• CONTENT
  • CONTENTS
  • Research Onion
  • Logic and Reasoning
  • Deductive research approach to theory building
  • Inductive Research Approach
  • Research paradigms and philosophies
  • Research Dimensions
  • Research Methodology
  • Sampling
  • Quantitative research preoccupations
  • Literature Review
  • Research Evidence Pyramid
  • Glossary

Research
A process of steps used to collect and analyse information to increase our understanding of a topic or issue (Creswell, 2008). It is a systematic, purposeful process for creating new knowledge, testing ideas, and solving problems in ways that others can scrutinise and build on. Research may also be defined as a systematic, objective analysis and record of controlled observation that leads to generalisations, principles, or theories, supporting prediction and control of events (Varpio et al., 2020). Research a process of steps to collect and analyse information to increase understanding of a topic or issue (Green, 2014).

Scientific knowledge
Scientific knowledge is knowledge obtained through the scientific method, including observation, experimentation, and critical analysis, and is objective, testable, and replicable. It is based on empirical evidence and organised systematically to explain and predict phenomena (Daud et al., 2025).

Criteria for Scientific Knowledge

No	Charactersics	Explanation
1	Systematic	Organized as an interrelated body of propositions and data with ordered relations.
2	Empirical	Grounded in structured observation and experimentation on factual (empirical) phenomena, not just raw facts, but facts interpreted through scientific activity.
3	Objective	Free from personal bias, likes/dislikes; statements must accurately reflect the object studied
4	Analytical	Breaks problems into parts to understand properties, relations, and roles, leading to specialized branches of science
5	Verificative	Its claims are open to testing and verification; truth must be checkable by others and is oriented toward scientific truth that allows explanation, prediction, and control of phenomena

Saunders Research Onion (Itswele)
A visual model that breaks the research process into layers; from philosophy through data techniques, helping researchers design coherent studies. Layers of the Research Onion include research philosophy, research approach, research strategy/design, methodological choice, time horizon, and techniques & procedures (Ahmed et al., 2025).

LAYER No	LAYER CONTENT	EXPLANATION AND EXAMPLES
LAYER 1	Philosophical Assumptions	Positivism, Critical Realism, Pragmatism, Postmodernism, Interpretivism
LAYER 2	Theory Development Approach	Inductive, Abductive, Deductive
LAYER 3	Method	Quantitative, Qualitative, Mixed Methods
LAYER 4	Strategy	Quantitative: Experimental, Survey Qualitative: Ethnographic, Grounded theory, Narrative, Case study, Archival research, Action research
LAYER 5	Time	Cross sectional, Longitudinal
LAYER 6	Data collection, Data analysis	Data collection, Data analysis

Logic
Logic is the process of using an argument to conclude. Logic is the system for determining the validity of an explanation (Babbie, 2025). Logic is the system of rules and structures of reasoning that guide how questions are formulated, data are interpreted, and conclusions are drawn. Logic forms the basis of both study design and the way scientists move from evidence to knowledge. Logic is related to rationality, ensuring arguments are valid, systematic, and free from basic reasoning errors (Grass, 2024). Logic is the set of construction rules that the research process must follow, structuring stages from problem to conclusion (Yao, 2024). Logic is viewed as a methodological discipline whose aim is to guide scientific practices, not to describe phenomena directly (Sagi, 2021).

1. Deductive logic
2. Inductive logic
3. Abductive logic

NO	LOGIC	EXPLANATION
1	Deductive	“What follows from this rule and these facts?” Logical model by which specific expectations of hypotheses are developed on the basis of general principles (Babbie, 2021, p. 23) Deductive reasoning is a form of reasoning where conclusions follow with logical necessity from given premises. If the premises are true and the inference is valid, the conclusion must be true; there is no room for “more or less likely” here, only true or false. Deductive reasoning is truth‑preserving reasoning: a valid deduction’s conclusion is true in every possible situation where all its premises are true (Abe et al., 2025). Deductive logic has logical validity: there are no counterexamples where the premises are true and the conclusion false (“Deduction,” 2021). All‑or‑nothing results: deductive logic requires binary responses (true/false, yes/no), reflecting that conclusions are either valid or not, with nothing in between Distinct from probabilistic reasoning: Probabilistic reasoning deals in degrees of belief and likelihood; deductive reasoning ignores prior knowledge and focuses only on the logical form of the premises
2	Inductive	“What general pattern do these cases propose?” Logical model in which general principles are developed from specific (particular) observations (Babbie, 2021, p. 23) Induction proceeds “from the particular to the general”: from individual facts or examples to general conclusions or theories (Crupi, 2015). Inductive reasoning is a way of thinking where people move from specific observations to broader patterns, rules, or predictions that are probable but not certain. The conclusions are not guaranteed; they are supported to some degree of probability, and multiple different general rules can often fit the same observations (Chen et al., 2025). Inductive reasoning means using existing knowledge or observations to make predictions about novel cases (Hayes & Heit, 2018).
3	Abductive	What would best explain this case? / What would best explain what we observe?”” A logical model by which the researcher grounds a theoretical understanding of the contexts and people he or she is studying in the language, meanings, and perspectives that form their worldview (Bryman, 2012, p. 401). Abductive reasoning is a type of reasoning people use to come up with plausible explanations for things they observe, especially when information is incomplete.  Abductive reasoning is inference to the most plausible (or best) explanation for a set of observations (Bai et al., 2024). Abduction starts from an observation, commonly surprising or puzzling Example: inferring that disease X has resulted in noted symptoms (Pareschi, 2025). Generates one or more hypotheses that, if true, would make the observation expected or understandable The hypotheses are tentative, not guaranteed true—more like informed guesses than proofs

