Abstract

The Stroop Color and Word Test (SCWT) is a short test that is widely used in neuropsychological assessment to evaluate the executive aspects of attention control, information processing speed, selective attention, cognitive flexibility, and executive function in terms of the ability to inhibit a usual response in favor of an unusual response. The aim of this study was to create normative data from the SCWT adjusted for age, sex, and educational level for its clinical use in a population of 55 years of age and above. The SCWT was administered to a population-based sample of 2,151 participants aged 55 or older, and the effect of sex, age, and educational level was determined in the SCWT by means of linear regression models. Normative tables were created following the traditional method based on the stratification by relevant variables and on regression models.

Introduction

The so-called word–color interference effect corresponds to an experimental paradigm in which it is shown that the reading of words printed in a different color is slower than the reading of the word or the color naming separately (Strauss, Sherman, & Spreen, 2006). The time difference between these cognitive tasks is a measure of the interference generated by two stimuli that come into conflict. The study of this phenomenon goes back to 1883, when Wilheim Wundt, founder of the first psychology laboratory in Leipzig, Germany, asked his student, James Cattell, to investigate the relative speed of the naming of words of colors and the naming of colors (Mitrushyina, Boone, Razani, & D'Elia, 2005) and published an article with the main results (Cattell, 1886).

The Stroop test, eponymous for John Ridley Stroop, who developed the first studies that described the word–color interference paradigm (Stroop, 1934), has become one of the psychometric tests most used in psychology for assessing executive function (Rabin, Barr, & Bruton, 2005) and on which numerous versions have been developed that are distinguished by the number of colors, the type of items, the form of presentation, or the scoring system, without these variations modifying the basic interference paradigm (Bush et al., 1998, 1999; Mitrushina, Boone, & D'Ella, 1999; Spreen & Strauss, 1998; Trenerry, Crosson, DeBoe, & Leber, 1989; Whalen et al., 1998). This test is considered useful for evaluating executive aspects of attention control (Banich et al., 2002), information processing speed, selective attention, cognitive flexibility, and executive function in terms of the ability to inhibit a usual response (reading of a word) in favor of an unusual response (naming of the color in which the word is printed; Bryan & Luszcz, 2000; Mitrushina et al., 1999; Rosselli et al., 2002; Spreen & Strauss, 1998; Van der Elst, Van Boxtel, Van Breukelen, & Jolles, 2006). Although neuroimaging studies generally indicate that when they are carried out, the frontal lobe is the neuroanatomical substrate that presents with a more consistent activity, the most specific areas are the orbitofrontal, prefrontal, dorsolateral, and anterior cingulate cortex (Egner & Hirsch, 2005; Harrison et al., 2005; Peterson et al., 1999). Clinical studies have shown that diseases as diverse as schizophrenia, depression, attention deficit with hyperactivity disorder, Parkinson's disease, chronic alcoholism, head trauma, child focal benign epilepsy, and dementia, among others, perform poorly on this test (Lezak, Howieson, & Loring, 2004; Strauss et al., 2006).

Regardless of the version used, the Stroop test offers information (in number of items or in number of seconds to complete the task) about the reading of words, colors, and words–colors. The interference in the Stroop test is an ipsative score calculated from the variations in the subject's skills for reading words and naming colors. In Golden's version of the Stroop test (Golden, 1978), which is one of the most broadly used, the interference score is based on the assumption that the time for naming a color printed in a word of a different color is a function that is additive to the time necessary for reading the word plus the time necessary for naming the color. According to this model, the interference is determined by means of an algebra formula that establishes the difference between the score obtained by the subject in the words–colors task, and the predicted score according to the number of words read and the number of colors denominated (Word × Color/Word + Color). In spite of the theoretical interest of this measure, it is surprising that not all the studies that have carried out normative studies of Golden's version of the Stroop test have included the interference score (Moering, Schinka, Mortimer, & Graves, 2004; Peña et al., 2009; Steinberg, Bieliauskas, Smith, & Ivnik, 2005). One possible explanation would be that the ipsative score of interference proposed by Golden has not had the same clinical utility for discriminating the location of the brain lesions as the patterns for scoring words, colors, and words–colors independently (Lanham, Vanderploeg, & Curtis, 1999; Strauss et al., 2006). Even Golden himself excluded this measure from his material of clinical cases, quoting its lack of utility (Golden, 1978).

The underlying model of the interference score proposed by Golden has been questioned from a conceptual standpoint, from the assumption that the effect of the word–color interference is not a consequence of an additive function of the time of reading the word plus the naming of the color, but that it is a consequence of a function of suppression or inhibition. Specifically, the interference effect would be produced as a consequence of the time necessary for inhibiting the reading of the word plus the time for naming the color. In accordance with this model, Chafetz and Matthews (2004) proposed a new formula for calculating the interference of Golden's version that provides a better adjustment with clinical and evolutionary data on cognitive function (Strauss et al., 2006). This formula includes the time for suppression of reading of the word (45/216 − Word) plus the time to name the color (45/Color), the predicted number of words–colors being of 45 s ((216 − Words) × Colors)/((216 − Words) + Colors) (Chafetz & Matthews, 2004).

Given the extensive use of the Stroop test both in the clinical and research settings, it is very important to determine and quantify the effect that the traditional sociodemographic variables of sex, age, and educational level have on the execution of this test. The aim of the present study was to offer normative population data for Golden's score version of the number of words, colors, words–colors, and the interference based on two different formulas for calculating this ipsative interference score, the one proposed by Golden and the one proposed by Chafetz and Matthews.

Materials and Methods

Study Population

The participants come from the Regicor study, which is a prospective and population cohort study about cardiovascular risk factors (http://www.regicor.org/). Specifically, it is comprised of three population cohorts of inhabitants of the province of Girona (Catalonia, Spain) who were recruited in the years 1995, 2000, and 2005 (Grau et al., 2007). The reference population was the residents in the province of Girona between 35 and 74 years in the year they were recruited and who were selected from census. The selection of 1,480 participants in 1995 consisted of a random sample that was stratified in two stages. In the first stage, 33 populations were randomly selected from the province of Girona, 8 of which having more than 10,000 inhabitants (considered urban areas) and the rest of the populations between 500 and 9,999 inhabitants (considered rural areas). The selection of 2,540 participants in the year 2000 was carried out similarly by means of a two-stage sampling from the census of 17 populations of the province of Girona (8 urban and 9 rural populations). The 3,551 participants of the 2005 cohort were selected from a random sample of the inhabitants of the city of Girona and of three neighboring rural municipalities. The rate of participation of the three cohorts was of 72.4%, 70.0%, and 73.8%, respectively.

Between the years 2008 and 2013, 7,571 participants of the three cohorts are being contacted once again to carry out the clinical follow-up. For the present study, we selected all the participants from the three cohorts of the Regicor project of an age of 55 and above who were examined during the period of 9 January 2008–10 April 2011. The protocol of the present study was assessed and approved by the Institutional Review Board of the Girona Healthcare Institute.

Procedure and Instruments

The data collection process of the Regicor study is focused on the healthcare centers of each participating population. In the said center, the examining team receives the participants between 8:00 a.m. and 11:00 a.m., after they have been informed by postal mail and they have received a telephone call to agree on the day of the examination. The examination is protocolized and all the participants are examined following the same order: first, drawing of blood and then an electrocardiogram, a carotid echo-Doppler, the measurement of weight and height, and finally, the clinical interview and completion of several health questionnaires. The neuropsychological examination includes several tests aside from the Stroop Color and Word Test (SCWT), and it is carried out after a small pause, after the health questionnaires have been completed.

Golden's version of the SCWT (Golden & Freshwater, 2002; Golden, 2005) was used, and it consists of three pages, one of words, another one of colors, and another one of words–colors, each of which has 100 items presented in five 20-item columns. In the page corresponding to words, the words corresponding to the colors “Red,” “Green,” and “Blue” are included, printed in black ink. The page corresponding to the colors includes series of four X painted in red, green, and blue. In the page corresponding to words–colors, the words corresponding to the colors “Red,” “Green,” and “Blue” are included, but printed in colors that do not correspond to the word (e.g., the word “Red” is printed in blue). Because the study population was bilingual (Castilian Spanish and Catalan), the SCWT was administered in the first language of the participants. We used the Castilian Spanish version of the SCWT edited by TEA Ediciones (Golden, 2005), and we translated the words of the colors into Catalan (Rojo, Vermell [for Red]; Verde, Verd [for Green]; Azul, Blau [for Blue]). The score for each of the pages is determined as the number of items read during a 45-s period and it corresponds to the score of words, colors, and words–colors. In a complementary form, the interference index was calculated following Golden's classic formula (words × colors)/(words + colors) (Golden & Freshwater, 2002) and by means of the formula proposed by Chafetz and Matthews that, from the assumption that the number of words–colors will be determined by the time necessary to suppress the reading of the word plus the time necessary to name the color, they propose the formula (((216 − words) × colors)/((216 − words) + colors)) (Chafetz & Matthews, 2004).

The SCWT was applied by five examiners who attended one training session in which they were trained for its correct administration.

Statistical Analysis

A descriptive analysis of the variables that are the objective of the study by means of absolute and relative frequencies for qualitative variables and by means of central tendency and dispersion measures for quantitative variables was carried out.

In order to determine the effect of age, sex, first language, and educational level in the execution of the SCWT, five linear regression models were adjusted using the score of the words page, the colors page, and the words–colors page as dependent variables, as well as the two interference indexes. The stepwise method was used to determine the contribution in terms of age, sex, first language, and educational level in the scores obtained in the SCWT. The contribution of each independent variable to the total variance was calculated, explained by each regression model, using the beta coefficient product by Pearson's correlation coefficient for each independent variable with the dependent variable (Guilford & Fruchter, 1973). In order to avoid multicollinearity issues, the variable age (age—67) was centered and the quadratic term of the variable age was then calculated, and the possible interactions were analyzed. The variable sex was coded (men = 0, women = 1), the variable first language was coded (Spanish Castilian = 0, Catalan = 1), and the variable education level was recorded according to the years of education of the participants, up to 8 years of education (LE low), between 8 and 12 years of education (LE average), and more than 12 years of education (LE high). Two dummy variables were created (LE low and LE high) using LE average as a reference category to include in regression models. For each adjusted model, the necessary requirements for applying a regression analysis were determined: homoscedasticity, normal distribution of residuals, absence of multicollinearity, and absence of extreme values. Homoscedasticity was determined by means of Levene's test of variance homogeneity for the residues of the score predicted by the stratified regression model according to the grouping per quartiles of the predicted score. The normal distribution of the residues was determined by means of the Kolmogorov–Smirnov test and the visual inspection of the histogram. The presence of multicollinearity was evaluated by means of the variance inflation factors (VIFs) which must not be above 10 (Belsley, Kuh, & Welsch, 1980). Cook's distance was calculated to identify possible influential cases defined from values ≥1 (Cook & Weisberg, 1982).

In order to offer normative data, two different strategies were applied. The first used the traditional method based on the score stratification according to the variables that presented with a significant effect on the execution of the SCWT (Mitrushyina, Boone, Razani, & D'Elia, 2005) and the second one based on regression models (Van Breukelen & Vlaeyen, 2005). According to the traditional method, the normative values were calculated for each score of the SCWT (mean, SD, and 5, 50, and 95 percentiles). In order to maximize the number of participants who contribute to the normative distribution for each mean value of the age interval, the strategy of interval superposition was adopted (Pauker, 1988). Hence, each mean age value provided norms for subjects of that age plus or minus one year, except for cases under 61 and above 82 years of age. The age range for each mean value was of 10 years, so that the mean age value of 60 years included the interval of participants between 55 and 65 years of age, the mean age value of 63 years included the interval between 58 and 68 years, and so forth. The age distribution allowed for the calculation of normative data for the following nine groups of mean age values: 55–61, 62–64, 65–67, 68–70, 71–73, 74–76, 77–79, 80–82, and ≥82 years.

According to the method based on regression models, the direct scores in the SCWT of one person become the standardized residual values, following three steps. First, the values predicted by the regression model are calculated; second, the residual values are calculated (ei = score observed − predicted score); and third, the residual values are standardized (Zi = ei/SD(residual)). Standardized residual values are interpreted according to a normal distribution table.

Results

The sample was formed by 2,564 participants of 55 years of age and above, of which 2.26% were excluded for presenting with visual, auditory, or motor disorders that could have interfered in the execution of the SCWT. Of the 2,506 candidates, 9.75% refused to participate, 2.99% did not complete the SCWT, and 1.9% presented with lost data, so that the final rate of participation was of 86.63% of the candidates (Fig. 1).

Fig. 1.

Participation algorithm.

Fig. 1.

Participation algorithm.

The mean age of the participants was 67.4 years (SD = 8.2) and 53.4% were women. 56.4% had a low educational level, 25.4% had a mid educational level, and 18.2% had a high educational level. The first language was Catalan in the 73.4% of the participants. Table 1 presents the distribution of age groups, the level of education, the first language, and the civil status stratified by sex. Study participants and non-participants differ significantly in age and in the level of education; the mean age of non-participants was 70.6 years (SD = 9.1) (Z for Mann–Whitney U = −6.4, p < .001), and 65.6% have low level education (vs. 56.4% in participants; χ2 = 11.3, df = 1, p = .001).

