To describe the methodology utilized to calculate reliability and the generation of norms for 10 neuropsychological tests for children in Spanish-speaking countries.
The study sample consisted of over 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Inclusion criteria for all countries were to have between 6 to 17 years of age, an Intelligence Quotient of≥80 on the Test of Non-Verbal Intelligence (TONI-2), and score of <19 on the Children’s Depression Inventory. Participants completed 10 neuropsychological tests. Reliability and norms were calculated for all tests.
Test-retest analysis showed excellent or good- reliability on all tests (r’s>0.55; p’s<0.001) except M-WCST perseverative errors whose coefficient magnitude was fair. All scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the models by country. The non-significant variables (p > 0.05) were removed and the analysis were run again.
This is the largest Spanish-speaking children and adolescents normative study in the world. For the generation of normative data, the method based on linear regression models and the standard deviation of residual values was used. This method allows determination of the specific variables that predict test scores, helps identify and control for collinearity of predictive variables, and generates continuous and more reliable norms than those of traditional methods.
Neuropsychology is a discipline of psychology that studies cognitive processes and their relationship with behavior in both healthy populations and individuals with brain injuries and/or pathologies (Lezak, Howieson, & Loring, 2004). One of the most important specialties within this discipline is child neuropsychology, which is responsible for studying behavior in relation to the development of different brain structures and systems in children and adolescents (APA, Division 40, 2001).
There are currently a number of disorders that affect the development and functioning of cognitive processes in children, such as autism, attention deficit/hyperactivity disorder, specific learning disorders, motor and behavioral disorders, traumatic brain injury, cerebral palsy, and other neurodevelopmental conditions (American Psychiatric Association, 2014). Due to the variety of symptoms associated with each of these disorders, clinicians perform neuropsychological evaluations to accurately diagnose them, document natural changes over time, and track improvements associated with specific interventions.
The use of neuropsychological tests during assessment is essential to obtaining an objective measure of the child’s cognitive functioning in different domains, such as memory, attention, language, visuospatial skills, executive functioning, motor skills, and behavioral and emotional functioning (Baron, 2004; Lezak et al., 2004; Strauss, Sherman, & Spreen, 2006). Because test performance is known to be influenced by many factors, having normative data adjusted to the sociodemographic (e.g. age, education, sex) and cultural characteristics of individuals is of vital importance in order to adequately interpret a person’s score on a particular neuropsychological test (Lezak, et al., 2004; Strauss et al., 2006; Van der Elst, Molenberghs, Van Boxtel, & Jolles, 2013). For this reason, researchers from different countries like the United States of America (Goodman, Delis, & Mattson, 2010), Korea (Kim & Na, 2008), Taiwan (Shu, Tien, Lung, & Chang, 2000), Italy (Cianchetti, Corona, Foscoliano, Conty, & Sannio-Fancello, 2007), Holland (Hiuzinga & Smidts, 2010), Australia (Davies, Field, Andersen, & Pestell, 2011), Portugal (Townes, Martins, Castro-Caldas, Rosenbaum, & Derouen, 2008), Israel (Vakil, Greenstein, & Blachstein, 2010), and Iran (Yousefi, et al., 1992), among others, have established normative data for some of the primary neuropsychological tests used with children and adolescents.
In Latin America and Spain, some of the neuropsychological tests most commonly used with children and adolescents are the Trail Making Test, the Digit and Symbols Test, the Stroop Color-Word Interference Test, the Wisconsin Card Sorting Test, the California Verbal Learning Test, the Rey Complex Figure Test, The Boston Naming Test, the Peabody Picture Vocabulary Test, and the Verbal Fluency Test (Arango-Lasprilla, Stevens, Morlett Paredes, Ardila, & Rivera, 2016; Olabarrieta-Landa et al., 2016). Unfortunately, despite their popular use, many of these tests have not been validated in research and lack country-specific normative data for the majority of these Spanish-speaking countries. Because of this, clinical neuropsychologists report using: 1) normative data from other countries, 2) personalized procedures through clinical practice, and 3) raw scores without a comparison to normative data (Arango-Lasprilla et al., 2016; Olabarrieta-Landa et al., 2016). Therefore, the objective of this study was to describe the methodological procedure used to develop normative data for 10 commonly used neuropsychological tests in a group of children and adolescents (6 to 17 years of age) from nine Latin American countries and Spain.