Deductive Research Approach
A deductive research approach (see Saunder’s research onion) tests existing theory by moving from general ideas to specific observations. The table and diagram below summarise the deductive approach.

No	Item	Description
1	Theory	The existing theory from available literature and current knowledge is described
2	Hypothesis	A hypothesis which explains the relationship of the variables is formulated.
3	Data	Data collection and analysis follow after a hypothesis has been stated.
4	Findings/Results	Interpret the data and findings.
5	Hypothesis outcome	The hypothesis is rejected or confirmed based on the results.
6	Theory revision	The results are disseminated, and the theory is revised and re-evaluated.

Inductive Research Approach
An inductive research approach builds theory from data. The inductive approaches start from raw data and build up concepts, patterns, or theories, instead of testing a hypothesis. 

No	Step	Explanation
1	Data cleaning	The raw data is cleaned into a format
2	Data transcription	The researcher transcribes the data and deeply familiarises himself with the data.
3	Coding	Open (initial) coding follows data transcription and familiarisation. Significant text segments are identified and coded (labelled)
4	Categorization	Overlapping codes are grouped into categories/concepts.
5	Theming	Merge the categories into themes that describe the experience or process.
6	Theory Development	Make a connection between themes in order to develop a conceptual framework or a theory.

Inductive reasoning vs Deductive reasoning

NO	Item	Inductive	Deductive
1	Direction	From particular to general	From general to particular
2	Conclusion certainty	Probable, not guaranteed	Guaranteed if premises true
3	Scientific role	Building new rules/theories from data	Applying established rules to cases

RESEARCH PARADIGMS AND PHILOSOPHIES
A paradigm is a basic set of beliefs or worldview that guides research actions (Niure & Sapkota, 2025). A pattern, lens, or framework for seeing the world and making sense of it (Scotland, 2012). A paradigm is a philosophical framework that helps a researcher articulate their position across research dimensions (ontology, epistemology, axiology, and methodology) (Buriro et al., 2021).