Table 1.

Sociodemographic characteristics of the study participants

 Men (n = 1,005; n [%]) Women (n = 1,146; n [%]) Total (n = 2,151; n [%]) 
Age (years) 
 55–59 188 (18.7) 226 (19.7) 414 (19.2) 
 60–64 224 (22.2) 277 (24.1) 501 (23.2) 
 65–69 199 (19.7) 212 (18.4) 411 (19.0) 
 70–74 145 (14.4) 178 (15.5) 323 (15.0) 
 75–79 140 (13.9) 151 (13.1) 291 (13.5) 
 80–84 89 (8.8) 82 (7.1) 171 (7.9) 
 85+ 23 (2.3) 24 (2.1) 47 (2.2) 
Education (years) 
 0 (without formal scholarship) 9 (0.9) 16 (1.4) 25 (1.2) 
 1–8 519 (51.7) 669 (58.3) 1.188 (54.9) 
 9–12 237 (23.6) 310 (27.0) 547 (25.4) 
 13–15 84 (8.4) 83 (7.2) 167 (7.8) 
 16+ 154 (15.4) 70 (6.2) 224 (10.3) 
Fist language 
 Catalan 738 (46.9) 835 (53.1) 1573 (73.1) 
 Spanish Castilian 266 (46.0) 312 (54.0) 578 (26.8) 
 Men (n = 1,005; n [%]) Women (n = 1,146; n [%]) Total (n = 2,151; n [%]) 
Age (years) 
 55–59 188 (18.7) 226 (19.7) 414 (19.2) 
 60–64 224 (22.2) 277 (24.1) 501 (23.2) 
 65–69 199 (19.7) 212 (18.4) 411 (19.0) 
 70–74 145 (14.4) 178 (15.5) 323 (15.0) 
 75–79 140 (13.9) 151 (13.1) 291 (13.5) 
 80–84 89 (8.8) 82 (7.1) 171 (7.9) 
 85+ 23 (2.3) 24 (2.1) 47 (2.2) 
Education (years) 
 0 (without formal scholarship) 9 (0.9) 16 (1.4) 25 (1.2) 
 1–8 519 (51.7) 669 (58.3) 1.188 (54.9) 
 9–12 237 (23.6) 310 (27.0) 547 (25.4) 
 13–15 84 (8.4) 83 (7.2) 167 (7.8) 
 16+ 154 (15.4) 70 (6.2) 224 (10.3) 
Fist language 
 Catalan 738 (46.9) 835 (53.1) 1573 (73.1) 
 Spanish Castilian 266 (46.0) 312 (54.0) 578 (26.8) 

All the necessary requirements to apply the regression analysis were met on the different scores of the SCWT. The Levene test for the residuals of each quartile SCWT score did not reject the assumption of homogeneity of the variances and the result of the application of the Kolmogorov–Smirnov test showed p-values of >.324 for residuals of each SCWT score. VIF values were not above 1.486 in any of the regression models. The presence of extreme values was not seen, as the highest Cook's distance was of 0.025.

Table 2 presents the results of regression models for the different SCWT scores. The age and the low educational level were associated with scores of words, colors, and words–colors. The sex variable was associated with the scores of colors and of interference. Age was the variable that presented with the greatest variance percentage explained in all regression models. None of the SCWT scores were affected by the first language nor by the quadratic term of age. A significant interaction of the educational level and the age was detected for both interference scores. Fig. 2 graphically shows the predicted score of both interference scores for men, in which the difference between subjects with a high and a low educational level is significantly modified in terms of age. It is important to point out that the percentage of variance explained in the interference score was much higher when calculated according to Chafetz's method (interference 2) than when calculated by Golden's method (interference 1) (13.2% compared with 2.5%).

Table 2.

Stepwise multiple linear regression of age, education, and sex on SCWT scores

Measure Variable β SE(βt Sdβ R2 
Word Constant 89.387 0.735 121.546** — .305a 
Age −0.821 0.046 −17.958** −0.332 .138 
LE low −13.575 0.899 −15.103** −0.329 .146 
LE high 3.820 1.132 3.376* 0.072 .020 
Color Constant 59.590 0.579 102.954** — .236a 
Age −0.567 0.031 −18.121** −0.352 .145 
Sex 2.304 0.510 4.517** 0.086 .008 
LE low −6.495 0.615 −10.565** −0.241 .079 
LE high 0.896 0.779 1.151 0.026 .004 
Word–Color Constant 34.439 0.412 83.591** — .226a 
Age −0.449 0.026 −17.512** −0.342 .137 
LE low −4.771 0.504 −9.476** −0.218 .073 
LE high 2.008 0.634 3.167* 0.071 .016 
Interference 1 Constant −1.108 0.428 −2.530* — .025a 
Age −0.170 0.047 −3.643** −0.164 . 017 
Sex −0.998 0.371 −2.687* −0.058 .003 
LE low −0.145 0.451 −0.327 −0.008 <.001 
LE high 0.910 0.579 1.571 0.041 .001 
Age × LE low 0.136 0.055 2.454* 0.099 .003 
Age × LE high −0.096 0.071 −1.350 −0.039 .001 
Interference 2 Constant −4.949 0.419 −11.813** — .156a 
Age −0.309 0.046 −6.775** −0.288 .074 
Sex −1.695 0.363 −4.666** −0.095 .010 
LE low −13.325 3.613 −3.688** −0.158 .040 
LE high 8.072 4.596 1.756 0.080 .016 
Age × LE low 0.157 0.054 2.895* 0.110 .016 
Age × LE high −0.093 0.070 −1.328 −0.036 <.001 
Measure Variable β SE(βt Sdβ R2 
Word Constant 89.387 0.735 121.546** — .305a 
Age −0.821 0.046 −17.958** −0.332 .138 
LE low −13.575 0.899 −15.103** −0.329 .146 
LE high 3.820 1.132 3.376* 0.072 .020 
Color Constant 59.590 0.579 102.954** — .236a 
Age −0.567 0.031 −18.121** −0.352 .145 
Sex 2.304 0.510 4.517** 0.086 .008 
LE low −6.495 0.615 −10.565** −0.241 .079 
LE high 0.896 0.779 1.151 0.026 .004 
Word–Color Constant 34.439 0.412 83.591** — .226a 
Age −0.449 0.026 −17.512** −0.342 .137 
LE low −4.771 0.504 −9.476** −0.218 .073 
LE high 2.008 0.634 3.167* 0.071 .016 
Interference 1 Constant −1.108 0.428 −2.530* — .025a 
Age −0.170 0.047 −3.643** −0.164 . 017 
Sex −0.998 0.371 −2.687* −0.058 .003 
LE low −0.145 0.451 −0.327 −0.008 <.001 
LE high 0.910 0.579 1.571 0.041 .001 
Age × LE low 0.136 0.055 2.454* 0.099 .003 
Age × LE high −0.096 0.071 −1.350 −0.039 .001 
Interference 2 Constant −4.949 0.419 −11.813** — .156a 
Age −0.309 0.046 −6.775** −0.288 .074 
Sex −1.695 0.363 −4.666** −0.095 .010 
LE low −13.325 3.613 −3.688** −0.158 .040 
LE high 8.072 4.596 1.756 0.080 .016 
Age × LE low 0.157 0.054 2.895* 0.110 .016 
Age × LE high −0.093 0.070 −1.328 −0.036 <.001 

Notes: β = regression coefficient; SE(β) = standard error of β; Sdβ = standardized regression coefficient; ΔR2 = variance explained by variable (Pearson's correlation × Sdβ).

aR2 = coefficient of determination.

*p < .05.

**p < .001.

Fig. 2.

Predicted Stroop interference scores 1 and 2 (men) by level of education based on age.

Fig. 2.

Predicted Stroop interference scores 1 and 2 (men) by level of education based on age.

Table 3 shows the normative data of the SCWT obtained by the traditional method. The mean score, standard deviation, and 5, 50, and 95 percentiles are stratified in accordance with the variables detected in regression models. The table is comprised of age intervals superimposed with mean age values in 3-year intervals (age ± 1 year). To be used, the age group with the mean age value closest to the age of the subject for whom the score is to be interpreted must be chosen. The scores included in the table allow to position the performance of the subjects compared with their normative group based on percentiles, but they also allow to transform scores into Z-values. For example, for a 64-year-old woman with a low educational level, with a score of 47 words, her performance is under the 5th percentile. Furthermore, the comparison with a mean score for her age group (79.5 words) shows a negative residual of −32.5 words, which corresponds to a Z-value of −1.85 (=47–79.5/17.6) and to a p-value of .03 that cannot be considered to be within the range of normality.

Table 3.

Normative data for the SCWT scores stratified by their relevant predictors (descriptive-based)

