Stroop Color-Word Interference Test: Normative data for Spanish-speaking pediatric population

2.1Participants

The sample consisted of 4,373 healthy children who were recruited from Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, Puerto Rico, and Spain. Participants were selected according to the following criteria: a) were between 6 and 17 years of age, b) were born and currently lived in the country where the study was conducted, c) spoke Spanish as their mother tongue, d) an IQ ≥80 on the Test Of Non-verbal Intelligence (TONI-2, Brown, Sherbenou, & Johnsen, 2009), and e) a score <19 on the Children’s Depression Inventory (CDI, Kovacs, 1992).

Children with history of neurologic or psychiatric disorders, as reported by the participant’s parent(s), were excluded due to its effects on cognitive performance. Participants in the study were recruited from public and private schools, and signed an informed consent. Socio-demographic and participant characteristics for each of the countries’ samples have been reported elsewhere (Rivera & Arango-Lasprilla, 2017). Ethics Committee approval was obtained for the study in each country.

2.2Instrument administration

The Stroop Color-Word Interference Test consists of three pages, each with 100 components randomly organized into five columns. In the first page the participant must read aloud the words “Red”, “Green”, and “Blue” printed in black ink. In the second one, “color naming”, the color (blue, green or red) of each element “XXXX” must be named. And in the last one, “interference”, the task is to name the color of the ink, inhibiting the reading of the word, which corresponds to the name of another color. The subject has 45 seconds to read aloud, as quickly as possible, the columns from left to right. Finally, the Interference Index was calculated with the formula: WC – [(W×C)/(W + C)], and indicates the degree to which the person has control over interference (Golden, 2007).

2.3Statistical analyses

Detailed statistical analyses used to generate the normative data for the Stroop Color-Word Interference test scores are described in Rivera & Arango-Lasprilla (2017). In summary, the scores were standardized using multiple linear regression analyses by means of a four-step procedure. 1) First, the Words, Color, Word-Color, and Interference scores were computed separately by means of the final multiple regression models. The full regression models included as predictors: age, age², sex, and mean level of parental education (MLPE). Age was centered (= calendar age – mean age in the sample by country) before computing the quadratic age term to avoid multicollinearity (Aiken & West, 1991). Sex was coded as male = 1 and female = 0. The MLPE variable was coded as 1 if the participant’s parent(s) had >12 years of education or 0 if participant’s parent(s) had ≤12 years of education. If predicted variables were not statistically significant in the multivariate model with an alpha of 0.05, the non-significant variables were removed and the model was run again. A final regression model was conducted yˆi=B0+B1·(Age-x¯Agebycountry)i+B2·(Age-x¯Agebycountry)i2+B3·Sexi+B4·MLPEi . 2) Residual scores (e_i) were calculated based on the final model (ei=yi-yˆi) . 3) Residuals were standardized using the residual Standard Deviation (SD_e) value provided by the regression model: z_i = e_i/SD_e. 4) Standardized residuals were converted to percentile values using the standard normal cumulative distribution function. This four-step process was applied for Words, Color, Word-Color, and Interference scores separately for each country.

For all multiple linear regression models, the following assumptions were evaluated: a) multicollinearity by the values of the Variance Inflation Factor (VIF), which must not exceed 10, and the collinearity tolerance values, which must not exceed the value of 1 (Kutner, Nachtsheim, Neter, & Li, 2005), and b) the existence of influential values by calculating the Cook’s distance. The maximum Cook’s distance value was related to a F (p, n - p) distribution. Influential values are considered when percentile value is equal or higher than 50 (Cook, 1977; Kutner et al., 2005). All analyzes were performed using SPSS version 23 (IBM Corp., Armonk, NY).

3.1Stroop Word

The final multivariate linear regression models for the ten country-specific Stroop Word score were significant (see Table 1). In all countries, the Stroop Word score increased linearly as a function of age. The Stroop Word score for all countries was affected by a quadratic age effect. Children from Chile, Mexico, and Puerto Rico whose parent(s) had a MLPE >12 years obtained higher Stroop Word score than children whose parent(s) had a MLPE ≤12 years. The child’s sex affected Stroop Word score for Spain such that girls scored higher than boys. The amount of variance these predictors explained in the Stroop Word score ranged from 26.3% (in Paraguay) to 63.8 (in Spain).