In the present study consisted of neuropsychological evaluations of 6,030 clinically healthy children and adolescents from ten Latin American countries (Chile, Colombia, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. This article will present the methodology used to generate normative data for this study. Since the normative data for Colombia (n = 1,657) are published in Arango-Lasprilla, Rivera, and Olabarrieta-Landa (2017), the sample for the present study consisted of 4,373 children and adolescents ages 6 to 17 who were evaluated in 21 centers in the 9 other Latin American countries and Spain (see Table 1). The demographic characteristics (age, sex, type of school, and parent education) by country can be found in Table 2.
|Total Puerto Rico||215||100.0%|
|Sample||Age||Sex||Type of school||MLPE|
|n Total||Maximum error||Mean (SD)||Girls||Boys||Public||Private||Mean (SD)|
|(Accuracy level)||n (%)||n (%)||n (%)||n (%)|
|Chile||387||0.050 (95.0%)||11.5 (3.5)||194 (50.1%)||193 (49.9%)||195 (50.4%)||192 (49.6%)||12.3 (3.0)|
|Cuba||381||0.050 (95.0%)||11.5 (3.5)||190 (49.9%)||191 (50.1%)||381 (100%)||0 (0.0%)||16.2 (1.8)|
|Ecuador||302||0.056 (94.4%)||11.4 (3.5)||175 (57.9%)||127 (42.1%)||159 (52.6%)||143 (47.4%)||14.4 (3.6)|
|Guatemala||203||0.069 (93.1%)||10.7 (2.5)||94 (46.5%)||108 (53.5%)||112 (55.2%)||91 (44.8%)||10.5 (4.1)|
|Honduras||300||0.056 (94.4%)||11.2 (3.2)||161 (53.7%)||139 (46.3%)||155 (51.7%)||145 (48.3%)||12.8 (3.7)|
|Mexico||934||0.032 (96.8%)||11.4 (3.5)||481 (51.5%)||453 (48.5%)||574 (61.5%)||360 (38.5%)||13.1 (3.9)|
|Paraguay||300||0.056 (94.4%)||11.6 (3.5)||161 (53.7%)||139 (46.3%)||141 (47.0%)||159 (53.0%)||14.1 (2.9)|
|Peru||348||0.053 (94.7%)||12.0 (3.3)||171 (49.1%)||177 (50.9%)||187 (53.7%)||161 (46.3%)||12.5 (2.4)|
|Puerto Rico||215||0.067 (93.3%)||12.2 (3.6)||120 (55.8%)||95 (44.2%)||133 (61.9%)||82 (38.1%)||14.5 (2.6)|
|Spain||1003||0.031 (96.9%)||11.3 (3.4)||518 (51.6%)||485 (48.4%)||546 (54.4%)||457 (45.6%)*||14.0 (4.0)|
Note: MLPE: Mean Level Parental Education; *Private/Concerted (private school partially publicly funded).
All participants met the following inclusion criteria: a) being between the ages of 6 and 17 years old, b) being born and currently living in the country where the protocol was administered, c) having Spanish as primary language, d) having an Intelligence Quotient (IQ) of≥80 according to the Test of Non-Verbal Intelligence (TONI-2; Brown, Sherbenou, & Johnsen, 2009), e) having a score of <19 on the Children’s Depression Inventory (CDI, Kovacs, 1992), and f) being enrolled in a regular private or public school.
Participants were excluded according to the following criteria: a) having a history of a central nervous system disease that is associated with neuropsychological problems (e.g. epilepsy, brain injury, movement disorders, multiple sclerosis, brain tumor, stroke), b) having a history of alcohol abuse and/or consumption of psychotropic substances, c) having some type of active or uncontrolled systemic disease associated with cognitive impairment (e.g. diabetes mellitus, hypothyroidism, vitamin B12 deficiency), d) having a history of psychiatric illness (e.g. mayor depression, bipolar mood disorder, psychosis), e) having severe sensory deficits (e.g. loss of vision and/or hearing) that affect the administration of or performance on the tests, f) being on psychiatric or other medications that could alter cognitive performance, g) having intellectual or learning disability or other neurodevelopmental disorders, h) having a history of pre- peri-, and post-natal problems (e.g. hypoxia, jaundice, seizures, hydrocephalus, spine bifida, neuromuscular disorders), i) having a score of >5 on the Alcohol Use Disorders Identification Test (AUDIT-C) for participants 12 years of age and older, and j) using psychoactive substances such as heroin, barbiturates, amphetamines, methamphetamines, or cocaine in the last 6 months for participants 12 years of age and older.
