Conferencia Magistral. El Planteamiento del Problema Define la Ruta de la Investigación

Good afternoon everyone,

warm greetings from the Pontifical

Catholic University of Argentina, from the

Central Library here in Buenos Aires.

In commemoration of the 20th anniversary of the CBCA (UCA

Library System),

we will now begin

the keynote address.

Opening remarks will be given by

Soledad Lago, Director of the

UCA Library System and

Coordinator of the UCA Library Network,

Organization of

Catholic Universities of Latin America and the

Caribbean. Good afternoon everyone, today, on

International Book Day, we are delighted to

welcome you to the keynote address: "The

Problem Statement Defines the

Path of Quantitative,

Qualitative, or Mixed Research." We

sincerely thank everyone for registering and

participating in this event. We are

thrilled to have the presence

of distinguished experts in the

academic field who are joining us today:

Dr. Roberto Hernández Sampieri,

Dr. Cristian Paulina Mendoza Torres, and

Dr. Sergio Méndez Valencia.

We also thank our

business partners, Elida Ramírez of Magril and

Susana of Silvestre Digital Content, who

have supported us in making this event possible.

We hope this

conference will be an

enriching space where we can delve

into fundamental topics for

academic innovation and

knowledge generation. Without further ado, let's

begin the conference. Good afternoon,

everyone. Thank you very much, Soledad. Now

we have the problem statement, which

defines the research path, by

Dr. Roberto Hernández

Sampieri. Good morning/afternoon, everyone. On

behalf of the three of us

who will be speaking today, we

want to

thank Dr. Soledad Lago,

director of the library system at

the Catholic University of Argentina, and

Professor Joselix Bermúdez from

content management at the same

institution for this invitation, as well as

our publishing house and

our speakers,

Dr. Cristian Paulina

Mendoza Torres and Dr. Sergio Méndez

Valencia. I will share my screen

first; I will speak.

Afterward, Dr. Cristian

Paulina will present software that

translates our ideas into reality.

Finally, Dr.

Sergio Méndez will talk about some

artificial intelligence tools applied to research methods

and

research methodology. Thank you so much to each

and every one of you who are here

today from all over the continent, from

Tijuana, Mexico, in northern Mexico on the

border with the United States,

to areas far south

of

Argentina and other

countries. I'd like to

share the presentation, and we'll

begin with great pleasure. It's an honor to

be here with

you. Congratulations to the

library system on its 20th anniversary and to

all the readers on this International Day. We all

know that to research

any topic, in any area, in

all sciences, in all

disciplines, we have three main

approaches or methodological paths:

qualitative, mixed, and non-qualitative. None of

the three is

superior to the others; all are very

valuable and have made

significant contributions to the

generation of knowledge in

different

sciences. The

quantitative path is based on the epistemology

of positivism and post-positivism. The

qualitative path is rooted in

constructivism, critical theory, and

hermeneutics. More recently, the

mixed methods approach is

based on

pragmatism. Throughout the 20th century, and I

want to point this out with all

humility, the sequence for conducting

research was: first, the

paradigm, the worldview, my

epistemology; from there, an approach was derived,

and a research problem was posed. And that's how we

all did it in the 20th century. What we

have been maintaining and

proposing in recent years is that,

for us, the sequence

begins with the problem statement. The problem statement is the

first step; it is, so to

speak, the king or

queen of

research. The problem is posed,

analyzed within its

context, the available resources are evaluated,

and, according to our

training, we choose the

most appropriate approach for that

statement. The

epistemological paradigm is what nourishes

the

process and the interpretation of

the results.

Whenever we pose a problem,

we are studying a reality or a

phenomenon, which can be social, economic,

health-related, etc. And that reality, from

the quantitative approach (quantitative route),

is conceived as something rather objective,

measurable, and that can be analyzed

numerically,

statistically. The

qualitative approach conceives of a

multiple reality understood through narratives

and expressions of human perceptions.

And the mixed route... It conceives of a

reality that has both an

objective, or more or less objective, dimension and a

more or less subjective dimension that is measurable

and is also captured by narratives,

by expressions of

perception. Thus, the reality of a

phenomenon or a research problem

has an objective dimension. For example,

a

university, a factory, or a company

has an objective dimension; we can

see it, we can appreciate it through our senses, we

can touch it, we can touch its

walls. For example, the

Catholic University of Argentina, a large

university—well, there are the buildings, we

see them, they're there. Or a factory, or

Stanford University, or any

company—well, we can

know how many people work there, we could

even weigh it, the

weight of its buildings, its

infrastructure. And the same with a

company: we can appreciate it, we see the

machinery there; we see it. This is the

objective part. And in the case of companies, we

can also quantify what they

sell per month, per year. If they are listed on a

stock exchange like the one in Buenos Aires, the one

in Bogotá in Colombia, or the one in

Mexico City, we can also

measure how much their shares are worth, how much their

market size is growing.

This is the

organizational aspect, but it also has a

qualitative dimension. What is it? Qualitative: Well, in a

university or a company, there are

people who develop, who have feelings,

emotions,

perceptions, attitudes, and

knowledge. There can be relationships,

for example, of love between two

people who work at the company or

between two university students. There is

solidarity, there is teamwork, there is enjoyment, there is

joy. This is the

qualitative aspect of the

organization. The same applies to a physical phenomenon,

such as

an earthquake. For example, the earthquakes

last year in Turkey and

Morocco. What is the

quantitative aspect of the earthquake, and what is the

qualitative aspect? The quantitative aspect, for example,

in Syria and Turkey, resulted in more than

8,000 deaths, more than 20,000 injuries,

more than 4,000 buildings affected, a

measurable magnitude of 7.4 on

the Ritter or Mercalli scale, with

an aftershock of 7.6, and a

horizontal seismic acceleration slightly greater than

196 cm per second squared. What does this

tell us? Well, imagine a

seismic wave traveling on the surface at

one-fifth the acceleration of

gravity. It travels very fast because it travels

underground and hits the Furthermore,

buildings not prepared for earthquakes,

lacking support beams and

vertical structures, instead have

heavy horizontal slabs

with a hypocenter 18

km deep, the same as in

Morocco or any other earthquake. These are the

quantitative figures, but there is also the

qualitative aspect. And what is the qualitative aspect? Well,

all the human suffering:

the people who lost their homes, who see their workplaces

destroyed,

or

worse, the pain of losing a

loved one, or having a well-

known person trapped in the rubble.

That human suffering is the

qualitative aspect. And ultimately, an earthquake is

the sum of both dimensions, the

quantitative and the qualitative. Or in

war, for example, the invasion of

Russia by Ukraine, the quantitative aspect is the number of

displaced people, the number of soldiers

fighting, the type of weapons, the

value of the weapons, etc. But there is

also the qualitative aspect: the child who is

orphaned and all the pain of being

displaced from the city where they

lived. All that human suffering is the

qualitative aspect. Or in

learning, the

quantitative aspect is the averages. of

learning in

courses, etc.

Quantitative evaluations exist, but there is also the

qualitative aspect: the joy of learning,

the pride of parents when they are

told that their children are doing very well in

school, or the frustration of a teacher

who cannot transmit their

teachings to the students, or the pride of

graduating from an undergraduate degree at a

university anywhere in the world.

Even in physical phenomena,

these two dimensions, quantitative and

qualitative, are present. For example,

human blood pressure has a

quantitative dimension; we can measure it, we can

evaluate it:

systolic, diastolic, high,

low, etc. And someone might say, "

That is totally quantitative. Where

is the qualitative aspect? If it is measured on a

scale through a very

precise instrument, which is the sphygmomanometer or the

blood pressure monitor, you are

mistaken. There is only something

quantitative there." But then one might say, "Yes, it is

measured, but is the

blood pressure of a newborn baby the same as that of

a teenager, a

young adult, a 60-65

year old, or an 85 year old?" Well, no, it

is not the same. Similarly,

blood pressure at sea level, like in Buenos

Aires, certain areas like Puerto Madero, or

Guayaquil (a low-lying city), or Cancun,

Mexico, is not the same as in a high-altitude city like

Huancayo at 3800 mph, Cusco in

Peru at

3300 mph, or Mexico City.

There's also a

subjective dimension, and time is another

example. I'm not talking about the perception of

time, which is quite subjective, and we all

see it in a soccer match or

any other sport. One

team is winning, and

the game is about to end.

The last five minutes seem incredibly

long to the fans of the winning team, while for the fans of the

losing team, time flies by. Or, I don't

know if you know this, but if I'm in

a large structure like

a pyramid or a

skyscraper, and I measure time inside

the building and someone else measures it outside with a

stopwatch, there will be differences, even if they're just

microseconds or milliseconds. There will be

a small difference, and everything is...

Relatively speaking, time at the

speed of light is another situation.

So even physical facts have

these two dimensions: quantitative and

qualitative. The birth of a baby

has a quantitative part: the baby has

a weight, a height, a

hair color. But it also has

temperament. Therein lies the qualitative aspect: the

way it looks, etc. So, the more objective and the more subjective parts are

always present in reality, in any

phenomenon we study.