Paradigm	EXPLANATION
Positivism	Positivism is a way of doing science and thinking about knowledge that treats reality as objective, observable, and governed by general laws. It has strongly influenced the natural and social sciences, especially quantitative research. Positivism is aligned with the hypothetico-deductive model of science that builds on verifying a priori hypotheses and experimentation by operationalizing variables and measures; results from hypothesis testing are used to inform and advance science (Park et al., 2020).   A research strategy and approach that is rooted on the ontological principle and doctrine that truth and reality is free and independent of the viewer and observer (Aliyu et al., 2014). Ontology (Real): 1. There is one objective reality, existing independently of our beliefs or perceptions (Department of Built Environment Studies & Technology, College of Built Environment, Universiti Teknologi MARA, Perak Branch, 32610, Seri Iskandar Campus, Malaysia & Mohammad Ali, 2024). 2. Social and natural worlds are both seen as governed by causal laws that can, in principle, be fully known (Wati, 2024). Epistemology (How We Know): 1. Only empirical, observable facts plus logical reasoning count as real knowledge (Kaluarachchi, 2025). 2. Knowledge should be value‑free, aiming for neutrality and objectivity (Mazur, 2020). 3. Claims that cannot be tested or verified empirically are treated as meaningless or non‑scientific (Fischer, 1998). Methodology (conducting research) 1. Uses surveys, experiments, measurement, statistics, hypothesis testing, large samples, and replication (Mr et al., 2025). 2. Seeks universal laws, explanatory associations, and causal relationships, often through quantitative designs (Scientific knowledge: Methodology and technology, 2019).
Pragmatism	A flexible research paradigm that prioritizes solving real-world problems and using whatever methods best answer the research question. Much of the literature links pragmatism to mixed methods designs, but there is also critical debate about its coherence and limits. Knowledge as arising from human experience and action, not from abstract, absolute truths (Allemang et al., 2022). Fallibilism: Knowledge is always provisional and open to revision; inquiry produces warranted assertions and not final truth (Yvonne Feilzer, 2010). The meaning of theories and methods lies in their consequences and problem‑solving capacity (Martela, 2015). Many accounts stress democratic values, participation, and ethical “ends‑in‑view” as core to inquiry (Khabibullah et al., 2025).
Interpretivism	Interpretivism is a major research paradigm in the social sciences that focuses on understanding how people make sense of their world, rather than measuring it as if it were a purely natural, objective system. It sees reality and knowledge as shaped by meanings, experiences, and social contexts, and is especially suited to studying human behaviour, relationships, and institutions. Research paradigm that focuses on understanding how people make sense of their social world (Nickerson, 2023) Interpretivism views reality as socially constructed and multiple rather than a single objective truth. Reality is understood through people’s meanings, interpretations, and experiences (Kouam Arthur William, 2024). Knowledge is subjective and contextual therefore what counts as true depends on participants’ viewpoints and the specific social and cultural situation (McChesney & Aldridge, 2019). Research aims to understand the “lived experience” from the point of view of those who live it, not to produce universal laws (Aguzzoli et al., 2024).
Critical realism	An objective reality exist however the human knowledge of this objective reality is socially organised and is liable to error (Zhang, 2023) Critical realism is a philosophical approach widely used in social research to understand not just what happens, but why it happens, by looking for underlying causes and structures. It is between strict positivism and strong constructivism (Allana & Clark, 2018). Realist ontology  Reality exists independently of our beliefs; social structures and mechanisms have causal powers that make events happen (Lawani, 2021). Relativist/interpretive epistemology Our knowledge of this reality is partial, theory-laden, and shaped by history and culture; different people know different things in different ways (Park & Peter, 2022).
Post modernism	Postmodernism as a research paradigm emerged in response to the dominance of positivism and post-positivism in social science, challenging ideas of a single, objective truth and universal methods. It is closely tied to qualitative and critical approaches, and increasingly shapes how researchers think about knowledge, power, and representation. Postmodernism rejects universal truths and fixed reality, emphasizing multiple, situated realities shaped by language, culture, power, and history (George, 2024). It treats knowledge as constructed and contingent, often skeptical of any one “correct” method or meta-narrative (Aryal, 2024). Postmodernism pays strong attention to power relations in knowledge production and to marginalized voices (e.g., post‑colonial, racial, gender perspectives) (Pathak & Thapaliya, 2022).

No	Item	Positivism	Interpretivism
1	Reality	Single and objective	Multiple, socially constructed
2	Goal	General laws, prediction	In-depth understanding of meanings
3	Data	Quantitative, measurable	Qualitative, rich, contextual
4	Values	Try to be value-free	Values and subjectivity are acknowledged

Research dimensions
Research is shaped by a small set of recurring philosophical dimensions. These dimensions shape what counts as reality, knowledge, good methods, and appropriate values in a study. The 7 most common research dimensions are ontological, epistemological, axiological, methodological [the initial 4 dimensions] (Pratiwi et al., 2024), sociological, teleological and valorisation [additional 3 dimensions] (Scotland, 2012), (Dominion Dominic, 2023).

DIMENSION	EXPLANATION
ONTOLOGICAL	The ontological dimension is a set of assumptions a study makes about what reality is like. It is one of the core philosophical foundations of research, along with epistemology (knowledge) and methodology (methods). The nature of reality, questioning whether truth is an objective, external entity or a subjective, socially constructed phenomenon. It defines what exists, shaping whether research focuses on measurable, fixed facts (realism) or multiple, evolving experiences
EPISTEMOLOGICAL	Philosophical study of knowledge. Investigates the nature, scope, and limitations of knowledge.
METHODOLOGICAL	Theory of how an inquiry should proceed.
SOCIOLOGICAL	Analyses how social structures, cultural norms, and institutions shape human behaviour, relationships, and collective life. “the study of companionship” or “the science of society,” Systematically analyse social structures and human behaviour
AXIOLOGICAL	Philosophical study of value, ethics, and aesthetics Philosophical study of goodness, beauty, and ethics—basically, the study of what is considered worthy or valuable.
TELEOLOGICAL	approach focused on understanding phenomena by identifying their ultimate purpose, goal, or intended outcome.
VALORISATION	Valorisation refers to how universities turn academic knowledge into societal and/or economic value, beyond publications and teaching. It is now a central dimension of university missions and research policy. Valorisation includes all activities that add value to academic knowledge: dissemination, co-production with non-academic partners, and commercial use, not just patents and spin‑offs (Ngwenya & Boshoff, 2018).