To determine if participants met the inclusion and exclusion criteria, the parents (or guardians) of each potential child or adolescent participants answered a sociodemographic and inclusion/exclusion questionnaire. All children and adolescents also completed the TONI-2 to determine their IQ and the CDI to assess depressive symptomatology. Those 12 years of age and older completed the AUDIT-C to assess alcohol consumption and answered questions regarding recent psychoactive substance use.
2.3Clinical and demographic interview for participants
A questionnaire was created to collect information about the child or adolescent related to the health status and clinical history. It was completed by the parent or guardian. With this information, it was possible to identify participants who met the exclusion criteria proposed for the present study. In the interview, the following information was obtained: demographic data, pregnancy, childbirth, and possible complications; motor, language, visual, and auditory problems; assistance received by different professionals (e.g. neurologist, psychiatrist, medical rehabilitation professional, occupational therapist, speech therapist, psychologist), the existence of psychological disorders, and pharmacological treatment. The parents/guardians also responded to a demographic questionnaire about themselves that included information on their age, education, monthly income, and occupation.
2.4.1Test of Non-Verbal Intelligence (TONI-2; Brown et al., 2009)
The TONI-2 is a test that evaluates intelligence in people between the ages of 5 and 85 years. The duration of the test is between 15 and 20 minutes. The test attempts to eliminate any influence of language, motor ability, and culture by assessing the ability to solve abstract tests. The TONI-2 positively correlates with the Performance IQ of the Wechsler Intelligence Scale for Children - 3rd edition (WISC-III), which provides support for adequate concurrent validity. The TONI-2 also has demonstrated support for good construct validity established by correlations between the TONI-2 and five of six WISC-III subtests, as well as the predictive value of the spelling, mathematical problem-solving, and reasoning tasks of the Stanford Achievement Test (SAT) (Bostantjopoulou, Kiosseoglou, Katsarou, & Alevriadou, 2001; Mackinson, Leigh, Blennerhassett, & Anthony, 1997).
2.4.2Children’s Depression Inventory (CDI; Kovacs, 1992)
The CDI is a self-report test composed of 27 items that measure depressive symptoms in children and adolescents. This measure is composed of five subscales: negative humor, ineffectiveness, low self-esteem, social withdrawal, and pessimism. Each item has three possible responses (0, 1, or 2) depending on the degree of depression, with 0 representing an absence of symptomatology and 2 representing severe depressive symptomatology. The suggested cut-off is 19 points, and the maximum score is 54 points.
2.4.3The AUDIT Alcohol Consumption Questions (AUDIT-C; Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998)
The AUDIT-C is the modified version of the AUDIT, which consists of three items from the traditional 10-item version. Each item is scored on a scale of 0 to 4 points. The AUDIT-C has demonstrated support for validity as a primary care screening test for heavy drinking and/or active alcohol abuse or dependence. In this study, only those participants 12 years of age and older completed this measure and those with a score >5 were excluded from participation.
2.4.4Checklist for use of psychoactive substances
This measure is a list containing the most commonly used psychoactive substances. Participants were excluded from the study if they reported having consumed any type of substance in the last six months, including heroin, barbiturates, amphetamines, methamphetamines, and cocaine. These questions were administered only to participants 12 years of age and older.