And they

exist in the universe. That's why we

've always asked, why can't we have the

objective quantitative view and the subjective

qualitative view, thus giving rise to

mixed methods? So,

phenomena and

research problems in any science

have this quantitative dimension—

numerical variables—and a

qualitative dimension—narrative and

symbolic data. Therefore, should a study

be quantitative or should it be

qualitative? Well, it all depends on the

research problem. And we always

want to draw an analogy

between household chores, challenges at

home, and

research itself. When I have a challenge at

home, for example, hanging a picture,

something as simple as hanging a picture, then I

need... Well, I need a nail and

a hammer. I put

the nail in the wall, I use the hammer.

I hang the picture and I've already fulfilled that

function with that domestic challenge. But on the

other hand, if the challenge is different, for example,

that the dining room or

kitchen table is made of wood and is splintered and rough, and

children or the

elderly hurt themselves on it, what tool do I need

to solve this problem? A

nail and a

hammer are useless. I need a

different tool, like

a polisher or sandpaper, and then I sand the

table, solve the problem, and remove

the splinters that hurt.

But

if the domestic challenge is very different,

like, for example, that the bathroom faucets

have come loose and water is leaking, a

nail, a hammer, a

polisher, or sandpaper are useless. I need a

very different tool, a wrench, to

reconnect the pipes and solve the

problem. And if the window is broken, that's a

different challenge and requires different tools.

That's how

research works too. It's like going on a

trip. What clothes do I take on a trip? Well, it

depends on where I'm going. If I'm going to the

mountains, to a very high area, to Cusco

in Peru, then I'll... I wear

jackets, coats, sweaters,

scarves, and gloves for the cold. On the

other hand,

if I'm going to the beach, I need a

different kind of clothing,

light clothes. If I'm going to play sports, I need

certain clothes, like

sneakers or athletic shoes. Or if I'm going

to the stadium to see my favorite soccer team, whatever it may be, I

wear the

team's jersey. Yes, it all depends on

where I'm going.

That's how research works because

quantitative ideas and approaches respond to the need to

measure and estimate magnitudes or quantities

of the phenomena or

research problems that will be studied. This is when I'm

going to determine how often

a phenomenon or problem occurs (frequency,

magnitude, quantity), and when the phenomenon or

research problem is

viewed as something objective,

measurable, and concrete, although we've already seen that

total objectivity doesn't exist. It's also when

the intention is not

only to measure the phenomenon and its

components but also to measure its

relationship with other phenomena,

its causes, effects, impacts, and

consequences.

Qualitative approaches, on the other hand, respond to the need to

understand and

interpret phenomena or

research problems from the narratives of

the people involved. This is when I

want to discover Patterns behind

verbal or oral narratives, visual narratives

such as photographs, paintings, written

documents, or those captured by other

means, for example, audiovisual media like

videos, when the

research problem is viewed

as something subjective, or rather, subjective and

not measurable. Although its essence can be

understood and is more abstract, and when

the intention is not only to capture the

essence of the phenomenon but also its

meanings, we seek to interpret it and

understand its relationship with other

phenomena. Therefore, in

quantitative approaches,

the problem is posed, the literature is reviewed, and the

problem statement is evaluated and revised and adjusted. In contrast,

qualitative approaches are

inductive: the problem is posed,

the literature is reviewed, the

context is understood, and if the context changes,

adjustments are made and the problem statement is revised.

Quantitative approaches

include variables, that is, aspects that

can be measured, which become

constructs. Hypotheses are tested

through a

design using quantitative measurements and

data collection instruments,

and statistical analysis is performed.

In contrast,

qualitative approaches are based initially on

concepts, categories, themes, and patterns that

become constructs and,

through a Approach: A design that

collects narrative and

symbolic data. We conduct

thematic analyses, and let's look at some

real examples. Imagine you are on

a research team, and

a group of

companies arrives. They could be from any

region within the

Buenos Aires metropolitan area, or from

Guatemala City, or the state of

Guanajuato in Mexico.

People from the automotive industry came to us

and

asked us to determine the

state of the

organizational climate in our companies. They asked us to

measure how we are doing in terms of climate, and

also to measure the different

dimensions of

organizational climate and analyze and evaluate

differences in climate based on

company size. They also asked us to evaluate the impact of

organizational climate and its variables on

productivity.

Impact measurements: What type of study

do we require for such a request? A

quantitative one? A qualitative one? Well, a

quantitative one because we have

climate variables such as job satisfaction,

motivation, autonomy at work,

perception of leadership, and the

feeling of pride in working for

the company. Measurements of

company size: number of employees, number of

subunits, and geographical dispersion.

The most appropriate approach is

quantitative. We measure

climate variables, calculate

descriptive statistics, then

link the variables using correlation coefficients,

and finally, through a

multiple linear regression model, establish the

impact of each dimension

on productivity. If we had

an ordinal measurement, we would use least

squares

statistical analysis. However, as happened

with a

Coca-Cola bottling company, a plant in

Cuernavaca, Mexico, the

general manager came to us and said, "I have

many problems in my company, a large

number of conflicts between the

production, quality control, and

maintenance departments, and I want to know what has

caused these conflicts and what

the solutions could be." They gave us

an example of how far

the level of conflict had gone. As you know, the

production of a soft drink—we call them

sodas or refreshing drinks in Mexico—

is relatively

simple. And although there are newer

production methods, most still follow

this process: you have the

container, the

product concentrate (in this case, Coca-Cola) is

added, then

water (distilled water), then

sweetener is added, and if it's a beverage... Soda, well,

gas is added, and for the

products to go through this simple

production process, conveyor belts are used

where the containers

go through each stage of this process. For

the bottles to slide

properly, certain

high-quality lubricants are required so that there are no

problems and they slide

properly.

The maintenance department is in charge of buying the lubricants.

Well, in order to make

the production department look bad,

they bought the worst and

incorrect lubricants with the objective that

the production line would jam all the time,

the bottles would get stuck, fall, and

break. And since the cost of breakage is

very high in a bottling plant, and also,

every time a bottle or

some bottles fell and broke, the

production line had to be stopped, and this is

very expensive. And then stopping the

production line and cleaning

took time and the cost was very high.

That was the level of conflict. To

analyze the causes and possible

solutions, what kind of study do

we need here? Measuring the

organizational climate no longer makes sense

because we already know that it will be terrible,

that the organizational climate is... The situation

here is very broken down; what is required is to

understand why these

levels of conflict were reached and how we can

address them through a collaborative approach. We conducted qualitative research, and

that's what we did. We interviewed all the

company's staff, especially those from

these three areas, in in-

depth interviews. We also held focus groups,

and it turns out that the origin of the conflict was

that the three managers had been in conflict with each other for about 20 years. They

verbally assaulted each other, and there were even

physical altercations. One of them couldn't even remember

why they had

clashed to that degree. And from then on,

consciously and

unconsciously, they transmitted

a kind of hatred to the staff under their supervision, which

grew and escalated to other

areas. The only solution was to

change the managers. It's one of those

situations where the conflicts

are so great that a

separation is necessary; it's no longer possible to

resolve them. Fortunately, the three

managers were eligible for retirement, and they

retired. But

if not, there was no other option.

New managers arrived, and the entire organization

entered a process of

organizational development. And I want to tell you that

two years later, this Coca-Cola bottling plant located in

the city of Cuernavaca

received productivity awards throughout

the entire global system of this brand, of this

company. This corporation won

marketing awards, and

the problem was solved. The appropriate approach here was

qualitative; if we had

added

measurements and impact on

productivity from quality indicators, we would have already

taken a

mixed approach. Similarly, if what I want

is to identify the

financial variables that affect the

profitability of companies listed

on the stock exchanges of Buenos Aires,

Bogotá, Lima, or

Mexico City, as well as the degree of correlation

between them in a given

period, this last fiscal year in the

corresponding country, here I am looking for

impacts and I have variables. What is most

appropriate? Of course, a

quantitative study where I have the

dependent variable or effect, which is

financial profitability, and the

independent variables, which are the

company's performance, the acid test, which is the

liquidity indicator, the Alpha variable,

which measures the effects of the

financing structure, and the net margin, and I

can analyze it by industrial sector, by

commercial sector, etc. Here I

require something quantitative because I am

measuring and seeing

impacts. The same applies if I am testing a

drug, the efficacy and safety of

a drug, as was the case with the vaccines

for SARS-CoV-2. 2. During the

COVID-19 pandemic, to evaluate

how well they prevent and neutralize

infection in human cells to

generate an immune response and

antibodies that neutralize the

coronavirus. What type of study do I require? Well,

something quantitative, and, to use the

expression, the most quantitative of the

quantitative, such as an experiment

where one group of people receives

the medical treatment, the

vaccine, and the other group does not, and I

compare them. What I have, or what I need,

is a quantitative study according to

this problem statement. On the other hand, it is

a study that we

did in Mexico. If my

research question is: What are the fears and

anxieties that adults are experiencing or

experienced upon

receiving a positive COVID diagnosis?