Research Methodology
Research methodology is the principles, logic, and step‑by‑step procedures used to conduct a study so that a research problem can be solved in a systematic, scientific way (Mwange et al., 2023)

Quantitative Research Process

1. Choose Topic
2. Focus research question
3. Design Study
4. Collect data
5. Analyse data
6. Interpret data
7. Inform others

Quantitative versus qualitative research

Item	Quantitative (Creswell pg 13)	Qualitative (Creswell pg 16)
Research problem	Described by variables relation	Explored to develop a phenomenon
Variables	Relations of variables explained	Variables unknown
Hypothesis	Tetsted	Formulated
Literature review	Very Important at beginning of research. Direct the research questions and hypothesis.	Less important at the beginning of the research. Does not direct the research questions available literature has little information on the phenomenon
Data collection	Instruments used – To apply the result to bigger population (Generalize)	Protocols used – To learn from participants
Data analysis	Mathematical = Statistics	Sentences = Text Segments
Sample size	Large	Focus group
Sampling	Probability Sampling 1. simple random sampling 2. systemic and random start 3. stratified random sampling 4. multistage cluster sampling	Non-probability sampling 1. Convenience sampling 2. Snowball sampling 3. Quota sampling 4. Judgmental / purposive sampling
Logic	Deductive	Abductive
Designs	Experimental Quasi-Experimental Correlational Descriptive	Grounded theory Ethnographic Narrative

SAMPLING
Sampling is the selection of a subset of a population to represent the whole group. Sampling is essential because studying entire populations is impossible, too slow, or too expensive. A sample is the smaller group actually studied (Djennad & Djellouli, 2025). The sampling frame is the list (registry, for example) from which the sample is drawn (Zrineh et al., 2026).

SAMPLING TYPES
There is probability sampling and non-probability sampling (Turner, 2020). In Probability sampling, everyone in the population has an equal chance of being selected, which supports generalisation and strong statistical inference (Ahmed, 2024). In non-probability sampling, participants are chosen based on accessibility or researcher judgment, not random chance (Marshall, 1996).

Item	Probability sampling	Non-Probability sampling
Random selection	Yes	No
Generalizability	Yes	No
Research Method	Quantitative	Qualitative
Examples	1. Simple random sample 2. Systemic sample + random start 3. Stratified random sample 4. Multistage cluster sample	1. Convenience / haphazard / Subject Availability sample 2. Snowball Sample 3. Quota sample 4. Judgmental / Purposive

QUANTITATIVE RESEARCH PREOCCUPATIONS
In research methodology, quantitative research preoccupations are the main concerns quantitative researchers focus on, prioritise, and seek to optimise when conducting studies. The 4 Quantitative research preoccupations include causality, measurement, generalisation and replication (Bryman, 2012, p. 175).

No	Preoccupation	Explanation
1	Causality (hypotheses testing, prediction and explanation)	Quantitative researchers want to explain a phenomenon, which means examining its causes. Goals are testing hypotheses and establishing cause–and–effect or predictive relationships between variables (Rauteda, 2025).
2	Measurement (and numerification)	Involves identifying abstract constructs and then conceptualising and operationalising these constructs in order to obtain empirical data (Lim, 2025).
3	Generalisation (generalizability)	The extent to which the findings of a study conducted on a sample can be extrapolated and applied to a wider population. Studies are usually designed so that results from a sample can be generalised to a wider population (Mohajan, 2020).
4	Replication	An attempt by a second researcher to replicate a previous study is an effort to determine whether applying the same methods to the same scientific question produces similar results (Yilmaz, 2013).

MEASUREMENT CONCEPTS

Conceptualisation
Conceptualisation is part of the measurement process. Conceptualisation refers to taking an abstract construct and refining it by giving it a conceptual or theoretical definition. Conceptualisation is the process of thinking through the various possible meanings of a construct (Neuman, 2011, p. 205). Conceptualisation is the process of clarifying what key ideas mean in a study or theory so they can be used precisely and consistently. Conceptualisation underpins how researchers define concepts, build frameworks, and make sense of complex phenomena (van der Waldt, 2020).

Operationalisation
Operationalisation is part of the preoccupation with measurement in quantitative research. Operationalisation links a conceptual definition to a set of measurement techniques or procedures, the construct’s operational definition (Neuman, 2011, p. 207). The intellectual operations used to decide how to observe a concept in reality (Burnette, 2007). The process of translating theoretical constructs into measurable variables or indicators that can be collected as data (Andrade, 2021).

Hypothesis / Hypotheses
A hypothesis is an empirically testable version of a theoretical proposition that has not yet been tested or verified with empirical evidence. It is most used in deductive theorising and can be restated as a prediction (Neuman, 2011, p. 68). “Hypotheses are statements in quantitative research in which the investigator makes a prediction or a conjecture about the outcome of a relationship among attributes or characteristics” (Creswell, 2012, p. 111). The five E’s may be used to define a hypothesis that is evidence-based, explicit, ex-ante, empirically testable, and explanatory (Ghasemi et al., 2025).