Participants who met the inclusion criteria were administered the following neuropsychological tests:
1. Rey Osterrieth Complex Figure Test (ROCF; Rey, 2009)
2. Stroop Color-Word Interference Test (Golden, 2010)
3. Modified Wisconsin Card Sorting Test (M-WCST; Schretlen, 2010)
4. Trail Making Test A-B (TMT A-B; Reitan & Wolfson, 1985)
5. Symbol Digit Modalities Test (SDMT; Smith, 2002)
6. Shortened version of Token Test (De Renzi & Faglioni, 1978)
7. Concentration Endurance Test (d2) (Brickenkamp, 2009)
8. Phonological and Semantic Verbal Fluency Tests (Benton & Hamsher, 1989)
9. Peabody Picture Vocabulary Test - PPVT-III (Dunn, Dunn, & Arribas, 2010)
10. Learning and Verbal Memory Test (TAMV-I; Rivera, Olabarrieta-Landa, & Arango-Lasprilla, 2017)
The present study began by recruiting the local researchers who would carry out the study in their individual countries. Universities, institutions, and research centers from Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain were contacted and invited to participate in the multi-center study. Next, the centers that agreed to participate in the study requested approval from their institution/center’s ethics committee. After approval, any copyrighted test materials (manuals, booklets, and stimulus cards) were purchased. In each center, a neuropsychology researcher was appointed to coordinate data collection for the study. Randomized lists were used to determine the order of test administration for each participant, aiming to avoid order bias and cognitive conditioning. For the creation of the list, the function fx = RANDOM () in Microsoft Excel© was used, and this configuration considered the interaction of verbal fluency tests with the TAMV-I verbal memory test. To enter the data, a template was designed in Microsoft Excel©, which was designed using the following configuration options to reduce data entry bias: Data validation = custom (numeric variables), drop-down lists (categorical variables), and configuration formats. All the neuropsychological tests were administered according to the specific manual guidelines of each test.
A pilot test was performed with 20 participants to ensure proper functioning and comprehension of Spanish-language test instruction. Data from the pilot test were eliminated from data analysis. Data collection began in January 2016 and finished in May 2017. The neuropsychological battery was administered individually in a single day to children and adolescents at schools and/or universities. Administration lasted approximately 120 minutes per participant. Prior to initiating the screening tests and battery of neuropsychological tests, parent questionnaires were received and reviewed, all parents/guardians and children 12 years of age and older signed the informed consent, and children under 12 years of age signed the assent. Informed consent included information related to the aim of the study, rights of participants, duration/place of the assessment, contact information for the local researcher responsible for the study, and the possibility to be re-evaluated in a set period of time (60– 120 days).
In each country, a randomly selected group of children and adolescents with a size of 5– 10% of the sample collected were selected for re-evaluation 60 to 120 days after the initial evaluation. Randomization was done using random sample cases in SPSS. The same procedures, including random test order generation, were used for the re-evaluation session. Unfortunately, 5 of the countries (Guatemala, Honduras, Paraguay, Peru, and Puerto Rico) began data collection late and were unable to re-evaluate any of the participants within the original study time frame. Thus, test-retest data for this sample was available from a sub-sample of 233 participants from Chile, Cuba, Ecuador, Mexico, and Spain.
2.7.1Accuracy of the final sample
The accuracy of the total sample size by country was established using classical estimation assuming infinite (very large) population sizes (Arrufat, Guàrdia-Olmos, & Blanxart, 1999), where the case of maximum uncertainty was assumed (π= 1 -π= 0.5) and a confidence interval of 95%.
The reliability of each neuropsychological test’s scores for the entire sub-sample was calculated through the test-retest method, which evaluates the temporal stability of the test scores. This type of reliability assumes that the scores of examinees will not have unexpected or fluctuating changes over time (Abad, Díez, Gil, & García, 2011). The reliability was calculated with intraclass correlation coefficients (ICCxtest-retest). The intraclass correlation coefficient is frequently used to report reliability, especially with variables that share metric and variance (McGraw & Wong, 1996; Weir, 2005).
In order to determine if there were significant differences between countries on the performance of each of the 10 neuropsychological tests, a multivariate analysis of variance (MANOVA) was performed to examine the effect of country on the test scores. Country was dummy coded and used as a fixed factor, and each of test scores served as dependent variables. Bonferroni adjustment alpha level of 0.005 to 10 pairwise comparisons was use (0.05/10).
Linear regression models and standard deviations of the residual values of the models (Van Breukelen & Vlaeyen, 2005) were used to generate normative data for each country. A multivariate regression model was fitted to the data. The multivariate regression model assumes that Yi = Xiβ + ɛi, where Yi is the vector of the measurements for children, Xi the design matrix for the fixed effects, β the vector of the regression coefficients (fixed effects), and ɛi the vector of the residual components. The main model included age, age2, sex of the child, and the mean level parental education (MLPE) as predictor variables. Age was centered (= Age – average age of the sample in each country), and then age2 was calculated from the centered age to avoid multicollinearity (Aiken & West, 1991). Sex was coded as boys = 1 and girls = 0. The MLPE variable was coded as 1 if the participant’s parent(s) had >12 mean years of education or 0 if participant’s parent(s) had≤12 mean years of education. The cut-off of 12 years of education was selected because it provided a relatively standard reference point and was defined as a crucial cut-off point for higher education in other studies with Spanish-Speaking countries in Latin America (Guàrdia-Olmos, Peró-Cebollero, Rivera, & Arango-Lasprilla, 2015) and Spain (Peña-Casanova et al., 2009).
