South American Research Journal, 3(1), 37-51

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ISSN 2806-5638

agrupados en 12 unidades comenzando por los más flexibles

(

cualitativos) y avanzando hacia los menos flexibles

cuantitativos) hasta en ocho niveles, para luego abordar las

New organization of research designs

particularidades de otros diseños incluyendo a los mixtos.

Además, se discuten algunos hallazgos en relación con la

naturaleza epistémica de los diseños que anteriormente no

fueron clasificados por los metodólogos. Esta propuesta

Nueva organización de los diseños de

investigación

proporciona

a los investigadores una guía clara y

_P_a_t_r_i_c_i_o_C_a_b_r_e_r_a_-_T_e_n_e_c_e_l_a1

estructurada para identificar estrategias más específicas al

desarrollar su investigación. Al comprender las

1

características

y

el alcance de cada diseño, los

University of Salamanca, Miguel de Unamuno Campus.

investigadores podrán seleccionar el enfoque más

apropiado.

Edificio FES Avda. Francisco Tomás and Valiente, s/n.,

Salamanca, Spain.

Palabras clave: diseño de investigación, cualitativo,

cuantitativo, mixto

E-mail: pcabrera08@usal.es

Received: March 1, 2023 - Accepted: June 14, 2023 - Published: June

1

4, 2023

INTRODUCTION

ABSTRACT

Up to now, research manuals have typically focused on

research approaches and their respective scopes, leaving

discussions on research designs until the end. However,

what if we prioritize research designs, starting from the

simplest and progressing towards the most complex ones?

The following is an attempt to explore this approach.

All cultures have been concerned with finding methods

that allow them to know themselves and the outside world.

In the beginning, these procedures depended on

superstitions, as well as on the development of technology,

but half a century ago, Farrington (1984) picked up

Aristotle's ideas to emphasize that the search for knowledge

is not limited to a particular religion, language, or people,

but to common cognitive interests that go beyond even

practical utilities. To know to feed curiosity is the most

important characteristic of science and of the scientist of all

times and places.

In the search for knowledge, scientists have invented

methods of observation and experimentation that scientific

communities endorse or reject for various reasons.

Generally, endorsed scientific methods are continually fed

back and, on rare occasions, revolutions occur that discard

previously accumulated knowledge because, as Bunge

(1983) argues, it regularly harmonizes with the bulk of

previous knowledge.

Identifying a research design is one of the most

common problems when planning a scientific project. In this

methodological proposal, we start from the most cited

manuals in research methodology to extract, complete and

reorganize research designs. The books by Bryman (2016),

Cohen et al. (2017) and Hernández Sampieri et al. (2014)

are the most cited in the last eight years and occupy the first

positions of relevance in Google Scholar Based on them and

on the recommendations of the latest edition of the APA

Manual, a synthesis of research designs grouped in 12 units

is proposed, starting with the most flexible (qualitative) and

moving towards the less flexible (quantitative) up to eight

levels, and then addressing the particularities of other

designs, including mixed designs. In addition, some

findings are discussed in relation to the epistemic nature of

designs that were not previously classified by

methodologists. This proposal aims to provide researchers

with a clear and structured guide to identify more specific

strategies in the development of their research. By

understanding the characteristics and scope of each design,

researchers will be able to select the most appropriate

approach.

Key words: research design, qualitative, quantitative,

mixed

In the 19th century, ways of systematically

investigating how individuals and society behave were

devised (Raynaud, 2022). However, as a reaction to

positivism, authors such as Dilthey (2000) argued that there

are human and cultural aspects that cannot be explained, but

at best interpreted. Under the shelter of philosophical

theories such as idealism, phenomenology, existentialism,

social constructivism, structuralism, psychoanalysis,

symbolic interactionism and postmodernism, inductive

research designs such as hermeneutics emerged,

ethnography, phenomenology, narrative, critical discourse

analysis, action research, among others, tried to understand

the perspective of both the participants of the study (emic)

and the researchers (etic) (Páramo Reales et al. , 2020). This

perspective generated a very marked division, since they

RESUMEN

Identificar un diseño de investigación es uno de los

problemas más comunes al plantear un proyecto científico.

En esta propuesta metodológica, se parte de los manuales

más citados en metodología de la investigación para extraer,

completar y reorganizar los diseños de investigación. Los

libros de Bryman (2016), Cohen et al. (2017) y Hernández

Sampieri et al. (2014) son los más citados en los últimos

ocho años y ocupan los primeros puestos de relevancia en

Google Académico. Con base en ellos

y en las

recomendaciones de la última edición del Manual APA, se

propone una síntesis de los diseños de investigación

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defend the thesis that the social and human sciences require

their own method, radically different from that of the natural

sciences. Therefore, the reliability and validity of qualitative

designs is exhausted in a process of triangulation between

different sources of information or study categories. Very

few researchers in this line have questioned the validity of

employing such subjective techniques in which it is

impossible to control the researcher's bias and the veracity

of the results (Rose and Johnson, 2020). Even though

authors such as Weber have made great generalizations and

have promoted interpretive research, under the

philosophical perspective that shelters them, their findings

are usually not generalizable.

On the contrary, the hypothetico-deductive method

used in the natural sciences maintained its course with the

concern to identify and try to explain reality with techniques

that attempt to control the researcher's bias. Many

researchers in the social sciences have continued along these

lines. The philosophical theories that cover this type of

research are positivism, realism, materialism, empiricism,

emergentism, as well as systemism. However, Bunge (2005)

argues that, although each scientist has his or her beliefs at

a personal level, when doing science, he or she tends to

make a pact with realism and materialism, consciously or

unconsciously, since the principles of these philosophies

facilitate research in the natural and social sciences. In this

case, research designs are twofold: observational or

experimental (Campbell and Stanley, 2015). In both cases,

there are research designs that are subject to certain rules

such as validity, reliability, randomization of samples,

replicability, among others, which demand specialized

knowledge to avoid statistical dishonesty (Abril and Abril,

Depending on the approaches outlined above, the

research possibilities may be flexible or not very malleable.

However, in a specific research, epistemic discourse is

superfluous, since what is important is to explain the method

that will be followed or has been followed in the research.

Therefore, when designing research, it is necessary to

identify specific research designs to avoid major surprises

when executing a project. For example, there are designs

ranging from tool construction (Carretero-Dios, 2007;

Gómez and Dorati, 2017), observational description or the

creation of experiments (Campbell and Stanley, 2015;

Cárdenas Castro, 2009), to the use of qualitative research

(Hernández Sampieri et al., 2014), and often these designs

are combined (Creswell, 2013). A research design presents

in an orderly fashion the strategies employed throughout an

investigation.