No	“E”	Explanation of the “E”
1	Evidence-based	A hypothesis is based on theory, knowledge, and logical assumptions (Yam & Taufik, 2021).
2	Explicit	The hypothesis must be clear regarding the target population and the relationship between variables (Thompson & Skau, 2023).
3	Ex-ante experiment	A hypothesis is stated before the study (evaluation) (Misra et al., 2021).
4	Empirically testable	The hypothesis must be proved/disproved, verified/falsified (Barroga & Matanguihan, 2022).
5	Explanatory	The hypothesis provides insight and logical explanations (Economic scope, 2020).

Null hypotheses
Null hypotheses make predictions that of all possible people whom researchers might study (i.e., called the general population), there is no relationship between independent and dependent variables or no difference between groups of an independent variable or a dependent variable (Creswell, 2012, p. 126).

Alternative hypotheses
Use an alternative hypothesis if you think there will be a difference based on results from past research or an explanation or theory reported in the literature. The two types of alternative hypotheses are directional and nondirectional. In a directional alternative hypothesis, the researcher predicts the direction of a change, a difference, or a relationship for variables in the total population of people (Creswell, 2012, p. 127).

HARKing (Hypothesising After the Results are Known)
HARKing means formulating or altering hypotheses based on results, then presenting them as if they were planned in advance. HARKing is a questionable research practice across. The 3 types of HARKing are CHARKing, RHARKing, and SHARKing (Rubin, 2017).

Reliability
Reliability refers to finding the same result over time (Olmsted, 2024). Reliability assesses the consistency of results. Obtaining identical results after repeating the same procedures several times. Reliability in research refers to the consistency and reproducibility of measurements. It assesses the degree to which a measurement tool produces stable and dependable results when used repeatedly under the same conditions (McLeod, 2024). Psychologists consider three types of consistency: over time (test-retest reliability), across items (internal consistency), and across different researchers (inter-rater reliability) (Chiang et al, 2015). Reliability refers to the consistency and dependability of the data collection process. Reliability is like having a steady hand that produces the same result each time it reaches for a task (QuestionPro Collaborators, 2023).

Validity
The validity of a research study refers to how well the results among the study participants represent true findings among similar individuals outside the study. The validity of a research study includes two domains: internal and external validity (Patino & Ferreira, 2018). A valid test is simply one that measures what it intends to measure (Swanson, 2014).The extent to which an empirical measure adequately reflects the real meaning of the concept under consideration (Barbie, 2025)

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QUALITATIVE RESEARCH PREOCCUPATIONS
The most notable qualitative research concerns are seeing through the subject’s eyes; flexibility and limited structure; process emphasis; description and context emphasis; and concepts and grounded theory data (Bryman, 2012, p. 399).

Qualitative Research Trustworthiness
Credibility, confirmability, dependability, and transferability are the 4 criteria used to assess the trustworthiness of qualitative research; these criteria are often referred to as the Lincoln and Guba framework (Amin et al., 2020). Other authors have added authenticity to the criteria (Kyngäs et al., 2020).

No	Criteria	Explanation
1	Credibility (≈ internal validity)	Confidence that findings are a truthful, plausible representation of participants’ views (Korstjens & Moser, 2018).
2	Confirmability (≈ objectivity/neutrality)	Degree to which results come from data rather than researcher bias or imagination (Kyngäs et al., 2020).
3	Dependability (≈ reliability)	Stability and consistency of findings over time and conditions, given the same design.
4	Transferability (≈ external validity/generalizability)	The degree to which findings can apply to other contexts, supported by rich description so readers can judge fit (Morrow, 2005).
5	Authenticity	Several sources add authenticity as a fifth criterion (Cope, 2014).

RESEARCH PROPOSAL

• Topic
• Title
• Introduction
• Background
• Problem statement
• Significance
• Questions
• Aim
• Obejectives
• Literature Review
• Research methodology
• Ethical considerations
• Concept definitions
• Chapter sequencing
• Limitations
• Conclusion

THEMATIC ANALYSIS
A method used to analyse qualitative data in order to extract the most relevant themes from a dataset (Naeem et al., 2023). Thematic analysis is linked to the “Concepts and grounded theory data” preoccupation of qualitative research.

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LITERATURE REVIEW

A literature review is an objective, critical summary of published research literature relevant to a topic under consideration for research. Its purpose is to create familiarity with current thinking and research on a particular topic, and may justify future research into a previously overlooked or understudied area.

A literature review is a review or discussion of the current published material available on a particular topic. It attempts to synthesize and evaluate the material and information according to the research question, thesis, and central theme.

