Factorial Structure of the EOCL-1 Scale to Assess Executive Functions

The process of assessing executive functions through behavioral observation scales is still under theoretical and empirical construction. This article reports on the analysis of the factorial structure of the EOCL-1 scale that assesses executive functions, as proposed by the theory developed by Luria, which has not been previously considered in this type of evaluation. In this scale, the executive functions taken into account are error correction, internal behavioral and cognition regulatory language, limbic system conscious regulation, decision making, future consideration of consequences of actions, goal-directed behavior, inhibitory control of automatic responses, creation of new behavioral repertoires, and cognitive–behavioral activity verification. A variety of validity and reliability analyses were carried out, with the following results: (a) an adequate internal consistency level of executive functions between α = 0.70 and α = 0.83, (b) significant convergent validity with a scale that assesses frontal deficits between r = −0.07 and r = 0.28, and (c) the scale’s construct validity that proposes a model with an executive central factor comparative fit index (CFI) = 0.93, root mean square error of approximation (RMSEA) = 0.04 (LO.04 and HI.04), standardized root mean square residual (SRMR) = 0.04, and x²₍₃₁₂₎ = 789.29, p = 0.001. The findings are discussed based on previous literature reports and in terms of the benefits of using a scale to assess the proposed executive functions.

Introduction

Alexander Luria developed one of the most interesting models for understanding the human brain’s functioning. This theory proposed that three functional units interact between different structures: the first is in charge of the regulation of wakefulness and tone; the second is in charge of the reception, process, and storage of information; the last unit is in charge of the programming, regulation, and verification of behavioral and cognitive activity (Luria, 1973, 1980, 1982).

The third functional unit is related to the work of the human prefrontal cortex (Derouesné, 2018), the structure responsible for the activation of the most developed mental activities of the nervous system, which are the executive functions (Cipolotti et al., 2020). These functions have been classically identified as the following mental abilities: (1) working memory, (2) inhibitory control, (3) emotional regulation, (4) monitoring, (5) planning, (6) organization, (7) initiative, and (8) cognitive flexibility (Téllez and Sánchez, 2016; Roche et al., 2020).

These eight executive functions have been described along with their theoretical development and in the different scales for their evaluation. However, this paper takes into consideration the functions of the frontal system described in Luria’s theory (Luris 1973, 1980): error correction, internal behavioral and cognition regulatory language, limbic system conscious regulation, decision making, future consideration of consequences of actions, goal-directed behavior, inhibitory control of automatic responses, creation of new behavioral repertories, and cognitive–behavioral activity verification, all functions that have not been included in the previous scales (Derouesné, 2018; Ramos-Galarza et al., 2019b).

Executive functions can be evaluated on three different levels: first, with specific experimental tasks that constitute neuropsychological tests developed with the aim of evaluating executive functions; second, with non-specific tasks that comprise tests created to evaluate different cognitive variables, such as intelligence, attention, and memory, which support the clinical evaluation of executive functions; and third, with scales or questionnaires based on daily life behavioral observation. It should be highlighted that the first two methods of evaluation have ecological validity limitations because the executive functions are not evaluated in real daily life. This explains why this paper proposes using the EOCL-1 scale (Appendix Table A1), which evaluates executive functions not previously considered based on the analysis of a subject’s daily situations (Damasio, 1994; García-Gómez, 2015; Pérez-Salas et al., 2016; Ramos-Galarza et al., 2019b).

The relevance of the assessment of executive functions through the application of questionnaires or scales lies in the contribution of daily life behaviors to the analysis. These types of tests have been found to have adequate ecological validity and reliability in evaluating these mental abilities in real contexts, which goes further than the artificial environment that a neuropsychological consulting room offers (Damasio, 1994; Pérez-Salas et al., 2016).

To determine the executive functions that were considered in previous scales, principal databases were reviewed, and scales such as Behavior Rating Inventory of Executive Functions (BRIEF), Prefrontal Symptoms Inventory (PSI), Executive Functioning Scale for Families (EFS-F), Scale of Executive Functions of the Cáceres group (EFECO), Adult Executive Functioning Inventory (ADEXI), Barkley Deficits in Executive Functioning Scale (BDEFS), and many others were identified as the main contributors in this research line. These scales focus their evaluation on the eight classically recognized executive functions in favor of behavioral regulation and metacognition. The revised scales are described in Table 1.

TABLE 1

Table 1. Revision of the scales to assess executive functions.

As the results of the literature revision on the scales emerged, it became evident that the interest in assessing the executive functions classically recognized in the theories of Lezak (1995); Isquith and Gioia (2008), Baddeley (2012); Barkley (2014) did not consider certain executive functions, such as verification, error correction, and awakening state, which are key functions of the frontal lobe. In this context, it also became apparent that to consider the executive functions described in Luria’s theory, as part of the neuropsychological evaluation through daily life assessment, would be both enriching and innovating.