According to Kirshenblatt-Gimblett (2006), choosing

a research design selects a general guideline for logically

and coherently integrating different components of a study

to ensure that the research problem is effectively addressed.

This course includes

a

plan for data collection,

measurement, and analysis. For their part, Creswell, and

Creswell (2017) explain that a collection of procedures and

techniques is used to collect and analyze the variables

specified in the research question. Under this concept, the

type of study, the research problem, the hypotheses, the

independent and dependent variables, the data collection

methods, and the statistical analysis plan are described.

According to Hernández Sampieri et al. (2014), a research

design is defined as "...a plan or strategy conceived to obtain

the desired information in order to respond to the problem

statement..." (p. 128). A research design, in addition to

specifying the path, facilitates the replicability of the project

to address the research problem in other places and at other

times.

2

021). Functionalist authors such as Durkheim, based on

these rules of the game, considered that the findings of this

type of research are susceptible to generalization.

In the face of the disputes that exist between the

quantitative and qualitative approaches, a third way has

emerged, hand in hand with pragmatism: mixed methods.

Almost all projects that adopt the hypothetico-

deductive method select designs prior to the research, so that

their new research, in practice, is guided mainly by theory

and/or the imagination of new hypotheses (Bunge, 2005).

Sometimes, when confronted with real-world data, novice

scientists are forced to replan their research. Re-planning"

does not necessarily mean "re-planning", but rather

adjusting the original design. It has happened to more than

one researcher that he or she has had to reconsider the

sample or the measurement instruments. However, if there

is no fraudulent intent to offer something and then deliver

something else, this should be considered a process

compatible with scientific fallibilism when it is not possible

to find an advanced state of the art due to the originality of

the research. In other cases, addressing novel problems

involves the creative and critical adaptation of existing

research designs. After all, the methods of the social

sciences, like those of the natural sciences, lie in testing

possible solutions to their problems (Popper et al., 2008). In

this sense, avoiding planning or replanning may imply

"

The philosophical basis of pragmatism allows and guides

mixed methods researchers to use a variety of approaches to

answer research questions that cannot be addressed by a

single method" (Doyle et al., 2009). However, it cannot be

taken for granted that the above debate has been overcome

in this way, since the mismatch between theory and reality

is not a problem for a type of research that is not, in

principle, guided by theory. Another possibility is to

consider mixed methods as part of interpretive studies since

they tolerate the error and bias that quantitative research

tries to control. However, authors such as Bryman (2016)

propose reverse embedded strategies, as according to him, a

qualitative design within a cross-sectional quantitative study

is possible. Whatever the epistemological background,

mixed methods, in practice, are conquering an increasingly

common place in the social sciences and health sciences¹.

1

who argues that there are no major diﬀerences between epidemiological

and social science research when it comes to developing quanꢀtaꢀve

research.

Regarding the similarity between social science and health science

research designs, there is at least one proposal developed by Supo (2012),

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attempts to force reality to conform to theory, i.e., a lack of

critical attitude on the part of the researcher. Thus, if a

researcher believed it possible to conduct an explanatory

study and only managed to conduct a relational study, or if

he intended to conduct an experiment but did not achieve

adequate control and ended up conducting a quasi-

experiment, his honesty should be translated into identifying

the new research design. The same cannot be said of those

who adopt the inductive and interpretative method, since in

their case they are not guided by theory but by particular

facts that shape ways of interpreting reality (Bunge, 2005).

In this case, the designs constitute fewer rigid referents, but

this does not mean that the possibility of having designs to

guide the researcher should not be ruled out.

To understand the above point of view, it is necessary

to analyze two situations: 1) Sometimes, research designs

such as instrumental research can contribute to the

qualitative research of those who seek to formulate

questions based on psychometric reliability and validity. 2)

Other designs, such as sentiment analysis, once exclusive to

hermeneutics, have been extrapolated to the field of data

analysis to study the public opinion of millions of users who

express themselves on social networks, which has given

way to quantitative research designs that will be discussed

below.

when biological studies applied to the health sciences have

very well defined research designs and it is easy for them to

guide students, masters, or doctoral students in conducting

research, while in the social sciences, methodologists still

debate whether to apply research designs in qualitative or

mixed fields.

As we have seen, these discussions are very productive

in the epistemological field. However, in the practical field

of research, they are a burden, as young researchers are

often preoccupied with defining the typology, approach,

level, and type of research, when they could provide greater

precision in pinpointing a research design. A research design

is derived from a focus and is precise in scope. The research

approach can be quantitative, qualitative, or mixed. The

research design is a plan of methodological strategies

recognized by the scientific community to address research

problems. While the scope of a research can be broad or

narrow, depending on the objectives and limitations of the

researcher.

Identifying a research design becomes much more

important if we consider that one of the most prestigious

systems of writing and writing, such as the APA (American

Psychological Association, 2019), in its principles of

academic writing and publication points out this typology

for articles and papers: quantitative, qualitative (including

case studies), mixed, replication, meta-analysis, literature

review, theoretical, methodological, student papers,

dissertation and thesis, others (reports, commentaries,

letters, abstracts, essays, etc.).

When the design is quantitative or mixed, the APA

itself states that the research design must be identified.

However, don't the remaining eight in which the manual

requires methodological transparency constitute designs

with possibilities of delimitation in themselves?

In practice, it appears that research designs can

develop much more conciliatory strategies than theoretical

or philosophical antagonisms.

A recurrent problem faced by new scientists is the

scarcity of unifying solutions that facilitate understanding

and methodological decision-making in the face of research

problems. In this sense, the advantage of demarcating

research in a research design is to assume the rigor of

procedures that have been tested by other scientists to avoid

improvisation. This situation becomes even more inevitable

Table 1

Most relevant research methodology sources in English and Spanish according to Google Scholar

Spanish

English

Relevance

Author

Quotations

875

Author

Quotations

1209

109

1

2

3

4

5

6

7

8

9

Pimienta-Prieto y De la Orden (2017)

Quezada Lucio (2021)

Wan, 2022)

1960

363

105

78

958

87

6757

252

Stokes y Wall (2017)

Harris et al. (2019)

Patten (2017)

Bryman (2016)

Marvasti (2018)

Lankoski et al. (2015)

Walliman (2021)

Busetto et al. (2020)

Cohen et al. (2017)

Oberti y Bacci (2018)

Villanueva Couhg (2022)

Toscano (2018)

73

2918

73772

71

168

3109

622

Arias Gonzáles y Covinos Gallardo (2021)

Balboa Barreiro (2018)

Bisquerra Alzina et al. (2019)

Luciano (2020)

1

0

Hernández Sampieri et al., (2014)

146279

80593

Note: In the case of Hernández Sampieri, although there are records on the Internet that there is a 2016 edition, the sixth confirmed edition is from 2014, it was

decided to keep it given the relevance of the citations of this source in Spanish. The number of citations does not necessarily reflect the quality of the work,

which is why it is necessary to analyze this indicator with caution, but without losing sight of the criterion of relevance offered by the search engine.

those in the first 10 positions, i.e., on the first page, as of

A review of the most cited books on "research

methodology" in Google Scholar shows the impact of some

sources in both English and Spanish. Table 1 summarizes

the author, the relevance according to Google's algorithm

and the number of citations. Because of their relevance,

2015 are selected. The search was performed with all the

words in Spanish: "metodología de la investigación" and, in

English: "research methods". These two terms are

equivalent in the sense that their exact translation generates

an imbalance in the number of citations.