Literature review 5 writing the literature reviewsteps

1. Identify key terms.
2. Locate literature in the library resources.
3. Critically evaluates the materials and makes a determination of its relevance.
4. Obtain the literature take note of abstracts.
5. Writing the literature review

Types of literature reviews

NO	REVIEW TYPE	EXPLANATION OF REVIEW TYPE
1	Narrative Review (traditional review)	Comprehensive, qualitative, and subjective analysis of existing literature on a topic, designed to summarize, interpret, and critique knowledge without a strict, reproducible methodology.
2	Systematic Review	A high-level, evidence-based research method that uses strict, pre-planned, and transparent procedures to identify, select, and critically appraise all relevant research on a specific topic
3	Scoping review	A type of knowledge synthesis that systematically maps the existing literature on a broad topic to identify key concepts, theories, and evidence gaps.
4	Critical review	An academic writing task that summarizes and evaluates a text (book, article, or medium) to assess its merit, quality, and contribution to a field
5	Realist Review	Theory-driven, interpretive, and systematic literature review method designed to explain how and why complex interventions work (or fail) in different contexts
6	Meta-narrative review (Grand narrative)	A qualitative review method for reviewing complicated topics by outlining how different research paradigms have reviewed them over the course of time. A meta-narrative is a grand story that seeks to explain smaller, more complicated stories and contextualise them.
7	Mixed-methods systematic review	A mixed-methods systematic review applies the principles of mixed-methods research to the review process, that is, studies from different research traditions (but focused on the same topic) are combined to generate evidence to guide decision-making (Pearson et al, 2015).
8	Rapid Review	A rapid review is a type of literature review in which some of the components of a standard systematic review process are omitted in order to simplify the process and produce information within a short time. Alternative terms for a rapid review include Rapid Evidence Review, Rapid Evidence Assessment, Rapid Systematic Review, Expedited, Review, Rapid Evidence Summary. A rapid review is a form of knowledge synthesis that accelerates the process of conducting a traditional systematic review through streamlining or omitting specific methods to produce evidence for stakeholders in a resource-efficient manner PICOT Criteria FINER Criteria
9	Framework-based synthesis	It involves reviewers in choosing a conceptual model likely to be suitable for the question of the review, and using it as the basis of their initial coding framework (Dixon-Woods, 2011).
10	Mini literature Review	Concise, focused summary of recent research on a specific topic, highlighting key findings, trends, and gaps without the exhaustive depth of a full review
11	Conceptual Literature Review	A conceptual literature review is a type of review that focuses on the theoretical frameworks and concepts related to a specific topic. Unlike systematic reviews that aim to synthesize empirical findings, a conceptual review seeks to explore and clarify the ideas, theories, and models that underpin a particular field of study.

6 STEPS AND 14 DECISION POINTS FOR SYSTEMATIC LITERATURE REVIEW

STEP	SLR STEP	TASKS	14 DECISION POINTS (dp)
Step 1	RESEARCH QUESTION AND THEORY. Define the theory-backed research question	Preparing the research by identifying a relevant gap in the literature Justifying the need to map a certain field of research Theoretical grounding of the SLR	dp 1. Specify the research gap and research question(s) dp 2.Choose a theoretical approach (inductive/abductive/deductive) dp 3. Define the main theoretical framework, constructs and coding scheme (deductive)
Step 2	CHARACTERISTICS. Determine the needed characteristics of primary studies	Define criteria for the inclusion and exclusion of literature to be reviewed	dp 4. Provide the inclusion and exclusion criteria
Step 3	LITERATURE SAMPLE. Retrieving a sample of potentially relevant literature	Collecting literature to be analyzed	dp 5. Define sources and databases dp 6. Define search terms and create a search string
Step 4	LITERATURE OF RELEVANCE. Select the relevant literature	Selection of relevant literature against the inclusion and exclusion criteria defined in step 2	dp 7. Include and exclude literature to analyse and synthesise
Step 5	LITERATURE SYNTHESIS. Synthesize the literature	Content analysis–based	dp 8. Select data extraction tool(s) dp 9. Code based on constructs dp 10. Conduct a subsequent (statistical) analysis (optional) dp 11. Ensure validity and reliability
Step 6	RESULTS. Report the results	Paper writing Journal choice for submission	dp 12. Structure the paper dp 13. Present a refined theoretical framework and discussing its contributions dp 14. Derive an appropriate journal from the analyzed papers and explicating theoretical links to it

Research evidence pyramid

The research evidence pyramid is a visual way to rank study designs by how strongly they can answer clinical questions and resist bias. 