Thus, this investigation proposes using the EOCL-1 scale, which allows for the assessment of executive functions as described in the theory developed by Luria (Ramos-Galarza et al., 2019b), which assesses frontal lobe abilities that were not considered previously in scales or questionnaires, thus richly contributing to the executive functions’ instruments frame, as well as to its theoretical construction. Among the previous investigations, there is no published study about this scale and its psychometric properties; therefore, this article reports the results of the investigations that analyzed the factorial structure, internal consistency, and convergent validity of the EOCL-1 scale.

Investigation Hypotheses

Based on Luria’s theoretical proposal, nine executive functions were identified that were not considered previously in the tests already developed: EF 1, Error correction; EF 2, Internal behavior and cognition regulatory language; EF 3, Limbic system conscious regulation; EF 4, Decision making; EF 5, Future consideration of consequences of actions; EF 6, Goal-directed behavior; EF 7, Inhibitory control of automatic responses; EF 8, Creation of new behavioral repertoires; and EF 9, Cognitive–behavioral activity verification. The content of the 27 items of the EOCL-1 scale were proposed by the research team after a deep analysis of Luria’s theory (Luria, 1973, 1980, 1982), together with the team’s clinical experience of treating people with frontal lobe impairments. This allowed comprehension of the differences between the pathological and normal states of executive functioning. The authors propose the following hypotheses in relation to EOCL-1 reliability:

H₁. The nine executive functions assessed with the EOCL-1 scale will show adequate psychometric properties in terms of internal consistency.

Most of the scales developed to evaluate the executive functions focus on the deficits in these mental abilities, which negatively influences self-reporting. One of the scales that mainly focus on deficits is the Prefrontal Symptoms Inventory (Ruiz-Sánchez de León et al., 2012), an instrument that assesses the executive functions inversely to the direction that the EOCL-1 promotes. Based on that description, the next hypothesis proposes a convergent validity based on an inverse relationship between the Prefrontal Symptoms Inventory and the EOCL-1 scale, since the high scores in the executive function deficits of the people evaluated will correlate with low scores in the EOCL-1 scale.

H₂. The EOCL-1 scale will present a statistically significant convergent validity based on an inversely proportional correlation to the Prefrontal Symptoms Inventory because the EOCL-1 scale assesses the ability of executive functions, while the Prefrontal Symptoms Inventory measures frontal cortex structure deficits.

Luria’s theory (Luria, 1973, 1980, 1982) proposes that the frontal brain system, the neuroanatomic structure responsible for executive functions, works on three levels: programming, verification, and regulation, and, as such, it allows humans to consciously regulate their behavior and cognition (Ramos-Galarza et al., 2019b). Based on this theoretical postulate, the next hypothesis about the scale’s factorial structure is proposed:

H₃. The EOCL-1 scale will show an adequate goodness-of-fit of the confirmatory factor model because it assesses nine executive functions organized in the three factors of second order proposed by Luria’s theory: programming, regulation, and verification (model 1, Figure 1).

FIGURE 1

Figure 1. Three-factor model of second order of the EOCL-1 scale (Model 1). Note. EF 1, error correction; EF 2, internal behavioral and cognition regulatory language; EF 3, limbic system conscious regulation; EF 4, decision making; EF 5, future consideration of the consequences of actions; EF 6, goal-directed behavior; EF 7, inhibitory control of automatic responses; EF 8, creation of new behavioral repertoires; EF 9, cognitive–behavioral activity verification.

The executive function models that have been developed include those based on one executive central factor (Norman and Shallice, 1986; Barkley, 1997), as well as multiple factors (Gioia et al., 2002; García-Gómez, 2015; Ramírez et al., 2017; Kamradt et al., 2019), and have proposed the theoretical organization of these mental abilities. It is important to highlight that there are no conclusive results as yet, and, based on this description, the following two hypotheses are proposed, one based on a central executive factor and the other based on multiple factors:

H₄. The EOCL-1 scale will present an adequate goodness-of-fit of the confirmatory factor analysis of the model of second order organized around one central factor (model 2, Figure 2).

FIGURE 2

Figure 2. Factorial model of second order with one executive central factor of the scale EOCL-1 (Model 2). Acronyms are the same as in Figure 1.

H₅. The EOCL-1 scale will show an adequate goodness-of-fit of the confirmatory factor analysis in the model of one executive factor of third order and the three factors of second order proposed in the theory developed by Luria: programming, regulation, and verification (model 3, Figure 3).

FIGURE 3

Figure 3. Factorial model of one factor of third order and three factors of second order of the scale EOCL-1 (Model 3). Note: Acronyms are the same as in Figure 1.

The authors consider that based on Luria’s theory, there will be a model with a higher heuristic to comprehend humans’ executive functioning, and the following hypothesis proposes the model that will have the best adjustment level:

H₆. The model of the EOCL-1 scale that rests on one factor of third order and three factors of second order (programming, regulation, and verification), based on Luria’s theory (model 3, Figure 3), will show a better statistically significant goodness-of-fit when compared with the rest of the models proposed.