A final regression model was conducted . 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. The established regression model and the standard deviation of the residual values (SDe) provided by the regression model were subsequently used to norm the score (Van Der Elst, Van Boxtel, Van Breukelen, & Jolles, 2006a; 2006b; Van der Elst, Hurks, Wassenberg, Meijs, & Jolles, 2011; Van der Elst, Dekker, Hurks, & Jolles, 2012). Standard deviation of the residual (SDe) value is calculated as follows: , where Mean Squared Error (MSE) is . Using each country’s dataset, these models were applied to each neuropsychological test’s scores separately.
For all multiple linear regression models, the following assumptions were evaluated: a) collinearity by a Variance Inflation Factor (VIF) not greater than 10 and a collinearity tolerance values not greater than 1 (Kutner, Nachtsheim, Neter, & Li, 2005), b) normality using Q-Q plots and histograms of residual values, and c) 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, where p is the number of regression parameter, including constant, and n is the sample size. Influential values are considered when percentile value is equal or higher than 50 (Cook, 1977; Kutner et al., 2005). The scores should be transform to other metrics (e.g. root square) if severe violation of normality assumptions happens. In case of multicollinearity, the main model should be change, and in case of influential values, outliers’ cases should be excluded for the analysis. All analyzes were performed using SPSS version 23 (IBM Corp., Armonk, NY).
3.1Accuracy of the final sample
The maximum error (e) of sample sizes range from 0.069 (Guatemala) to 0.031 (Spain). All country sample size maximum error (e) and accuracy levels can be found in Table 2. Regarding the sample, non-proportionate quota sampling was used, looking for a symmetrical distribution for strata of age, sex, and type of school for each country, except for Cuba, where the education is entirely public (Gasperini, 2000).
The subsample used to calculate the test-retest reliability consisted of 232 participants from Chile (n = 29; 12.4%), Cuba (n = 38; 16.3%), Ecuador (n = 16; 6.9%), Mexico (n = 74; 31.8%), and Spain (n = 76, 32.6%). Sixty percent were boys and the average age was 11.5 (SD = 3.6) years. The average time elapsed between the two tests administrations was 78.2 (SD = 11.7; range = 60– 115) days. In Table 3 shows the interclass coefficients for each neuropsychological test score for the entire sub-sample.
|Test scores||ICCXtest Xre-test|
|FOCR Immediate Recall||0.88**||0.85||0.91|
|M-WCST Correct categories||0.80**||0.74||0.85|
|M- WCST Perseveration errors||0.54**||0.41||0.65|
|M-WCST Total errors||0.79**||0.73||0.84|
|TMT – A||0.68**||0.58||0.75|
|TMT – B||0.76**||0.69||0.82|
|TAMVI Total Recall||0.70**||0.61||0.77|
|TAMVI Delayed Recall||0.68**||0.59||0.76|
|d2 – TN||0.77**||0.70||0.82|
|d2 – CR||0.89**||0.85||0.91|
|d2 – TP||0.79**||0.72||0.84|
|d2 – CP||0.81**||0.75||0.85|
Note: ** p < 0.001; ICC: Intraclass correlation coefficient; TN: Total number of items processed, CR: Total number of correct responses, TP: Total performance, CP: Concentration performance.
The scores demonstrated temporal stability, with interclass coefficients greater than 0.54 (r = 0.54 to r = 0.95) that were statistically significant (p’s<0.001). In general, good and high magnitudes of temporal stability were observed for the scores of all tests examined, indicating a consistency of the measures across time periods, which fulfilled the assumption that participant scores would not demonstrate unexpected or fluctuating changes (Abad et al., 2011; Cicchetti, 1994; Strauss et al., 2006).