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It is important to note that the first page returned by the

search engine contains texts such as book chapters, a

syllabus of a university course, a scientific article, and

slides. In all other cases, books are found. In fact, it is books

that have the highest number of citations. In Spanish, the

text by Quezada Lucio (2021), in third place; Bisquerra

Alzina et al. (2019), in eighth place; and Hernández

Sampieri et al. (2014), in tenth place, stand out. However, if

the number of citations is considered, the positions would

be reversed, with the text by Hernández Sampieri et al.

being extremely higher with respect to all the others. In

English, the three most cited books are Walliman's (2021),

which occupies the eighth position; Bryman's (2016), which

is in the fifth position; and Cohen et al. (2017), which is in

the tenth position. In this case, the texts of Bryman and

Cohen et al. have a close number of citations, but they are

much higher than those of Walliman.

Instead of dividing science into quantitative and

qualitative research or proposing the holistic use of mixed

methods, research designs are presented with relevant

properties that characterize them and that can be used

regardless of the approach.

Most of the designs are collected from the sources

referenced in the introduction (Bryman, 2016; Cohen et al.,

2017; Hernández Sampieri et al., 2014), as well as from

other research designs that exist in the literature.

The methodological proposal is divided into 12 units

of study. It begins with highly flexible research designs,

such as qualitative design (1) and data science (2), which are

more inductive in their approach.

This is followed by designs more closely aligned with

the realm of scientific publications, which include

theoretical (3) and bibliographic (4) designs. Next are the

methodological (5) and instrumental (6) designs that

propose paths or tools for research.

Then the quantitative designs themselves are

addressed, namely those that are observational (7) and those

that are experimental (8). Before concluding the proposal, a

clarification is made on two types of designs that are

difficult to classify: historical designs (10) and case studies

(11). Finally, we return to a flexible perspective with mixed

designs (12).

These books present some differences that could

generate confusion in researchers when offering the name of

a

research design. Bryman exclusively highlights

observational and experimental designs as research designs,

while explaining the workings of the qualitative approach

and mentioning techniques such as ethnography, interviews,

virtual document analysis and focus groups. Cohen et al.

similarly mention the previously mentioned designs, but

also include systematic review and meta-analysis as

research designs and refer to netnographic designs.

Hernández-Sampieri et al., for their part, identify the

quantitative research designs mentioned above, but also

indicate qualitative designs and several mixed designs.

However, this author does not point out theoretical designs,

nor reviews, nor data science. It is risky to speak of a

historical design, since there is still a debate as to whether it

belongs to the qualitative, quantitative, or mixed approach,

but Cohen points out strategies for its employment. In

contrast, none of the books cited identifies theoretical,

methodological, instrumental, and epidemiological designs

This methodological proposal seeks to provide

researchers with a variety of research designs to address

problems of reality, allowing them to select those that best

fit their needs and research objectives.

PROPOSAL

This section presents the proposal following the order

proposed in the methodology, describing design by design

and indicating when each one should be used. The

enumeration of research designs, instead of being ordered

according to the three research approaches (quantitative,

qualitative, and mixed), proposes an alternative of starting

with flexible qualitative research to gradually increase the

rules and conditions required by the quantitative approach

and, only after that, mixed designs are listed.

(

although the latter is to be expected as it has specialized

literature). In these sources, quantitative designs are always

presented before qualitative designs. In view of this, it is

necessary to develop an organization that resolves the

designs starting from the most flexible to the least flexible,

in an ascending order of control and complexity. It is

important to bear in mind that these sources directly or

indirectly identify the research designs that should be

recognized, since, as will be seen in the proposal, they are

not always declared as such.

Based on the above, the present study proposes to

organize the research designs according to the criterion of

flexibility in the use of methodological research rules,

starting from the most flexible (qualitative) to the least

flexible (quantitative), but adding other designs that, due to

their nature, are difficult to classify in this order.

1

2

.

Qualitative: This is a general group that includes

various qualitative research approaches.

Data science: Different data analysis approaches

used in research are listed, such as netnography,

sentiment analysis, bibliometrics, big data, and

others.

3

4

5

.

Theoretical: Research designs based on the

construction or development of theories.

Bibliographic: Research designs that focus on the

review and analysis of bibliographic sources.

Methodological design: Research designs that

involve the application of a specific methodology to

address a research problem or question.

METHODOLOGY

The purpose of this article is to identify common

research designs to facilitate decision making when

researchers are faced with real-world problems. The

methodological design is described, discussed, and

organized in a didactic way so that the reader can judge its

relevance.

6. Instrumental: Research designs that make use of

specific instruments or tools to collect data.

7. Observational: Research designs in which the

researcher observes and records phenomena as they

occur in their natural environment.

8. Experimental: Research designs in which variables

are manipulated to establish causal relationships.

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9

.

Epidemiological: Research designs used in the field

of epidemiology to study the distribution and

determinants of disease in a population.

identification of the different designs in the methodological

proposal. In this regard, instead of focusing on a theoretical

definition that can be expanded in the methodology books

presented in Table 1, the design or group of designs is

identified according to the researcher's need to adhere to an

already developed research design. For each case, an

example is given to illustrate processes carried out by

researchers in various parts of the world to solve their

research problems, or a combination of these. Figure 1

illustrates the content of each of the groups of research

designs.

1

0. Historical: Research designs that focus on the study

of past events and their influence on the present.

1. Case: Research designs that focus on the study of

individual or specific cases.

2. Mixed: Research designs that combine qualitative

and quantitative elements in their approach.

This new organization, according to the author's criteria

and experience, facilitates the understanding and

Figure 1

Resumen de la propuesta de organización de los diseños de investigación agrupados según su flexibilidad

5

Methodological

design

1

Qualitative

2 Data science

3 Theoretical

4 Bibliographic

6 Instrumental

•

Ethnographic,

• Netnography,

• Theoretical,

Philosophical

• Traditional review,

Systematic or

systematized review,

Meta-analysis

• Review of existing or • Reliability and

Phenomenological,

Grounded Theory,

Narrative,

sentiment analysis,

bibliometrics, big data,

text mining, other

new research proposals concordance,

Confirmatory Factor

Analysis, Exploratory

Factor Analysis,

Hermeneutical, Action

Research

Analysis of invariance.