Method

Sample

Multiple-level sampling was done, first, by a selection of zones from the urban-metropolitan district of Quito-Ecuador, next, of 134 neighborhoods from this district, and, finally, by the random choice of 771 participants within each neighborhood. The number of participants allows counting with an adequate statistical power for this research. The scale consists of 27 items and therefore is equivalent to 28.55 people for each proposed item.

The application of the questionnaires took place between September 2019 and January 2020. The authors and clinical psychologists trained in their implementation applied the instruments. The first step of the data analysis was obtaining the main descriptive statistics of the sample and comparing them with those of the general city’s population. The average age of the participants was 39.86 years (SD = 15.47), which is very close to the average age of Quito’s population, which is 39 years old (National Institute of Statistics and Censuses [INEC], 2010).

The inclusion criteria were as follows: (a) living within Quito-Ecuador’s urban metropolitan district, (b) not presenting a disability, (c) not having a record of psychopathological diagnosis, (d) having the capacity to understand and answer the items presented, (e) signing the informed consent of voluntary participation, and (f) being at least 18 years old. The exclusion criteria were as follows: (a) having a record of psychopathological diagnosis or any psychopharmacological consumption, (b) having a disability, and (c) showing an unwillingness to participate in the research.

Table 2 presents other statistics of interest. As can be seen, the percentage of women surveyed (50.5%) was very close to the percentage of men surveyed (49.5%). Additionally, the educational level most frequently reached by the respondents was secondary school (48.5%), which is precisely the predominant educational level of Quito’s adult population (National Institute of Statistics and Censuses [INEC], 2010, 2013). Based on these data, it can be concluded that the sample extracted is an adequate representation of Quito’s population.

TABLE 2

Table 2. Sample characteristics (N = 771).

Measurements

In this investigation, two instruments were applied: the EOCL-1 scale (Ramos-Galarza et al., 2019b) and the Prefrontal Symptoms Inventory (Ruiz-Sánchez de León et al., 2012). The EOCL-1 scale was developed based on Luria’s theory, in which executive functions not considered in the scales previously developed were included for assessment. The reason for the absence of these executive functions could be because the scales developed so far were created by the classic authors in the field. However, according to Luria’s proposed model, mental abilities of the frontal cortex that are used in daily life behavior are included, as follows: EF1, error correction; EF2, internal behavioral and cognition regulatory language; EF3, limbic system conscious regulation; EF4, decision making; EF5, future consideration of consequences of actions; EF6, goal-directed behavior; EF7, inhibitory control of automatic responses; EF8, creation of new behavioral repertoires; and EF9, cognitive–behavioral activity verification. One of the benefits of this scale is the directionality in the redaction of its composing items, since it assesses the functions positively, which allows for analyzing the ability of executive functioning in the diverse contexts in which an adult develops. It is also a free-use scale (Table A1).

The second scale used to evaluate EOCL-1 converged validity, the Prefrontal Symptoms Inventory, is a questionnaire that assesses the signs and symptoms of prefrontal cortex impairments from a behavioral perspective. Therefore, when applying these two scales, convergent validity is expected, which will show an inversely proportional relationship among the executive function abilities assessed with the EOCL-1 scale and their impairment, as measured by the Prefrontal Symptoms Inventory.

The Prefrontal Symptoms Inventory has a negative narrative in that it focuses on deficits in executive functioning. The following factors are evaluated with this instrument: (a) social–behavioral problems, (b) behavioral–control problems, and (c) emotional–control problems. Adequate values of validity and reliability have been reported using this instrument (Ruiz-Sánchez de León et al., 2012; Table A2).

Procedure

This study started with the approbation from the Ethical Committee of the Universidad Indoamérica del Ecuador. The application of the instruments was done at each participant’s house from the randomized assignation. In every case, an informed consent of volunteer and anonymous participation was obtained. Throughout the study, the participants’ physical and psychological integrity were safeguarded. Once the instruments were applied, the data were registered and organized in the databases in preparation for the statistical analysis.

Statistical Analysis

The statistical procedures of central tendency and dispersion were applied to evaluate the descriptive data. To analyze the reliability of each subscale, Cronbach’s alpha and McDonald’s Omega procedures were applied. To analyze convergent validity, Pearson’s correlation procedure was applied. Finally, a confirmatory factor analysis was conducted to evaluate the construct validity of the model hypothesized. The values to determine an acceptable index of goodness-of-fit in the factorial analysis were comparative fit index (CFI) ≥ 0.95, root mean square error of approximation (RMSEA) < 0.06, and standardized root mean square residual (SRMR) ≤ 0.08 (Schreiber et al., 2005).

These analyses were carried out in the statistical package SPSS v. 25 and Amos v. 23.

