Assessing Statistical Knowledge and Training Needs among Professionals in Ecuadorian Businesses: Implications for Education

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International Journal of Instruction April 2024 ● Vol.17, No.2
e-ISSN: 1308-1470 ● www.e-iji.net p-ISSN: 1694-609X
pp. 351-366
Citation: Mosquera-Gutierres, J., Avilés-González, J., Córdova-León, F., & Duque-Espinoza, G.
(2024). Assessing statistical knowledge and training needs among professionals in ecuadorian
businesses: Implications for education. International Journal of Instruction, 17(2), 351-366.
https://doi.org/10.29333/iji.2024.17220a
Article submission code:
20230421174510
Received: 21/04/2023
Revision: 26/09/2023
Accepted: 13/10/2023
OnlineFirst: 17/01/2024
Assessing Statistical Knowledge and Training Needs among Professionals
in Ecuadorian Businesses: Implications for Education
Julio Mosquera-Gutierres
Universidad del Azuay, Ecuador, juliomosquera@uazuay.edu.ec
Jonnatan Avilés-González
Universidad del Azuay, Ecuador, javiles@uazuay.edu.ec
Fernando Córdova-León
Universidad del Azuay, Ecuador, jfcordova@uazuay.edu.ec
Gabriela Duque-Espinoza
Universidad del Azuay, Ecuador, gduque@uazuay.edu.ec
A study was conducted to assess the knowledge and frequency of use of statistics
among professional roles from various businesses in different economic sectors in
Ecuador. A tool was designed, based on the Guide for Evaluation and Instruction
in Statistical Education and the Handbook of Statistical Methods by the National
Institute of Standards and Technology, which made it possible to analyze the
aptitudes of different professionals in relation to the use of statistics along their
enterprises from different economic sectors. The data was collected by interviews
and surveys to 246 people working in factories around the country. For instance,
the results showed around 75% of the commerce sector requires to be more trained
in reliability techniques, unfortunately the expertise of the majority of the sample
shows that this part of the statistics it not a strength. The results also showed a lack
of knowledge of statistical topics among the different professionals according to
their professional roles. This study suggests that there is a need and an opportunity
to increase education and training in the use of statistics within professional roles
in several businesses around Ecuador.
Keywords: statistics training, statistics knowledge, economic sectors, use of statistics,
professional roles
INTRODUCTION
National monetary evaluation, measured by gross domestic product (GDP), is an
important measure of economic growth or decline of a country. Yearly increases in
GDP requires highly trained professionals within different economic sectors in the
collection, management and interpretation of data that allows them to make better
business decisions. It is advantageous for business professionals to acquire and use

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statistical analysis to summarize, interpret and act on evidence-based information
(Cardenas Poblador & Jimenez Valderrama, 2014). Frequently, business managers in
developing countries still lack the skills to produce, analyze and use the appropriate
statistical methods required to support adequate decision making. In addition, many
resources needed for increasing the GDP in developing countries are limited; therefore,
good data management is important to help ensure that resources are used in the most
effective way possible (Poverty, 2007).
The last two decades have seen a radical change in the dominant logic of the design of
higher educational policies in universities of Ecuador directed toward higher levels of
economic growth. Universities are encouraged to create a positive change in advanced
statistical use and knowledge. These policy changes are based on the growth factors
influencing society at large (Salgado_Arteaga & Cobos-Cali, 2018), which requires the
development of programs designed to strengthen the production of goods and services
throughout the business sector. In addition, universities are encouraged to develop
opportunities within small communities to bring micro artisanal businesses toward the
mainstream. As quantitative research skills become increasingly relevant for non-
academic business professionals, an important objective of teaching programs at
universities is to generate graduates who are interested in economic research and who
are also capable of integrating science and practice into a set of skills, knowledge, and
professional attitudes (Schuyten & Ferla, 2007).
Khan (2013) suggested that the use of statistics allowed an appropriate analysis of
information from different perspectives, which improved decision making. That is to
determine what can be inferred from data or statistical results and whether the
justifications led to valid conclusions (Lestari et al., 2022). Implementation and
validation of new statistical work tools can lead to new and better business strategies to
increase effective and efficient performance (Donoso-Diaz et al., 2020).
Within a university setting, statistics education has the potential to help students to
develop critical problem-solving skills in social contexts (Souza et al., 2020); however,
the efforts of universities are not necessarily aligned with the requirements of economic
sectors within a developing country. It could be inferred that educators prepare young
people for a productive market around their perspectives, while many employers suffer
a lack of skills of newly trained professionals. One possible explanation for the different
perceptions is that employers and educators have different interpretations of the skills
valued in the economic sectors; that is, there is a separation of criteria between these
actors (Cunningham & Villaseñor, 2016). These factors have led universities to
redesign their curricula in order to address global trends, both in the academic and
business fields (Cardenas Poblador & Jimenez Valderrama, 2014). The redesign of the
academic environment, including experiments and practices, have strengthened and
improved the education of students around the needs of modern society (Li et al., 2011).
Keh et al. (2016) explain how a constructivism theoretical framework might develop in
an easy manner the strategies required to teach mathematics and relates science. The
author explains how this way is effective to achieve news strategies. But the question
here is which statistics knowledge are the most relevant for the different professional
carrier?