That is, how did they feel when they were

told, "Unfortunately,

you have COVID-19," or "You tested positive"?

And we want to analyze these fears,

the appropriate thing here is a qualitative study, and that is what we

did with health personnel and the general

population. Categories

of people's perceptions emerged:

specific fears,

insomnia, general

health concerns, psychological distress, post-traumatic stress disorder,

somatization,

depression, anxieties, and In the case of

healthcare workers, for example, many

people suffered from

burnout because they were

working there and felt

valued. And yes, we must

recognize the great work

done worldwide by healthcare workers—

doctors,

nurses,

administrative staff, and so on—because they were

true heroes. That's why we

dedicated our latest book edition, in part, to these

workers, and I believe they deserve all the

recognition. But, well, the appropriate approach here was a

qualitative study to understand

these fears and concerns. The

main fear that emerged in our

study was infecting loved ones; in fact,

the fear of

infecting a loved one, especially

someone at high risk, was greater than the fear

of losing one's own life. We categorized the perspectives on the

future, the lessons learned, and so on.

We examined how

broad these categories were, and there is

a certain degree of quantification in a

qualitative study of this nature.

We linked the categories to each other to

obtain a grounded theory of

perceptions of

COVID-19. If we had added

variables such as stress or depression,

and Associated with

qualitative categories, we would have already taken it to

a mixed plane, or for example, a study

we did with INAF, the

National Institute of

Sport and Physical Activity of Chile, to

see how professional soccer players

from different leagues in

Spain, from teams like Real Madrid and

Barcelona, ​​and from various countries like

Argentina, Paraguay, Chile,

Ecuador, Peru, Mexico, etc., Costa

Rica, well, to see how they used

social networks and for what purpose. Well, here

something

quantitative and

descriptive is required: through what devices

they connect, what

social networks they use on

average, which social networks they

use, which are the most

important, and

what is the purpose of using these

social networks, for example, to find

sponsors, to facilitate the search for a

club, to increase their market value,

to approach the sports press,

to project their future, to develop their

own brand, and so we did with these

professional soccer players. In

this sample, for example, the famous

Spanish player Jenny Hermosillo was included, who

led the Spanish national team to the World Championship,

which later, well, was

the situation with the former president of

the Spanish Football Federation, but

here what was required was Something

quantitative changed, like a

doctoral thesis study directed by Sergio

Méndez Valencia, where the student

analyzed

the perceptions of

Colombian businesspeople in the chemical sector

regarding the

Mercosur economic integration agreements. Here,

something qualitative is needed, or another study,

as we mentioned, to estimate which

methods

exist for calculating the mass of

environmental control systems in

turbofan and

turbojet aircraft during the design phase. What are

the methods, and how can a

more efficient method be developed to calculate the

size and quantity of mass

in these aircraft? What is

required is

something quantitative, and so it was done.

An algorithm was developed

to see which parameters should be

included and which method was the most

efficient, and to propose a new method

for calculating the mass of these

aircraft. Here, something quantitative is needed if what

I want is to test the degree of

learning using different

teaching methods. Well, also quantitative, or

like a study that was done in Salta,

Argentina, where

the student of the thesis we directed was looking for...

We aim to

understand what young people feel

when they are about to undergo

high-risk surgery in order to design a therapy

tailored to them and alleviate their fears

and anxieties. Well, here, to understand what it

means for a

young person to undergo

high-risk surgery, such as

open-heart surgery,

aneurysm repair,

hip replacement, etc., a qualitative approach was

required. And so,

to conclude my presentation, we also have the

mixed methods approach, where we collect and

analyze both quantitative and qualitative data, blending and

merging them to obtain insights and

conclusions from the entire study. Because, as we've

seen, no study is

purely quantitative, since

numbers are interpreted, nor purely

qualitative, because categorization is

also important; the frequency of

each category matters to see its significance.

And so, we have the possibility today,

and this is what we have been

proposing, of

mixing quantitative and

qualitative approaches to understand phenomena

from both objective and

subjective dimensions, and their interactions.

Mixed methods are not meant

to replace... to

quantitative OR qualitative research, but to

add to

it. A very clear example we had

in the

pandemic. How the pandemic began to be

studied: the first

cases appeared in December, November, and December

2019 in the city of Gujan,

Jube province, in China. Imagine the first

doctors who received the patients.

Well, they began to

study them qualitatively,

observing them and inducing categories: what

the patients had, what

their symptoms were, qualitatively, and

they began to categorize them as

atypical pneumonias. But there was already a need to

start measuring what the symptoms were, like

fever, and they began to study them

quantitatively through

deduction and verification, as

Dr. Lean and Dr. Aen did

in the manual that we have available, and which we

will mention in the questions section. These are

free resources that

you have in our online resource centers,

the manual of

epidemiological research. And well, the

categorization begins. If the quantitative part begins,

analyzing the genome of this

new coronavirus, comparing it with others,

theories begin to emerge, the

theoretical framework about Taking into account

previous pandemics and epidemics, especially

MERS and SARS,

and how

the virus spreads, how it

disperses, forms of contagion, it becomes

a mixed part, for example,

for symptomatology with

qualitative issues, we analyze, well, headache, sore

throat, the famous

loss of smell and taste, nausea, through

qualitative observation,

skin lesions, and also with measurements of

fever and other symptoms,

the famous indicators of

inflammation in the blood, and so it

moves between the inductive

qualitative and the deductive until we

reach the

comorbidities that we all know:

hypertension, diabetes, the presence

of cancer cells,

chronic obstructive pulmonary disease, or what

some discovered about these

indicators of inflammation in the blood,

among them my father, who is one of

the founders of epidemiology in

Mexico, who passed away two years ago, not

from COVID, but from old age, but well,

issues arose to

analyze complex phenomena such as the

automotive industry, where we are

doing an analysis that involves

quantitative and qualitative issues,

or the study of Learning in robotics,

and with that I'll be wrapping up my

presentation. It's a study we're

conducting in Abu Dhabi, the United Arab Emirates, and

in Mexico, comparing the factors that

influence robotics education

for children. We started with the

qualitative part, interviewing experts

and teachers, reviewing experiences until we

arrived at a model that includes

the variables and factors influencing

teaching competencies. These variables include the

student's prior knowledge of

computing and robotics, the teacher's knowledge,

teachers' attitudes toward

technology, educational change,

computing and robotics, access to

coworking spaces, peer work, and

mentoring. We discovered in the process

that holding tournaments is very

important. And with the new

tools of

artificial intelligence, Big Reality,

data visualization, and revitalizing methods, what I

want to get at is that when

we have a household problem like

hanging a picture, sanding a

table, or fixing a window or the roof,

etc., what we should have at

home is a room where we keep

different tools, or a

tool board where we keep nails,

hammers, and so on. The context changes; for

example, if the wall is thicker,

we'll need wall

plugs and a

drill. We need drills, wall plugs,

cables, etc., to fix or

face any domestic challenge. In

research, we must have a kind

of methodological framework

where we know different methods,

from the most quantitative, such as

experiments,

network analysis, and quantitative evaluation, to

the most qualitative, such as

narrative life histories and qualitative case studies, all the way to

the middle

ground, such as grounded theory and

participatory research. And when faced with a

problem, we choose the method or

mix of methods and designs to

address it. So, how is

research practiced in

reality? We have a problem statement, and

according to this methodological framework,

we choose the most appropriate methods to

study or address that

problem statement and implement a

research process using three types of

thinking: critical thinking,

creative thinking, and

dynamic thinking. Even more so nowadays, with so much

information, and through analysis and

synthesis—because

synthesis is also very important in

research—

we solve research problems.

We ask ourselves,

for example, in industry, we

have a problem, the question of...

Research: What is the problem? What

are its causes? What is the cost? How

can it be solved? With what investment and

profitability? We have a

quality problem; we can conduct an experiment and

focus groups to understand it,

apply the research process, and

confront the problem. That's why we say

that the problem statement defines

the research path. And I

conclude: God, that great researcher,

granted humanity the capacity to

investigate. Now it is up to us to make it

a tool to create a

better world and facilitate the

integral well-being of all human beings.

Thank you very much, and I give the floor to

Dr. Cristian

Paulina. Thank you very much.

Thank you to Dr. Roberto

Hernández Sampieri. Let us remember that he holds a

degree in

communication sciences from

Anahuac University, a master's degree in administration from the

Institute of University Studies, a

diploma in consulting, a specialization in

organizational communication from the

Edenberg School for Communication and

Journalism at the University of Southern

California, and a doctorate in administration

from the University of Celaya. For

44 years, he has been a professor in

higher education, mainly in

research methodology courses, at

institutions such as the University of Anahuac, the

National Polytechnic Institute, and

the University of [unclear - possibly "University ... Celaya. Furthermore, for over

30 years he has taught courses,

seminars, and workshops in Mexico,

Guatemala, Ecuador, Honduras, Costa Rica, Colombia, Peru, the

Dominican Republic,

Panama, Chile, Spain, and Argentina. He is

currently the director of the

research center and coordinator of the

doctoral program in administration at the

University of Celaya. He also

teaches doctoral research seminars

and the research methodology diploma program

at the University of

Celaya. Thank you very much, Dr.