Measure Sex LE 55–61 62–64 65–67 68–70 71–73 74–76 77–79 80–82 +82 
Word 
 Mean (SDM and F 82.1 (17.1) 79.5 (17.6) 77.4 (18.3) 74.0 (18.6) 71.2 (18.8) 68.9 (19.4) 66.0 (19.2) 65.9 (19.0) 64.9 (18.8) 
 PC 5/50/95 55/83/110 50/80/108 48/78/106 41/75/102 38/73/100 34/71/99 31/67/97 31/66/96 30/66/95 
n 476 513 521 515 469 444 401 300 185 
 Mean (SD94.9 (15.3) 94.4 (15.8) 92.6 (15.9) 89.5 (16.1) 85.3 (16.8) 81.0 (16.0) 79.7 (17.0) 77.0 (15.0) 75.5 (16.3) 
 PC 5/50/95 69/95/123 69/95/123 67/94/123 63/90/119 59/86/110 56/82/103 55/79/100 52/77/100 52/75/98 
n 334 300 249 193 139 115 100 71 46 
 Mean (SD98.8 (14.7) 98.2 (14.5) 97.5 (15.2) 96.0 92.3 (15.4) 87.4 (15.1) 84.9 (14.4) 83.5 (15.4) 81.0 (14.6) 
 PC 5/50/95 74/98/123 74/98/123 73/97/123 73/96/123 70/92/122 63/88/113 62/85/106 54/84/106 54/81/105 
n 227 202 176 137 109 91 85 59 31 
Color 
 Mean (SD56.6 (12.1) 54.5 (12.5) 52.7 (13.0) 51.1 (13.1) 50.2 (12.1) 48.6 (12.2) 47.8 (12.2) 46.2 (12.4) 43.4 (11.8) 
 PC 5/50/95 35/56/77 34/54/75 30/53/74 30/50/73 30/50/69 29/48/68 25/48/68 25/47/66 23/44/64 
n 207 216 222 217 188 189 183 146 92 
 Mean (SD  63.9 (11.1) 63.4 (11.3) 62.5 (10.8) 60.6 (11.2) 58.5 (12.8) 53.4 (12.3) 53.2 (12.2) 51.4 (10.1) 50.1 (10.4) 
 PC 5/50/95 42/63/83 42/62/82 42/62/80 40/61/80 38/58/80 34/52/78 34/52/77 34/52/72 29/52/71 
n 141 134 108 76 61 51 46 34 21 
 Mean (SD64.6 (12.0) 64.0 (11.7) 62.8 (12.1) 62.2 (12.9) 59.2 (12.3) 54.7 (11.6) 54.4 (11.7) 53.0 (11.9) 53.1 (11.2) 
 PC 5/50/95 46/65/83 45/65/83 42/63/83 41/61/82 39/59/82 37/55/77 37/54/75 33/51/72 33/51/72) 
n 124 123 111 94 75 63 58 39 23 
 Mean (SD58.9 (11.9) 57.5 (11.8) 56.8 (11.9) 54.8 (11.7) 53.1 (11.6) 51.9 (11.3) 49.6 (10.7) 48.0 (10.9) 47.2 (11.4) 
 PC 5/50/95 39/60/79 38/59/78 37/57/78 37/55/75 36/53/75 33/52/73 32/49/68 30/48/65 26/46/65 
n 269 297 299 298 281 255 218 154 93 
 Mean (SD65.4 (11.0) 64.7 (10.8) 63.2 (10.7) 62.0 (11.5) 58.6 (11.3) 55.8 (9.9) 54.7 (10.0) 53.0 (10.6) 51.6 (11.0) 
 PC 5/50/95 48/63/85 48/63/87 44/63/82 42/61/82 40/59/81 40/57/75 34/57/68 33/57/66 29/57/66 
n 193 166 141 117 78 64 54 37 25 
 Mean (SD66.4 (11.5) 65.0 (11.4) 63.6 (10.5) 61.9 (12.1) 61.3 (13.5) 59.1 (11.8) 57.5 (11.1) 56.9 (11.5) 54.0 (10.5) 
 PC 5/50/95 49/66/90 47/65/84 46/65/83 40/61/83 40/60/82 39/60/81 38/59/79 37/59/79 37/59/62 
n 103 79 65 43 34 28 27 20 
Word-Color 
 Mean (SDM and F 31.6 (9.2) 30.9 (9.3) 30.4 (9.9) 28.9 (10.1) 28.3 (10.5) 26.7 (9.3) 25.3 (9.1) 24.7 (9.1) 23.1 (7.9) 
 PC 5/50/95 17/31/47 16/30/46 15/29/46 14/28/46 14/27/46 14/26/42 12/24/42 12/24/41 10/23/37 
n 476 513 521 515 469 444 401 300 185 
 Mean (SD37.9 (9.5) 37.2 (9.4) 35.9 (9.5) 33.9 (9.3) 32.2 (9.2) 29.8 (8.8) 29.1 (8.8) 27.0 (8.3) 25.7 (8.7) 
 PC 5/50/95 22/38/55 22/37/55 20/35/54 19/33/50 18/31/48 17/30/46 15/30/44 14/26/44 13/24/44 
n 334 300 249 193 139 115 100 71 46 
 Mean (SD40.3 (9.1) 39.7 (8.9) 38.2 (9.7) 36.0 (11.1) 33.0 (10.6) 30.8 (11.6) 29.6 (10.7) 29.2 (11.0) 29.3 (12.6) 
 PC 5/50/95 26/41/55 25/40/54 23/38/53 16/36/53 14/33/50 13/29/50 13/28/48 13/28/48 13/27/58 
n 227 202 176 137 109 91 85 59 31 
Interference 1 
 Mean (SD−1.7 (7.9) −1.1 (7.7) −1.1 (9.2) −1.4 (9.1) −0.5 (9.9) −1.5 (8.5) −1.0 (8.9) −0.5 (9.1) −1.0 (8.7) 
 PC 5/50/95 −14/−2/12 −13/−1/12 −14/−2/13 −14/−2/12 −14/−1/17 −14/−2/14 −12/−2/12 −13/−2/12 −12/−2/9 
n 207 216 222 217 188 189 183 146 92 
 Mean (SD−0.5 (7.1) −0.5 (7.4) −0.8 (7.7) −0.8 (7.6) −0.4 (7.3) −0.7 (6.2) −2.1 (6.5) −3.9 (6.2) −4.9 (6.9) 
 PC 5/50/95 −14/0/11 −13/0/12 −13/−1/12 −13/−2/12 −13/−1/11 −13/−1/9 −15/−2/9 −17/−3/7 −18/−3/9 
n 141 134 108 76 61 51 46 34 21 
 Mean (SD1.5 (8.3) 1.2 (7.5) 0.5 (8.4) −1.4 (8.4) −3.0 (7.6) −2.6 (10.7) −3.1 (10.5) −1.8 (7.5) −2.2 (13.7) 
 PC 5/50/95 −10/1/13 −10/1/12 −14/1/14 −15/−1/9 −16/−2/9 −17/−2/15 −20/−3/9 −19/−2/10 −20/−3/12 
n 124 123 111 94 75 63 58 39 23 
 Mean (SD−2.2 (8.3) −2.1 (8.9) −1.5 (8.9) −1.7 (8.8) −1.8 (9.7) −2.2 (8.9) −3.3 (8.8) −3.6 (8.6) −4.5 (7.3) 
 PC 5/50/95 −16/−3/12 −15/−3/13 −15/−2/15 −15/−3/13 −16/−3/13 −16/−3/12 −17/−4/10 −17/−4/9 −17/−5/6 
n 269 297 299 298 281 255 218 154 93 
 Mean (SD−0.3 (7.7) −1.0 (7.7) −1.6 (8.0) −3.2 (7.5) −3.5 (7.4) −4.0 (8.2) −3.5 (8.6) −3.9 (9.7) −4.5 (7.3) 
 PC 5/50/95 −13/−1/12 −14/−1/12 −15/−2/10 −16/−3/9 −16/−3/7 −16/−3/12 −16/−3/14 −17/−6/14 −17/−7/13 
n 193 166 141 117 78 64 54 37 25 
 Mean (SD1.6 (7.9) 0.5 (7.5) −0.5 (7.7) −1.7 (8.9) −3.2 (8.9) −4.0 (7.8) −4.5 (7.3) −2.7 (7.5) −2.8 (8.1) 
 PC 5/50/95 −12/2/13 −11/0/13 −12/0/15 −16/−1/18 −21/−3/15 −22/−3/11 −22/−3/8 −21/−3/9 −12/−5/10 
n 103 79 65 43 34 28 27 20 
Interference 2 
 Mean (SD−7.2 (7.7) −7.1 (7.6) −7.2 (8.8) −7.9 (8.9) −7.7 (8.8) −9.1 (7.1) −9.3 (7.1) −8.8 (6.9) −9.0 (6.8) 
 PC 5/50/95 −20/−7/7 −19/−7/5 −19/−7/5 −21/−9/5 −21/−9/5 −21/−10/3 −21/−10/3 −21/−10/3 −21/−10/1 
n 207 216 222 217 188 189 183 146 92 
 Mean (SD−3.3 (7.7) −3.5 (7.9) −4.2 (8.2) −5.1 (8.0) −4.8 (8.6) −5.8 (7.9) −7.7 (8.3) −10.7 (6.1) −12.1 (6.8) 
 PC 5/50/95 −17/−2/10 −17/−3/10 −18/−3/10 −18/−5/10 −18/−5/9 −18/−6/7 −19/−8/6 −23/−10/4 −25/−12/1 
n 141 134 108 76 61 51 46 34 21 
 Mean (SD−0.7 (8.3) −1.0 (7.6) −1.6 (8.6) −3.6 (9.1) −6.4 (7.9) −6.7 (11.2) −8.0 (11.2) −7.1 (12.5) −7.9 (14.1) 
 PC 5/50/95 −15/−1/13 −14/−2/12 −15/−2/12 −17/−3/8 −20/−6/7 −22/−8/8 −22/−9/7 −22/−8/7 −22/−10/6 
n 124 123 111 94 75 63 58 39 23 
 Mean (SD−8.6 (7.7) −8.8 (8.5) −8.8 (8.3) −9.6 (8.3) −9.9 (9.3) −10.7 (8.2) −11.8 (8.3) −12.8 (8.2) −13.0 (6.7) 
 PC 5/50/95 −21/−9/5 −21/−9/5 −21/−9/4 −21/−10/4 −22/−11/4 −23/−11/3 −25/−12/0 −25/−12/0 −25/−12/−2 
n 269 297 299 298 281 255 218 154 93 
 Mean (SD−3.7 (8.4) −4.5 (8.3) −5.6 (8.2) −7.7 (7.7) −9.4 (7.4) −10.6 (7.8) −9.9 (8.6) −9.9 (9.3) −9.8 (10.2) 
 PC 5/50/95 −18/−4/10 −19/−5/10 −21/−5/9 −21/−7/5 −23/−9/4 −23/−11/4 −23/−10/4 −23/−10/4 −24/−10/4 
n 193 166 141 117 78 64 54 37 25 
 Mean (SD−1.0 (7.9) −1.9 (7.9) −2.9 (8.5) −4.6 (10.3) −6.5 (10.5) −9.0 (8.7) −10.0 (6.8) −8.7 (6.4) −13.0 (6.7) 
 PC 5/50/95 −16/−1/13 −17/−1/13 −17/−2/13 −20/−5/13 −22/−6/12 −24/−9/11 −24/−10/8 −24/−10/2 −25/−12/−2 
n 103 79 65 43 34 28 27 20 
Measure Sex LE 55–61 62–64 65–67 68–70 71–73 74–76 77–79 80–82 +82 
Word 
 Mean (SDM and F 82.1 (17.1) 79.5 (17.6) 77.4 (18.3) 74.0 (18.6) 71.2 (18.8) 68.9 (19.4) 66.0 (19.2) 65.9 (19.0) 64.9 (18.8) 
 PC 5/50/95 55/83/110 50/80/108 48/78/106 41/75/102 38/73/100 34/71/99 31/67/97 31/66/96 30/66/95 
n 476 513 521 515 469 444 401 300 185 
 Mean (SD94.9 (15.3) 94.4 (15.8) 92.6 (15.9) 89.5 (16.1) 85.3 (16.8) 81.0 (16.0) 79.7 (17.0) 77.0 (15.0) 75.5 (16.3) 
 PC 5/50/95 69/95/123 69/95/123 67/94/123 63/90/119 59/86/110 56/82/103 55/79/100 52/77/100 52/75/98 
n 334 300 249 193 139 115 100 71 46 
 Mean (SD98.8 (14.7) 98.2 (14.5) 97.5 (15.2) 96.0 92.3 (15.4) 87.4 (15.1) 84.9 (14.4) 83.5 (15.4) 81.0 (14.6) 
 PC 5/50/95 74/98/123 74/98/123 73/97/123 73/96/123 70/92/122 63/88/113 62/85/106 54/84/106 54/81/105 
n 227 202 176 137 109 91 85 59 31 
Color 
 Mean (SD56.6 (12.1) 54.5 (12.5) 52.7 (13.0) 51.1 (13.1) 50.2 (12.1) 48.6 (12.2) 47.8 (12.2) 46.2 (12.4) 43.4 (11.8) 
 PC 5/50/95 35/56/77 34/54/75 30/53/74 30/50/73 30/50/69 29/48/68 25/48/68 25/47/66 23/44/64 
n 207 216 222 217 188 189 183 146 92 
 Mean (SD  63.9 (11.1) 63.4 (11.3) 62.5 (10.8) 60.6 (11.2) 58.5 (12.8) 53.4 (12.3) 53.2 (12.2) 51.4 (10.1) 50.1 (10.4) 
 PC 5/50/95 42/63/83 42/62/82 42/62/80 40/61/80 38/58/80 34/52/78 34/52/77 34/52/72 29/52/71 
n 141 134 108 76 61 51 46 34 21 
 Mean (SD64.6 (12.0) 64.0 (11.7) 62.8 (12.1) 62.2 (12.9) 59.2 (12.3) 54.7 (11.6) 54.4 (11.7) 53.0 (11.9) 53.1 (11.2) 
 PC 5/50/95 46/65/83 45/65/83 42/63/83 41/61/82 39/59/82 37/55/77 37/54/75 33/51/72 33/51/72) 
n 124 123 111 94 75 63 58 39 23 
 Mean (SD58.9 (11.9) 57.5 (11.8) 56.8 (11.9) 54.8 (11.7) 53.1 (11.6) 51.9 (11.3) 49.6 (10.7) 48.0 (10.9) 47.2 (11.4) 
 PC 5/50/95 39/60/79 38/59/78 37/57/78 37/55/75 36/53/75 33/52/73 32/49/68 30/48/65 26/46/65 
n 269 297 299 298 281 255 218 154 93 
 Mean (SD65.4 (11.0) 64.7 (10.8) 63.2 (10.7) 62.0 (11.5) 58.6 (11.3) 55.8 (9.9) 54.7 (10.0) 53.0 (10.6) 51.6 (11.0) 
 PC 5/50/95 48/63/85 48/63/87 44/63/82 42/61/82 40/59/81 40/57/75 34/57/68 33/57/66 29/57/66 
n 193 166 141 117 78 64 54 37 25 
 Mean (SD66.4 (11.5) 65.0 (11.4) 63.6 (10.5) 61.9 (12.1) 61.3 (13.5) 59.1 (11.8) 57.5 (11.1) 56.9 (11.5) 54.0 (10.5) 
 PC 5/50/95 49/66/90 47/65/84 46/65/83 40/61/83 40/60/82 39/60/81 38/59/79 37/59/79 37/59/62 
n 103 79 65 43 34 28 27 20 
Word-Color 
 Mean (SDM and F 31.6 (9.2) 30.9 (9.3) 30.4 (9.9) 28.9 (10.1) 28.3 (10.5) 26.7 (9.3) 25.3 (9.1) 24.7 (9.1) 23.1 (7.9) 
 PC 5/50/95 17/31/47 16/30/46 15/29/46 14/28/46 14/27/46 14/26/42 12/24/42 12/24/41 10/23/37 
n 476 513 521 515 469 444 401 300 185 
 Mean (SD37.9 (9.5) 37.2 (9.4) 35.9 (9.5) 33.9 (9.3) 32.2 (9.2) 29.8 (8.8) 29.1 (8.8) 27.0 (8.3) 25.7 (8.7) 
 PC 5/50/95 22/38/55 22/37/55 20/35/54 19/33/50 18/31/48 17/30/46 15/30/44 14/26/44 13/24/44 
n 334 300 249 193 139 115 100 71 46 
 Mean (SD40.3 (9.1) 39.7 (8.9) 38.2 (9.7) 36.0 (11.1) 33.0 (10.6) 30.8 (11.6) 29.6 (10.7) 29.2 (11.0) 29.3 (12.6) 
 PC 5/50/95 26/41/55 25/40/54 23/38/53 16/36/53 14/33/50 13/29/50 13/28/48 13/28/48 13/27/58 
n 227 202 176 137 109 91 85 59 31 
Interference 1 
 Mean (SD−1.7 (7.9) −1.1 (7.7) −1.1 (9.2) −1.4 (9.1) −0.5 (9.9) −1.5 (8.5) −1.0 (8.9) −0.5 (9.1) −1.0 (8.7) 
 PC 5/50/95 −14/−2/12 −13/−1/12 −14/−2/13 −14/−2/12 −14/−1/17 −14/−2/14 −12/−2/12 −13/−2/12 −12/−2/9 
n 207 216 222 217 188 189 183 146 92 
 Mean (SD−0.5 (7.1) −0.5 (7.4) −0.8 (7.7) −0.8 (7.6) −0.4 (7.3) −0.7 (6.2) −2.1 (6.5) −3.9 (6.2) −4.9 (6.9) 
 PC 5/50/95 −14/0/11 −13/0/12 −13/−1/12 −13/−2/12 −13/−1/11 −13/−1/9 −15/−2/9 −17/−3/7 −18/−3/9 
n 141 134 108 76 61 51 46 34 21 
 Mean (SD1.5 (8.3) 1.2 (7.5) 0.5 (8.4) −1.4 (8.4) −3.0 (7.6) −2.6 (10.7) −3.1 (10.5) −1.8 (7.5) −2.2 (13.7) 
 PC 5/50/95 −10/1/13 −10/1/12 −14/1/14 −15/−1/9 −16/−2/9 −17/−2/15 −20/−3/9 −19/−2/10 −20/−3/12 
n 124 123 111 94 75 63 58 39 23 
 Mean (SD−2.2 (8.3) −2.1 (8.9) −1.5 (8.9) −1.7 (8.8) −1.8 (9.7) −2.2 (8.9) −3.3 (8.8) −3.6 (8.6) −4.5 (7.3) 
 PC 5/50/95 −16/−3/12 −15/−3/13 −15/−2/15 −15/−3/13 −16/−3/13 −16/−3/12 −17/−4/10 −17/−4/9 −17/−5/6 
n 269 297 299 298 281 255 218 154 93 
 Mean (SD−0.3 (7.7) −1.0 (7.7) −1.6 (8.0) −3.2 (7.5) −3.5 (7.4) −4.0 (8.2) −3.5 (8.6) −3.9 (9.7) −4.5 (7.3) 
 PC 5/50/95 −13/−1/12 −14/−1/12 −15/−2/10 −16/−3/9 −16/−3/7 −16/−3/12 −16/−3/14 −17/−6/14 −17/−7/13 
n 193 166 141 117 78 64 54 37 25 
 Mean (SD1.6 (7.9) 0.5 (7.5) −0.5 (7.7) −1.7 (8.9) −3.2 (8.9) −4.0 (7.8) −4.5 (7.3) −2.7 (7.5) −2.8 (8.1) 
 PC 5/50/95 −12/2/13 −11/0/13 −12/0/15 −16/−1/18 −21/−3/15 −22/−3/11 −22/−3/8 −21/−3/9 −12/−5/10 
n 103 79 65 43 34 28 27 20 
Interference 2 
 Mean (SD−7.2 (7.7) −7.1 (7.6) −7.2 (8.8) −7.9 (8.9) −7.7 (8.8) −9.1 (7.1) −9.3 (7.1) −8.8 (6.9) −9.0 (6.8) 
 PC 5/50/95 −20/−7/7 −19/−7/5 −19/−7/5 −21/−9/5 −21/−9/5 −21/−10/3 −21/−10/3 −21/−10/3 −21/−10/1 
n 207 216 222 217 188 189 183 146 92 
 Mean (SD−3.3 (7.7) −3.5 (7.9) −4.2 (8.2) −5.1 (8.0) −4.8 (8.6) −5.8 (7.9) −7.7 (8.3) −10.7 (6.1) −12.1 (6.8) 
 PC 5/50/95 −17/−2/10 −17/−3/10 −18/−3/10 −18/−5/10 −18/−5/9 −18/−6/7 −19/−8/6 −23/−10/4 −25/−12/1 
n 141 134 108 76 61 51 46 34 21 
 Mean (SD−0.7 (8.3) −1.0 (7.6) −1.6 (8.6) −3.6 (9.1) −6.4 (7.9) −6.7 (11.2) −8.0 (11.2) −7.1 (12.5) −7.9 (14.1) 
 PC 5/50/95 −15/−1/13 −14/−2/12 −15/−2/12 −17/−3/8 −20/−6/7 −22/−8/8 −22/−9/7 −22/−8/7 −22/−10/6 
n 124 123 111 94 75 63 58 39 23 
 Mean (SD−8.6 (7.7) −8.8 (8.5) −8.8 (8.3) −9.6 (8.3) −9.9 (9.3) −10.7 (8.2) −11.8 (8.3) −12.8 (8.2) −13.0 (6.7) 
 PC 5/50/95 −21/−9/5 −21/−9/5 −21/−9/4 −21/−10/4 −22/−11/4 −23/−11/3 −25/−12/0 −25/−12/0 −25/−12/−2 
n 269 297 299 298 281 255 218 154 93 
 Mean (SD−3.7 (8.4) −4.5 (8.3) −5.6 (8.2) −7.7 (7.7) −9.4 (7.4) −10.6 (7.8) −9.9 (8.6) −9.9 (9.3) −9.8 (10.2) 
 PC 5/50/95 −18/−4/10 −19/−5/10 −21/−5/9 −21/−7/5 −23/−9/4 −23/−11/4 −23/−10/4 −23/−10/4 −24/−10/4 
n 193 166 141 117 78 64 54 37 25 
 Mean (SD−1.0 (7.9) −1.9 (7.9) −2.9 (8.5) −4.6 (10.3) −6.5 (10.5) −9.0 (8.7) −10.0 (6.8) −8.7 (6.4) −13.0 (6.7) 
 PC 5/50/95 −16/−1/13 −17/−1/13 −17/−2/13 −20/−5/13 −22/−6/12 −24/−9/11 −24/−10/8 −24/−10/2 −25/−12/−2 
n 103 79 65 43 34 28 27 20 