The Study Scenario

This study was conducted in the capital of Ecuador, the city of Quito. Ecuador is a middle-income developing country located in South America. Ecuador has an approximate population of 17 million inhabitants, while the city of Quito has, in its metropolitan area, a population close to 3 million inhabitants. Ecuador’s economic system is based on the private property model, and its official currency is the US dollar (National Institute of Educational Evaluation, 2019). Thus, the following presented results could be useful not solely in the context in which this research took place but also in other contexts that share similar described characteristics.

Results

Descriptive Data

Table 3 presents the descriptive data values obtained in the nine executive functions evaluated with the EOCL-1 scale.

TABLE 3

Table 3. Scale factors’ descriptive statistics.

Internal Consistency—First Hypothesis

To analyze the internal consistency of the executive functions, Cronbach’s alpha and McDonald’s Omega procedures were applied, as follows. The possible variability in the ordinal measurement of each item was controlled, and the internal consistency of the applied scale can be highly understood (Elosua and Zumbo, 2008; Lozano et al., 2008). The found values in this analysis were EF1, α = 0.82 and ω = 0.77; EF2, α = 0.83 and ω = 0.85; EF3, α = 0.83 and ω = 0.84; EF4, α = 0.77 and ω = 0.72; EF5, α = 0.75 and ω = 0.72; EF6, α = 0.73 and ω = 0.78; EF7, α = 0.72 and ω = 0.69; EF8, α = 0.76 and ω = 0.74; EF9, α = 0.70 and ω = 0.68.

The values found provide empirical evidence in favor of the first hypothesis proposed, since the levels of internal consistency are in the range of acceptable reliability.

Convergent Validity—Second Hypothesis

For the analysis of convergent validity, the Prefrontal Symptoms Inventory was applied (Ruiz-Sánchez de León et al., 2012). The following scales were used: (a) social–behavioral problems, (b) behavioral–control problems, and (c) emotional–control problems (Table 4 shows the descriptive analysis).

TABLE 4

Table 4. Descriptive data of the Prefrontal Symptoms Scale.

Subsequently, the analysis of the correlation between Luria’s EOCL-1 scale and the Prefrontal Symptoms Inventory resulted in a statistically significant inversely proportional correlation between the variables (Table 5).

TABLE 5

Table 5. Correlation between EOCL-1 and prefrontal symptoms.

The results found in the correlational analysis provide empirical evidence in favor of the second hypothesis proposed; since the correlation is inversely proportional, it is proposed that the EOCL-1 scale has a positive directionality in favor of the executive functions, while the Prefrontal Symptoms Inventory is aimed at assessing the deficits in executive functioning.

Confirmatory Factor Analysis: Model of Second Order in Three Executive Abilities—Third Hypothesis

A model of second-order factors was proposed following Luria’s theory, in which the three levels of the frontal lobe brain system were considered, i.e., the programming, regulation, and verification of cognitive and behavior activity. The model hypothesized was composed of three principal factors: programming (comprises decision making, future consideration of consequences of actions, and goal-directed behavior), regulation (comprises internal behavioral and cognition regulatory language, conscious regulation of the limbic system, and control of automatic responses), and verification (comprises error correction, creation of new behavioral repertoires, and cognitive–behavioral activity verification) (Figure 1 shows the hypothetical model, and Table 6 shows the regression weights). The results provide empirical evidence in favor of the third hypothesis proposed, since the goodness-of-fit parameters found contribute to the model proposed: CFI = 0.93, RMSEA = 0.04 (LO.04 and HI.05), SRMR = 0.04, and x²₍₃₁₂₎ = 789.29, p = 0.001.

TABLE 6

Table 6. First model’s regression weights.

Confirmatory Factor Analysis: Model of Second Order Organized in a Central Executive Factor—Fourth Hypothesis

Furthermore, a model of a unique factor of executive functions was analyzed, which found a goodness-of-fit of CFI = 0.93, RMSEA = 0.04 (LO.04 and HI.05), SRMR = 0.04, and x²₍₃₁₅₎ = 791.05, p = 0.001. These results provide empirical evidence in favor of the proposed hypothesis (Figure 2 shows the hypothetical model, and Table 7 shows the regression weights).

TABLE 7

Table 7. Second model’s regression weights.

Confirmatory Factor Analysis: Model of the Third Factor in One Executive Central Factor and Three Executive Factors of Second Order—Fifth Hypothesis

Next, a model of a unique factor of executive functions was analyzed, which found a goodness-of-fit of CFI = 0.93, RMSEA = 0.04 (LO.04 and HI.04), SRMR = 0.04, and x²₍₃₁₂₎ = 789.29, p = 0.001. These values provide evidence in favor of the proposed hypothesis (Figure 3 shows the hypothetical model, and Table 8 shows the regression weights).

TABLE 8

Table 8. Third model’s regression weights.