Sampieri. We will continue the talk with

the presentation of the Idea software, a

research project generator,

by Dr. Cristian Paulina

Mendoza Torres, who holds a degree in Administrative

Sciences, a master's degree in

Administration with a specialization in

market research, and a doctorate in

Administration with a focus on

organizational development. She is a member of various

research networks and

editorial committees in Latin America,

research coordinator for the

Radar network in Latin America, and co-author of

Magrao Hill's works:

Research Methodology for High School,

Fundamentals of Research Methodology, and

Research Methodology: Quantitative,

Qualitative, and Mixed Methods, as well as the

online resource center for these works. She has

published book chapters, articles,

and other scientific works on

SME administration, marketing, and

education. She has also been a consultant for... She has been a

professor at the

undergraduate and graduate levels since 2012

at various public and

private universities, as well as a lecturer and

workshop facilitator in Latin America and Spain. She

received an honorary title as a

professor in the category of

female research

from the Private Technological University

of Santa Cruz, Bolivia, and an honorary doctorate

from the Autonomous University of Ica in Lima,

Peru. Currently, she works as a professor at the

National Technological Institute of Mexico and the

University of Celaya at the

undergraduate and graduate levels, respectively. She

participates in the

British Consult Mentoring in Science program directed

by Dr. Hernández

Navarro. Well, thank you very much,

everyone. And well, let's

talk for a moment about the S

software idea, following up on

this first conversation with

Dr. Roberto Hernández Sampieri. So, I'll now

share my

presentation to explain

this software, which emphatically

seeks to facilitate the development of

research projects. Yes, so, well,

in particular,

the software arose from all these

situations that occurred with professors,

which really happen on a daily basis.

Also with students, and well,

we were collecting opinions and

experiences in some

Latin American countries where the constant was...

Well, I want to see an example of what

a protocol would be, with

elements that can be consistent

universally regardless of

the institution. And others

shared situations with us where they

pointed out that it takes a lot of time to do the

style review itself, and in the

background, the

substantive review is overlooked when it should be

the opposite. So,

these concerns gave rise to what is

the idea research software, which

is nothing more than an

add-in program because it attaches to the

Microsoft Word toolbar.

So, it will guide us step by step in

the development of a

research project, which is properly a

protocol. And well, of course, we

see it as a tool that

complements all the

electronic and printed material on

research methodology, the quantitative,

qualitative, and mixed approaches that you, of

course, already know: research fundamentals

and research methodology

for high school.

So, we see that we will find the program

in these three works that

appear on your screen at this moment,

which are precisely the... That I just

mentioned, and I reiterate, well, it seeks to

be a

technological research tool that

assists them in the development but also guides them

step by step with their

students so that they develop what

is a research protocol.

So, Idea works under three

approaches, which are, of course, the ones that

Dr. Roberto just explained to us. It

is precisely the researcher who

determines which path to choose in order to

develop the protocol.

So we have quantitative,

qualitative, and mixed; that is to say, Idea will be

able to work with these. Three approaches.

Of course, once we

have clarity regarding the

problem and the approach,

we will tell the software

which approach we want to work with. So,

that's important to

point out. Now, what other issue

should we consider? Is it compatible

with our equipment? This software

will be compatible with the

Microsoft Office 365 suite for both

Microsoft Windows

and macOS operating systems. iOS, which would be

Apple, in both versions. This is

offline; we can work with it, but we

can also do it online.

So, this is what we can

consider before installation. On

how many devices can we use it?

Once we have our

code to work with, we will

see that we

can install it on up to two

devices. Although it can only be used

on one at a time, we

can install it on our

desktop computer and our

smartphone, but

we will only be able to work on one at a time

to carry out each of the

stages that the software will guide us through

in terms of developing

our protocol. How are we going to

download this

application? Well, once you... Whether

you purchase the printed book or

the electronic version, you will find

these instructions. In the case of the

printed book, they are on the first page. In the case of

the electronic version,

the information is shared so

you can make the

necessary download. So, we're going to open

Microsoft 365, open Microsoft Word, and

in the File menu, we're going to create a

new document. In the Insert menu,

we're going to choose the Add-ins option.

This will allow us to locate the

Office Add-ins window, where we're going

to look for the

Idea application. This is how we're going to add it

to our add-ins.

Once we've added it,

where will it be installed or where will we

see it? In the Word toolbar,

where all the menus are,

Idea will also be there. In a moment,

we'll see an example, and when you click

Start, it will ask for a

code number. That's where you

have to write to the email address that

appears on your screen. Again,

you can also

find this email address in the instructions of the

printed book and also in the electronic version. I

know you're purchasing an

ebook, which is the one we're looking at right now,

and in the email we need

to include the invoice or receipt of

our purchase along with, let's say,

some other element they request

to verify that we

are indeed purchasing an original book. Yes,

in the case of the digital book, they ask for

the purchase order number. So, let's say

those would be the elements they would be

requesting. They will

automatically send us the code,

and that's how

we can finish downloading

the program. Yes, so that

would be the installation part, and as

you can see here on the screen, we'll

have it installed along with the rest

of the Microsoft Word menu. Now, how does this program

work? This program, which, I

reiterate, seeks to facilitate the

creation of protocols. Once

we have it installed, we

click, and at that moment we'll

identify the three approaches that

we were talking about, which Dr. Roberto already explained.

Well,

definitely, the issue here is the

approach so that we can

align, select, and identify the one that

best suits our needs.

So, let's say that would be

the first stage. The second stage, a key

moment, is identifying that the software

works in three stages. So, let's

say the first stage will be to

develop the

problem statement and the literature review. That would be the

first stage. The second stage

would be the method, which in this case... Well,

the stages will vary

depending on the approach we have

selected. And finally, the third

stage, which would be our

proposal. So, as we already

mentioned, the first stage, where

we will work with the entire

problem statement and the

literature review, will consider the steps

you already know. And here we

can go hand in hand with the material from

printed works, which would be the

purpose of our research, the

objective of the

research, our

research question, the justification, the

feasibility, and the

literature review. Let's say those are the

first six steps that

the Idea software will ask us to

develop. The second

stage, let's say, if our choice

was quantitative, the quantitative approach

under which we will develop our

project, then it asks us for other steps.

Yes. What would those steps be? Well, the research

approach, the scope of the research.

The

hypothesis, the research design,

the sampling and analysis unit, the

population, the sample, the

data collection instrument, the pilot test, and

everything that would be the

analysis strategy. Of course, that will

depend on the research question and

the hypothesis. So, let's say those

would be the steps for

developing the method if our

choice were a quantitative approach. The

last point I

mentioned a few moments ago was

precisely the one that refers to the whole

issue of formatting.

And at this point, what

the software will ask us to do is put together our

timeline, have a visualization

of what the general index of a

results report would be, our profile,

the references, the appendix, the cover page,

which will also depend a lot on the

criteria requested by the

institution or the journal or the place to which

we are going to send the

document, the summary, the abstract, of

course, the keywords, the table of

contents, and the introduction. So, let's

say this would be the second big

moment. Well, or rather the third, as we

mentioned earlier, the

research question and the method. The third would be

the whole formatting structure. Well,

this last one isn't going to change either,

regardless of the approach we

set. Yes, so, let's say it

wasn't quantitative, but rather the

approach was going to be qualitative. We reflected, we recapped,

and

then, well, we realized that

the approach we wanted to

work with was qualitative. So

we can change it without any problem,

except that it's going to send us a notification. It's

going to say, "Change of

method detected. If you continue, all these

changes you made in your

document will be lost. Do you wish to

continue?" Well, let's say

yes. Then everything that has already been

done would be lost. Similarly, we

can copy and paste into another

document, save what has already been done, if at

some point

we want to rectify it again

and continue with the protocol.

So, let's say here we say yes.

Okay, we're going to work with a

qualitative approach. What would the

stages be? Or what changes here in

our method? We're going to consider

other steps that would have to

be linked to the

research approach, the context, or the environment.

The design or approach itself, the unit

of sample and/or analysis, the population, the

initial sample, data collection,

data analysis, and the

qualitative rigor with which we will work on

our project. So, let's say

these would be the steps if

we decide that our

protocol will be approached using a

qualitative method. Well, then, let's say

those would be the elements that

change. In the other two, we continue

working under the same dynamics.

Remember: the problem statement and

finally, the proposal format. But

here we again

consider that we will probably not

work with a quantitative or

qualitative approach, but rather with a

mixed approach. So, what would happen?

We see that the steps also

change. Yes, I reiterate, that is what will

change between approaches.