Other designs

7

Observational

8 Experimental

9 Epidemiological

10 Historical

11 Case

12 Mixed

•

Cross-sectional

• Single-grouppretest-

• Cohort, Case-control, • Historical,

• Case, Case Study, or

Case Study Design

• Concurrent, Sequential

descriptive, Relational posttestpretest,Single

Case-control,

Archaeological,

Documentary

or correlational,

Explanatory or causal, pretest-posttestpretest,

measurementcasestudy

Ecological design,

Prevalence, Incidence

Longitudinal trend

analysis, Longitudinal

panel study

Singlemeasurementcase

studypretest-posttest

quasiexperimental,

Quasiexperimental,Time

seriesortimeseries

quasiexperimental,Two

independentgroup

experimental,Solomon

experimental

Note. The order from the most flexible to the least flexible only goes up to point 8. The epidemiological designs indicated are not included in the other designs

indicated in this proposal, while clinical trials and community trials can be recognized as experimental designs. On the other hand, replications, reports,

commentaries, letters, book summaries or essays have not been classified as designs due to the personalized nature that each author can offer.

leading to synthesize an information or generate a new

1

. Qualitative designs: When it is assumed that social

theory (Charmaz, 2014). For example, the study conducted

by Barandiarán Irastorza et al. (2022) shows that, by asking

questions to politicians, officials, and organizations of a

program, it proposes some key ideas to explain what moves

to trust in collaborative governance in a community.

Another widely used qualitative design is the narrative, in

this case it is the life stories and memories of the participants

that give an account of circumstances that allow a better

understanding of a period. For example, the study on the

human rights violations of a military operation through the

narration of the victims (Mantilla Millán et al., 2022). A

design used to interpret texts and images is hermeneutic, for

example, Tomaylla Quispe and Gutiérrez Aguilar (2023) in

their study interpreted the personal notebooks of the artist

Nereida Apaza pointing out some characteristic aspects of a

decade. Finally, the action research design not only tries to

understand a reality out of scientific curiosity, but also

assumes an active role to transform a reality, thus, when

evaluating a process, the researcher is also considering his

or her actions as part of a group (Bryman, 2016). For

reality cannot be explained numerically or logically, but

only by interpreting individual meanings, a qualitative

design is used. If the purpose is to understand the

particularities of small groups considering their cultural,

linguistic, religious, origin, etc. context, an ethnographic

design is used. For example, in a study on the mismatches

between family and teachers' perceptions of parental

involvement, the author (Alonso Carmona, 2021) recounts

the points of view by quoting relevant statements, paying

special attention to the culture of those involved. For its part,

the phenomenological design attempts to reveal what

individuals feel and think about a particular experience

(

Hernández Sampieri et al., 2014). They are very useful for

understanding those who have gone through painful

processes such as a natural catastrophe, an illness, or the

unexpected loss of someone (Palacios-Ceña and Corral

Liria, 2010). Grounded theory is another qualitative design

widely used to analyze social interactions, for which it

identifies patterns that intertwine producing categories,

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example, a group of researchers in South India assessed the

nutritional status of five families and found that most of the

women suffered from anemia. To address this problem, the

researchers engaged with the families and taught them

organic farming and food preparation techniques to find

practical solutions. This approach involved making

modifications to the daily activities of the families.

On the other hand, given the flexibility of the

interpretive method, it is not surprising that some qualitative

studies imitate quantitative studies in certain strategies. For

example, a study conducted on 25 Argentine writers to

compare their commercial perceptions over 20 years, in

which, the author (Rimoldi, 2019), instead of employing a

design with an already known name, decides to use the name

longitudinal study which is very common among

quantitative studies. There are other research designs of

which examples are not developed such as semiotic and

critical discourse analysis that are also part of the qualitative

approach.

published on Twitter. When you want to know what is most

consumed in bibliographic terms, it is possible to apply a

bibliometric design. This is the case of a study by Xu et al.

(

2021) who studied 1,044 documents published in Web of

Science, but since it is almost impossible to read them all,

they used artificial intelligence to identify the most

outstanding characteristics of the literature on

entrepreneurship and crises over 36 years. Data science can

be very useful to study a wide range of behaviors such as

marketing, biology, psychology, language, sociology,

among others. The sample sizes in these cases have no

limits, however, the designs depend on the strategies used to

collect information, because once structured data are

available, the analysis is performed according to statistical

rules. Indeed, when databases are structured, many

researchers prefer to declare that they have used the big data

design which, apart from variety, speed, and veracity, is

finally subject to statistical analysis.

3

. Theoretical designs: When there are no general

In the choice of sample size in qualitative research

designs, a precise value cannot be determined, since it

depends on the research objectives, unless it is desired to

control for error. However, it is suggested to use theoretical

saturation as a guide to know when to stop, i.e., when

participants no longer contribute new information about the

problem. This allows informed sample size decisions to be

made based on theoretical completeness rather than

statistical criteria.

principles to explain the phenomena or when a reflection

and adjustment of existing principles is required. Borsboom

et al. (2004) proposed an interesting methodology for theory

building. Although the designation as a specific design is

proposed by the author, it is possible to include

philosophical approaches in this design, since both focus on

the analysis of fundamental postulates of a theory.

Moreover, in both cases empirical evidence can be

dispensed with. Theoretical design does not contain strict

rules, since it is often dressed in essayistic styles, but what

cannot be lacking are written assertions in the form of

reasoning that, in the best of cases, can be identified as

axiomatic propositions, or simply as hypotheses, theoretical

propositions or logical inferences of one or several

disciplines. Theoretical design formulates relevant concepts

that explain how reality works. For example, cognitive

theory, feminist theory, evolutionary theory, the standard

model of particle physics, neoclassical microeconomics,

and so on. The principles of these theories guide research

and their validity will depend on their compatibility with

empirical evidence; therefore, proposals of this nature are

fundamental to guide the hypothetico-deductive

methodology that will devise ways to test hypotheses

2

.