Comparison of the Different Factorial Models—Sixth Hypothesis

To analyze the model that presented the best goodness-of-fit, comparing the three models proposed, it was necessary to consider each model’s adjustment values, as well as their goodness-of-fit values. As a result, it was found that the three models obtained similar values, which is consistent with Luria’s theory. However, the model that proposes an executive central factor shows a slightly better adjustment, and because of this, the empirical evidence does not provide conclusive evidence to accept the sixth hypothesis of this investigation.

Invariance Analysis

The invariance analysis was carried out for the model that presented the better goodness-of-fit, following the steps recommended by Byrne (2016). As a first step, the configurational or no restriction model (Model 1) was estimated; the same shape of model was applied to both groups, but with no equivalence restriction among the parameters. After this, Model 2 was estimated, in which all the factorial loading indicators were the same among the groups. Finally, in Model 3, in addition to the Model 2 restrictions, it was imposed that the structural coefficient of the paths remains the same throughout the groups. The base model for the comparison was Model 1.

For the comparisons between the models, the criteria of Cheung and Rensvold (2002); Chen (2007) were followed, and the chosen comparison indexes were the variations of CFI and RMSEA (ΔCFI and ΔRMSEA), since CFI and RMSEA are less sensitive to the sample size than the chi-square index (χ²). Additionally, and as per the recommendations of the cited authors, the cutting points to understudy the invariance were fixed in a maximum change of absolute value of CFI of.010, together with a maximum change in absolute value of RMSEA of.015.

As shown in Table 9, the values of ΔCFI and ΔRMSEA, as a result of the comparison between Models 2 and 3 against Model 1, all the cases obtained less values than the cutting selected points. These results support the existence of the model’s invariance, meaning that the proposed model is equivalent for both genders, men and women.

TABLE 9

Table 9. Statistical adjustment for invariance analysis.

Discussion

This article reports an analysis of the factorial structure of the EOCL-1 scale to assess executive functions based on Luria’s mental abilities theory. The aim of this investigation is to generate a significant contribution to the executive function research line, with the proposal of a measurement instrument to evaluate a variety of these mental abilities, which were not considered previously in other scales that assess daily life executive functions (Ramos-Galarza et al., 2019b).

The executive functions that are proposed in this investigation, within the scale’s factorial structure and after a revision of the previous scales, are a novel contribution to this research line. These functions include the executive frontal abilities of error correction, internal behavioral and cognition regulatory language, limbic system conscious regulation, decision making, future consideration of consequences of actions, goal-directed behavior, inhibitory control of automatic responses, creation of new behavioral repertoires, and cognitive–behavioral activity verification.

In the analysis of the EOCL-1 scale factorial structure, it was found that the three proposed models showed an adequate adjustment; however, the empirical evidence is not sufficient to accept one model as more valid than another, but the models proposed allow us to understand the executive functions’ organization from the proposed theory. The model of the unique factor could have a better adjustment, although future investigations are necessary to analyze the proposed hypothesis with greater depth.

This investigation contributes to the development of two new aspects within the executive function line of research. The first relates to theoretical construction, since, at the moment, there is not an absolute consensus as to how many executive functions can be generated by the prefrontal cortex. The second aspect pertains to the instruments that measure these mental abilities, since many of the observational scales for executive functions have arisen from the theories of classic neuropsychology and, as explained in this work, are missing the relevant mental abilities proposed by Alexander Luria.

Regarding the content of this instrument, it is important to highlight the purpose of the proposed items, since they are meant to evaluate the positive aspects of executive abilities, with the benefit of evaluating these in the different contexts in which human beings develop, such as in work, education, and social environments. Thus, the confusion that is often generated in the inversely proportional correlations could be avoided if the evaluation were allowed to focus naturally on people’s executive abilities, without the bias of evaluating their deficits.

In reference to the convergent validity, the contribution of this scale in favor of the assessment of executive functions is evident. Since an inverse relationship was found with the scale that evaluates the deficit of frontal functions, interest in developing different research lines around executive functions in other contexts naturally arises. Furthermore, the possibility is opened for applying the proposed scale in a clinical environment to study people with neuropsychological alterations and to look closer into human cognition and behavior in other areas of interest.

The inversely proportional correlation found in the convergent validity analysis is in concordance with our hypothesis assumptions, since the low punctuation in the EOCL-1 scale indicates a low level of executive functioning, inversely to the higher punctuations in the scale that assesses frontal lobe deficits. Therefore, the EOCL-1 scale is sensitive in detecting executive functioning deficits, as well as in assessing the typical performance of these mental abilities.

The results found and presented in this article are in accordance with previous literature, as well as with the proposals of the following scales: BRIEF (Roche et al., 2020), Executive Skills Questionnaire (Strait et al., 2019), ADEXI (Holst and Thorell, 2018), BDEFS (Barkley, 2011), the BDEFS–Children and Adolescents (Mashhadi et al., 2020), and the Multidomain Self-Report Assessment of Fronto-Executive Complaints in Spanish-Speaking Adults (Miranda et al., 2019), which have described the psychometric properties in favor of evaluating the executive functions related to behavioral conscious regulation, cognition, and emotion.