So, here we have the first

step that this mixed approach requires us to develop,

which would be the sequence: the

design, the design and integration phase,

the mixed hypothesis that we are or

will design, the relationship between

samples, the data conversion, the

mixed data analysis, and our

mixed rigor. Let's say again, these would be

the steps that are... They're going to consider

a mixed approach, okay then.

You're going to tell me, "Well,

I can do that in a

Word document without any problem, right?

And I can develop each

of the stages with the support of the

printed material or the methodology ebook." Yes,

but here's where one

of the

benefits of working with the

IDE software comes in. So, having

two key input elements for

each of the stages is key. So, what's

going to change? Let's say we're

going to develop our

research objective. If we're going to

work on our

research objective or any other stage, we're

going to consider two

key tools. The first one is the

wizard. Yes, the wizard will always

tell us what characteristics we should

consider for the development of that

stage. In this case, it tells us that

the objectives aim to

indicate what the

research aspires to. They must be expressed

clearly, as they are study guides, and we have to

start with an

infinitive verb. Yes, that's what

the wizard tells us for the purpose of

writing our objective. So,

when we're writing it, if something...

Soon, it makes us wonder what it

meant, and we want to see

immediately that we have the assistant, another

tool that, well, is

extremely beneficial when working

with this software. Our

example is that in each of the stages we just

mentioned, you will have

this tool, which is the

example. So, here we are

exemplifying, based on a

project that was done, a

study where we wanted to know, not specifically,

what the ideal couple is.

So, for this, the first objective

was to identify the

factors or characteristics that describe

the relationship of young

university students from Celaya, and the second

was to determine if there are

differences in these factors or

characteristics between men and women.

Well, not only do we have the

characteristics, but we also have

the example, which, I

reiterate, will guide us in

each of the stages to exemplify, in

addition to all the material we already

have in the book, what we

should work on, what we

should design in that stage,

in that step we are working on. Now,

what other question should we...

Well, all these stages will be

developed in the

Word document. How would this

protocol that we worked on with the

software look in the end? We'll see an example in a second.

I'll stop sharing this and show you

what an example of

a protocol that was developed

with IDEA would look like.

So there we

see it.

Well, of course, these elements were

placed on our cover page. Then

we have the summary, the abstract, the

keywords that are still missing, and the table of

contents. Aha. And here it begins. Let's just say

that all of this was developed

with the IDEA software, considering the

wizard and also the

examples tool. And from there,

well, IDEA guided us,

structuring and

organizing the information with

general topics and subtopics, anticipating

all the universal elements

for the development of this type of

scientific document. So there we have

our first part. The second

part, which was the method, this

document was developed from a

quantitative perspective. That's why you

see these specific elements

aligned with that approach,

justifying each choice. And the

third part, which we already mentioned,

was the style section.

Timeline, general composition, tentative,

our profile, the references, the

appendix. Well, let's say that this entire

document was structured with the help of the

software. One of the

most frequent questions is whether it also

helps us in the preparation of

references. For that, we have to

look for another tool or

do it manually. So

that would be the result of working with this

idea. Now, we

say, in

particular, the users of the document,

we as authors, that the

key point of the idea is precisely to

facilitate, not to facilitate in a simple,

quick, and higher-quality way, this

standardized format of

research protocols, and that of course, well,

also as teachers, it helps us in

the work of reviewing

research projects. So, in

general, this was one of the

tools we wanted to present

during this

presentation. I thank you very much for

listening. And well, I'm going to

stop sharing that at the end we can

answer any questions you may

have. So, next, my

colleague Dr. Sergio Méndez is going to

share other

artificial intelligence tools in this

whole exercise to carry out

research projects. Thank you

very much. Thank you, Dr. Paulina.

Next, we'll discuss

Artificial Intelligence tools for

research, presented by Dr. Sergio Méndez

Valencia. He holds a degree in

International Business and a Master's in

Marketing Administration from the

University of Celaya, where he also earned his

doctorate in

Administration with a focus on

Finance. He completed a postdoctoral fellowship

at the National Technological Institute in

2005 and has taught at

both the undergraduate and graduate levels at various Mexican universities. He is the co-author of the books "Research Methodology for High School" and "Research Fundamentals," both

published by Magrao Gil,

among others, as well as numerous chapters

and scientific articles. He has participated

as a speaker and lecturer at

international congresses and events in

Europe and Latin America. Since 2015, he has been a

tenured professor at the University of

Guanajuato and currently holds

recognition as a National Researcher

from SNIT and CONIT, and as a Professor with a

Desirable Profile from

PRODE. Thank you all very much.

Thank you for this invitation;

we are very happy to be talking

with you today. We

also appreciate your patience because I

know this is a training session. It's

a little long, but I think that... well, it's about

sharing

tools that we've been

working with or

discovering, which is what I'm

going to talk to you about today. Look,

I know that for all of

you, this topic of Artificial Intelligence

still raises many questions. Some of you have already started

using them, others have

n't. There are doubts, and that's natural.

We'll talk a little more about that later,

but

before we begin this last

part, I

want to emphasize that what we're going to

share with you are

tools. Yes, tools. I want to

highlight this point: they're going to help

with research. But these are

tools; these

platforms don't do the research for

us. They are, again, tools,

ways that can facilitate

some processes, but it will depend on how we

use them. Whether they're

useful, whether they

work, etc. So, I

want to make this point very clear.

We're going to show some tools;

we'll talk a little more about them

later. Well, I

also want to

divide this talk into three parts. The first part

is about reflecting

on the use of these

Artificial Intelligence tools.

As you know, this topic is very

new. Basically, it was revealed last year,

and we began to see a

very important and rapid generation

of different platforms that

used Artificial Intelligence

and that could be used for

different

aspects, for different jobs.

One of these jobs is

research, but we'll see that its

incorporation into research

requires reflection. What you

see here is an editorial published by News

Education in

Practice, which is indexed in

SVIER. The

editorial, called CHPT,

reflects on the use of

CHPT in generating these

types of documents. They analyze two

concepts they call accountability and

contribution. Let's say the translation isn't

very simple, but accountability—

the concept of

accountability—has to do with

this. To take charge, let's say,

and contribute, well, with the contribution,

and this is what they are analyzing. Notice

that this editorial arises because in a

previous one it was signed as

oconor and chpt, so there comes this

question: was it

correct or incorrect? And the

ethics committee of this journal met

precisely to analyze this situation. Was it

correct that

chat gpt was considered a co-author? And

they begin to reflect, they meet, they

discuss, etc., and in short,

they come to the conclusion that chat gpt cannot be

considered a co-author,

given two issues. What

they reach is the first is

that chat gpt cannot take charge of what is

generated, no, no, no, he is not

responsible, let's say,

for the information that he offers, with which that

editorial, that

academic document, is enriched. And second, the

concept of

contribution is analyzed, obviously, from this

perspective of who is an author, who

contributes to a document, and in

short, what

the ethics committee reaches From this, from this, uh,

journal... Well, they say no,

ch gpt cannot be considered a

contributor. I don't know if that's the right word, it's

a literal translation of this, from this,

this document, insofar as it's not a

person. Is that correct? Isn't that correct? Well,

these are the conclusions

they reached, but this

leads to a first reflection: we

need

universities, editorial committees,

academic committees to meet to

deliberate, to

reflect, to define how we are going to

use these

tools in our institutions,

in our journals, in our

publications, in our

academic environment. So there's this

first

part, excuse me, of this discussion:

the need to reflect on

the use of artificial intelligence.

Artificial intelligence is already here, and

now what we have to do is define its

correct use.

Well, for that, we will have to

make use of ethics as a...

From the philosophy and its different

approaches, what we have been

reflecting on is from the perspective of

action and cognition. In this

sense, morality is similar to a

social paradigm that dictates or judges

people's behavior in a

specific period of history. Therefore, perhaps we are currently facing

a change in morality because of the

emergence of

artificial intelligence, and we must reflect

on its use in different

contexts,

specifically in

research. The values

demonstrated in ethics seem to have

an inherited origin and have

allowed for the survival of the species.

Therefore, it is inscribed in

consciousness and changes according to our

cognition and experience. This

back and forth between theory and action,

action and theory, these changes

originate from

extraordinary situations, not like those

presented here. The individual is responsible

for their own knowledge and

actions, as the motto tells us. Ethics, then,

will be the individual's capacity to

reflect on what is right or

wrong, good or bad. And

this will be reflected in

action. Why do we believe that

These aspects need to be reviewed because

ultimately, we will be facing

our students, and we need to

guide them. The students will be using

artificial intelligence for their

work; they are already using it.

We, as teachers, as educators,

as directors, need to

reflect on

this and establish the

correct use within our classrooms and

our context.

The use of

artificial intelligence technologies must

adhere to a socially

established moral framework. The most important thing is the

ethical reasoning behind its use by

the researcher, and, as I

mentioned, the teacher-researcher

must guide the

students so that they not only

use the tools but

also reflect on the

consequences of their actions and their

use. Well, I'll leave

this first part here, this need to

reflect on its use, to

establish norms and rules for how to

establish processes and procedures for

how these

artificial intelligence tools can be used

and how we can benefit in

research processes. So, that's the first

part. The second part is this:

perhaps some of you are

familiar with what you see on the

screen, which is a typical dish. From

Mexico, what is mole? You might

say, "This Sergio, he's crazy,

why is he using a picture of molle?"