8	Meta analysis	A meta-analysis is a statistical technique that combines the results of multiple independent studies on the same question to produce a single, more precise estimate of the effect. Systematic reviews and meta-analyses of randomised controlled trials (RCTs) sit at the top of the evidence hierarchy, above individual RCTs and observational studies
7	Systemic reviews	Systematic reviews sit high in evidence-based medicine because they synthesise all relevant studies on a question using explicit, reproducible methods to minimise bias and provide robust effect estimates.
6	Critical appraisal	Critical appraisal is the core step that turns published studies into trustworthy guidance for practice. It links study design (and its “level”) with judgments about validity, bias, and applicability. Critical appraisal assesses the validity, reliability, applicability, and generalisability of individual studies
5	RCT’s	Randomised controlled trials (RCTs) are widely placed at or near the apex of evidence pyramids because randomisation helps isolate whether an intervention truly causes an outcome. RCTs are placed high because randomisation balances known and unknown prognostic factors, reducing confounding and making causal inference stronger than with other designs
4	Cohort studies	Cohort studies sit mid‑high in the evidence hierarchy, below randomised trials but above case reports and many other observational designs. Cohort studies are observational designs used to understand how exposures (like risk factors or treatments) relate to outcomes over time, and most evidence hierarchies place them in the middle–upper tiers of study designs used for clinical decisions.
3	Case control studies	Case–control studies are analytical observational designs where participants are chosen based on outcome status: those with the disease or condition (cases) and those without it (controls). Researchers then look back in time to assess past exposures in each group. They are typically retrospective and non‑interventional (no manipulation of exposure)
2	Case report / case series	A case report is a detailed narrative of a single patient (sometimes 1–2), describing presentation, investigations, treatment, and outcome. A case series follows a group of patients with a similar diagnosis or procedure over time, typically more than four patients, and remains uncontrolled and descriptive.
1	EXPERT OPINION	Expert opinion is usually placed at the bottom of evidence pyramids, yet it remains widely used and, in some contexts, indispensable.

GLOSSARY

TERM	DEFINITION
Concept	An abstract idea that defines a phenomenon and distinguishes it from others.
Conceptualisation	The process of defining a concept or construct precisely enough to guide theory, framework building, and eventual measurement.
Construct	A theoretical abstraction that is used to represent a real or posited phenomenon so researchers can define it, relate it to other concepts, and measure it indirectly through observations. The construct definition comes before measurement, and vague definitions weaken validity, theory building, and comparison across studies.
Correlational research	Research that examines associations between variables without manipulating them, so it is useful for description and prediction, but does not by itself justify causal claims.
Dataset	The product of related data properly grouped for a specific utilisation.
Double-blind	The tester and participant do not know the identity of the assigned treatment.
Empirical	Empirical refers to that which is derived from experiments and observations. The empirical approach is also referred to as an evidence-based approach that results in knowledge gained from direct observations and experiments.
Event-driven (EDCT)	Event driven clinical trials. A trial the stages of analysis and all termination is determined by prespecified clinical events.
Generalizability	“In quantitative research, the researcher is usually concerned to be able to say that his or her findings can be generalized beyond the confines of the particular context in which the research was conducted” (Bryman, 2012, p. 177) “The concern with generalizability or external validity is particularly strong among quantitative researchers using cross-sectional and longitudinal designs. There is a concern about generalizability among experimental research” (Bryman, 2012, p. 177).
HARKing	HARKing (Hypothesising After the Results are Known) means creating or revising hypotheses based on results, then presenting them as if they were planned in advance. The presentation of a post hoc hypothesis as if it were a priori hypothesis.
Logic	The process of using an argument to arrive at a conclusion. Logic is the system of determine the validity of an explanation (Babbie, 2025).
Open Coding	The initial, inductive process of breaking qualitative data apart, naming segments with emergent codes, and constantly comparing them to build categories that underpin later thematic or theoretical development.
Operationalisation	The process of translating an abstract concept into observable indicators, variables, or procedures so it can be studied empirically.
Paradigm	A paradigm is a model or framework for observation and understanding that shapes both what we see and how we understand it (Babbie, 2021, p. 218). A general organizing framework for theory and research that includes basic assumptions, key issues, models of quality research, and methods for seeking answers (Neuman, 2014, p. 96).
Placebo-controlled	Utilisation of an inactive substance that is compared to the test drug to test whether the tested study drug (tested drug) has an effect.
Population	The entire group of people, cases, or entities a study is about, from which a sample may be drawn and to which findings are meant to apply.
Proposition	A theoretical statement about the relationship between two or more concepts.
Quasi-experiment	A quasi-experimental study compares outcomes between groups where the intervention is not randomly assigned due to infeasibility or unethicality.
Random selection	A sampling method where all the subjects/units/elements in the population have an equal and unbiased chance of being selected into the subset (sample).
Randomization	This is the process of assigning participants in a trail to two separate groups i.e. the treatment group and the non-treatment group or the intervention group and the non-intervention group. In this allocation system both, the researcher and the subjects do not choose which groups the subjects are allocated to. Randomisation helps to prevent selection bias and accidental bias. The methods of randomisation are simple randomisation, block randomisation, stratified randomisation and unequal randomization
Randomized	The medication/drug being studied is assigned to a participant by chance.
Rationality	Rationality is a logical system of explanation that is typically associated with science. (Latin noun ratio = “calculate”, “account”, “reckon”) (rationalitas = reasonableness)
Reflexivity	In qualitative research, reflexivity is about researchers critically examining how who they are, what they believe, and the context in which they work shape the study.
Rigor	Well-aligned research questions and designs, proactive control of bias, transparent methods and analysis, and continuous checking of reliability and validity.
Sample	The smaller group of people, cases, or observations studied to represent a larger population.
Scientific method	Procedures followed by researchers to arrive at conclusions.
Sensitivity	Sensitivity approaches diagnostic accuracy from the point of view of diseased subjects. The probability of a subject who is affected by a certain disease to test positive for that particular disease is termed sensitivity. Sensitivity can also be defined as the ability of a diagnostic tool to accurately identify diseased subjects. It is the ratio of true positive test results over the total of all the diseased subjects. If a testing modality has a high sensitivity for a specific disease and is negative then it rules out that disease. Sensitivity is not affected by the prevalence of the disease since the total is all the diseased patients.
Specificity (true negative rate)	In contrast to sensitivity, specificity focuses on healthy subjects. The probability of a subject who does not have a certain disease testing negative for that particular disease is termed specificity. Specificity evaluates how well a diagnostic tool can correctly identify subjects without a certain disease as being negative for that disease. It is the ratio of true negative test result over the total number of all healthy patients.
Survey research	A method for collecting information (attitudes, opinions and trends) about a specific population from a random sample of that population by asking standardised questions, then using those responses to describe a larger population. Survey research is mostly utilised by psychology, sociology, political science, and behavioural economics.
Tharking	An alternative to HARKing; is Transparent post hoc analysis. The transparent post hoc analysis section must be clearly labelled in the study’s discussion.
Triangulation	The idea that looking at something from multiple points of view improves accuracy (Neuman, 2014, p. 166). Triangulation means studying one phenomenon from multiple view points to improve credibility and richness of understanding. It can involve different data, methods, investigators, and theories, and is used for convergence, complementarity, and sometimes to surface useful divergences.
True experiment	True experiments aim to establish cause-and-effect by manipulating at least one independent variable and observing its impact on a dependent variable under controlled conditions. The manipulation of an independent variable, random assignment to at least one control and one experimental group, and tight control over other factors.
Validity (measurement)	Validity is a concept pertaining to the measurement preoccupation of quantitative research. The degree to which a measure of a concept truly reflects that concept (Bryman, 2012, p. 713). A term describing a measure that accurately reflects the concept it is intended to measure (Babbie, 2021, p. 218).