In relation to the impact of the scale proposed, it is important to highlight its free use in the clinical context as a screening tool to assess executive functions from the ecological perspective of daily life. Moreover, when considering the measurement of executive functions not previously considered in these types of observational scales of behavior, the possibility emerges of executing new investigations in which the impact of the executive functions is considered in the novel contexts in which individuals develop in educational, family, social, and personal arenas.

In addition, when considering novel executive functions and an assessment proposal, this study makes a significant contribution to the line of research on executive functions, which is still under theoretical construction and in which the methods for assessing these mental abilities are still in development. In this way, the proposed research has an impact in opening new lines of research in executive functions not previously considered through this method of evaluation, such as error correction, future consideration of consequences of actions, decision making, and other functions included in the scale.

Another theoretical implication that emerges from this research and from a broad theoretical review of the scales developed to evaluate executive functions lies in the use and foundation of classical theoretical models (Musso, 2009; Barkley, 2011; Baars et al., 2015; Herreras, 2016; García et al., 2018; Holst and Thorell, 2018; Antoniak et al., 2019; Korzeniowski and Ison, 2019; Strait et al., 2019; Wang et al., 2019; Mashhadi et al., 2020; Roche et al., 2020). This research attempts to go beyond the established models by proposing such an assessment from the perspective of the behavioral observation of novel executive functions. Moreover, with this contribution, new theoretical models can be created that allow us to further understand the complexity that executive functions imply and to continue contributing to the study of the relations of these mental abilities of the frontal lobe and the processes of intervention in individuals who are suffering from acquired brain damage.

According to the influence of sociodemographic variables in the tested model, it was found that the gender variable during the invariance analysis had no influence in the EOCL-1 model, which explains why this construct has a similar behavior when applied to men and women. Finally, the limitation that must be declared about this investigation relates to the place where the sample was taken, in a specific city from Latin America, which has to be taken into account when generalizing the results. However, it is this very point on which future investigation lies, which is the interest in analyzing the psychometric behavior of the EOCL-1 scale in the different geographical contexts and cultures in which human beings develop in order to gain a deeper comprehension of the role that the executive functions play in daily life performance.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by the Comité de Ética de Investigación con Seres Humanos Universidad Tecnológica Indoamérica. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

CR-G contributed to the conceptualization, investigated the data, carried out the formal analysis and project administration, wrote the original draft, and reviewed and edited the manuscript. JC-C investigated the data, carried out the formal analysis, and reviewed and edited the manuscript. MB-P investigated the data, wrote the original draft, and carried out the formal analysis. PA-R investigated the data, wrote the original draft, and reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was funded by the Universidad Tecnológica Indoamérica de Ecuador and Pontificia Universidad Católica del Ecuador.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Antoniak, K., Clores, J., Jensen, D., Nalder, E., Rotenberg, S., and Dawson, D. (2019). Developing and validating a big-store multiple errands test. Front. Psychol. 10:2575.

Google Scholar

Baars, M., Nije, M., Tonnaer, G., and Jolles, J. (2015). Self-report measures of executive functioning are a determinant of academic performance in first-year students at a university of applied sciences. Front. Psychol. 6:1131.

Google Scholar

Baddeley, A. (2012). Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29. doi: 10.1146/annurev-psych-120710-100422

PubMed Abstract | CrossRef Full Text | Google Scholar

Barkley, R. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol. Bull. 121, 65–94. doi: 10.1037/0033-2909.121.1.65

PubMed Abstract | CrossRef Full Text | Google Scholar

Barkley, R. (2014). Attention-Deficit Hyperactivity Disorder: a Handbook for Diagnosis and Treatment. New York: Guilford Publications.

Google Scholar

Barkley, R. A. (2011). Barkley Deficits in Executive Functioning Scale (BDEFS). New York, NY: Guilford Press.

Google Scholar

Byrne, B. M. (2016). Structural Equation Modeling with Amos, Basic Concepts, Applications, and Programming, 3ra Edn. New York, NY: Routledge.

Google Scholar

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct. Equ. Modeling 14, 464–504. doi: 10.1080/10705510701301834

CrossRef Full Text | Google Scholar

Cheung, G. W. Y., and Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Struct. Equ. Modeling 9, 233–255. doi: 10.1207/s15328007sem0902_5

PubMed Abstract | CrossRef Full Text | Google Scholar

Cipolotti, L., Molenberghs, P., Domínguez, J., Smith, N., Smirni, D., Xu, T., et al. (2020). Fluency and rule breaking behaviour in the frontal cortex. Neuropsychologia 137:107308. doi: 10.1016/j.neuropsychologia.2019.107308

PubMed Abstract | CrossRef Full Text | Google Scholar

Damasio, A. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. New York: G. P. Putnam.