MANOVAs using country as a fixed factor and all neuropsychological test scores as dependent variables showed a significant difference across the ten countries (Wilks’ Lambda = 0.349, F (252,31359) = 17.210, p < 0.001, partial η2 = 0.116). Subsequent analyses of the group differences within each of these dependent variables were examined in tests of Between-Subjects Effects (see Table 4).
|Dependent Variable||F||Sig.||η2 (90% CI)|
|FOCR Copy||47.896||<0.001||0.108 (0.09; 0.12)|
|FOCR Immediate Recall||28.595||<0.001||0.067 (0.05; 0.08)|
|Stroop Word||10.239||<0.001||0.025 (0.01; 0.03)|
|Stroop Color||7.160||<0.001||0.018 (0.01; 0.02)|
|Stroop Word-Color||10.001||<0.001||0.025 (0.01; 0.03)|
|M-WCST Correct categories||32.255||<0.001||0.075 (0.06; 0.09)|
|M-WCST Perseveration errors||13.063||<0.001||0.032 (0.02; 0.04)|
|M-WCST Total errors||42.470||<0.001||0.097 (0.08; 0.11)|
|TMT – A||16.386||<0.001||0.040 (0.03; 0.05)|
|TMT – B||15.622||<0.001||0.038 (0.03; 0.05)|
|SDMT||9.672||<0.001||0.024 (0.01; 0.03)|
|Token||37.775||<0.001||0.087 (0.07; 0.10)|
|Letter F||9.973||<0.001||0.024 (0.01; 0.03)|
|Letter A||2.464||0.012||0.006 (0.00; 0.01)|
|Letter S||4.058||<0.001||0.010 (0.00; 0.01)|
|Category Animals||19.984||<0.001||0.048 (0.03; 0.06)|
|Category Fruits||10.752||<0.001||0.026 (0.02; 0.03)|
|Peabody||26.638||<0.001||0.063 (0.05; 0.07)|
|TAMVI Total Recall||25.869||<0.001||0.061 (0.05; 0.07)|
|TAMVI Delayed Recall||14.073||<0.001||0.034 (0.02; 0.04)|
|TAMVI Recognition||15.024||<0.001||0.036 (0.02; 0.05)|
|d2 – TN||11.238||<0.001||0.027 (0.02; 0.04)|
|d2 – CR||6.443||<0.001||0.016 (0.01; 0.02)|
|d2 – TP||7.272||<0.001||0.018 (0.01; 0.02)|
|d2 – CP||7.425||<0.001||0.018 (0.01; 0.02)|
Note: TN: Total number of items processed, CR: Total number of correct responses, TP: Total performance, CP: Concentration performance.
3.3.1Verification of the assumptions in the regression models for the scores
The summary of verification of assumptions for the final models is shown in Table 5. The visual analysis of the Q-Q normality diagrams and histograms of the residual values for the final models fulfilled the assumption of normality and indicated a non-problematic distribution in each of the models studied. No evidence of multicollinearity was found since all VIF values were lower than 1.311 and tolerance values did not exceed 1. No influential cases were present since the Cook distance values were less than 0.251. The maximum Cook’s distance value was 0.251 in a F (3,212) distribution which correspond to percentile 14. Because the percentile value did not exceed 50 the influence of outliers was not deemed sufficient to require remedial measures.
|FOCR Immediate Recall||1.060||0.080|
|M-WCST Correct categories||1.015||0.094|
|M-WCST Perseveration errors||1.051||0.158|
|M-WCST Total errors||1.048||0.155|
|TMT – A||1.008||0.242|
|TMT – B||1.012||0.115|
|TAMVI Total Recall||1.063||0.238|
|TAMVI Delayed Recall||1.066||0.190|
|d2 – TN||1.199||0.159|
|d2 – CR||1.010||0.081|
|d2 – TP||1.201||0.094|
|d2 – CP||1.022||0.079|
Note: TN: Total number of items processed, CR: Total number of correct responses, TP: Total performance, CP: Concentration performance.
The methodology to generate normative data based on linear regression provides normative data calculations using a four-step procedure described by Van Breukelen and Vlaeyen (2005) and Van der Elst et al. (2006a; 2006b; 2011; 2012):
1. The expected test score of tested children (i) is computed based on the fixed effect parameter estimates of the established regression model: .
2. To obtain the residual value , a subtraction between the raw score of the neuropsychological test (Yi) and the predicted value previously calculated was performed , as shown in the following formula: .
3. Using the residual standard deviation (SDe) value provided by the regression model, residuals were standardized: .
4. Finally, the exact percentile corresponding to the z-score previously calculated using the cumulative probability function of the standard normal distribution must be found. This can be done using conversion tables (e.g. Strauss et al., 2006) or using online calculators. In the online calculator, the z-score is entered, and the percentile of the unilateral test is then chosen. For example, the corresponding percentile of is 38.