3.2Stroop Color

The final multivariate linear regression models for the ten country-specific Stroop Color score were significant (see Table 2). In all countries, the Stroop Color score increased linearly as a function of age. The Stroop Color score for Ecuador, Mexico, Peru, and Spain was affected by a quadratic age effect. Children from Mexico, Puerto Rico, and Spain whose parent(s) had a MLPE >12 years obtained higher Stroop Color score than children whose parent(s) had a MLPE ≤12 years. The child’s sex affected Stroop Color score for Mexico, such that boys scored higher than girls. The amount of variance these predictors explained in the Stroop Color score ranged from 20.2% (in Paraguay) to 53.4% (in Spain).

3.3Stroop Word-Color

The final multivariate linear regression models for the ten country-specific Stroop Word-Color score were significant (see Table 3). In all countries, the Stroop Word-Color score increased linearly as a function of age. The Stroop Word-Color scores for Ecuador, Mexico, and Paraguay were affected by a quadratic age effect. Children from Ecuador, Guatemala, Mexico, Puerto Rico, and Spain whose parent(s) had a MLPE >12 years obtained higher Stroop Word-Color scores than children whose parent(s) had a MLPE ≤12 years. The child’s sex did not affect Stroop Word-Color scores for any country. The amount of variance these predictors explained in the Stroop Word-Color scores ranged from 26.5% (in Guatemala) to 51.8% (in Spain).

3.4Stroop Interference Index

The final multivariate linear regression models for the ten country-specific Stroop Interference score were significant (see Table 4). In all countries except in Guatemala, the Stroop Interference score increased linearly as a function of age. The Stroop Interference score for Cuba, Guatemala, and Spain was affected by a quadratic age effect. Children from Ecuador, Mexico, and Spain whose parent(s) had a MLPE >12 years obtained higher Stroop Interference score than children whose parent(s) had a MLPE ≤12 years. The child’s sex only affected Stroop Interference score for Honduras. The amount of variance these predictors explained in the Stroop Interference score ranged from 2.8% (in Guatemala) to 15.6% (in Paraguay).

The assumptions of multiple linear regression analysis were met for all final models. There was not multicollinearity (the VIF values were below 10; VIF ≤1.116; collinearity tolerance values did not exceed the value of 1) or influential cases (the maximum Cook’s distance value was 0.154 in a F _(2,298)  distribution which correspond to percentile 14).

3.5Normative procedure

Norms (e.g., a percentile score) for the different Stroop Color-Word Interference Test scores by country were established using the four-step procedure described in the statistical analysis section. An example will be provided to facilitate an improved understanding of the procedure used to obtain the percentile associated with a score on this test. Let’s assume we need to find the percentile score for an 8-year-old Guatemalan girl who scored a 39 on the Stroop Color and whose parent(s) have a mean of 10 years of education (MLPE). The steps to obtain the percentile for this score are: 1) Find Guatemala in Table 2, which provides the final regression models by country for the Stroop Color score. Use the B weights to create an equation that will allow you to obtain the predicted Stroop Color score for this child using the coding provided in the statistical analysis section. The corresponding B weights are multiplied by the centered age (= calendar age – mean age in the Guatemalan sample which is equal to 10.7 years). Age², sex, and MLPE were not significant predictors, and therefore are not included in this model. See Rivera & Arango-Lasprilla (2017) to figure out the mean age of each country’s sample. Then the result is added to the constant generated by the model in order to calculate the predicted value.

In the case of the Guatemalan girl, the predicted Stroop Color score would be calculated using the following equation: yˆi=52.382+[2.816·(Agei-10.7)] . The girl’s age is 8. Thus, the predicted value equation is: yˆi=52.382+[2.816·(8-10.7)]=52.382+(-7.604)=44.778. 2) In order to calculate the residual value (indicated with an e_i in the equation), we subtract the actual Stroop Color score (she scored 39) from the predicted value we just calculated (ei=yi-yˆi) . In this case, it would be e_i = 39 - 44.778 = -5.778. 3) Next, consult the SD_e column in Table 2 to obtain the country-specific SD_e (residual) value. For Guatemala it is 10.619. Using this value, we can transform the residual value to a standardized z score using the equation z_i = e_i/SD_e. In this case, we have -5.778/10.619 = -0.544. This is the standardized z score for a 8-year-old Guatemalan girl who scored a 39 on the Stroop Color who has parents with 10 years of education (MLPE). 4) The last step is to use the tables available in most statistical reference books (e.g., Strauss et al., 2006). In this example, the z score (probability) of -0.544 corresponds to the 29^th percentile. It is important to remember to use the appropriate tables that correspond to each test (Stroop Color, Word, Word-Color, and Interference) when performing these calculations.