Notes: M = men; F = women; L = low; A = average; H = high; PC = percentile.

The use of data of regression models to obtain normative values requires for users to carry out some calculations. The normative data are obtained from the values predicted by the regression model in combination with the standard deviation of residuals. First, the score predicted by the regression model must be calculated based on the characteristics of age, educational level, and sex by means of the regression coefficients presented in Table 2. Second, the residual value must be calculated, which corresponds to the difference between the value predicted and the value obtained (ei = value obtained-value predicted). Third, from the standard deviations of the residual values (Table 4), it is possible to transform them into Z-values (Zi = ei/SD(residual)). For the previous example of a 64-year-old woman with a low educational level, the predicted score would be of 78.275 words (=89.387 + (−0.821 × −3) + (−13.575 × 1) + (3.82 × 0)). The residual value would be of −31.275 words (ei = 47 − 78.275) and the standardized residual value would be of −1.775 (=−31.275/17.619) which corresponds to a p-value of .03.

Table 4.

Standard deviations of residuals for the predicted SCWT scores

 Predicted score SD (residual) 
Word ≥92.386 15.509 
Between 92.385 and 81.180 15.867 
Between 81.179 and 73.349 17.619 
≤73.350 19.082 
Color ≥62.203 11.341 
Between 62.202 and 57.100 11.643 
Between 57.099 and 51.997 12.240 
≤51.996 11.494 
Word–Color ≥35.997 9.661 
Between 35.996 and 31.909 9.632 
Between 31.908 and 27.873 10.022 
≤27.872 8.912 
Interference 1 ≥−0.887 7.829 
Between −1.605 and −0.886 8.765 
Between −2.296 and −1.604 8.693 
≤−2.295 8.651 
Interference 2 ≥−4.787 8.752 
Between −4.786 and −7.780 10.487 
Between −7.779 and −9.461 12.185 
≤−9.460 13.512 
 Predicted score SD (residual) 
Word ≥92.386 15.509 
Between 92.385 and 81.180 15.867 
Between 81.179 and 73.349 17.619 
≤73.350 19.082 
Color ≥62.203 11.341 
Between 62.202 and 57.100 11.643 
Between 57.099 and 51.997 12.240 
≤51.996 11.494 
Word–Color ≥35.997 9.661 
Between 35.996 and 31.909 9.632 
Between 31.908 and 27.873 10.022 
≤27.872 8.912 
Interference 1 ≥−0.887 7.829 
Between −1.605 and −0.886 8.765 
Between −2.296 and −1.604 8.693 
≤−2.295 8.651 
Interference 2 ≥−4.787 8.752 
Between −4.786 and −7.780 10.487 
Between −7.779 and −9.461 12.185 
≤−9.460 13.512 

Tables 5–9 present normative data from the method based on regression models for 5-year groups to make their use easier. For their clinical use, if the person that is to be evaluated does not have the exact age used for the creation of the table (55, 60, 65, … ), it must be rounded to the closest age of reference.

Table 5.

Normative data for the SCWT Word score stratified by age and level of education (regression-based)

Z-value Cumulative probability. 55 60 65 70 75 80 85 
LE low 
 1.64 .95 111.1 107.0 102.9 98.8 94.7 90.6 86.5 
 1.28 .90 105.5 101.4 97.3 93.2 89.1 85.0 80.9 
 0.84 .80 98.7 94.6 90.5 86.4 82.3 78.2 74.1 
 0.0 .50 85.7 81.6 77.5 73.3 69.2 65.1 61.0 
 −0.84 .20 69.6 65.5 61.4 57.3 53.2 49.1 45.0 
 −1.28 .10 61.2 57.1 53.0 48.9 44.8 40.7 36.6 
 −1.64 .05 54.4 50.3 46.2 42.1 37.9 33.8 29.7 
LE average 
 1.64 .95 124.7 120.6 116.5 112.4 108.3 104.1 100.0 
 1.28 .90 119.1 115.0 110.9 106.8 102.7 98.6 94.5 
 0.84 .80 112.3 108.2 104.1 100.0 95.8 91.7 87.6 
 0.0 .50 99.2 95.1 91.0 86.9 82.8 78.7 74.6 
 −0.84 .20 83.2 79.1 75.0 70.9 66.8 62.7 58.6 
 −1.28 .10 74.8 70.7 66.6 62.5 58.4 54.3 50.2 
 −1.64 .05 67.9 63.8 59.7 55.6 51.5 47.4 43.3 
LE high 
 1.64 .95 128.5 124.4 120.3 116.2 112.1 108.0 103.9 
 1.28 .90 122.9 118.8 114.7 110.6 106.5 102.4 98.3 
 0.84 .80 116.1 112.0 107.9 103.8 99.7 95.6 91.5 
 0.0 .50 103.1 99.0 94.8 90.7 86.6 82.5 78.4 
 −0.84 .20 87.0 82.9 78.8 74.7 70.6 66.5 62.4 
 −1.28 .10 78.6 74.5 70.4 66.3 62.2 58.1 54.0 
 −1.64 .05 71.8 67.7 63.6 59.4 55.3 51.2 47.1 
Z-value Cumulative probability. 55 60 65 70 75 80 85 
LE low 
 1.64 .95 111.1 107.0 102.9 98.8 94.7 90.6 86.5 
 1.28 .90 105.5 101.4 97.3 93.2 89.1 85.0 80.9 
 0.84 .80 98.7 94.6 90.5 86.4 82.3 78.2 74.1 
 0.0 .50 85.7 81.6 77.5 73.3 69.2 65.1 61.0 
 −0.84 .20 69.6 65.5 61.4 57.3 53.2 49.1 45.0 
 −1.28 .10 61.2 57.1 53.0 48.9 44.8 40.7 36.6 
 −1.64 .05 54.4 50.3 46.2 42.1 37.9 33.8 29.7 
LE average 
 1.64 .95 124.7 120.6 116.5 112.4 108.3 104.1 100.0 
 1.28 .90 119.1 115.0 110.9 106.8 102.7 98.6 94.5 
 0.84 .80 112.3 108.2 104.1 100.0 95.8 91.7 87.6 
 0.0 .50 99.2 95.1 91.0 86.9 82.8 78.7 74.6 
 −0.84 .20 83.2 79.1 75.0 70.9 66.8 62.7 58.6 
 −1.28 .10 74.8 70.7 66.6 62.5 58.4 54.3 50.2 
 −1.64 .05 67.9 63.8 59.7 55.6 51.5 47.4 43.3 
LE high 
 1.64 .95 128.5 124.4 120.3 116.2 112.1 108.0 103.9 
 1.28 .90 122.9 118.8 114.7 110.6 106.5 102.4 98.3 
 0.84 .80 116.1 112.0 107.9 103.8 99.7 95.6 91.5 
 0.0 .50 103.1 99.0 94.8 90.7 86.6 82.5 78.4 
 −0.84 .20 87.0 82.9 78.8 74.7 70.6 66.5 62.4 
 −1.28 .10 78.6 74.5 70.4 66.3 62.2 58.1 54.0 
 −1.64 .05 71.8 67.7 63.6 59.4 55.3 51.2 47.1 
Table 6.