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One problem that it is possible to infer, in this point, is that the training in the statistics
methods, is not aligned to the techniques required in the factories and business. This
could be studied from two perspectives, the university curricular design (syllabus) and
the requirements of the economic sector and their weakness. The study reported here is
designed to analyze the frequency of knowledge and use of statistics by professionals in
different business roles from different economic sectors within main cities in Ecuador,
with the finality to create a method to obtain evidence to improve the education
curriculum in a future.
Literature Review
Economic Sector
The economic progress of a nation involves the use of capital to enhance human
productivity, create prosperity, and boost the overall national income. This process is
accompanied by various social and cultural transformations. Therefore, a country's
economic growth is influenced not solely by the quantity of labor and capital but also
by institutional, cultural, educative and technological elements that dictate how labor
and capital are employed (Massey et al., 2005).
Economic changes of a country is the result of its dynamics between production and
consumption. Countries with higher economic growth generally have greater resources,
which improves productivity and the standard of living of the population.
Presently, numerous organizations are leveraging data to enhance the quality of their
strategic and operational choices. The practice of utilizing data for decision-making is
not novel; in fact, since the introduction of data warehouse systems in the early 1990s,
business entities have been storing and analyzing vast amounts of data. Nevertheless,
the type of data accessible to most organizations is evolving, and these transformations
introduce challenges in effectively handling the increasing volumes and complexity of
data analysis (Daniel, 2015).
Statistics, both as a science and in practice, has contributed significantly to the decision-
making process in businesses; and in government. Its importance is so great that
practically all governments have internal departments or committees dedicated to
carrying out statistical analyses (Ignácio, 2010). Therefore, one key role of statistics is
in the planning and successful development of each productive sector of a nation (Khan,
2013). For this reason, this science has a critical role in the development of countries
(Shangodoyin & Lasisi, 2011).
Statistics in higher education
Higher education institutions are currently functioning within a more intricate and
competitive setting. They face mounting demands to adapt to changes on a national and
global scale, including the necessity to boost student enrollment in specific fields,
incorporate workplace-related skills into graduate attributes, and ensure that their
educational programs maintain relevance at both national and global levels. The
pressures are driven by economic, political, and social factors, making it essential for
institutions to navigate these challenges effectively (Daniel, 2015).