3.4User-friendly normative data
The four-step normative procedure explained above offers clinicians the ability to determine the exact percentile of a child’s test score. However, this method can be prone to human error due to the number of hand computations required. To enhance user-friendliness, the authors have computed these steps for a range of raw test scores based on age, sex, and MLPE as necessary, and created tables for clinicians to more easily obtain a percentile range/estimate associated with a given raw score on a specific test. The percentile obtained using these user-friendly tables could be slightly different from the hand-calculation (which although prone to human error, is a more precise method) because the user-friendly table is based on a limited number of percentile values.
The purpose of this paper was to describe the methodology and procedures involved in generating normative data for 10 neuropsychological tests for children and adolescents from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain.
Test-retest reliability was also examined. The interclass coefficients obtained in this study have good (0.60– 0.74) or excellent (0.75– 1.00) magnitudes of reliability according to Cicchetti (1994), except M-WCST perseverative errors, whose coefficient magnitude is fair (r = 0.54; p < 0.001). Thus, almost all of the neuropsychological test scores showed temporal test stability. In addition, the stability coefficients obtained in this study can provide valuable information about the replicability of the test results (Strauss et al., 2006). Similarly, other studies looking at the same neuropsychological tests have shown similar test-retest reliability (e.g. Hurks, 2012; Reese & Read, 2000; Tombaugh, Kozak, & Rees, 1999).
Regarding norms, researchers have pointed out the need of normative data adjusted to the demographic characteristics of a specific population (Strauss et al., 2006; Van der Elst et al., 2013) because the raw scores on neuropsychological tests can be impacted by these types of variables (e.g. age, education, sex, race, ethnicity). Despite the wide use of neuropsychological tests for the evaluation and diagnosis of cognitive problems in Latin America and Spain, to date there are few studies with normative data for neuropsychological tests for Spanish-Speaking children and adolescents (Arango-Lasprilla et al., 2016; Olabarrieta-Landa et al., 2016).
Existing normative data studies have several limitations, such as 1) the use of mean and standard deviation within each subgroup (e.g. Malloy-Diniz et al., 2007; Oliveira, Mograbi, Gabrig, & Charchat-Fichman, 2016), 2) conversion of raw scores to metrics such as Z or T values (e.g. Golden, 2010; Rey, 2009; Schretlen, 2010), and 3) having no representative samples in their studies (e.g. Malloy-Diniz et al., 2007; Matute, Rosselli, Ardila, & Morales, 2004). Each of these limitations presents significant problems, which will be explained in detail in the next paragraphs.
The use of mean and standard deviations within subgroups can have two major drawbacks (Van Breukelen & Vlaeyen, 2005). First, normative data tables are generated, assuming that predictive variables of the test scores are known, as not all variables assumed are relevant. For example, normative data generated by sex may not be important for a test if this variable does not influence performance. Second, taking into account traditional demographic variables (e.g. sex and age) and dividing the sample into subgroups implies a considerable loss of information. For example, when dividing the sample by sex (males vs. females), the sample size is reduced by approximately 50%. In addition, if the sample is divided into five age groups, it reduces the size of the sample per subgroup to 10% of the total sample. As a result, the distribution of test scores, including its mean and standard deviation, are not reliable since it can lead to random trends in norm tables, where the average can vary and make large jumps across the age groups. Conversion of raw scores to other metrics (e.g. standard or z-scores) are common practice; but, this simple conversion of raw scores has no effect on the shape of the distribution. If raw scores are normally distributed, the resulting z-scores will be as well. However, if the raw scores have an asymmetric distribution, then the z-scores will be similarly skewed (Crawford, 2004).
Finally, non-representative sample sizes can lead to problems when generating normative data to be generalized to the general population. The sample should reflect its composition— that is, be representative— to ensure the external validity of the research and thus the replicability of the results.
In order to address these limitations, the present study used a method based on multiple regression models and standard deviation of residual values (Van Breukelen & Vlaeyen, 2005). This method provides information on which variables predict test scores and which variables are relevant for the development of valid normative data. In this study, age, age2, sex, and MLPE were used as predictors as part of a main model for each of the test scores. The final models were constructed from the hierarchical elimination of non-significant predictor variables (Van der Elst, Molenberghs, van Tetering, & Jolles, 2017), or variables with a p value greater than 0.05 (p > 0.05). However, most predictors in the final models had a p value less than 0.001 (p < 0.001), controlling for possible type I errors (Van der Elst et al., 2006a).