Well, it's simply to provoke

reflection. But now it has to

do with processes. Dr.

Hernández

and Dr. Cristian Paulina Mendoza

Torres spoke to us this morning about

three research approaches, three

different processes for conducting

research. As they explained, they start from the problem

statement, from

precisely the

research problem that we define.

Under what process, under what approach, under

what method should

that research be carried out? And therein lies the

need to reflect, to teach

our students to think—these

skills that

Dr. Hernández Sanier also spoke to us about.

Research requires reflection.

Research is not a cooking recipe; that's why I'm

presenting this

dish. Research is indeed

a series of systematic,

empirical, and critical steps, but precisely that's what

requires reflection. It's not just

following the steps for the sake of following them; we

must always

think, reflect on

them: What are we doing?

Where are we headed? Okay, so what are we looking for? Well,

this is the second

point of my intervention:

the need to

always conduct research

thoughtfully, not just following steps,

but reflecting on each

stage and doing them

correctly. Okay, so there's my

second point. Now, the third point

is to look at tools—again,

artificial intelligence tools for

doing research. These tools are

n't ChatGPT, they aren't Gemini, they aren't

even U. They are tools that, as

you will see, are designed precisely

for researchers, for

academics. The first of them is

Consensus Vyan. You

can even explore them as we go along, so that we can get used to them, familiarize ourselves with them.

It's a search engine that

uses language models,

and what it does is highlight and

synthesize statements from

research articles. Here's the

difference, the difference that exists with

respect to ChatGPT, Gemini, or U.

Why? Because with these, we don't know where

the information comes from. In U, yes, but

the information that U uses isn't

academic. Yes,

because Consensus comes from

academic research articles and is

generated from a database

called Semantic Scholar. At the

time of my first review, it

had close to 200 million

scientific articles from different

domains: nutrition, administration,

medicine, history, etc. And this

Consensus will operate using

credits. Those already working with

artificial intelligence on different

platforms will probably be

familiar with these coins, these

credits, these little coins that are

spent each time we

use them. And if the use is very

intensive, then a

payment must be made. This platform has

three plans: one is free, Premium,

and Enterprise for institutions,

universities, and companies. The advantage is

also that it offers discounts to

students, which we know is not always the case with

analytics platforms or those

related to research.

This one

does have a student discount.

So that's another advantage I

see in Consensus, and I'm going to step out of

the presentation format to

switch between screens.

Please... Saying yes,

they can see it. Okay, then I'm going to do

a new

way of sharing and I'm going to go

directly to Consensus. Please, the

administrator, can you confirm that

the Consensus website is visible? It

seems so, right, Noelia? Yes,

thank you. I have it here. Thank you, Doctor. It's visible.

Perfect. So, this is the

Consensus website. Look, one way

we can engage

students with these

more specific tools for

academic work is by showing them that it's not

just for doing homework or

research projects; it's useful

for everyday life. I give

my students the following example, or in

these talks we give to

students: I say, "Look, here in the

audience I see people who are in shape,

guys and girls, who look like they're in

shape, who exercise. Many of

them have probably approached or

looked for information about

supplementation. Should I take protein?

Should I take creatine? Many times there are

different supplements, and we do

n't know which one to

prioritize." Well, Consensus

has an example of this. For

example, here, of course, we at Consensus

work with the typical...

Question space. It doesn't say "ask" here, or "

ask a question." Ask the research question,

but I normally work with this one

they use as an example, which is

creatine. Does it help build

muscle? This supplement has

become popular precisely in

the sports aspect of

sports supplementation. But I want to

know if there's really

scientific evidence that it works. Okay, so

I could ask this question: Does

creatine really help

build muscle? So I'm going to

click here just for the

example and see what

Consensus answers. What does Consensus offer? Well,

Consensus has many tools. Here,

quickly, because of time, I'll

show the most obvious ones. It

has a synthesizer, a copilot that

also helps us ask more

specific questions, this

summary answer, and another one, which is... I don't know

how you pronounce it, excuse me,

MET, something like that, like

consensus measurement. In general, what does it tell us?

Well, here it gives us a summary of 10

scientific articles analyzed first.

So here it tells us, "Look,

these studies suggest that

creatine supplementation promotes

muscle strength, increases

lean mass, and can improve..."

The growth of

muscle fibers during

resistance training, etc., that is, it already gives us a

first clue. Furthermore, it tells us,

look, of the 13 articles analyzed, which

are the ones it considers the main ones,

92% tell us that yes, it does help

muscle building, and 80%

tell us that it possibly does. So what I

tell my students here is,

what decision would you make? But now, it's

not what the coach tells you, it's not what

the nutritionist tells you, it's not what

the GPT chat tells you.

These answers are based on

scientific documents. Now, you, for example,

here Tito Morelia, might be thinking, "

Well, but that's still a

general answer." Well, but if you

go here to what the

key clues from the Consensus Copilot results give us,

Tito, you'll see that it gives you

these key ideas, but it tells you

which article

these key ideas come from. So here it tells you, "

Look, from the Journal of

Physiology, the Journal of Applied Physiology, the

European Journal of

Applied Physiology, etc." So if you

click there, it can take you to the

specific article here. Look, you click here and it

takes you to the article. But also, within

this answer, well, it gives you a

conclusion where it says that yes, it does

help or promote

muscle growth. Here are the

articles it

used to give that answer. So you

can go directly to those articles to

review them and say, "Agustina, no, I don't believe it.

I want to see the

information clearly." Well, of course, that's

the difference. So I

click here, which is the first article that

says yes, it does help

muscle growth. So I click and I go

to the complete abstract

to review what was done, if it was an

experiment, what the

main results were, etc. And I

could even go to the full text. Of course,

this will depend on the

databases where we are logged in at that

moment. For example, here now, since I

'm on the University of

Guanajuato network, my network allows me to

see this complete article, which is

free for me. So I can review

that article in detail. So there

we see

precisely the advantages of Consensus, which

is the first tool we're going to

look at today. Well, Obviously,

I have different tools prepared for you.

If

the administrator could help me again,

tell me how much time I have to

present so I can keep track of my

progress. I

have about 15 minutes.

So, I'll hurry. Look, don't

worry, I'll focus on the ones I

consider essential, from my point of

view, the ones that have worked for me.

In these 15 minutes, I just want to

tell you that they are very intuitive to use

because we've all been

working with one or another

artificial intelligence tool; it's more or less the

same process. So, I'm going

to show you the ones I have

prepared, and then you

can explore them. Does that sound good? This

way, we make the presentation more efficient. Okay,

the next tool is

Elicit. Elicit is similar to Consensus.

Here's the website so you can

write it down and

explore it later on. It

works very similarly to

Consensus and helps you do a

faster literature review. Or,

sometimes, let's say I think there

can be two scenarios with these

tools that I'm

showing you. One is that you want to

familiarize yourself with a topic as a

researcher or

academic, or the other is that you've already

done your literature review and

simply want to verify that you're not

missing anything. You

can also use it for that. So,

Elicit is a tool that helps,

as I said, to do a

literature review. It also automates

systematic reviews and even

meta-analyses, and it's an excellent option,

as I mentioned, for learning about a new

domain. It also works with the

Semantic Scholar database and

has different payment plans. It also

works with credits. So, let's

say that, again, due to

time constraints, I won't go into that one, but it

works very similarly to

Consensus. The other tool is

Site. What difference does Site have

compared to the previous ones? It does a bit of the

same thing, that is, it also helps you

search for articles on a

specific domain based on

questions. But it also helps you because it

has a concept they called

Smart Citations, which favors The

discovery and evaluation of

scientific articles—we're going to see how it works. It

also has an

assistant that helps you identify

relevant articles and answers the

questions the user generates, just like

Consensus and Elicit did, with

their different payment methods:

Individual, Enterprise. Okay, let's see

what Smart Citations refers to.

Smart Citations works like this: you have

an article—suppose you want to evaluate

an article that was published in 2010

because it appears as relevant in the

searches you've been doing,

but you want to see how

that article has been used. Well, that's what

Smart Citations does.

For example, you have an article from

2022, and Smart Citations helps you see

how the previous article was used.

Suppose Tito Morelia, from Morelia,

Mexico, published this article in 2010, and

Agustina is interested in how that

article by Tito Morelia from 2010 has been used. So,

the

site will give you these articles that have been

used. The one from Tito Morelia 2010, and they'll

tell you specifically how it's been

used. For example, here it says, "

Oh look, this

2022 article cites Tito Morelia in the

introduction, mentioning him.