Normative data for the SCWT Color score stratified by age, sex, and level of education (regression-based)

Z-value Cumulative probability 55
 
60
 
65
 
70
 
75
 
80
 
85
 
 
LE low 
 1.64 0.95 78.5 80.8 75.7 78.0 72.8 75.1 70.0 72.3 67.2 69.5 64.3 66.6 61.5 63.8 
 1.28 0.90 74.4 76.7 71.6 73.9 68.7 71.0 65.9 68.2 63.1 65.4 60.2 62.5 57.4 59.7 
 0.84 0.80 69.4 71.7 66.6 68.9 63.8 66.1 60.9 63.2 58.1 60.4 55.3 57.6 52.4 54.7 
 0.0 0.50 59.9 62.2 57.1 59.4 54.2 56.5 51.4 53.7 48.6 50.9 45.7 48.0 42.9 45.2 
 −0.84 0.20 50.2 52.5 47.4 49.7 44.6 46.9 41.7 44.0 38.9 41.2 36.1 38.4 33.2 35.5 
 −1.28 0.10 45.2 47.5 42.4 44.7 39.5 41.8 36.7 39.0 33.8 36.2 31.0 33.3 28.2 30.5 
 −1.64 0.05 41.0 43.4 38.2 40.5 35.4 37.7 32.5 34.8 29.7 32.0 26.9 29.2 24.0 26.3 
LE average 
 1.64 0.95 85.0 87.3 82.2 84.5 79.3 81.6 76.5 78.8 73.7 76.0 70.8 73.1 68.0 70.3 
 1.28 0.90 80.9 83.2 78.1 80.4 75.2 77.5 72.4 74.7 69.6 71.9 66.7 69.0 63.9 66.2 
 0.84 0.80 75.9 78.2 73.1 75.4 70.3 72.6 67.4 69.7 64.6 66.9 61.7 64.0 58.9 61.2 
 0.0 0.50 66.4 68.7 63.6 65.9 60.7 63.0 57.9 60.2 55.1 57.4 52.2 54.5 49.4 51.7 
 −0.84 0.20 56.7 59.0 53.9 56.2 51.1 53.4 48.2 50.5 45.4 47.7 42.6 44.9 39.7 42.0 
 −1.28 0.10 51.7 54.0 48.8 51.2 46.0 48.3 43.2 45.5 40.3 42.6 37.5 39.8 34.7 37.0 
 −1.64 0.05 47.5 49.8 44.7 47.0 41.9 44.2 39.0 41.3 36.2 38.5 33.4 35.7 30.5 32.8 
LE high 
 1.64 0.95 85.9 88.2 83.1 85.4 80.2 82.5 77.4 79.7 74.5 76.9 71.7 74.0 68.9 71.2 
 1.28 0.90 81.8 84.1 79.0 81.3 76.1 78.4 73.3 75.6 70.5 72.8 67.6 69.9 64.8 67.1 
 0.84 0.80 76.8 79.1 74.0 76.3 71.1 73.5 68.3 70.6 65.5 67.8 62.6 64.9 59.8 62.1 
 0.0 0.50 67.3 69.6 64.5 66.8 61.6 63.9 58.8 61.1 56.0 58.3 53.1 55.4 50.3 52.6 
 −0.84 0.20 57.6 59.9 54.8 57.1 52.0 54.3 49.1 51.4 46.3 48.6 43.5 45.8 40.6 42.9 
 −1.28 0.10 52.6 54.9 49.7 52.0 46.9 49.2 44.1 46.4 41.2 43.5 38.4 40.7 35.6 37.9 
 −1.64 0.05 48.4 50.7 45.6 47.9 42.8 45.1 39.9 42.2 37.1 39.4 34.3 36.6 31.4 33.7 
Z-value Cumulative probability 55
 
60
 
65
 
70
 
75
 
80
 
85
 
 
LE low 
 1.64 0.95 78.5 80.8 75.7 78.0 72.8 75.1 70.0 72.3 67.2 69.5 64.3 66.6 61.5 63.8 
 1.28 0.90 74.4 76.7 71.6 73.9 68.7 71.0 65.9 68.2 63.1 65.4 60.2 62.5 57.4 59.7 
 0.84 0.80 69.4 71.7 66.6 68.9 63.8 66.1 60.9 63.2 58.1 60.4 55.3 57.6 52.4 54.7 
 0.0 0.50 59.9 62.2 57.1 59.4 54.2 56.5 51.4 53.7 48.6 50.9 45.7 48.0 42.9 45.2 
 −0.84 0.20 50.2 52.5 47.4 49.7 44.6 46.9 41.7 44.0 38.9 41.2 36.1 38.4 33.2 35.5 
 −1.28 0.10 45.2 47.5 42.4 44.7 39.5 41.8 36.7 39.0 33.8 36.2 31.0 33.3 28.2 30.5 
 −1.64 0.05 41.0 43.4 38.2 40.5 35.4 37.7 32.5 34.8 29.7 32.0 26.9 29.2 24.0 26.3 
LE average 
 1.64 0.95 85.0 87.3 82.2 84.5 79.3 81.6 76.5 78.8 73.7 76.0 70.8 73.1 68.0 70.3 
 1.28 0.90 80.9 83.2 78.1 80.4 75.2 77.5 72.4 74.7 69.6 71.9 66.7 69.0 63.9 66.2 
 0.84 0.80 75.9 78.2 73.1 75.4 70.3 72.6 67.4 69.7 64.6 66.9 61.7 64.0 58.9 61.2 
 0.0 0.50 66.4 68.7 63.6 65.9 60.7 63.0 57.9 60.2 55.1 57.4 52.2 54.5 49.4 51.7 
 −0.84 0.20 56.7 59.0 53.9 56.2 51.1 53.4 48.2 50.5 45.4 47.7 42.6 44.9 39.7 42.0 
 −1.28 0.10 51.7 54.0 48.8 51.2 46.0 48.3 43.2 45.5 40.3 42.6 37.5 39.8 34.7 37.0 
 −1.64 0.05 47.5 49.8 44.7 47.0 41.9 44.2 39.0 41.3 36.2 38.5 33.4 35.7 30.5 32.8 
LE high 
 1.64 0.95 85.9 88.2 83.1 85.4 80.2 82.5 77.4 79.7 74.5 76.9 71.7 74.0 68.9 71.2 
 1.28 0.90 81.8 84.1 79.0 81.3 76.1 78.4 73.3 75.6 70.5 72.8 67.6 69.9 64.8 67.1 
 0.84 0.80 76.8 79.1 74.0 76.3 71.1 73.5 68.3 70.6 65.5 67.8 62.6 64.9 59.8 62.1 
 0.0 0.50 67.3 69.6 64.5 66.8 61.6 63.9 58.8 61.1 56.0 58.3 53.1 55.4 50.3 52.6 
 −0.84 0.20 57.6 59.9 54.8 57.1 52.0 54.3 49.1 51.4 46.3 48.6 43.5 45.8 40.6 42.9 
 −1.28 0.10 52.6 54.9 49.7 52.0 46.9 49.2 44.1 46.4 41.2 43.5 38.4 40.7 35.6 37.9 
 −1.64 0.05 48.4 50.7 45.6 47.9 42.8 45.1 39.9 42.2 37.1 39.4 34.3 36.6 31.4 33.7 
Table 7.

Normative data for the SCWT Word–Color score stratified by age and level of education (regression-based)

Z-value Cumulative probability 55 60 65 70 75 80 85 
LE low 
 1.64 0.95 50.9 48.7 46.4 44.2 41.9 39.7 37.4 
 1.28 0.90 47.4 45.2 42.9 40.7 38.4 36.2 34.0 
 0.84 0.80 43.2 40.9 38.7 36.4 34.2 31.9 29.7 
 0.0 0.50 35.1 32.8 30.6 28.3 26.1 23.8 21.6 
 −0.84 0.20 27.6 25.3 23.1 20.8 18.6 16.3 14.1 
 −1.28 0.10 23.6 21.4 19.2 16.9 14.7 12.4 10.2 
 −1.64 0.05 20.4 18.2 16.0 13.7 11.5 9.2 7.0 
LE average 
 1.64 0.95 55.7 53.4 51.2 48.9 46.7 44.4 42.2 
 1.28 0.90 52.2 49.9 47.7 45.5 43.2 41.0 38.7 
 0.84 0.80 47.9 45.7 43.5 41.2 39.0 36.7 34.5 
 0.0 0.50 39.8 37.6 35.3 33.1 30.8 28.6 26.4 
 −0.84 0.20 32.3 30.1 27.9 25.6 23.4 21.1 18.9 
 −1.28 0.10 28.4 26.2 23.9 21.7 19.4 17.2 14.9 
 −1.64 0.05 25.2 23.0 20.7 18.5 16.2 14.0 11.7 
LE high 
 1.64 0.95 57.7 55.4 53.2 50.9 48.7 46.5 44.2 
 1.28 0.90 54.2 52.0 49.7 47.5 45.2 43.0 40.7 
 0.84 0.80 50.0 47.7 45.5 43.2 41.0 38.7 36.5 
 0.0 0.50 41.8 39.6 37.3 35.1 32.9 30.6 28.4 
 −0.84 0.20 34.3 32.1 29.9 27.6 25.4 23.1 20.9 
 −1.28 0.10 30.4 28.2 25.9 23.7 21.4 19.2 17.0 
 −1.64 0.05 27.2 25.0 22.7 20.5 18.2 16.0 13.7 
Z-value Cumulative probability 55 60 65 70 75 80 85 
LE low 
 1.64 0.95 50.9 48.7 46.4 44.2 41.9 39.7 37.4 
 1.28 0.90 47.4 45.2 42.9 40.7 38.4 36.2 34.0 
 0.84 0.80 43.2 40.9 38.7 36.4 34.2 31.9 29.7 
 0.0 0.50 35.1 32.8 30.6 28.3 26.1 23.8 21.6 
 −0.84 0.20 27.6 25.3 23.1 20.8 18.6 16.3 14.1 
 −1.28 0.10 23.6 21.4 19.2 16.9 14.7 12.4 10.2 
 −1.64 0.05 20.4 18.2 16.0 13.7 11.5 9.2 7.0 
LE average 
 1.64 0.95 55.7 53.4 51.2 48.9 46.7 44.4 42.2 
 1.28 0.90 52.2 49.9 47.7 45.5 43.2 41.0 38.7 
 0.84 0.80 47.9 45.7 43.5 41.2 39.0 36.7 34.5 
 0.0 0.50 39.8 37.6 35.3 33.1 30.8 28.6 26.4 
 −0.84 0.20 32.3 30.1 27.9 25.6 23.4 21.1 18.9 
 −1.28 0.10 28.4 26.2 23.9 21.7 19.4 17.2 14.9 
 −1.64 0.05 25.2 23.0 20.7 18.5 16.2 14.0 11.7 
LE high 
 1.64 0.95 57.7 55.4 53.2 50.9 48.7 46.5 44.2 
 1.28 0.90 54.2 52.0 49.7 47.5 45.2 43.0 40.7 
 0.84 0.80 50.0 47.7 45.5 43.2 41.0 38.7 36.5 
 0.0 0.50 41.8 39.6 37.3 35.1 32.9 30.6 28.4 
 −0.84 0.20 34.3 32.1 29.9 27.6 25.4 23.1 20.9 
 −1.28 0.10 30.4 28.2 25.9 23.7 21.4 19.2 17.0 
 −1.64 0.05 27.2 25.0 22.7 20.5 18.2 16.0 13.7 
Table 8.

Normative data for the SCWT Interference 1 score stratified by age, sex, and level of education (regression-based)

Z-value Cumulative probability 55
 
60
 
65
 
70
 
75
 
80
 
85
 
LE low 
 1.64 0.95 12.0 14.7 11.8 10.8 11.7 10.7 11.5 10.5 11.3 10.3 11.1 10.1 11.0 10.0 
 1.28 0.90 9.2 11.9 9.0 8.0 8.8 7.8 8.7 7.7 8.5 7.5 8.3 7.3 8.2 7.2 
 0.84 0.80 5.7 8.4 5.6 4.6 5.4 4.4 5.2 4.2 5.1 4.1 4.9 3.9 4.7 3.7 
 0.0 0.50 −0.8 1.8 −1.0 −2.0 −1.2 −2.2 −1.4 −2.4 −1.5 −2.5 −1.7 −2.7 −1.9 −2.9 
 −0.84 0.20 −8.1 −5.4 −8.3 −9.3 −8.5 −9.4 −8.6 −9.6 −8.8 −9.8 −9.0 −10.0 −9.1 −10.1 
 −1.28 0.10 −11.9 −9.2 −12.1 −13.1 −12.3 −13.3 −12.4 −13.4 −12.6 −13.6 −12.8 −13.8 −12.9 −13.9 
 −1.64 0.05 −15.0 −12.3 −15.2 −16.2 −15.4 −16.4 −15.5 −16.5 −15.7 −16.7 −15.9 −16.9 −16.1 −17.1 
LE average 
 1.64 0.95 13.8 12.8 12.8 11.9 12.1 11.1 11.2 10.2 10.4 9.4 9.5 8.5 8.7 7.7 
 1.28 0.90 11.0 10.0 9.9 9.1 9.3 8.3 8.4 7.4 7.6 6.6 6.7 5.7 5.9 4.9 
 0.84 0.80 7.5 6.5 6.5 5.7 5.8 4.8 5.0 4.0 4.1 3.1 3.3 2.3 2.4 1.4 
 0.0 0.50 0.9 −0.1 −0.1 −0.9 −0.8 −1.8 −1.6 −2.6 −2.5 −3.5 −3.3 −4.3 −4.2 −5.2 
 −0.84 0.20 −6.3 −7.3 −7.3 −8.2 −8.0 −9.0 −8.9 −9.9 −9.7 −10.7 −10.6 −11.6 −11.4 −12.4 
 −1.28 0.10 −10.1 −11.1 −11.2 −12.0 −11.8 −12.8 −12.7 −13.7 −13.5 −14.5 −14.4 −15.4 −15.2 −16.2 
 −1.64 0.05 −13.3 −14.3 −14.3 −15.1 −15.0 −16.0 −15.8 −16.8 −16.7 −17.7 −17.5 −18.5 −18.4 −19.4 
LE high 
 1.64 0.95 15.8 14.8 14.5 13.5 13.2 12.2 11.8 10.8 10.5 9.5 9.2 8.2 7.9 6.9 
 1.28 0.90 13.0 12.0 11.7 10.7 10.4 9.4 9.0 8.0 7.7 6.7 6.4 5.4 5.0 4.0 
 0.84 0.80 9.6 8.6 8.2 7.2 6.9 5.9 5.6 4.6 4.3 3.3 2.9 1.9 1.6 0.6 
 0.0 0.50 3.0 2.0 1.7 0.7 0.3 −0.7 −1.0 −2.0 −2.3 −3.3 −3.7 −4.7 −5.0 −6.0 
 −0.84 0.20 −4.3 −5.3 −5.6 −6.6 −6.9 −7.9 −8.3 −9.3 −9.6 −10.6 −10.9 −11.9 −12.3 −13.3 
 −1.28 0.10 −8.1 −9.1 −9.4 −10.4 −10.7 −11.7 −12.1 −13.1 −13.4 −14.4 −14.7 −15.7 −16.1 −17.1 
 −1.64 0.05 −11.2 −12.2 −12.5 −13.5 −13.9 −14.9 −15.2 −16.2 −16.5 −17.5 −17.8 −18.8 −19.2 −20.2 
Z-value Cumulative probability 55
 
