlike any study before it.