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Today, the rapid growth of publicly available data reinforces the need for public
understanding of statistics and data interpretation. These skills are important for data-
based decision making at individual-level and corporate level. However, statistics have
been accused as a complex educational topic (Legaki & Hamari, 2020). This complexity
causes undergraduates finding some difficulties in learning and understanding the
courses if such complexity is conventionally studied using pencalculator, for that
reason, lecturers should provide adequate opportunities for undergraduates to acquire
the abilities in the classroom (Mairing, 2020).
However, the main objective of statistics education is to create adults who are proficient
in statistical thinking and can use it effectively. Among all disciplines, statistics
uniquely allows students to acquire the necessary knowledge in just one introductory
course. It is in this introductory course that teachers hold the responsibility to either
inspire or fail to motivate students in developing the essential statistical skills that will
be valuable in both their professional and personal endeavors (Ramirez et al., 2012).
On the other hand, the advance of information technologies, and in general, the
knowledge that society has gained, specifically in the area of the use of statistics, has
increased greatly in the last 50 years. Statistics is one of the most used disciplines
because of its holistic and universal support in the science of business (Barreto-
Villanueva, 2012).
Studies have shown the importance of statistical knowledge in various professional
fields. For example, Swarnalatha and Ramakrishna (2022) have shown the importance
of the knowledge and use of statistics by health professionals’ areas. Likewise,
statistical knowledge is crucial for several careers such as biomedicine, psychology,
sociology, commerce, business, engineering and education.
Reality in Ecuador
The historical evolution of the scientific-technological activity of Ecuador has led to
different stages of transformation in the productive matrix that influenced the statistics
of national production (Ronquillo & Ronquillo, 2017). However, the way of teaching
statistics has not changed much over the years. Some researchers have tried to leave
aside the traditional way of teaching statistics in order to adapt to the needs of constant
change in the professional world. Related to this Cruz (2020) shows how collaborative
learning can be used in the subject of statistics, with the aim of forming students with
greater skills to understand data. In this sense, it is increasingly important to seek new
ways to align the teaching of statistics with the professional reality of the country, since
statistics plays a fundamental role in the improvement of a country's productive growth.
In this sense, Khan (2013) shows how statistics have left a successful mark in diverse
areas of economic science. A clear example of this relates to the local agricultural
sector, where, by using statistical techniques, evidence is provided that allows
governmental policy to be extended beyond the simple analysis of tons produced per
hectare (Bonilla Bolaños & Singaña Tapia, 2019). Another important productive sector
in Ecuador that benefits from the use of statistics is in the textile industry. For example,
it is now possible to know the relationship that exists between technology and

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productivity, and thus determine how the production processes of companies in this
sector make them increasingly effective, efficient and competitive (Ibujes Villacis &
Benavides Pazmino, 2018). The importance of the use of statistics within the banking
sectors is also well known, for example: Ignacio (2010)explains that using statistical
techniques, analyzes were carried out on the influence of productive credits from public
and private banks in certain economic sectors. In the industrial sector, statistical
techniques are used to monitor and control the quality of products and processes.
Evidence suggests that there is a great advantage and need for the increased knowledge
and use of statistical analysis in business sectors in general. However, there are very
few studies that have been done to access the knowledge and use of statistical analysis
in the business sectors of developing countries. The study presented here addresses this
gap in knowledge.
METHOD
The study reported here was carried out using a survey given to a cohort of 384 business
professionals (finit sample size) from different economic sectors of Ecuador, 246
belong to sectors of interest, the others are sectors with a low impact in the economy.
The sample was balanced for each sector, the people who participated in the study were
selected considering professionals who are in positions of leadership, management,
administrative, or who have personnel under their charge.
A stratified sampling procedure was used along with a tool developed by the authors
based on the Guide for Assessment and Instruction in Statistics Education (GAISE)
(Carver et al., 2016) and the NIST-SEMATECH guide ((Natrella, 2010). The
individuals who participated in the study had a mean age of 34 years (sd = 8.7 years)
and a mean work experience of 9 years (sd = 8 years).
It is important to mention that the GAISE can be used as a macro scheme to create an
educational evaluation around the statistics, as a validated method for the statistics
teaching, while the NIST-SEMATECH could be used as a micro structure to identify
requirements in the economic sectors from a technical and business point of view.
Before the survey was administered, the main economic sectors of Ecuador were
identified. There are 21 productive sectors throughout the world according to the
International Standard Industrial Classification ISIC 4.0. Using this classification, a
Pareto analysis was carried out (Figure 1) in order to determine the most representative
sectors in Ecuador. When considering the income generated by the corporate business
fabric of Ecuador in 2019 (the year prior to the COVID-19 pandemic), six sectors
accounted for more than 80% of this income: 1) commerce, 2) manufacturing industries,
3) agriculture-livestock-forestry-fishing, 4) transport and storage, 5) information and
communication, and 6) construction.

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Figure 1
Pareto analysis for selection of productive sectors
A total of 384 observations were classified into the different sectors as shown in Figure 2.
Figure 2
Sample stratification used in the study
Within the six productive sectors shown above, the following factors were investigated: a)
an analysis of the frequency of use of statistics by professionals along with their perceived
needs of statistics related to different data analyses in the given role into their businesses; b)
the identity of the depth of the expertise and knowledge of these professionals regarding the
use of statistics in their given role into their business. To achieve these objectives, a survey
composed of eight questions was designed to measure the frequency of use and the expertise
of the participants in different aspects of statistics, see Figure 3. Each participant had to
choose the economic sector in which he/she identifies according to the role he/she plays in
his/her business.
GAISE, developed by the American Statistical Association (ASA), was used (Franklin et al.,
2007). GAISE groups statistical knowledge into sections based on competencies, which
were the same groups outlined in the Handbook of Statistical Methods (Natrella, 2010).
These were: identification of patterns, measurement instruments, data behavior, predictions,
optimization, control, comparison and confidence.