Data science: When working from an

interdisciplinary perspective in which computer science is

combined with statistics, not only to analyze information but

also to collect it, data science is employed. This is a

booming field for which there is little philosophy and

theorizing, however, some particularities can be identified

that could lead to specific research designs. For example,

when a qualitative database is very large, it is impossible to

interpret each of the collected data (interviews, texts,

images, network data, multimedia data), so it becomes

inevitable to transform them into quantitative data. This

transformation is done with pattern recognition techniques

using coding (like grounded theory design) that can be

replicated by computers using artificial intelligence (Bryant

and Charmaz, 2019). When a researcher wants to understand

the interaction behavior in social networks on the Internet,

the content of web pages, online interviews, or blogs,

obtained through application programming interfaces

(

Bunge, 2005). Contrary evidence may lead to adjustments

in the theory or to its discrediting. While it is true that

theoretical designs are used to develop theories that explain

reality, not all theoretical designs formulate relevant

concepts that can be empirically verified. Some may be

more focused on the elaboration of mathematical or logical

models within a formal science that are not compatible with

verification.

(

APIs), he or she can resort to a netnographic design whose

name is an acronym of "net" with "ethnography" (Jeacle,

021). For example, two researchers set out to analyze

2

insecurity in some Mexican cities through what was

published in the digital press between 2010 and 2019 by

collecting certain predominant terms in newspaper articles

4

. Bibliographic designs: When there is an abundant

scientific production that needs to be synthesized to draw

the most general conclusions, bibliographic designs are

used. The bibliographic design is not carried out by means

of direct empirical evidence, but its units of analysis are

scientific publications (Petticrew and Roberts, 2008). If the

way this review is conducted is neither methodical nor

transparent, it is considered a traditional review (or non-

systematic review). But if it is reliably identified by

(

Soto-Canales and Padilla-Herrera, 2023). Another

interesting example is that carried out by Ceron et al. (2016)

who have made ex ante electoral forecasts with an error

level not far from the electoral trend polls, for which they

employ sentiment analysis, text mining, big data, supervised

machine learning and the use of algorithms to identify

patterns and trends in hundreds of thousands of microblogs

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establishing a time frame, inclusion and exclusion criteria,

and a systematic search using strings or formulas and

evaluations of the information collected are performed, it

can be said to be a systematic or systematized review

beings for which they have created a new instrument, the

High Five Inventory, whose construct has been validated by

confirmatory factor analysis (CFA) and internal consistency.

But not only are tools being built, but they are also being

adapted. For example, an adaptation of an instrument to

assess metacognition in a cultural context different from the

one originally proposed (Zhunio-Falcones and Cabrera-

Tenecela, 2022). Another example is the reconfiguration and

creation of a short version of a tool to measure work ethics

that was originally extensive (Zúñiga et al., 2022).

Statisticians usually classify CFA among explanatory

designs; however, the goal of instrumental designs is not to

explain the causes of things but to ensure the quality of the

measurements. In these cases, it is advisable to have samples

of at least 200 data or at least 10 observations for each

question (item) to facilitate model fitting (Hair et al., 2010).

When working with ordinal or categorical variables, there

are often problems of multivariate normality, so there are

estimation techniques such as robust maximum likelihood

(MLR), unweighted least squares (ULS), diagonally

weighted least squares (DWLS), among others, which use

polychoric correlations and are available in free platforms

such as R or Python. However, instead of using Cronbach's

alpha, McDonalds' omega can be used to verify internal

concordance. The possibilities of this design are broad

because, in addition to validating in a population, factorial

invariance evaluations can be performed to verify if the

instrument is not biased in any subgroup, as well as to

compare with other instruments to test concurrent or

discriminant validity. If the study is very new, it is advisable

to start by designing a good instrument by means of an

exploratory factor analysis (EFA), which will be discussed

in one of the observational designs.

7. Observational designs: When the aim is to learn

about behavior without manipulating reality, the

observational design, also known as non-experimental, is

used (Campbell and Stanley, 2015). If the information is

collected on a single occasion, the design is known as cross-

sectional, in which case the designs can be specified as

exploratory, descriptive, relational, and explanatory. If the

problem is new and there is little state of the art (or the

researcher does not agree with the measures used in the state

of the art), it is necessary to develop an exploratory design.

For example, a researcher wanted to measure blended

learning in higher education, but the variables to be assessed

were not clear. He invented an instrument and tested it on a

group of 413 teachers. In addition, he conducted an

exploratory factor analysis (Anthony Jr., 2022). Exploratory

designs should not be conceived for their level of difficulty

because they require a great deal of creativity to understand

the problem and devise solutions without sufficient

information, for this reason, the exploration can be carried

out with simple descriptive statistical models up to complex

analytical models. The descriptive cross-sectional design is

one of the most used designs to present its results in

(

Codina, 2018). For example, in the article 'Bibliographic

review of control systems for micro-grid energy

management' by Sampietro Saquicela and Pico-Valencia

(

2018) or the one conducted on studies of electoral

forecasting through big data (Cabrera-Tenecela, 2021) or

through surveys (Andrade-Bayona, 2021). Generally,

systematic reviews are limited to describing this

information; however, when the sample is considerable,

more advanced statistical techniques can be used to explain

what may be occurring with respect to a theory. This type of

design is no longer known simply as a systematic review,

but as a meta-analysis (sometimes both terms are used)

(

Codina, 2018). For example: Effect of the low-calorie

ketogenic diet on body composition in overweight and obese

adults: systematic review and meta-analysis (Díaz Muñoz et

al., 2021). Note the similarity of this design to the

bibliometric design described in data science.

5

. Methodological designs: When existing methods

are insufficient or when new procedures have been

discovered to address a research problem, methodological

designs are used, which can be found as innovative

methodological design or research design with new

methods. These designs involve a systematic and organized

approach to gathering information and analyzing the data

collected (Ezzy, 2002). Research designs guide the way in

which information can be effectively obtained or processed.

There are methodological designs as open-ended as the

present case, as well as very specific designs that show step-

by-step how to download information from Twitter APIs to

process it through algorithms in the R programming

language to finally provide a route to perform ex ante

election forecasts without surveys (Ceron et al., 2016).

Another example of methodological design could be to

generate new software to select study samples attending to

the statistical power of the tests to be used to contrast

hypotheses (Faul et al., 2007). When it is not clear how to

proceed, it is important to review a methodological design

before committing to the objectives of a research study.

2

6

. Instrumental designs : When existing observation

instruments are insufficient, artifacts, tests, questionnaires,

observation sheets, etc. are created or adapted with the

instrumental design (Carretero-Dios, 2007). This is used in

both mechanical and behavioral processes in living beings

(

including humans). In the mechanical case applied to oral

health, for example, both an engineer and a health researcher

can test whether a 3D scanner is reliable, concordant,

accurate and valid before using it for intraoral

measurements, as shown in the study by Soto-Alvarez et al.

Another example, two psychologists (Cosentino and Castro

Solano, 2017), instead of focusing on pathological aspects

want to focus on emotionally positive aspects of human

2

Instrumental design for validation and reliability, as in the present case,

should not be confused with instrumental variables used to solve

endogeneity problems.