This study is well-studied in the

literature, and it's supported by

Tito Morelia

2010. Then it uses it again in the

discussion,

citing it as consistent with

Tito Morelia 2010." So, that's how

we can also see... Well,

first we can evaluate an article, we can

review how it's been

used, we can even evaluate

our own work, maybe an

article we published a while ago,

how

other researchers have been using it. So

this is the advantage that

Smart Citations gives us, and as I

said, it does the same as the

previous two. Look, here it gives you a summary

and tells you where

those key ideas come from. Here we can

see it in a general way. Well, there's the

site. Another tool that I really like

is one called ResearchRabbit.

ResearchRabbit is a

tool that supports

literature review and allows you to locate

related articles. Starting with one that you

might be interested in, the advantage is

that it does it interactively

through images, and it also

allows you to collaborate with other

researchers. Another advantage is that it's

free, so that helps

us as professors, but especially

the students. I'm going

to stop here because I think it can

be very useful for you. Let me

change screens.

Let me move this little

bar and go to ResearchRabbit.

Well, I have a search that I

had already done. If I had had time, I

could have done it right now as an

exercise, but since we don't have much

time, I want to show you the one I've already done. I

started with this

document. Obviously, you ask it to

offer you documents

based on a topic, in this case,

organizational climate and culture, and it offered me,

among others, this one, and this is the one I want to

relate to others that have been

done previously or

subsequently. It offered me options

that I incorporated. Here, if you look, it

says "add papers." Of those options it

gave me, I'll show you... I kept

adding these, and I want to know if they're

related or not. So

the advantage here is that when you

click on "

connections," it tells you, "Oh, look, of those that

interested you, these are connected, and

these aren't." Okay, so now I have a

first idea of

where these are going. These suggest

one approach, and these others, let's say,

propose another approach. Okay,

those are the ones it offered me

immediately, directly from

the first search I did.

But I want it to offer

similar works. Okay, I'm going to click on it, and it will

offer me

similar works that I can click on and

go to. Obviously, some will be

open, others won't. But it also

shows them to me again visually.

See? So here I clearly see

the approaches. Another advantage is that

here I can quickly see authors. I mean,

in this search for "climate and

organizational culture," those who have

worked with these constructs

will realize that these are very

relevant authors for the topic. So here

we see that these are related. Here we see,

and these others aren't, but they're

also relevant authors, though perhaps they

establish other

positions. We see that it

offers us related previous works, for

example, here I can give you previous

or

subsequent ones. There it is, this

Research Rabbit tool, which I really

like. Well, again I'm going to

run a little fast because of time constraints,

but let's see which ones we

can cover. We also have

Hard Discovery, which works

very similarly to the first ones we saw,

Consensus, ELCIT, etc. So I won't dwell

on it, just make a note of it so

you can

explore it. Art Discovery, this one is

perhaps a little newer than the

previous ones. What's

relevant here is that it claims to exclude

predatory content, offers alerts for

new content, and allows you—

with a paid subscription—to listen to the content of

articles. That is, you can say, let's say,

Jelis is interested in an article, she

can download it to her phone and while she's on her

way home or from home to

work, she can listen to it to

see if it's really relevant to

the work she's doing,

etc. So, let's say it offers

this type of tool. It also

allows you to create and,

uh, it can be synchronized With

reference managers, not like the ones

you know. Well, there it is

for information retrieval.

The following tools, for

information analysis, we have this tool

called ChatPDF. What it does is

no longer answer questions

based on information from

scientific articles on the web,

but rather, you give it the

article you want to analyze.

Again, for this presentation, I have a

developed exercise. I'm not going to go anywhere; I'm going to

change pages again and

go to ChatPDF. In this case, I

'm already working. Look here, as it

says "Drop PDF here," and I take the

article I want to review, the one I already

identified with the

previous tools. I downloaded it, and I want to know

if it's useful for my work, so I

load it here, and I'm going to work from

it. It also works

through questions. For

example, I already have the article I want to

analyze, and I'm going to do it again with

questions. Here we have the

space to ask questions. Here, for

reasons of time, I'm going to use

example questions, for example, "What is the

difference between my

organizational culture?" but I want you to...

Answer from the article. Not from the

network. Here we are already working with

documents that we possibly found

with the previous tools. So, I want you to

tell me. I want to know if this

article can tell me what the

difference is between organizational climate and culture.

Well, and here it will generate the

answer. So it will tell me, look, the

difference between organizational climate and culture

is this: they are

related but

different concepts, and they are

included within

organizational behavior. And here it gives me what

climate and culture are,

specifically from this article. So I

can also say, "Well, but where does this

information come from?" Ah, well, here I click and it

will show me where the

information is from which it is

giving me this answer. Let's see here,

here it sends me to this other one. So

this is no longer so much for

searching for information, but rather

for analyzing the

documents that we found.

So there we have ChatPDF. Again,

I'm going to

change a tool similar to

ChatPDF: UMATA.

UMATA does the same thing as ChatPDF.

So you can explore it, you can

search for it exactly like that in the... A

search engine like Umata

Punai will do the same thing; it's a

chatbot that works with

artificial intelligence and operates based on the

documents the user provides. It

summarizes, answers questions, extracts data,

and writes based on it. There are various

versions, so here we have Umata.

Finally, to wrap things up,

artificial intelligence is already

appearing in software

geared towards the different stages

of the research process. For

example, these that I showed you

help with planning,

literature review, construction of the

theoretical framework, and even the method when

we are defining how we

could approach it, or

specifically, the

tools, approaches, etc.,

for analysis.

Artificial intelligence has also been incorporated

into these specific analysis software programs.

For example, here in the

presentation you can see

Atlas.net and Max, which are

software programs for qualitative analysis. And

recently,

artificial intelligence tools have been incorporated

for quantitative analysis. As far as I understand, it hasn't been

incorporated yet. That's it, but there's already a

GPT chat add-in for Excel, so it's

starting to be incorporated into

these analysis software programs.

Finally, there's

Quillbot, which also helps us. I

highly recommend it. It

will help us with the academic writing process. It

can help paraphrase,

offers synonyms, reviews grammar in

different languages, including

Spanish, helps with citations, checks for

plagiarism, and translates. Although some of these

tools require payment,

I use it a lot

for grammar checking, at least

sometimes when you have doubts about periods, commas,

etc., and Quillbot helps a lot. It

's very user-friendly, and that part is

free. And finally, there's Gam. Gam helps

to make presentations. In fact,

this presentation

you see, the graphic design of the

presentation, I made with Gam. In fact,

here it says "Made with Gamma," and you

can work from scratch, from

a template based on a

quick outline, from notes, or, as in my case,

by importing a file. I generated my

presentation, imported it into Gam, and Gam

made this presentation a bit more

eye-catching than you see. Pretty, huh? So,

well, I think I'll stop

there due to

time constraints. I'll just reiterate that what has been

presented are tools

that support research and

academic work, which do not replace

the experience and knowledge of a

researcher. We believe their use is

welcome as long as it is done with sound

judgment and ethics. Tools are not

good or bad; it depends on how

we use them. For example,

if I use a hammer to

drive a nail and then hang a

picture, then its use is good. But if I use

that same hammer to hit

someone, then the use is bad. It's not the

tool itself that is good or bad, but

the use we give it that

defines these aspects. Ultimately,

this moment makes us think about the

people who lived through the

Industrial Revolution. Artificial intelligence isn't going to take your job; what

will take it is surely someone

who knows more about artificial intelligence

than you. This was mentioned by Cristina

Villarroya, Director of

Digital Strategy and Media at

BBVA. What follows is the reflection, the discussion, the

necessary forums in our

institutions to continue defining the

guidelines in the Use of these tools.

Thank you very much. Sorry for rushing,

but time is always finite. Thank you

very much. Thank you very much to Dr.

Sergio. We are open to the

question round. Some

questions have been asked through the chat. We have one

for Dr. Sampieri who says that if

the objective were to demonstrate

the relationship between the organization

of school library spaces

and the services they provide,

what would be

quantitatively measurable? Sorry, I was

muted. Well, the

quantitative aspect would be to look at the

appropriate variables of use, perceptions

of

use, library usage behaviors,

etc.

And the qualitative aspect would be

service assessments.

And here, the most

appropriate approach would be a mixed-methods approach. I don't know if

I answered the question.

If you need to leave another question,

you can leave it in the chat so we

can review them. There were also

many questions about the software. Dr.

Paulina asked where we can register

to get more information about the

software. Well, Dr. Cristian,

Paulina had to leave us because she

had an important commitment, but

regarding the acquisition of the software, it

comes with any of our works. Our

research methodology includes mixed-methods approaches.

Research Fundamentals, an

introductory book on research and

research methodology for

high school students, comes free with the purchase

of any copy. Some people have

asked if they can't find it in their

country, so I don't know if the people at George

Hill, if Elid is around and could

give us the Macril website for

South America, or if we could write to him in the chat. For

those who have been answering comments in the chat, some people

also ask another question: Is it

acceptable in thesis presentations or

research documents to mention the

source of reference for these

artificial intelligences? Go

ahead, Dr.