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The guide presents six main objectives: 1) the teaching of statistics-based thinking, which
focuses on problem solving, decision making, and multivariate perspectives, 2) focused
teaching on conceptual understanding, 3) the integration of real data around context and
purpose, 4) to encourage active learning, 5) to use technology to explore concepts and
analyze data, 6) to use monitoring and assessment to evaluate and improve student learning
(Natrella, 2010)
To modify these objectives to better fit the study reported here, an evaluation tool was
designed around the technical criteria of the NIST-SEMATECH guide, which are: explore,
measure, characterize, model, improve, monitor, compare, and analyze reliability. A
schematic of the tool is presented in Figure 3
Figure 3
Tool for evaluating use frequency and perception of expertise (knowledge) around the se of
statistics in different given role in businesses
Note: For each question, a Likert scale between 0 and 5 was used, for the frequency of use where 0 = never; 5
= always, and for knowledge expertise the same scale where 0 = nothing; 5 = expert.
Data were obtained from different professionals within the main cities from Ecuador. An
attempt was made to collect the information around a balanced sample according to the main
productive sectors of the country. The sample number included 25 observations per sector
corresponding to the number of participants who agreed to participate in the study in each

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group. A classification analysis was performed using a decision tree algorithm and
regression clustering, due to the subjective characteristics of the data. It is important to note
that non-parametric classification trees are a supervised learning method used for
classification and regression that make no assumptions about the distribution of the
underlying data. The purpose is to classify data around grouping labels (Fletcher & Islam,
2019). For this grouping, a classification was made considering the following aspects:
frequency: Likert scales from 0 to 2 is classified as "Use Little", from 3 to 5 "Use A Lot";
expertise: Likert scales from 0 to 2 is classified as “Knows Little”, from 3 to 5 “Knows A
Lot”.
FINDINGS AND DISCUSSION
The tool was validated with regard to its design and construction using four categories;
adequacy, clarity, consistency, relevancy; presented by Escobar and Cuervo (2008). The
Kendall W was evaluated with an expert group of 8 people and its p-value is 0.00254. The
reliability of answers scale was stablished using Cronbach's Alpha. The values obtained for
the parts that measure the frequency of use and knowledge expertise were 0.88 and 0.92
respectively. Figure 4 shows the number of professionals and their frequency of use of
statistical tools by the specified criteria (see Table 1). Figure 5 indicates the perception of
knowledge according to the opinion of each individual.
Table 1
Symbols used in Figures 4 and 5
Symbol
F1 – E1
F2 – E2
F3 – E3
F4 – E4
Criteria
Explore
Measure
Characterize
To Model
Symbol
F5 – E5
F6 – E6
F7 – E7
F8 – E8
Criteria
Improve
Monitor
Compare
Reliability
Figures 4 and 5 show that individuals believe that they know little about statistical topics,
and they consider that they use few statistics methods in their daily professional activities.
This could be inferred as an educational abandonment around statistics and its uses, is
probable that the statistics should be used higher during a making decision situation.
Use little
Use a lot
Use little
Use a lot
Use little
Use a lot
Use little
Use a lot
Use little
Use a lot
Use little
Use a lot
Use little
Use a lot
Use little
Use a lot
F1
F2
F3
F4
F5
F6
F7
F8
Figure 4
Amount of use (frequency) of each of the parts of the tool by sector

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Knows little
Knows a lot
Knows little
Knows a lot
Knows little
Knows a lot
Knows little
Knows a lot
Knows little
Knows a lot
Knows little
Knows a lot
Knows little
Knows a lot
Knows little
Knows a lot
E1
E2
E3
E4
E5
E6
E7
E8
Figure 5
Amount of expertise of each of the parts of the tool by sector
Based on these results, an analysis was conducted using an event classification
approach associated with decision trees. The objective was to identify the areas of
application of statistics most used by the respondents along with their perception of
expertise in the area. The resulting decision trees allowed the study of these application
areas; some of which are shown in Figures 6 and 7 as examples.
Question 2: In the development of your profession, have you described and analyzed the
behavior of data observed in a population?
Question 8: In the development of your profession, have you used techniques to
guarantee the confidence of your results?
Figure 6
Classification trees for questions F2 and F8 for frequency of use sector