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percentages, measures of central tendency and variability

without establishing probabilistic relationships between

variables. An example can be the pathological description

made by a group of researchers to the testes of 11 fatal cases

of Covid-19 (Duarte-Neto et al., 2022). Another example is

the description of genital tract infections in a group of

women attending obstetric consultation (Loachamin, 2023).

The samples in this design can be as small as the previous

example to as large as the results of a national census. When

the researcher has an acceptable sample (with a statistical

power of at least 80%), relational hypotheses can be

established. To carry out this process, a relational design,

also known as a correlational design, is used. It is important

to note that the term "relational" refers to the connection or

association between variables, and should not be confused

with the correlation coefficient, which is a specific measure

of the relationship between two variables. Within the

intervention, in 287 industries in Nepal. An important

reflection before closing this section is that, although some

philosophers of science, such as Pearl (2012), consider that

observational methods serve to model causality, as is the

case of those who point out that an SEM model infers the

underlying causal relationships (and not only correlations),

most researchers avoid using the name explanatory or causal

design because they are aware that the level of control of an

observational design is not always sufficient to provide a

conclusive explanation, even more so when dealing with

low predictive levels, common in the social sciences. For

this reason, social scientists often state that their study is

relational or correlational, however, when researchers seek

to demonstrate causality, they can add more control through

longitudinal designs.

Finally, within the observational designs is the

longitudinal design. This is like the previous ones, but with

a difference: there are several measurements over time to

several samples over time, to different age groups or to the

same sample on several occasions. When one wants to know

the changes in a population by working with different time

samples, the trend design is used. For example, a study

conducted in Brazil at four points in time (waves) showed

that fear of crime is positively associated with support for

less democratic forms of government (Pereira and de

Andrade Dornelles, 2021). When studies are conducted to

know the evolution according to age, the cohort, group

evolution or accelerated longitudinal design is used. An

example of this is the study conducted on 2,278 children

from 4 to 14 years of age, which showed that there is a

significant relationship between sports participation and

social competence (social skills), especially in late

childhood and early adolescence (Bedard et al., 2020).

Finally, when the same sample is evaluated over time, on the

other hand, it is called a panel design. An example of such a

study is the one conducted on 396 Filipino adolescents

through a three-wave panel structural equation model, with

which the researchers demonstrated the association between

the valuation of happiness and positive affect (Datu et al.,

2021). Longitudinal designs control error and bias better

than cross-sectional designs, however, they tend to be more

costly and time-consuming. While it is true that the models

used tend to employ statistics like explanatory designs, they

do not have the naming conflict.

"

relational design", a term that the author prefers to use,

various statistical testing techniques can be used to make

comparisons between groups and analyze the association

between variables (correlation is just one of them). An

example of this design is the one used by Kuru Alici and

Ozturk Copur (2022) who measured anxiety and fear of

Covid-19 in 234 undergraduate nursing students showing

that these two variables were highly correlated. Up to this

point, it is worth mentioning a characteristic of exploratory,

descriptive, and relational observational designs: in their

ultimate objectives they use independent variables because

they are not concerned with measuring the dependence or

causality of one variable on another. However, studies are

often found that claim that their design is relational when

they incur in causality, which, as will be seen below, may

occur due to modesty, fear, or ignorance of explanatory

designs.

When researchers attempt to model dependence on one

or more independent variables, they refer to explanatory or

causal designs (erroneously classified as correlational-

causal). These designs usually make use of linear, ordinal or

logistic regressions to establish the level of explanation of

the independent variables with respect to the dependent

variables. There is a wide variety of statistical models in this

respect, such as multivariate regression, multivariate

regression, multilevel analysis, confirmatory factor

analysis, among others. One of the most useful is the use of

structural equation modeling (SEM), which allows complex

information to be modeled. A good example is the study by

Kakemam et al. (2022) who determined how

professionalism and systems thinking explain the safety

competence of 358 nurses caring for Iranian patients. While

it is true in their design, they modestly point out that their

design is a cross-sectional survey (which would be a part of

the design), statistically they cannot avoid writing the

explanatory level of their model in terms of regression. An

example opposite to the previous one, in which they use the

name explanatory design without hesitation, indeed they

combine it with the exploratory design, is that of

Rajbhandari et al. (2022) who examined the relationship of

some variables, including skilled labor, with respect to

technological innovation, mediated by government

8. Experimental designs: When the researcher

intentionally manipulates variables to control the internal

and external validity of his results, he employs the

experimental design. For Campbell and Stanley (2015), if

the researcher only has a group of participants in which the

design intervenes, it can be called pre-experimental. Within

this design the most common is the pre-test and post-test of

a single group. For example, researchers at an institute of

medical sciences tested a self-instruction module on

parental drug administration in 50 nurses, achieving an

increase from 64% (pre-test) to 88% (post-test) of

appropriate practices. Pre-experimental designs are also

considered to be those that apply only a post-test, this would

be the case study with a single measurement or the

comparison with a static group, in both cases, the control is

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minimal because there is no pre-test, so it is impossible to

evaluate the differences (Jaan et al., 2022).

educational in nature and students may be biased by

receiving a pretest, two more groups (one intervention and

one control) are added to the above design in which the

pretest is not applied, but the posttest is retained in all of

them, and is referred to as the Solomon design. An example

of such a study was developed by Golaki et al. (2022) to test

knowledge retention by employing the inverted classroom.

For this purpose, four Solomon groups were randomly

assigned, a pre-test was administered to an intervention

group and a control group, and then all four groups received

When working with groups that were formed prior to

the researcher's intervention, pre-testing and post-testing are

performed, a quasi-experimental design is used. One of the

most used quasi-experimental designs is when the pretest

and posttest are used in an intervention group and in a

control group, this is called a separate sample design in

pretest and posttest in intact groups, which are also known

as non-equivalent control groups (Cohen et al., 2017). For

example, at one university they wanted to provide better

mentoring to culturally and linguistically diverse students,

for which they designed a blended program and compared

mentoring competencies before and after the training to an

intervened group of 49 students who received the training

including an additional component and a non-intervened

one of 62 students who only received the training

a

follow-up post-test of knowledge retention. The

intervention groups used the inverted class, and the control

groups the conventional class, however, when evaluating the

results two months after the treatment and comparing the

results with the pre-test, as well as with the control, there

was no significant increase in the test. There are other

designs such as A-B-A-B that collect baseline information,

implement the treatment, and evaluate its effects, and then

re-evaluate the return to baseline by withdrawing the

treatment and then reapplying it to measure the changes.