Google Scholar

Derouesné, C. (2018). AR Luria and the role of frontal lobes in the regulation of higher mental activities and behavior. Revue Neurologique 174, 739–740. doi: 10.1016/j.neurol.2018.09.007

CrossRef Full Text | Google Scholar

Elosua, P., and Zumbo, B. (2008). Coeficientes de fiabilidad para escalas de respuesta categórica ordenada. Psicothema 20, 896–901.

Google Scholar

García, T., Álvarez-García, D., Areces, D., Segurola, L. A., González-Castro, P., and Rodríguez, C. (2018). Psychometric characteristics of the executive functioning scale for families (EFS-F). Revista Iberoamericana De Diagnóstico y Evaluacion Psicológica 2, 71–82.

Google Scholar

García, T., González-Pienda, J., Rodríguez, C., Álvarez, D., and Álvarez, L. (2014). Psychometric characteristics of the BRIEF scale for the assessment of executive functions in Spanish clinical population. Psicothema 26, 47–52.

Google Scholar

García-Gómez, A. (2015). Development and validation of an observation questionnaire for the evaluation of executive functions in childhood. Revista Intercontinental de Psicología y Educación 17, 141–162.

Google Scholar

Gioia, G., Isquith, P., Retzlaff, P., and Espy, K. (2002). Confirmatory factor analysis of the behavior rating inventory of executive function (BRIEF) in a clinical sample. Child Neuropsychol. 8, 249–257. doi: 10.1076/chin.8.4.249.13513

PubMed Abstract | CrossRef Full Text | Google Scholar

Herreras, E. (2016). BRIEF-A (short): analysis psychometric properties in a spanish sample. [BRIEF-A (forma abreviada): análisis propiedades psicométricas en una muestra española]. Archivos De Neurociencias 21, 6–15.

Google Scholar

Holst, Y., and Thorell, L. B. (2018). Adult executive functioning inventory (ADEXI): validity, reliability, and relations to ADHD. Int. J. Methods Psychiatr. Res. 27:e1567. doi: 10.1002/mpr.1567

PubMed Abstract | CrossRef Full Text | Google Scholar

Isquith, P., and Gioia, G. (2008). Behavior Rating Inventory of Executive Function: Feedback Report. Florida: PAR Psychological Assessment Resources, Inc.

Google Scholar

Kamradt, J., Nikolas, M., Burns, G., Garner, A., Jarrett, M., Luebbe, A., et al. (2019). Barkley deficits in executive functioning scale (BDEFS): validation in a large multisite college sample. Assessment 28, 946–976.

Google Scholar

Korzeniowski, C., and Ison, M. (2019). Executive functioning scale for schoolchildren: an analysis of psychometric properties. Psicol. Educ. 25, 147–157.

Google Scholar

Lezak, M. (1995). Neuropsychological Assessment, 3th Edn. Oxford: Oxford University Press.

Google Scholar

Lozano, L., García-Cueto, E., and Muñiz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology 4, 73–79. doi: 10.1027/1614-2241.4.2.73

CrossRef Full Text | Google Scholar

Luria, A. (1973). The Working Brain: an Introduction to Neuropsychology. New York: Basic Books.

Google Scholar

Luria, A. (1980). Higher Cortica Functions in Man. New Yotk: Basic Books.

Google Scholar

Luria, A. (1982). Language and Cognition. New York: John Wiley & Sons.

Google Scholar

Mashhadi, A., Maleki, Z., Hasani, J., and Tabatabaei, S. K. R. (2020). Psychometric properties of Persian version of the Barkley deficits in executive functioning scale–children and adolescents. Appl. Neuropsychol. Child 18, 1–8. doi: 10.1080/21622965.2020.1726352

PubMed Abstract | CrossRef Full Text | Google Scholar

Miranda, A., Rivadero, L., Serra, S., and Soria, E. (2019). Multidomain self-report assessment of fronto-executive complaints in Spanish-speaking adults. Psychol. Neurosci. 13, 357–374. doi: 10.1037/pne0000187

CrossRef Full Text | Google Scholar

Musso, M. (2009). Assessment of executive functions in children: analysis and adaptation of tasks in a school context. Revista Iberoamericana De Diagnóstico y Evaluación Psicológica 1, 157–178.

Google Scholar

National Institute of Educational Evaluation (2019). Education in Ecuador: Achievements and New challenges. Mexico: National Institute of Educational Evaluation.

Google Scholar

National Institute of Statistics and Censuses [INEC] (2010). Census Results. Available online at: http://www.ecuadorencifras.gob.ec/censo-de-poblacion-y-vivienda/ (accessed May 05, 2020).