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Figure 7
Classification trees for questions E2 and E8 for knowledge expertise
The decision trees in Figures 6 and 7 show the answers to each of the questions in the
tool in a binary way in order to make a classification by assigning weights to each of the
sectors. For each question, the classification algorithm selects an answer that will be
evaluated in a binary way: branches to the left of each node represent “yes” answers and
branches to the right indicate “no” answers. For example, Figure 6 a) analyzes the
response to the question F2, “Use a Lot" regarding the use of scales and measurement
instruments. The branch to the left shows the classification weights for different roles
within each sector, with the Construction sector as the most important. Therefore, the
roles at Construction sector have a great need for the F2 component of the tool whereas
the roles at Transportation sector do not have as great a need for this component
because it is located on the right branch of the tree.
It is important to note that the value of the weight must be greater than 0.17 to be able to
classify a sector in any branch. Since there are 6 sectors, 0.17 is the value of the
common weight for all; therefore, weights greater than 0.17 imply greater importance.
This same analysis was performed for the knowledge expertise component.
With the information obtained through the classification weights of the trees, the
following tables were designed as a summary of the results for all the questions of the
tool (see Tables 2 and 3).
An analysis of Tables 2 and 3 highlight the fact that professionals belonging to the
Commerce sector indicate that their roles have a significant need to know statistical
tools related to patterns and trends (Table 2), but their knowledge on this aspect is
scarce (Table 3). Additionally, it is also possible to identify parameters in which
professionals have a lot of knowledge on a specific subject, but it is not widely used in
their business roles, for example, with regard to question 8, “guaranteeing confidence of
your results” in the Transport sector.
The results of the study reported here agree with the conclusions of Schuyten and Ferla
(2007), who suggests that people who have the skills and willingness to be involved in
the use of quantitative tools are more prepared and comfortable analyzing data. These
competencies and skills are not only necessary in an information and evidence-based
society, but are also necessary to bridge the gap between science and practice. Evidence
suggests that when people within an economic sector take advantage of the value of

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data analysis, people who work there have the capacity to make better decisions.
However, this is only possible when data analyses allow the creation of relevant
information within the business area, which is sustained through studies and statistical
approaches. The identification and proper use of statistical skills will allow timely
decisions to be made and remove the potential risks that surround any economic activity
in a country.
Table 2
Summary of results for frequencies of use
Evaluation of Statistics Frequency Aspects
Sectors
Identify
Patterns
or
Trends
Prepare, use
scales,
measuring
instruments
Analyze
data
behaviors in
a population
Approach
reality and
make
predictions
Optimize
Monitor
and
control
Compare
Information
Verify and
ensure data
reliability
Agriculture
Commerce
Construction
Industry
Information
Transport
Note: symbols present in the table have the following interpretation: the green mark
indicates that the professionals of a sector use a lot the specified statistical aspect; the red x
indicates that this statistical aspect is not widely used by the different professionals and the
yellow circle indicates that these statistical aspects may be used sporadically.
Table 3
Summary of results for knowledge expertise
Evaluation of Statistics Frequency Aspects
Sectors
Identify
Patterns
or
Trends
Prepare, use
scales,
measuring
instruments
Analyze
data
behaviors in
a population
Approach
reality and
make
predictions
Optimize
Monitor
and
control
Compare
Information
Verify and
ensure data
reliability
Agriculture
Commerce
Construction
Industry
Information
Transport