MZ. Well, remember, what

this artificial intelligence or

these artificial intelligence tools do is

bring the

documents closer to you. What should be

referenced are the documents themselves, not so much

the tool. It's like

when we consult

databases; you don't reference the

database, but rather

the specific article you find in

a journal. And that journal is

indexed in these

databases, so yes, that's correct.

Thank you very much, Dr. Sergio. I'm

asking here; I'm interested in taking the

diploma course in

research methodology. Who do I contact, and is

there an online option?

Thank you. Oh, thank you

very much. Well, I'll give you my

email address. I'll write it in the chat for

any information. Right now, I'll put

my email address here, and you can

write to me.

Among other questions, they also

asked if the talk would be

recorded. It's available on our

library's YouTube channel. It will be

recorded

there. And for those who need a

certificate or would like to have a

certificate of attendance, I

'll also leave the library's email address

in the chat so they can

request it directly. They

must have been registered and,

obviously, participated in the chat.

Here we have more questions for

Dr. Sanier, and what influence does

the type of study, whether quantitative or

qualitative, have on the formulation of the hypothesis or

hypotheses, and what recommendation could you

give us for it?

Yes, well, what I want to point out is that

hypotheses only occur in the

quantitative approach. In the qualitative approach, there is

no hypothesis testing, logically,

because what is a hypothesis? It's a

statement about the possible relationship between

two or more variables. The variables are not

quantitative; they are measured, therefore they don't form

hypotheses. Qualitative because in

the qualitative world we don't work with

variables but with

constructs. So in

qualitative research, there is

no hypothesis testing. There can't be

hypotheses due to the very nature of the

research because there are no variables. In

mixed methods, there can be hypotheses for the

quantitative part, and from a

qualitative study, as a result of the study,

hypotheses can be proposed for

future quantitative studies.

Thank you very much.

Well, many thanks for the

chat. So, thank you all very much for

your messages. I don't know if you want to ask another

question. In any case, you can also

raise your hand and I'll activate your

audio for a second. That way we can have, I don't know, another

5 minutes of questions if you

want. Here's

Tito Morelia from Mexico. I'll

activate it for you. Thank you very much. Well, thank you.

First of all, to the Catholic University

of Argentina and the organizing team for

this interesting keynote address.

Also, to Dr. Sampieri's team

for this interesting presentation. I

greet you from Mexico. I am a professor of

research methodology. I just

acquired your book, latest edition, Doctor.

Very interesting. It's just that the

IDEA code hasn't wanted to work.

But well, I'll see if I can contact

a technician or someone who can help me. Thank you,

an interesting presentation.

I think it has been very beneficial for

everyone. Very kind. Thank you very much, Tito.

Now, María Sánchez, I'm activating you,

María. Thank you very much. Well, from

Peru, I send you all my heart. I am very

excited to have heard the

participation of excellent

professionals in the field, and

especially thank you very much, Dr.

Roberto Hernández. I follow his book. I teach

methodology, I write

theses, and all this topic as well, and I am

always following his book. And

something curious happened to me. So, when

one wants to buy a book, one always wants the

latest edition, right? Well, I

saw that it was in the second edition, and I

thought, "That's strange." So I looked it up, and it

came up that it was in the sixth

edition. So,

Dr. Hernández, I just wanted to ask if you could

clarify for me. And excuse my ignorance, perhaps it's

still in the second

edition or already in the sixth? That's why I could

n't find it here, and that

was the problem. Yes, yes,

thank you very much. It's that in

Research Methodology, it had a first stage with

some co-authors,

and it was, for seven editions,

that book that was called

Research Methodology. In

2014, we published a book, the

Research Methodology book, with the

subtitle "Quantitative,

Qualitative, and Mixed Methods," and a change in

co-authors. Dr.

Cristian Paulina Mendoza Torres was added. So,

when they say "second edition," it's the

second edition from last year,

and it has a cover.

I'll quickly

share

the

book cover, which is this cover with some

arrows. This is the latest edition,

from August/September of last year. It's the

second edition with

Dr. Cristian Paulina as co-author, and it's the

last edition. The next one will

also include Dr. Sergio Méndez,

and it will be the third edition, or perhaps

a first edition, depending on the

situation. So, this is it. And I also

want to briefly mention

that the book, the

Methodology book, has an online resource center. The

address is there; I'll

paste it in the chat. There, you can

get additional chapters of the book and manuals, such as

manuals for

research in epidemiology or

public health, and other manuals. These are

library resources,

free resources for

users of the works.

So, this is the cover.

I insist, the which has three blue and

yellow arrows. This is the

last one, I don't know if it

responds. Thank you very much, Doctor.

I'll leave this in the chat for

any questions. Elida also

left it in the chat so you

can contact the

publisher directly. I don't know, Elida, if you want to add

anything

else. Good afternoon to everyone present.

Thank you very much, Doctor Roberto, and also

to Doctor Paulina, who had to

leave, and Doctor Sergio. Um, for Peru, they

were

asking. We have Manuel

Reyes as the distributor. I've passed along his contact information; his

email is Manuel.reyes@gmail.com,

where you can find the latest edition

of the work. For

Argentina, we have distributors like

SBS Corpus. In some cases,

the new edition is in transit, so

if it hasn't arrived yet, it

will be arriving soon. You

also have our

website, which is

mill.com.co for Colombia, which is for

South America, or .mx for Mexico. You

can also purchase from that

page. Thank you very much, Elida.

Um, Doctor, we have one more question, and if

you want, we can close

for today. Um, a

question for Doctor Sampieri: what

recommendations do you have for students and

researchers who face

difficulties? Formulating the

research problem: Well, that's the most

important challenge. The software helps, but

the question

and the

research itself are crucial.

Read the recommendations for the idea you

have to move on to formulating

the problem. Read about the topic and

clarify it many times. We

suggest in our works representing it

graphically. What are your variables?

How do you want to link them? We

suggest this in our works to

formulate the research problem

graphically. If it's

qualitative, also graphically. And

realize that

research is for everyone. It's not just

for privileged minds; anyone

can do research. Have the

enthusiasm, the proactive attitude, and you'll see that

you yourself will develop it. What

recommendation? Put in a lot of effort, a lot of enthusiasm.

Research is very

beautiful and will give you a lot of satisfaction.

And Dr. Sergio Méndez wanted to add:

Thank you very much.

Look, for example, what we

are proposing now in these

new works is that

Artificial Intelligence can help us

in these situations. Of course,

again, the use. What use are we going to give to

this Artificial Intelligence? Or

how are we going to take advantage of their use?

Look, for example, if I have an

introductory course—it often happens in many degree programs—

I have an

introductory research course.

Normally, that course is given in the

first semesters. I think that's more or

less the case in different

universities. But we, as

teachers, ask the students to

generate a research question

related to their field of study. But if the

students are only in their first, second, or even

third semester, they haven't seen

much about their field. Well, they

can use

artificial intelligence tools, even the

GPT chat. How can they do it? Well, they can,

for example,

ask the student to write a first

draft of their idea. What is it they would

like to know? Well, they'll

tell you in their

own words: "I want to know

how the colors of a

commercial establishment influence the

customer's purchase motivation." So you

can put it, for example,

in the GPT chat and say, "This helps

generate research ideas

related to it." And you go on discussing

this. This helps to re-engage

students. Why?

Because you're already

helping them generate concrete ideas for their

project. Some will say no, but

how is this? Where is their reflection?

Where is it? Well, in this case,

when it comes to introductory courses,

these first courses you're going to

give them so they know where to search, how to

search, how to make a proposal,

etc. Sometimes the most important thing is to

engage, familiarize, and inspire the

student with research, and have them go

through the process little by

little, etc. In other words, the process

is what's interesting, the search. And if you

engage them with topics that are of

interest to them, then you've already

achieved, I think, part of the objective of

this course. Why? Because you're going to be

teaching them about their interests,

about, for example, sports and the

relationship between sports and

marketing, the relationship between

soccer and sports teams and

marketing, to give you an example.

So, in that way, you

awaken the students' interest, and then

encourage them to

practice.

The important thing is that we lose our

fear. The important thing is that we give ourselves the

opportunity. The important thing is that we

practice ourselves, and that in

this way we can hook and

inspire students with this

beautiful thing called

research, because it allows us, or

allows them, to learn for

themselves. Thank you very much to Dr.

Sergio, and well, we've left the

emails here. The library's email is the

same one where you received the invitation. So, I'll be happy to

answer your questions there.

We are truly delighted with the

participation of these excellent

speakers today, and we

thank everyone for their participation and

their time. Thank you very much, and until the

next

event. Goodbye. Thank you

all.

[Music] Let's see

if the doctor

wanted to.

Thank you, Dr. Soledad Lago Jos, to everyone,

thank you Elida Ramírez, and to the

editorial staff, Maila Martín Chue, the

vice president who is from

Argentina, Latin America, and thank you very much,

Paulina. God bless you.

Goodbye to you and your families until

next time. Thank you very much, goodbye. Thank you, goodbye.