60
 
65
 
70
 
75
 
80
 
85
 
LE low 
 1.64 0.95 12.0 14.7 11.8 10.8 11.7 10.7 11.5 10.5 11.3 10.3 11.1 10.1 11.0 10.0 
 1.28 0.90 9.2 11.9 9.0 8.0 8.8 7.8 8.7 7.7 8.5 7.5 8.3 7.3 8.2 7.2 
 0.84 0.80 5.7 8.4 5.6 4.6 5.4 4.4 5.2 4.2 5.1 4.1 4.9 3.9 4.7 3.7 
 0.0 0.50 −0.8 1.8 −1.0 −2.0 −1.2 −2.2 −1.4 −2.4 −1.5 −2.5 −1.7 −2.7 −1.9 −2.9 
 −0.84 0.20 −8.1 −5.4 −8.3 −9.3 −8.5 −9.4 −8.6 −9.6 −8.8 −9.8 −9.0 −10.0 −9.1 −10.1 
 −1.28 0.10 −11.9 −9.2 −12.1 −13.1 −12.3 −13.3 −12.4 −13.4 −12.6 −13.6 −12.8 −13.8 −12.9 −13.9 
 −1.64 0.05 −15.0 −12.3 −15.2 −16.2 −15.4 −16.4 −15.5 −16.5 −15.7 −16.7 −15.9 −16.9 −16.1 −17.1 
LE average 
 1.64 0.95 13.8 12.8 12.8 11.9 12.1 11.1 11.2 10.2 10.4 9.4 9.5 8.5 8.7 7.7 
 1.28 0.90 11.0 10.0 9.9 9.1 9.3 8.3 8.4 7.4 7.6 6.6 6.7 5.7 5.9 4.9 
 0.84 0.80 7.5 6.5 6.5 5.7 5.8 4.8 5.0 4.0 4.1 3.1 3.3 2.3 2.4 1.4 
 0.0 0.50 0.9 −0.1 −0.1 −0.9 −0.8 −1.8 −1.6 −2.6 −2.5 −3.5 −3.3 −4.3 −4.2 −5.2 
 −0.84 0.20 −6.3 −7.3 −7.3 −8.2 −8.0 −9.0 −8.9 −9.9 −9.7 −10.7 −10.6 −11.6 −11.4 −12.4 
 −1.28 0.10 −10.1 −11.1 −11.2 −12.0 −11.8 −12.8 −12.7 −13.7 −13.5 −14.5 −14.4 −15.4 −15.2 −16.2 
 −1.64 0.05 −13.3 −14.3 −14.3 −15.1 −15.0 −16.0 −15.8 −16.8 −16.7 −17.7 −17.5 −18.5 −18.4 −19.4 
LE high 
 1.64 0.95 15.8 14.8 14.5 13.5 13.2 12.2 11.8 10.8 10.5 9.5 9.2 8.2 7.9 6.9 
 1.28 0.90 13.0 12.0 11.7 10.7 10.4 9.4 9.0 8.0 7.7 6.7 6.4 5.4 5.0 4.0 
 0.84 0.80 9.6 8.6 8.2 7.2 6.9 5.9 5.6 4.6 4.3 3.3 2.9 1.9 1.6 0.6 
 0.0 0.50 3.0 2.0 1.7 0.7 0.3 −0.7 −1.0 −2.0 −2.3 −3.3 −3.7 −4.7 −5.0 −6.0 
 −0.84 0.20 −4.3 −5.3 −5.6 −6.6 −6.9 −7.9 −8.3 −9.3 −9.6 −10.6 −10.9 −11.9 −12.3 −13.3 
 −1.28 0.10 −8.1 −9.1 −9.4 −10.4 −10.7 −11.7 −12.1 −13.1 −13.4 −14.4 −14.7 −15.7 −16.1 −17.1 
 −1.64 0.05 −11.2 −12.2 −12.5 −13.5 −13.9 −14.9 −15.2 −16.2 −16.5 −17.5 −17.8 −18.8 −19.2 −20.2 
Table 9.

Normative data for the SCWT Interference 2 score stratified by age, sex, and level of education (regression-based)

Z-value Cumulative probability 55
 
60
 
65
 
70
 
75
 
80
 
85
 
LE low 
 1.64 0.95 8.4 6.7 7.6 5.9 6.9 5.2 6.1 4.4 5.4 3.7 4.6 2.9 3.8 2.1 
 1.28 0.90 5.2 3.5 4.5 2.8 3.7 2.0 3.0 1.3 2.2 0.5 1.4 −0.3 0.7 −1.0 
 0.84 0.80 1.4 −0.3 0.6 −1.1 −0.1 −1.8 −0.9 −2.6 −1.6 −3.3 −2.4 −4.1 −3.2 −4.9 
 0.0 0.50 −6.0 −7.7 −6.7 −8.4 −7.5 −9.2 −8.2 −9.9 −9.0 −10.7 −9.8 −11.5 −10.5 −12.2 
 −0.84 0.20 −17.3 −19.0 −18.1 −19.8 −18.8 −20.5 −19.6 −21.3 −20.3 −22.0 −21.1 −22.8 −21.9 −23.6 
 −1.28 0.10 −23.3 −24.9 −24.0 −25.7 −24.8 −26.5 −25.5 −27.2 −26.3 −28.0 −27.1 −28.7 −27.8 −29.5 
 −1.64 0.05 −28.1 −29.8 −28.9 −30.6 −29.6 −31.3 −30.4 −32.1 −31.2 −32.9 −31.9 −33.6 −32.7 −34.4 
LE average 
 1.64 0.95 13.1 11.4 11.6 9.9 10.0 8.3 8.5 6.8 6.9 5.2 5.4 3.7 3.8 2.1 
 1.28 0.90 10.0 8.3 8.4 6.7 6.9 5.2 5.3 3.6 3.8 2.1 2.2 0.5 0.7 −1.0 
 0.84 0.80 6.1 4.4 4.6 2.9 3.0 1.3 1.5 −0.2 −0.1 −1.8 −1.6 −3.3 −3.2 −4.9 
 0.0 0.50 −1.2 −2.9 −2.8 −4.5 −4.3 −6.0 −5.9 −7.6 −7.4 −9.1 −9.0 −10.7 −10.5 −12.2 
 −0.84 0.20 −12.6 −14.3 −14.1 −15.8 −15.7 −17.4 −17.2 −18.9 −18.8 −20.5 −20.3 −22.0 −21.9 −23.6 
 −1.28 0.10 −18.5 −20.2 −20.1 −21.8 −21.6 −23.3 −23.2 −24.9 −24.7 −26.4 −26.3 −28.0 −27.8 −29.5 
 −1.64 0.05 −23.4 −25.1 −24.9 −26.6 −26.5 −28.2 −28.0 −29.7 −29.6 −31.3 −31.1 −32.8 −32.7 −34.4 
LE high 
 1.64 0.95 16.1 14.4 14.1 12.4 12.1 10.4 10.1 8.4 8.0 6.4 6.0 4.3 4.0 2.3 
 1.28 0.90 12.9 11.2 10.9 9.2 8.9 7.2 6.9 5.2 4.9 3.2 2.9 1.2 0.9 −0.8 
 0.84 0.80 9.1 7.4 7.1 5.4 5.1 3.4 3.1 1.4 1.0 −0.7 −1.0 −2.7 −3.0 −4.7 
 0.0 0.50 1.7 0.0 −0.3 −2.0 −2.3 −4.0 −4.3 −6.0 −6.3 −8.0 −8.3 −10.0 −10.3 −12.0 
 −0.84 0.20 −9.6 −11.3 −11.6 −13.3 −13.6 −15.3 −15.6 −17.3 −17.7 −19.4 −19.7 −21.4 −21.7 −23.4 
 −1.28 0.10 −15.6 −17.3 −17.6 −19.3 −19.6 −21.3 −21.6 −23.3 −23.6 −25.3 −25.6 −27.3 −27.6 −29.3 
 −1.64 0.05 −20.4 −22.1 −22.4 −24.1 −24.4 −26.1 −26.5 −28.2 −28.5 −30.2 −30.5 −32.2 −32.5 −34.2 
Z-value Cumulative probability 55
 
60
 
65
 
70
 
75
 
80
 
85
 
LE low 
 1.64 0.95 8.4 6.7 7.6 5.9 6.9 5.2 6.1 4.4 5.4 3.7 4.6 2.9 3.8 2.1 
 1.28 0.90 5.2 3.5 4.5 2.8 3.7 2.0 3.0 1.3 2.2 0.5 1.4 −0.3 0.7 −1.0 
 0.84 0.80 1.4 −0.3 0.6 −1.1 −0.1 −1.8 −0.9 −2.6 −1.6 −3.3 −2.4 −4.1 −3.2 −4.9 
 0.0 0.50 −6.0 −7.7 −6.7 −8.4 −7.5 −9.2 −8.2 −9.9 −9.0 −10.7 −9.8 −11.5 −10.5 −12.2 
 −0.84 0.20 −17.3 −19.0 −18.1 −19.8 −18.8 −20.5 −19.6 −21.3 −20.3 −22.0 −21.1 −22.8 −21.9 −23.6 
 −1.28 0.10 −23.3 −24.9 −24.0 −25.7 −24.8 −26.5 −25.5 −27.2 −26.3 −28.0 −27.1 −28.7 −27.8 −29.5 
 −1.64 0.05 −28.1 −29.8 −28.9 −30.6 −29.6 −31.3 −30.4 −32.1 −31.2 −32.9 −31.9 −33.6 −32.7 −34.4 
LE average 
 1.64 0.95 13.1 11.4 11.6 9.9 10.0 8.3 8.5 6.8 6.9 5.2 5.4 3.7 3.8 2.1 
 1.28 0.90 10.0 8.3 8.4 6.7 6.9 5.2 5.3 3.6 3.8 2.1 2.2 0.5 0.7 −1.0 
 0.84 0.80 6.1 4.4 4.6 2.9 3.0 1.3 1.5 −0.2 −0.1 −1.8 −1.6 −3.3 −3.2 −4.9 
 0.0 0.50 −1.2 −2.9 −2.8 −4.5 −4.3 −6.0 −5.9 −7.6 −7.4 −9.1 −9.0 −10.7 −10.5 −12.2 
 −0.84 0.20 −12.6 −14.3 −14.1 −15.8 −15.7 −17.4 −17.2 −18.9 −18.8 −20.5 −20.3 −22.0 −21.9 −23.6 
 −1.28 0.10 −18.5 −20.2 −20.1 −21.8 −21.6 −23.3 −23.2 −24.9 −24.7 −26.4 −26.3 −28.0 −27.8 −29.5 
 −1.64 0.05 −23.4 −25.1 −24.9 −26.6 −26.5 −28.2 −28.0 −29.7 −29.6 −31.3 −31.1 −32.8 −32.7 −34.4 
LE high 
 1.64 0.95 16.1 14.4 14.1 12.4 12.1 10.4 10.1 8.4 8.0 6.4 6.0 4.3 4.0 2.3 
 1.28 0.90 12.9 11.2 10.9 9.2 8.9 7.2 6.9 5.2 4.9 3.2 2.9 1.2 0.9 −0.8 
 0.84 0.80 9.1 7.4 7.1 5.4 5.1 3.4 3.1 1.4 1.0 −0.7 −1.0 −2.7 −3.0 −4.7 
 0.0 0.50 1.7 0.0 −0.3 −2.0 −2.3 −4.0 −4.3 −6.0 −6.3 −8.0 −8.3 −10.0 −10.3 −12.0 
 −0.84 0.20 −9.6 −11.3 −11.6 −13.3 −13.6 −15.3 −15.6 −17.3 −17.7 −19.4 −19.7 −21.4 −21.7 −23.4 
 −1.28 0.10 −15.6 −17.3 −17.6 −19.3 −19.6 −21.3 −21.6 −23.3 −23.6 −25.3 −25.6 −27.3 −27.6 −29.3 
 −1.64 0.05 −20.4 −22.1 −22.4 −24.1 −24.4 −26.1 −26.5 −28.2 −28.5 −30.2 −30.5 −32.2 −32.5 −34.2 

Discussion

The aim of this study was to examine the effect of demographic variables on the execution of the SCWT (Golden's version) and to offer normative population data from a broad community sample of people of 55 years and older, so as to provide their clinical interpretation and their use in research. Specifically, the effect of age, sex, first language, educational level, and their possible interactions on scores of the number of words, colors, and words–colors read/named in 45 s were analyzed. Complementarily, their effect was determined on a measure of calculated interference in two different ways, one by means of the formula proposed by Golden and the other by means of the formula proposed by Chafetz.