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Note: symbols present in the table have the following interpretation: the green mark
indicates that the professionals of a sector know a lot about the specified statistical aspect;
the red x indicates that this statistical aspect is not well known by the different professionals
and the yellow circle indicates that these statistical aspects may be known sporadically.
This information show that it is necessary to initiate a change in the curricula around the
statistics teaching. New techniques, like simulations, artificial intelligence, creative and
contemporary curriculums are necessary to develop a new education process (Waluya et
al., 2018).
For instance, this study compares a set of curriculums of the Ecuadorian universities
and its commerce, administration, and similar careers, where usually the majority of
graduate students work in the area of the commerce and business. It is necessary to
mention that 12 curriculums of several universities in Ecuador were studied and the
framework of their syllabus is similar to the showed in this section. According to the
evaluation and the results obtained the students in this area should focus at least in these
specific education areas of the statistics to accomplish the activities in the commerce
sector in Ecuador: Analyze data behaviors in a population, Approach reality and make
predictions, Optimization, Monitor and control. Unfortunately, the curriculums (see
Table 4) shows that they focus on the area of Analyze data behaviors in a population
leaving 75% without enough attention, so, it is necessary to create a change into the
traditional curriculum.
Table 4
Example of comparison for the commerce area between the classical curriculum in
Ecuador and the requirements obtained in this study
Macro Section Syllabus
Commerce Sector
Relative Percentage
of hours suggested by
section
Classification according to
Evaluation Method
Requirement
Requirement form the
business sector
Generalities
7 %
N / A
Data Description
21 %
Analyze data behaviors in a
population
Analyze data behaviors in
a population
Probability
10 %
N / A
Distributions
13 %
Identify Patterns or Trends
Index
3 %
Compare Information
Sampling
9 %
Prepare, use scales, measuring
instruments
Confidence Intervals
9 %
Monitor and Control
Monitor and Control
Hypothesis test - Errors
19 %
Analyze data behaviors in a
population
Analyze data behaviors in
a population
Regression and
Correlation**
8 %
Approach reality and make
predictions
Approach reality and make
predictions
Optimization
**This theme usually differs in the quantity of hours designated
N/A not apply
Table 4 reveals that not all the areas required by the commerce sector are being
addressed within this framework. In fact, the areas that are focused on are not
necessarily the ones required by the sector organizations. A similar perspective is
presented by Wallin et al. (2014), who argue that the challenges of the business world
demand successful companies to act proactively. This is why the fundamental challenge

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addressed in a university-industry collaborative approach is to enable each organization
to leverage knowledge, skills, and techniques that are currently limited to a single
organization. Companies need to comprehend how to optimally utilize knowledge.
The curriculum has clear objectives and expected results. However, the approach to
achieving these objectives typically follows a structure guided by books. At this point,
this study presents evidence that the syllabus structure could be enhanced by aligning it
with the voice of the business and considering academia's input. This result aligns with
the viewpoints expressed by Radermacher and Walia (2013), where the authors suggest
that there is literature-based evidence demonstrating the gap between industry and
academia. In fact, the authors explain that several changes in the curriculum across
diverse careers are necessary, as they are embedded in other organizations. Universities
need to gain a deep understanding of the conditions in the industrial environment.
It is important to mention that, these results have been limited to the Ecuadorian context
and focused in the main economic sectors, taking in count for instance the commerce
sector.
CONCLUSION
This study identifies the statistical needs of the organizations, with evidence that will
allow for future improvements in university curricular content, considering the opinions
of the various stakeholders. The tool developed through GAISE and NIST enables the
identification of areas where curriculum content is misaligned with organizational
needs. In the Ecuadorian context, higher education is usually based on traditional and
highly conceptual theories. Therefore, it is necessary to design or implement new
approaches along study planification in order to create a flexible modern education. In
other words, there is a need to redesign curricula to incorporate the flexibility of modern
teaching.
From another point of view, there are evidence of a possible divergence in the curricula
of the higher education in the Ecuadorian context when the business requirements are
studied. For instance, in the commerce sector as an example, it is shown that there is
statistical knowledge that is neglected or little studied, which require greater attention
from universities. The results show that there are opportunities for improvement in the
statistics teaching model in Ecuador. In light of this, universities could reallocate
curricular hours and redesign their curricula to align with the genuine needs of the
country's economic sector. This approach would foster a constructivist framework
involving all stakeholders.
For future research, it is recommended to expand the sample size focusing on specific
roles, and then contrast the results of their needs with the Ecuadorian university as a
whole, the results obtained requires to be contrasted applying the same methodology to
a different balanced sample.
From the educational point of view, it is required to generate a pilot test where the
curricular structure is modified in a university house of study, in this way the necessary
data could be obtained to measure the improvement and the impact suggested in this
study. Finally, the methodology proposed here could be used as a quantitative basis for
decision making regarding business, educational, and scientific needs, by considering

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profile evaluation, as well as generating possible indications to initiate deeper scientific
studies, among common business areas, educational levels, or curriculum analysis,
among others.
ACKNOWLEDGEMENTS
The authors wish to thank the Universidad del Azuay and the Research Vice-Rectora
for financing this study. The authors also thank the Statistics Center Group of the
University for their support and guidance throughout the investigation
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