Another design encountered is the factorial design, but it

refers more to the statistical technique that can be used

interchangeably in various designs. The samples of the

experimental designs usually vary as they can be small as

well as large, being advisable to identify the statistical

power of at least 80% for the statistical tests that are

intended to be performed.

9. Epidemiological designs: When you want to study

public health behavior in a population, the epidemiological

design is used. Different designs mentioned above can be

used to describe or analyze reality. The most common

designs in epidemiology are the prevalence design and the

incidence design, which make it possible to examine the

frequency of a disease in a population at a given time or

during a given period. In addition, epidemiology also

employs other specific designs that are not commonly used

in other fields, which are relevant and deserve to be

mentioned in this section. If the researcher has access to

information that summarizes the behavior of one or more

populations (and cannot directly access data from

individuals), he or she can make use of the ecological

(sometimes also called cluster) design (Cataldo et al., 2019).

For example, one researcher conducted an analysis of

Covid-19 mortality in 30 countries by taking data from the

Our World in Data organization from the time the first case

appeared until two years later and demonstrated the impact

that vaccination had had on reducing such mortality (L. M.

Cabrera-Tenecela and Macancela-Sacoto, 2022). When

there is direct information on individuals (whether

retrospective or prospective) and it is desired to compare

those who present a disease with those who do not, the case-

control design is used. This design uses a type of categorical

statistic to separate those who present certain health

conditions (with which the level of risk posed by certain

characteristics can be studied) from those who do not

present those health conditions. For example, a study on

risk factors for child pregnancy conducted among 180

adolescents showed that those who were victims of domestic

violence were 6 times more likely to become pregnant than

(

Oikarainen et al., 2022). Another quasi-experimental

design is that of time series or time series that consists of

making observations at different moments over time to the

same study units, in which natural or artificial events can

occur that can affect the behavior of the variables being

evaluated every so often (daily, monthly, quarterly, etc.). A

good example is the study by Chiatchoua et al. (2020) who

studied

some

econometric

indicators,

including

governmental economic intervention, before and after

Covid-19 in Mexico, managed to calculate the effect of the

pandemic on the economy as well as to propose a forecast

with the ARIMA model. The design of the time series is

debatable if it belongs to longitudinal studies, however, as it

studies the impact that certain events have, they are

regularly identified as quasi-experiments. Although less

common, there is a quasi-experimental design with a

separate pretest/posttest sample that compares a group that

has been previously evaluated but not intervened with one

that has been intervened and evaluated only afterwards. A

study that is intended to be a pure experiment, but fails to

meet the control, can be reduced to a quasi-experimental or

pre-experimental study.

Finally, when a researcher randomly groups the

samples to ensure that the groups evaluated are equivalent,

the pure experiment is chosen. In this case, the most used

experimental design is the two independent groups

experimental design or also known as control group with

pre- and post-test. For example, a study that tested a vaccine

for Covid-19 worked with 450 individuals, convalescing

from the disease, who were randomly assigned to two

groups, one experimental (n=344) and one control (n=86)

and was able to demonstrate that the dose applied safely

reinforced the pre-existing natural immunity (Ochoa-Azze

et al., 2022). Another interesting example was applied to

online gamblers to assess their behaviors when betting on

soccer or online roulette games, who were given a monetary

endowment warning them that the excess would be for them.

In this case the intervention group (n=254) received colored

warnings about the safety of the game and the control group

(

n=252) did not receive such warnings. However, the

protective effect of the safer play message was not tested (de

Vries et al., 2022). Sometimes when the intervention is

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those who had not been victims (Castañeda Paredes and

Santa-Cruz-Espinoza, 2021). Another example may be the

comparison of dopamine production between cannabis-

using students and non-using students (Acuff et al., 2023).

These types of studies are not limited to presenting the

results in a descriptive way, but often use biostatistics to

model the information using bivariate or multivariate

inferential techniques.

opts for the mixed design. These designs have the possibility

of combining all the designs they consider pertinent, and can

be exploratory, descriptive, relational or explanatory. Let us

consider an example. A study related to adherence to

antiretroviral treatment in 86 women with HIV used a mixed

quantitative and qualitative method. This study started with

an explanatory design in which they contrasted variables

such as age, educational level, and income to explain

adherence to treatment. They then used the

phenomenological (qualitative) design, only in women who

did not adhere to treatment to find out why they did not

adhere. If the researcher employs one design in order to then

employ the other, as in the present case, the design is

sequential, it is clear that the first design is an explanatory

one and the second is a phenomenological one, in which

case, the name usually given is sequential explanatory

mixed. Let us look at another example in which neither

method depends on the other to exist, i.e., the application of

one design or the other is independent. In a poor urban

district, several methods were combined to promote health

literacy, empowerment, citizen participation and

intersectoral collaboration. To this end, de Jong et al. (2019)

provided evidence regarding the application of health

literacy questionnaires, documentary, and photographic

analysis, as well as, interviews, to answer four research

questions. As the qualitative design work independently,

this design could be called concurrent mixed action

research-action research, while other authors would prefer

to call it concurrent transformative mixed, however, the

authors of the cited article have preferred to call it action

research-guided mixed methods. There are also authors who

would call the first example QUAN-which, due to the

importance of the quantitative method, while the second

would call it QUAL-which, also due to the importance of

the qualitative method. However, in view of the very wide

possibilities of naming a design, and the impractical options

offered by methodologists, it is preferable to indicate

whether the design is mixed sequential or concurrent and

then, as in our second example, to mention the designs

traditionally used, as this would offer greater precision to

the reader.

1

0. Historical designs: When seeking to investigate

past events and write or rewrite history, use is made of

historical design, which is based on the analysis of

secondary sources and primary documents (Bloch, 2018).

Boch suggested that historian must involve other disciplines

but that the historian's interpretation is fundamental. This

interpretation can be flexible and open according to the

principles of hermeneutics, or it can be rigorous and logical,

even statistical-inferential, depending on the approach taken

by the historian. For example, the work of Vásquez Ruiz

(

2014) is an interpretive study that analyzes the uprising that

occurred in 1932 in El Salvador. Although the black legend

had been spread that radical communists were responsible,

the author reviews the contribution of other groups in that

anti-government rebellion. Another example is the work of

Carretero Poblete and Samaniego Erazo (2017), which is

based on primary archaeological sources to explain the

Puruhá culture's trade relations with the Cañari and the

Coast through the careful analysis of 2,198 pieces, including

ceramics, lithics and bones belonging to the late Late

Formative phases of the Ecuadorian Central Highlands. This

study could be considered an archaeological design.

Another historical design is the documentary design, which

involves the analysis of past records. Although these

historical designs are not well known, those who engage in

them agree that a triangulation of different sources and the

comparison of multiple perspectives are required to

approach the validity and reliability of the results.