The broad use of the SCWT has prompted the publication of normative data for different languages and versions (see the reviews by Lezak et al., 2004; Mitrushina, Boone, Razani, & D'Elia, 2005; Steinberg et al., 2005; Strauss et al., 2006). The need for normative data for neuropsychological tests is especially important in elderly subjects, as this group is at greater risk for presenting with cognitive impairment, and SCWT is useful for the diagnostic process of dementia (Kryscio, Schmitt, Salazar, Mendiondo, & Markesbery, 2006; Sano, 2006). Initially, most of the normative data of the SCWT in elderly populations were from Anglo-Saxon countries and with subjects of a relatively high educational level (Golden & Freshwater, 2002; Ivnik, Malec, & Smith, 1996; Mitrushina et al., 1999; Spreen and Strauss, 1998; Trenerry et al., 1989). However, normative data based on population samples from other countries and languages such as Holland (Van der Elst et al., 2006), Korea (Seo et al., 2008), Greece (Zalonis et al., 2009), or France (Bayard, Erkes, & Moroni, 2011) have been published. The normative data of the SCWT in Spanish come from different types of samples. For example, Artiola, Hermosillo, Heaton, and Pardee (1999) administered Golden's version of the SCWT to a sample of 250 subjects with ages between 18 and 65 who reside in Madrid and to 185 Spanish–speaking subjects residing in the border of Mexico with the USA; Rosselli and colleagues (2002) provide normative data from a sample of 40 monolingual English subjects, 71 bilingual Spanish-English subjects, and 11 monolingual Spanish-speaking subjects in the south of Florida; Armengol (2002) provided normative data for children; and recently, in the context of the NEURONORMA project, Peña and colleagues (2009) provided normative data, adjusted for age and educational level, of Golden's version, from a convenience sample of 344 subjects with a normal cognitive function of 50 years of age or older who attended a neurology department in Spain.

The results obtained show that the performance of the SCWT is associated with demographic variables such as age, sex, and level of education. Age is the variable that presents with an important percentage of variance explained for all the scores of the SCWT. Generally speaking, all the studies identified a progressive reduction in the performance in the SCWT with advanced age (Golden and Freshwater, 2002; Graf, Uttl, & Tuokko, 1995; Houx, Jolles, & Vreeling, 1993; Ivnik et al., 1996; Klein, Ponds, Houx, & Jolles 1997; Lucas et al., 2005; Moering et al., 2004; Steinberg et al., 2005; Van der Elst et al., 2006). The aging process produces a slowing-down that mainly affects the naming of colors and increases the interference effect (Bryan & Luszcz, 2000; Cohn, Dustman, & Bradford, 1984; Graf et al., 1995; Lucas et al., 2005; Moering et al., 2004; Uttl & Graf, 1997; Van der Elst et al., 2006). These differences due to age have been attributed to a general effect of cognitive slowing-down (Bugg, DeLosh, Davalos, & Davis, 2007), to the greater difficulty in the inhibitory control (Bugg et al., 2007; Connor, Franzen, & Sharp, 1988; Troyer, Leach, & Strauss, 2006), or to a reduced visual capacity (Van Boxtel, Ten Tusscher, Metsemakers, Willems, & Jolles, 2001). Unlike the results by Van der Elst and colleagues (2006), we did not find a quadratic effect of age over any of the scores of the SCWT, possibly because the age range of our sample was of 55 years of more, whereas the range of the study in the Netherlands included subjects between 24 and 81 years of age. The educational level also showed a significant effect, although of a smaller size in all the scores of the SCWT (Golden & Freshwater, 2002; Houx et al., 1993; Ivnik et al., 1996; Moering et al., 2004; Van der Elst et al., 2006), and in our study, there was a greater effect of a low educational level compared with a mid educational level than a high educational level compared with a mid educational level. As expected in this cohort population of inhabitants with 55 years and older, there were differences in the level of education according to the gender, and women had lower level of education than men. A similar result has been observed in other cohorts with similar age or with older age from the province of Girona (López-Pousa, Vilalta-Franch, Llinàs-Regla, Garre-Olmo, & Román, 2004). Regarding sex, data available in literature is more contradictory than the ones in terms of the effect of age and educational level on the scores of the SCWT. There are studies that report a nil or minimum effect, which is why they do not recommend sex adjustments in the normative data (Armengol, 2002; Golden & Freshwater, 2002; Houx et al., 1993; Ivnik et al., 1996; Lucas et al., 2005; Peña et al., 2009; Trenerry et al., 1989), whereas others report a better performance in women (Moering et al., 2004; Van der Elst et al., 2006), especially in the color-naming score (Golden, 1978; Jensen & Rohwer, 1966; Strickland, D'Elia, James, & Stein, 1997; Swerdlow, Filion, Geyer, & Braff, 1995; Stroop, 1934). Our results indicate a mild effect of sex on the color-naming score and on the interference score, with a better performance of women in color naming and a better performance of men in the interference score. Despite the modest effect of sex, due to the large size of the sample in this study, we stratified the normative data in the SCWT scores into the ones this variable was associated with.

As for the interference score, our results show that, regardless of the form it is calculated, there is an association with age, sex, and educational level, although the percentage of total variance explained by these variables is much less than the one corresponding to words, colors, and words–colors scores. Once again, the limited age range of the participants may be a factor that explains this discrepancy compared with other studies in population samples in which age and educational level explained a greater percentage of the variance of this score (Bayard et al., 2011; Van der Elst et al., 2006; Zalonis et al., 2009). This result is comparable with the one obtained by the study carried out in Korea on a sample of people of 60 years of age and older, for which the regression analysis only explained 2.2% of the variance of the interference score by means of the educational level variable (Seo et al., 2008). The interaction of age and educational level detected for the interference score is congruent with the results obtained by Van der Elst and colleagues (2006), although not for the SCWT version of Hammes (1973), in which the result of the time necessary to read/master the stimuli presented is used as a variable. This result indicates that the interference presents with a reduction with age and the magnitude of the downward slope is influenced by the educational level. This result agrees with the cognitive reserve hypothesis, which proposes that the degree of cognitive impairment associated with age or in subjects with neurodegenerative diseases is conditioned by the educational level, so that subjects with a low educational level would be more vulnerable (Jorm et al., 1994). However, the graphic representation of the interference score for the formula proposed by Golden according to age and stratified by educational level shows that from 68 years of age and onwards, subjects with a mid educational level have a worse score than subjects with a low educational level, and from 72 years of age onwards, a similar phenomenon occurs in subjects with a high educational level. This result is counterintuitive, given that it is incomprehensible that, at a greater age, subjects with a lower educational level have a better performance than individuals with a greater educational level. In order to interpret this result adequately, it is necessary to take into consideration the cognitive model underlying the interference score proposed by Golden. As was pointed out in the introduction, this model is based on the assumption that the subject is incapable of inhibiting the reading of words before naming the color (and the interference results from an additive model of the necessary time to read the word and the naming of the color). This assumption may not necessarily be valid and/or applicable to subjects with a low educational level because, for them, reading the word would not be a response that is sufficiently automatic to interfere with color naming. In other words, in elderly subjects with low educational level, words might have the same effect that Xs produce in the task of naming colors. According to this hypothesis, for subjects with a low educational level, there should be an inverse correlation between the number of words and Golden's interference score that would increase with age. Although the comparative analysis of both interference scores surpasses the objectives of the present study, a preliminary analysis of the data appears to confirm the hypothesis. Thus, Pearson's correlation coefficient between the number of words and Golden's interference score for the group between 55 and 64 years of age with a low educational level was −.307 (n = 410; p < .001), for the group between 65 and 74 years of age, it was −.259 (n = 451; p < .001), and for the group of 75 years and older, it was −.466 (n = 352; p < .001). On the other hand, for the same groups, Pearson's correlation coefficients for the Chafetz interference score were of .132 (n = 410; p = .007), .106 (n = 451; p = .024), and −.134 (n = 352; p = .012), respectively. Therefore, the interference index proposed by Golden should be interpreted cautiously in this population group. The results obtained for the calculated interference according to the formula by Chafetz show a progressive reduction in the interference score with age, but maintaining the difference between the groups according to the educational level, and it is also compatible with the cognitive reserve hypothesis.

The interpretation of the results of the study must be carried out taking into account its strengths and limitations. Among its strengths, it is important to point out the high size and the population-based nature of the sample. In this sense, the normative data broadly cover all groups of age, sex, and educational levels analyzed, and the random selection of the municipalities (both rural and urban) and of the subjects from the population census provide an elevated degree of external validity. Furthermore, in order to maximize the number of participants to obtain reliable normative data in accordance with the classic stratification method by relevant variables, we have used the interval superposition strategy. The use of this strategy allowed for the calculation of statistical data of the position of different scores of the SCWT to be carried out for most of the strata with a sample size above 20–30 subjects. Another strength of the study is the complementary calculation of the normative data on the basis of the regression-based method (Van Breukelen & Vlaeyen, 2005). This method avoids the problem of generating normative data of a limited reliability due to small sample sizes for some groups as a consequence of the stratification by relevant variables and provides more exact estimations, as the data are based on the equations obtained from all the participants included (Van Breukelen & Vlaeyen, 2005; Zachary & Gorsuch, 1985). Among the limitations of the study, it is important to point out that the study was carried out in a bilingual geographical area (Catalan and Castilian Spanish) and that the SCWT was administered in the first language of the participants. However, the study results did not show evidence of any effect of the first language on the SCWT performance. In this sense, it is important to bear in mind that our population was 55 years and over and, although the Catalan was the first language for the most of them, their education was based in Spanish (the Catalan was introduced in the educative system in Spain during the late 70's). So, the combination of first language–education language may have diminished the potential effect of the first language on the SCWT performance. Although the previous studies related to this question are not comparables with our population-based sample, the evidence available does not indicate that this fact may have somehow biased the data. Roselli and colleagues (2002) studied the effect of language in the performance of SCWT in monolingual and bilingual Spanish- and English-speaking subjects and did not detect any significant differences between them. Moreover, Alegret and colleagues (2012), in a study that provided normative data for a neuropsychological battery of tests that included the SCWT, did not detect any significant differences between the subjects assessed in Castilian Spanish or in Catalan. Another limitation to consider is the rate of participation. Those who refused to participate were older and had lower levels of education. Finally, it is also necessary to remember that normative studies are important, but have a limited temporal duration (Mitrushina et al., 2005). Social and cultural changes that affect the populations from which the samples are taken, mainly the ones associated with the different educational opportunities—currently affect the elderly—or with significant changes in the immigration rate, warrant new normative studies in the future (Ivnik, 2005; Manly, 2005).

Funding

This work was supported by the Fondo de Investigación Sanitaria from the Instituto de Salud Carlos III (PS09/02591).

Conflict of Interest

None declared.

Acknowledgements

We are grateful to all the Regicor Study participants who have generously given their time and collaborated in the study.