Another advantage for the regression approach to developing normative data is that norms are continuous and more reliable than those obtained by tabulating the mean and standard deviation of the score scales for different age groups, level of education, and sex. In addition, normative data based on regression allows for exploration of more than one type of function (linear vs. quadratic) for age. For example, a curvilinear effect of age on cognitive processes has been shown in past research. This is of special relevance in studies with children, given that a single year can have a tremendous difference in terms of rapid cognitive development compared to adults. In addition, cognitive development is not always present in linear functions depending on the cognitive process under study (Grady, 2012). Another important variable that should be considered in developmental studies is parental education. Parental education has been found to predict cognitive development (Meador et al., 2011; Schady, 2011), educational level (Dubow, Boxer, & Huesmann, 2009; Ermisch, & Pronzato, 2010) and occupation (Dubow et al., 2009) of children in the future. That is why, unlike other normative data studies, parents’ educational level was introduced in the models as a predictive variable to generate normative data. In a study by Van der Elst et al. (2011), parental level of education was used as a predictive variable in their normative study, and results indicated that parents’ education influences verbal fluency performance among children.
Another point in favor of this method is the identification and control of collinearity in the predictive variables through VIF. In the present study, VIF values were less than 1.311 and tolerance values did not exceed 1, well below threshold values. Finally, this method allows for the standardization of raw scores. Transforming raw scores into percentiles allows z-scores to be standardized by using a table of the areas under the normal curve (Crawford, 2004). This method has been used recently in different studies to generate normative data for several neuropsychological tests (Guàrdia-Olmos et al., 2015; Van der Elst et al., 2011, 2012).
Finally, the present study used a total sample of 4,373 participants from nine Latin American countries and Spain, making this the first and largest normative data study done in pediatric and adolescent populations in the world. Moreover, the subsamples of each country have precision levels that oscillate between 93.1% (Guatemala) and 96.9% (Spain), which approaches the best possible representation for each population.
This study has several limitations. First, although the method used in this study allows knowing which variables predict test performance in children (even after having included MLPE as a predictor), there may be other variables that affect test performance that were not presently examined. Therefore, future studies should take into account other potentially important variables (e.g. bilingualism etc.).
Second, to avoid possible human error when calculating test percentiles, this study provides tables with approximate percentiles. It is hoped that such tables will facilitate use of appropriate norms by professionals and allow more accurate interpretation of results. However, these tables are based on a limited number of percentile values and for that reason may be slightly different from hand-calculations.
Although having an overall large number of participants, there were countries with a relatively small sample (e.g. Guatemala and Puerto Rico). This, however, did not prevent the generation of valid normative data for these countries. Future studies should increase the sample size in these countries in order to reduce potential bias due to sampling error. Moreover, excluding samples from Chile, Mexico, Paraguay, Puerto Rico, and Spain, in the remaining countries the samples were collected from only one geographical area, potentially affecting generalization of norms to the entire country. Finally, all data was collected from urban areas. Future studies should expand data collection to other geographical areas of these countries and assess children from rural schools to improve representativeness and generalizability.
Despite these limitations, this is the largest normative data study in the world with a total sample of 4,373 Spanish-speaking children and adolescents in 9 Latin American countries and Spain. The selection of neuropsychological tests in this study was according to the frequency of use by neuropsychologists in Latin America and Spain (Arango-Lasprilla et al., 2016; Olabarrieta-Landa et al., 2016). For the generation of normative data, the method based on linear regression models and the standard deviation of residual values was used. This method allows determination of the variables that predict test scores, helps identify and control for collinearity of predictive variables, and generates continuous and more reliable norms than those of traditional methods (e.g. obtaining just the mean and standard deviations). In addition, to generate normative data, the MLPE was included as a predictive variable, which is especially relevant in child development studies. Finally, this study describes the methodology used to generate normative data for 10 neuropsychological tests for children and adolescents in nine countries from Latin America and Spain. Futures studies can utilize the presented procedures to create normative data for these tests in other Spanish-speaking countries or to develop new norms for other neuropsychological tests with the goal of continuing the development and improvement of clinical practice in these countries.
Conflict of interest
None to report.
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