11. Case designs: When one wants to offer a detailed

and exhaustive analysis of one or some cases with the aim

of understanding a fact, event or problem, the case design,

case study, or case study is used (Cohen et al., 2017). Popov

et al. (2019) made a study of four canine patients in the

recovery of bone defects after resection of osteosarcomas,

in their study they provide a detailed review of the process

employed by them to conclude that all operated animals

began to actively use their restored limbs and showed good

functional results. According to Yin (2017) case studies are

like experimental studies in that they argue how it was done

and why it was done, unlike observational studies where it

is difficult if not impossible to answer. While case studies

tend to have small samples, this is due to their in-depth and

comprehensive approach to a specific case, which allows for

a detailed and contextualized understanding of the event

under study. Through rigorous and systematic analysis, case

studies seek to provide solid evidence that, through broader

studies, can be made generalizable.

DISCUSSION

The qualitative approach provides a solid basis for the

organization of research designs, as its flexibility allows for

a more accessible methodological understanding. As

constraints and levels of complexity are added to designs, a

gradual increase in the quantitative approach can be seen.

While it is true that qualitative designs require experience

and disciplinary training (designs are minimalist and

flexible and gradually adapt to the context according to

Bisquerra Alzina et al., 2019), their ability to make decisions

in a flexible manner is remarkable and contrasts with the

rigidity inherent in the quantitative approach. The adoption

of this initial organization based on the qualitative approach

improves the overall understanding of research designs,

1

2. Mixed designs: When the researcher considers

that the hypothetico-deductive method or the interpretative

method is insufficient and assumes that in the combination

of the two there is a better solution to his problem, then he

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allowing a more effective approach to methodological

aspects. This order has not been considered by Hernández

Sampieri or by the other methodologists mentioned above.

Interesting techniques have emerged from these

designs, which today can be worked on by means of data

science. On the other hand, although it is true that theoretical

and philosophical designs are characterized only by stating

the principles guiding the research, and their validity

depends on their compatibility with empirical evidence,

bibliographic studies are a channel for verifying whether the

hypotheses derived from these theories are plausible, as

Popper would say. However, even though Bryman, Cohen et

al. (2017) and Hernández-Sampieri et al. (2014) make

suggestions on how to structure theoretical frameworks and

states of the art, they do not recognize that research designs

of this nature exist, as has been done in the present case.

This aspect has been worked on by Borsboom et al. (2004)

who suggest the use of certain rules to design theories to

guide research and scientific practice. Of course, the

structuring of theories is a problem widely studied by

epistemology (Popper, 2008; Popper et al., 2008), a branch

of philosophy that discusses the importance of the

structuring of theories and how it relates to the acquisition

of scientific knowledge.

designs stand out or are equivalently combined. In any case,

the choice of mixed design should be guided by the specific

needs of the study. Whatever the designation used, it is

essential that investigators provide a clear and detailed

description of the methods used in the mixed design. This

will ensure methodological transparency and allow readers

to assess the validity and reliability of the results.

One of the most revealing findings of this study is the

tendency of many researchers to confuse data collection or

analysis techniques and instruments with the research

designs themselves. For example, the structured

questionnaire design (instrument) or the confirmatory factor

analysis design (statistical technique) are mentioned as if

they were designs in themselves. In the context of

methodological design studies, it is crucial to address this

aspect, otherwise any resource could be misinterpreted as a

research design. To avoid this confusion, it is essential to

understand that tools and statistics are tools that are at the

service of research designs, not the other way around. Each

research project should make clear the scope and

limitations, emphasizing the importance of using techniques

and instruments appropriately within a sound research

design.

Finally, it is important to reiterate the importance of

organizing and classifying research designs, since, at the

time of publication, journal editors, guided by systems such

as APA or Vancouver, seek to be as clear as possible about

the method adopted by the researcher. In this sense, a

methodology that does not contain research designs would

only reflect epistemologically on the approach adopted or

confuse it with logistical elements of the research, such as

tools or techniques for information analysis.

Limitation. This study offers an approach to research

designs, but it is important to emphasize that it is not

intended to be exhaustive in its coverage. Rather, it is

presented as an overview of the most consulted

methodological sources, especially in the social sciences. In

the future, it would be advisable to develop an

interconnected structure of research designs, employing a

broader coding process that considers the specific purposes

of each, particularly as they relate to the health sciences. In

this way, a fuller and deeper understanding of the different

approaches and methodological designs used in research

could be achieved.

Authors who have published the most influential

recent works in research methodology, in terms of citations,

cannot ignore the important work of Campbell and Stanley

(

1963), who published experimental and quasi-experimental

designs in social research. These authors present a range of

research designs that allow causal relationships to be

established and address the challenges of conducting

experiments in uncontrolled settings. The authors discuss

key concepts, such as randomization, control of extraneous

variables, and internal and external validity, that are

fundamental to establishing reliable conclusions in

empirical research.

It is interesting to note that historical designs are not

widely mentioned in the most cited works. Although history

and archaeology are disciplines with very particular

methods, it is increasingly evident that, through

interdisciplinary approaches, these should be considered

and included in classifications of research designs in science

in general. Historical research provides a unique perspective

to the study of past events and their influence on the present,

which can enrich and complement other forms of research.

It is important to foster a broader dialogue and recognition

of historical designs within the scientific community to

embrace the diversity of methodological approaches more

fully to research.

CONCLUSION

Finally, regarding mixed designs, Hernández-Sampieri

summarizes the ideas of Creswell and Creswell (2017) to

mention eight research designs. However, when looking for

examples, there is no such abundance of designs. Most

researchers prefer to note whether it is a concurrent or

sequential design. In the face of the great confusion that may

be involved in combining research designs such as those

proposed by the above researchers, to avoid ambiguous or

subjective labels, it is preferable to specify whether the

design is sequential or concurrent and then mention which

This article has presented various research designs

grouped into 12 categories, showing the wide range of

approaches used by researchers in the social sciences and

health sciences. These designs go beyond mere observation

and experimentation, including also theoretical,

bibliographical, and instrumental study.

An original contribution of this proposal is the didactic

organization of designs from the most flexible to the least

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flexible up to eight levels, an approach that has not been

previously explored in the methodological literature. It is

essential that researchers become familiar with these

different designs and understand their applications and

limitations. Choosing the appropriate design is crucial to

effectively address research problems and obtain valid and

reliable results.

It is also important to emphasize the need to provide a

clear description of the techniques used in each design,

avoiding confusion between the tools and statistics

employed. It should be recognized that these statistical tools

and methods are at the service of the research designs, and

not the other way around.

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