References

Alegret
M.
Espinosa
A.
Vinyes-Junqué
G.
Valero
S.
Hernandez
I.
Tárraga
L.
, et al.  . 
Normative data of a brief neuropsychological battery for Spanish individuals older than 49
Journal of Clinical and Experimental Neuropsychology
 , 
2012
, vol. 
34
 (pg. 
209
-
219
)
Armengol
C.
Stroop test in Spanish: Children's norms
The Clinical Neuropsychologist
 , 
2002
, vol. 
16
 (pg. 
67
-
80
)
Artiola
L.
Hermosillo
D.
Heaton
R.
Pardee
R. E.
Manual de normas y procedimientos para la batería neuropsicológica en español
1999
Tucson
AZ Press
Banich
M. T.
Milham
M. P.
Atchley
R.
Cohen
N. J.
Webb
A.
Wszalek
T.
, et al.  . 
fMRI studies of Stroop tasks reveal unique roles of anterior and posterior brain systems in attentional selection
Journal of Cognitive Neuroscience
 , 
2002
, vol. 
12
 (pg. 
988
-
1000
)
Bayard
S.
Erkes
J.
Moroni
C.
Victoria Stroop Test: Normative data in a sample group of older people and the study of their clinical applications in the assessment of inhibition in Alzheimer's disease
Archives of Clinical Neuropsychology
 , 
2011
, vol. 
26
 (pg. 
653
-
661
)
Belsley
D. A.
Kuh
E.
Welsch
R. E.
Regression diagnostics: Identifying influential data and sources of collinearity
1980
New York
John Willey
Bryan
J.
Luszcz
M. A.
Measurement of executive function: Considerations for detecting adult age differences
Journal of Clinical and Experimental Neuropsychology
 , 
2000
, vol. 
22
 (pg. 
40
-
55
)
Bugg
J. M.
DeLosh
E. L.
Davalos
D. B.
Davis
H. P.
Age differences in Stroop interference: Contributions of general slowing and task-specific deficits
Aging, Neuropsychology, and Cognition
 , 
2007
, vol. 
14
 (pg. 
155
-
167
)
Bush
G.
Frazier
J. A.
Rauch
S. L.
Seidman
L. J.
Whalen
P. J.
Jenike
M. A.
, et al.  . 
Anterior cingulate cortex dysfunction in Attention-Deficit/Hyperactivity Disorder revealed by fMRI and the counting Stroop
Biological Psychiatry
 , 
1999
, vol. 
45
 (pg. 
1542
-
1552
)
Bush
G.
Whalen
P. J.
Rosen
B. R.
Jenike
M. A.
McInerney
S. C.
Rauch
S. L.
The Counting Stroop: An interference task specialized for functional neuroimaging—validation study with functional MRI
Human Brain Mapping
 , 
1998
, vol. 
6
 (pg. 
270
-
282
)
Cattell
J.
The time it takes to see and name objects
Mind
 , 
1886
, vol. 
11
 (pg. 
63
-
65
)
Chafetz
M. D.
Matthews
L. H.
A new interference score for the Stroop test
Archives of Clinical Neuropsychology
 , 
2004
, vol. 
19
 (pg. 
555
-
567
)
Cohn
N. B.
Dustman
R. E.
Bradford
D. C.
Age-related decrements in Stroop color test performance
Journal of Clinical Neuropsychology
 , 
1984
, vol. 
40
 (pg. 
1244
-
1250
)
Connor
A.
Franzen
M.
Sharp
B.
Effects of practice and differential instructions on Stroop performance
International Journal of Clinical Neuropsychology
 , 
1988
, vol. 
10
 (pg. 
1
-
4
)
Cook
R. D.
Weisberg
S.
Residuals and influence in regression
1982
New York
Chapman & Hall
Egner
T.
Hirsch
J.
The neural correlates and functional integration of cognitive control in a Stroop task
Neuroimage
 , 
2005
, vol. 
24
 (pg. 
539
-
547
)
Golden
C. J.
Stroop Color and Word Test. A manual for clinical and experimental uses. Wood Dale
Illinois: Stoelting
 , 
1978
Golden
C. J.
Freshwater
S. M.
The Stroop Color and Word Test: A manual for Clinical and Experimental Uses
Stoelting Co: Chicago
 , 
2002
Golden
C. J.
Test de colores y palabras (Stroop)
2005
Madrid
TEA Ediciones
Graf
P.
Uttl
B.
Tuokko
H.
Color- and picture-word Stroop tests: Performance changes in old age
Journal of Clinical and Experimental Neuropsychology
 , 
1995
, vol. 
17
 (pg. 
390
-
415
)
Grau
M.
Subirana
I.
Elosua
R.
Solanas
P.
Ramos
R.
Masiá
R.
, et al.  . 
Trends in cardiovascular risk factor prevalence (1995-2000-2005) in northeastern Spain
European Journal of Cardiovascular Prevention and Rehabilitation
 , 
2007
, vol. 
14
 (pg. 
653
-
659
)
Guilford
J. P.
Fruchter
B.
Fundamental statistics in psychology and education
1973
5th ed.
New York
McGraw-Hill
Hammes
J.
The Stroop color-word test: Manual
1973
Amsterdam
Swets & Zeitlinger
Harrison
B. J.
Shaw
M.
Yücel
M.
Purcell
R.
Brewer
W. J.
Strother
S. C.
, et al.  . 
Functional connectivity during Stroop task performance
Neuroimage
 , 
2005
, vol. 
24
 (pg. 
181
-
191
)
Houx
P. J.
Jolles
J.
Vreeling
F. W.
Stroop interference: Aging effects assessed with the Stroop Color-Word Test
Experimental Aging Research
 , 
1993
, vol. 
19
 (pg. 
209
-
224
)
Ivnik
R. J.
Normative psychology: a professional obligation
Clin Neuropsychol
 , 
2005
, vol. 
19
 (pg. 
159
-
161
)
Ivnik
R. J.
Malec
J. F.
Smith
G. E.
Neuropsychological tests norms above age 55: COWAT, BNT, MAE Token, WRAT-R Reading, AMNART, STROOP, TMT, and JLO
Clinical Neuropsychology
 , 
1996
, vol. 
10
 (pg. 
262
-
278
)
Jensen
A. R.
Rohwer
W. D.
The Stroop Color-Word Test: A review
Acta Psychologica, 25
 , 
1966
, vol. 
1
 (pg. 
36
-
93
)
Jorm
A. F.
Henderson
A. S.
Scott
R.
Korten
A. E.
Christen
H.
Mackinnon
A. J.
, et al.  . 
Does education protect against cognitive impairment? A comparison of the elderly in two Australian cities
International Journal of Geriatric Psychiatry
 , 
1994
, vol. 
9
 (pg. 
357
-
363
)
Klein
M.
Ponds
R. W. H. M.
Houx
P. J.
Jolles
J.
Effect of test duration on age-related differences in Stroop interference
Journal of Clinical and Experimental Neuropsychology
 , 
1997
, vol. 
19
 (pg. 
77
-
82
)
Kryscio
R. J.
Schmitt
F. A.
Salazar
J. C.
Mendiondo
M. S.
Markesbery
W. R.
Risk factors for transitions from normal to mild cognitive impairment and dementia
Neurology
 , 
2006
, vol. 
66
 (pg. 
828
-
832
)
Lanham
R. A.
Vanderploeg
R. D.
Curtis
G.
The lack of construct validity of the Stroop Color and Word Test with traumatic brain injury
Archives of Clinical Neuropsychology
 , 
1999
, vol. 
14
 (pg. 
782
-
783
)
Lezak
M. D.
Howieson
D. B.
Loring
D. W.
Neuropsychological assessment
2004
4th ed.
New York
Oxford University Press
López-Pousa
S.
Vilalta-Franch
J.
Llinàs-Regla
J.
Garre-Olmo
J.
Román
G. C.
Incidence of dementia in a rural community in Spain: The Girona cohort study
Neuroepidemiology
 , 
2004
, vol. 
23
 (pg. 
170
-
177
)
Lucas
J. A.
Ivnik
R. J.
Smith
G. E.
Ferman
T. J.
Willis
F. B.
Petersen
R. C.
, et al.  . 
Mayo's older African Americans normative studies: Norms for the Boston naming test, controlled oral word association, category fluency, animal naming, token test, WRAT-3 reading, trail making test, Stroop test, and judgement of line orientation
The Clinical Neuropsychologist
 , 
2005
, vol. 
19
 (pg. 
243
-
269
)
Manly
J. J.
Advantages and disadvantages of separate norms for African American
The Clinical Neuropsychologist
 , 
2005
, vol. 
19
 (pg. 
270
-
275
)
Mitrushina
M.
Boone
K. B.
Razani
J.
D'Elia
L. F.
Handbook of normative data for neuropsychological assessment
2005
2nd ed.
New York
Oxford University Press
Mitrushina
M. N.
Boone
K. B.
D'Ella
L.
Handbook of normative data for neuropsychological assessment
1999
New York
Oxford University Press
Moering
R. G.
Schinka
J. A.
Mortimer
J. A.
Graves
A. B.
Normative data for elderly African Americans for the Stroop color and word test
Archives of Clinical Neuropsychology
 , 
2004
, vol. 
19
 (pg. 
61
-
71
)
Pauker
J. D.
Constructing overlapping cells tables to maximize the clinical usefulness of normative test data: rationale and an example from neuropsychology
Journal of Clinical Psychology
 , 
1988
, vol. 
44
 (pg. 
930
-
933
)
Peña
J.
Quiñones
S.
Gramunt
N.
Quintana
M.
Aguilar
M.
Molinuevo
J. L.
, et al.  . 
Spanish multicenter normative studies (NEURONORMA project): Norms for the Stroop Color-Word Interference Test and the Tower of London-Drexel
Archives of Clinical Neuropsychology
 , 
2009
, vol. 
24
 (pg. 
413
-
429
)
Peterson
B. S.
Skudlarski
P.
Gatenby
J. C.
Zhang
H.
Anderson
A. W.
Gore
J. C.
An fMRI study of Stroop word-color interference: Evidence for cingulated subregions subserving multiple distributed attentional systems
Biological Psychiatry
 , 
1999
, vol. 
45
 (pg. 
1237
-
1258
)
Rabin
L.
Barr
W. B.
Bruton
L. A.
Assessment practices of clinical neuropsychologist in the United States and Canada: A survey of INS, NAN, and APA division 40 members
Archives of Clinical Neuropsychology
 , 
2005
, vol. 
20
 (pg. 
33
-
65
)
Rosselli
M.
Ardila
A.
Santisi
M. N.
del Rosario Arecco
M.
Salvatierra
J.
Conde
A.
, et al.  . 
Stroop effect in Spanish–English bilinguals
Journal of the International Neuropsychological Society
 , 
2002
, vol. 
8
 (pg. 
819
-
827
)
Sano
M.
Neuropsychological testing in the diagnosis of dementia
Journal of Geriatric Psychiatry and Neurology
 , 
2006
, vol. 
19
 (pg. 
155
-
159
)
Seo
E. Y.
Lee
D. Y.
Choo
I. H.
Kim
S. G.
Kim
K. W.
Youn
J. C.
, et al.  . 
Normative study of the Stroop Color and Word Test in an educationally diverse elderly population
International Journal of Geriatric Psychiatry
 , 
2008
, vol. 
23
 (pg. 
1020
-
1027
)
Spreen
O.
Strauss
E.
A compendium of neuropsychological test
 , 
1998
2nd ed.
New York
Oxford University Press
Steinberg
B. A.
Bieliauskas
L. A.
Smith
G. E.
Ivnik
R. J.
Mayo's older Americans normative studies: Age- and ID- adjusted norms for the trail-making test, the Stroop test, and MAE controlled oral word association test
The Clinical Neuropsychologist
 , 
2005
, vol. 
19
 (pg. 
329
-
377
)
Strauss
E.
Sherman
E. M. S.
Spreen
O.
A compendium of neuropsychological tests. Administration, norms, and commentary
2006
5th ed.
New York
Oxford University Press
Strickland
T. L.
D'Elia
L. F.
James
R.
Stein
R.
Stroop color-word performance in African Americans
The Clinical Neuropsychologist
 , 
1997
, vol. 
11
 (pg. 
87
-
90
)
Stroop
J. R.
Studies of interference in serial verbal reactions
Journal of Experimental Psychology
 , 
1934
, vol. 
121
 (pg. 
15
-
23
)
Swerdlow
N. R.
Filion
D.
Geyer
M. A.
Braff
D. L.
“Normal” personality correlates of sensoriomotor, cognitive, and visuospatial gating
Biological Psychiatry
 , 
1995
, vol. 
37
 (pg. 
286
-
299
)
Trenerry
M. R.
Crosson
D.
DeBoe
J.
Leber
W. R.
The Stroop Neuropsychological Screening Test
1989
Florida
Psychological Assessment Resources
Troyer
A. K.
Leach
L.
Strauss
E.
Aging and response inhibition: Normative data for the Victoria Stroop test
Aging, Neuropsychology, and Cognition
 , 
2006
, vol. 
13
 (pg. 
20
-
35
)
Uttl
B.
Graf
P.
Color-Word Stroop Test performance across the adult life span
Journal of Clinical and Experimental Neuropsychology
 , 
1997
, vol. 
19
 (pg. 
405
-
420
)
Van Boxtel
M. P. J.
Ten Tusscher
M. P. M.
Metsemakers
J. F. M.
Willems
B.
Jolles
J.
Visual determinants of reduced performance on the Stroop Color-Word Test in normal aging individuals
Journal of Clinical and Experimental Neuropsychology
 , 
2001
, vol. 
23
 (pg. 
620
-
627
)
Van Breukelen
G.
Vlaeyen
J.
Norming clinical questionnaires with multiple regression: The pain cognition list
Psychological Assessment
 , 
2005
, vol. 
17
 (pg. 
336
-
344
)
Van der Elst
W.
Van Boxtel
M. P.
Van Breukelen
G. J.
Jolles
J.
The Stroop color-word test: Influence of age, sex, and education; and normative data for a large sample across the adult age range
Assessment
 , 
2006
, vol. 
13
 (pg. 
62
-
79
)
Whalen
P. J.
Bush
G.
McNally
R. J.
Wilhelm
S.
McInerney
S. C.
Jenike
M. A.
, et al.  . 
The emotional counting Stroop paradigm: A functional magnetic resonance imaging probe of the anterior cingulate affective division
Biological Psychiatry
 , 
1998
, vol. 
44
 (pg. 
1219
-
1228
)
Zachary
R. A.
Gorsuch
R. L.
Continuous norming: Implications for the WAIS-R
Journal of Clinical Psychology
 , 
1985
, vol. 
41
 (pg. 
86
-
94
)
Zalonis
I.
Christidi
F.
Bonakis
A.
Kararizou
E. E.
Triantafyllou
N. I.
Paraskevas
G.
, et al.  . 
The Stroop effect in Greek healthy population: Normative data for the Stroop neuropsychological screening test
Archives of Clinical Neuropsychology
 , 
2009
, vol. 
24
 (pg. 
81
-
88
)