3.6User-friendly normative data

The four-step normative procedures explained above offers the clinician the ability to determine an exact percentile for a child who has a specific score on the Stroop Color-Word Interference Test. However, this method can be prone to human error due to the number of required computations by hand. To enhance user-friendliness, the authors have completed these steps for a range of raw scores based on age, sex, and MLPE and created tables for clinicians to more easily obtain a percentile range/estimate 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 39 on the Stroop Color test for a Guatemalan girl who is 8 years old and whose parent(s) have 10 years of education) using the simplified normative tables provided in the Appendix, the following steps must be followed. (1) First, identify the appropriate table ensuring the appropriate country and test (Stroop Color, Word, Word-Color, and Interference). In this case, the table for Stroop Color score for Guatemala can be found in Table A15. (2) Find the appropriate age of the child, in this case, 8 years old. (3) Next, look in the 8 years’ age column to find the approximate location of the raw score obtained on the test. Within the 8 years’ column, the score of 39 obtained by this Guatemalan girl corresponds to an approximate percentile of 30.

The percentile obtained using this user-friendly table sometimes could be slightly different than the hand-calculated, more accurate method (29^th vs. 30^th) because the user-friendly table is based on a limited number of percentile values. Individual percentiles cannot be presented in these tables due to space limitations. If the exact score is not listed in the column, you must estimate the percentile value from the list of raw scores available.

4.1Limitations and future directions

Despite the importance of this study, since it is the largest in the world that has been developed for validation and standardization of the Stroop Color-Word Interference Test in Spanish-speaking children, the results of the study should be interpreted in light of the following limitations: This study presents normative data for the Stroop Color-Word Interference for nine countries from Latin America and Spain. For this reason, it is not advisable to use these norms in the pediatric population of those Spanish-speaking countries where the study was not performed. Future studies should be conducted to standardize this test in other Spanish-speaking countries.

Although the norms of the present study could be used by neuropsychologists in other countries to evaluate Spanish-speaking immigrant children from the countries where the sample was collected for this study, they should be used with caution since other variables such as level of acculturation, bilingualism, the number of years living in the country, and so on, could influence performance. In addition, the quality of education of both the child and the parent(s) is another aspect that may influence the cognitive performance of children.

On the other hand, it is very important to keep in mind that no clinical diagnosis should be made based solely on the scores of this test. This test should be integrated as part of a much larger battery that evaluates these processes in more detail. Because there are a limited number of tests and norms in Latin America and Spain to evaluate these processes, more efforts should be made in the future to have other similar tools.

Although the size of the sample was adequate in each of the countries where the study was conducted, it is very important to note that only the sample in Chile, Mexico, Paraguay, Puerto Rico, and Spain was obtained from several regions of the country, while in the remaining countries were collected from only one geographic area. Future studies should expand the sample in other geographical areas of these countries with the objective to be able to have a greater representativeness of the sample.

The children who participated in the present study had Spanish as their first language. Although Spanish is the first language of the majority of the population in Latin America and Spain, it is important to keep in mind there is a great cultural and linguistic richness to the point that in many of these countries, sometimes the first language of many children may be completely different from Spanish (e.g., Portuguese, Euskera, Catalan, Guaraní, Maya, Quechua). For this reason, caution should be used when using these norms in children whose first language is not Spanish.

Finally, it is important to keep in mind that the present study was performed with normal healthy population. Therefore, future studies should be performed with clinical population to establish the sensitivity and specificity of this test.

4.2Implications and conclusions

Despite the aforementioned limitations, the present study stands out for being the first to offer normative data for the Stroop Color-Word Interference Test in Spanish-speaking children, taking into account cultural and socio-demographic characteristics. Similarly, the sample of 4,373 children and adolescents from nine countries in Latin America and Spain, make it the largest multicenter study worldwide. In addition, up-to-date approaches have been used for the creation and development of normative data in order to allow more precise calculations, such as the use of multiple regressions and residual values rather than average scores and standard deviations.

The additions of parents’ education, as well as the quadratic function of age in the final regression models, are one of the main advantages this study has in comparison to other normative studies where these variables have not been taken into account (e.g. Armengol, 2002; Barbarotto et al., 1998; Lee et al., 2002; Martins et al., 2005; Oliveira et al., 2016). Therefore, the results of this study are expected to contribute to the improvement of quality standards in the neuropsychological evaluation of pediatric population in these countries.