Google Scholar

National Institute of Statistics and Censuses [INEC] (2013). National Survey on Household Income and Expenditure 2011-2012. Available online at: http://www.ecuadorencifras.gob.ec/documentos/web-inec/Estadisticas_Sociales/Encuesta_Nac_Ingresos_Gastos_Hogares_Urb_Rur_ENIGHU/ENIGHU-2011-2012/Metologia_ENIGHUR_2011-2012_rev.pdf (accessed February 05, 2020).

Google Scholar

Norman, D., and Shallice, T. (1986). “Attention to action: willed and automatic control of behavior,” in Consciousness and Self-Regulation, eds R. En, G. Davidson, and D. Shapiro (New York: Editorial Plenumm Press).

Google Scholar

Pedrero, E., Ruiz, J., Rojo, G., Morales, S., Pedrero, J., Lorenzo, I., et al. (2016). Inventario de síntomas prefrontales (ISP): validez ecológica y convergencia con medidas neuropsicológicas. Revista de Neurología 63, 241–251. doi: 10.33588/rn.6306.2016143

CrossRef Full Text | Google Scholar

Pérez-Salas, C., Ramos, C., Oliva, K., and Ortega, A. (2016). Bifactor modeling of the behavior rating inventory of executive function (BRIEF) in a Chilean sample. Percept. Mot. Skills 122, 757–776. doi: 10.1177/0031512516650441

PubMed Abstract | CrossRef Full Text | Google Scholar

Ramírez, P., Pérez-Salas, C., and Ortega, A. (2017). Hierarchical structure analysis of behavior rating inventory of executive functions self-report in a sample of university student’s. Revista Iberoamericana De Diagnostico y Evaluacion Psicologica 3, 175–188.

Google Scholar

Ramos-Galarza, C., Acosta-Rodas, P., Bolaños-Pasquel, M., and Lepe-Martínez, N. (2019a). The role of executive functions in academic performance and behaviour of university students. J. Appl. Res. Higher Educ. doi: 10.1108/JARHE-10-2018-0221 Online ahead-of-print.

CrossRef Full Text | Google Scholar

Ramos-Galarza, C., Benavides-Endara, P., Bolaños-Pasquel, M., Fonseca-Bautista, S., and Ramos, D. (2019b). Scale of clinical observation to valuate the third functional unit. Revista Ecuatoriana de Neurología 28, 83–91.

Google Scholar

Ramos-Galarza, C., Bolaños-Pasquel, M., Garcia-Gomez, A., Siiarez, P., and Jadan-Guerrero, J. (2019c). Efeco scale for assessing executive functions in self-report format. Revista Iberoamericana De Diagnóstico y Evaluación Psicológica 51, 83–93.

Google Scholar

Ramos-Galarza, C., Villegas, C., Ortiz, D., García, A., Bolaños, M., Acosta, P., et al. (2018). Evaluation of the skills of the prefrontal cortex: the Efeco II-VC and II-VR. Revista Ecuatoriana de Neurología 27, 36–43.

Google Scholar

Roche, J., Cámara-Costa, H., Roulin, J., Chevignard, M., Frappaz, D., and Guichardet, K. (2020). Assessment of everyday executive functioning using the BRIEF in children and adolescents treated for brain tumor. Brain Injury 34, 583–590. doi: 10.1080/02699052.2020.1725982

PubMed Abstract | CrossRef Full Text | Google Scholar

Ruiz-Sánchez, de León, J., Pedrero-Pérez, E., Lozoya-Delgado, P., Llanero-Luque, M., Rojo-Mota, G., et al. (2012). Prefrontal symptoms inventory for clinical evaluation of addictions in everyday life: development process and psychometric properties. Revista de Neurología 54, 649–663.

Google Scholar

Schreiber, J. B., Stage, F. K., King, J., Nora, A., and Barlow, E. A. (2005). Reporting structural equation model-ing and confirmatory factor analysis results: a review. J. Educ. Res. 99, 323–337.

Google Scholar

Strait, J. P. D., Walther, C., Strait, G., Barton, A., and McClain, M. (2019). Refinement and psychometric evaluation of the executive skills questionnaire-revised. Contemp. Sch. Psychol. 24, 378–388. doi: 10.1007/s40688-018-00224-x

CrossRef Full Text | Google Scholar

Téllez, A., and Sánchez, T. (2016). Luria’s model of the functional units of the brain and the neuropsychology of dreaming. Psychol. Russia State Art 9, 80–93.

Google Scholar

Wang, L., Pollastri, A., Vuijk, P., Hill, E., Lee, B., Samkavitz, A., et al. (2019). Reliability and validity of the thinking skills inventory, a screening tool for cross-diagnostic skill deficits underlying youth behavioral challenges. J. Psychopathol. Behav. Assess. 41, 144–159. doi: 10.1007/s10862-018-9703-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Appendix

TABLE A1

TABLE A1. OECL-1 Scale (Ramos-Galarza et al., 2019b).

TABLE A2

TABLE A2. Prefrontal Symptoms Inventory (Ruiz-Sánchez de León et al., 2012).