References 

REFERENCES

Abe, H., Ando, R., Morishita, T., Ozeki, K., Mineshima, K., & Okada, M. (2025). Abductive reasoning with syllogistic forms in large language models. In Lecture Notes in Computer Science (pp. 3–17). Springer Nature Switzerland. Aguzzoli, R., Lengler, J., Miller, S. R., & Chidlow, A. (2024). Paradigms in qualitative IB research: Trends, analysis and recommendations. Management International Review : MIR : Journal of International Business, 64(2), 165–198. https://doi.org/10.1007/s11575-024-00529-5 Ahmed, A., Idris, S. H., Yahaya, H., Jidda, A., & Ngohi, M. B. (2025). The Research Onion as a teaching tool in nursing education: Enhancing research competence among nursing students. International Journal of Research and Scientific Innovation, XII(V), 1309–1319. https://doi.org/10.51244/ijrsi.2025.120500125 Ahmed, S. K. (2024). How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. Oral Oncology Reports, 12(100662), 100662. https://doi.org/10.1016/j.oor.2024.100662 Allana, S., & Clark, A. (2018). Applying meta-theory to qualitative and mixed-methods research: A discussion of critical realism and heart failure disease management interventions research. International Journal of Qualitative Methods, 17(1), 160940691879004. https://doi.org/10.1177/1609406918790042 Allemang, B., Sitter, K., & Dimitropoulos, G. (2022). Pragmatism as a paradigm for patient-oriented research. Health Expectations: An International Journal of Public Participation in Health Care and Health Policy, 25(1), 38–47. https://doi.org/10.1111/hex.13384 Amin, M. E. K., Nørgaard, L. S., Cavaco, A. M., Witry, M. J., Hillman, L., Cernasev, A., & Desselle, S. P. (2020). Establishing trustworthiness and authenticity in qualitative pharmacy research. Research in Social & Administrative Pharmacy, 16(10), 1472–1482. https://doi.org/10.1016/j.sapharm.2020.02.005 Andrade, C. (2021). A student’s guide to the classification and operationalization of variables in the conceptualization and design of a clinical study: Part 1. Indian Journal of Psychological Medicine, 43(2), 177–179. https://doi.org/10.1177/0253717621994334 Auriacombe, C. J., & Holtzhausen, N. (2014). Theoretical and philosophical considerations in the realm of the Social Sciences for Public Administration and Management emerging researchers. Administratio Publica, 22(4), 8-25. Aryal, R. (2024). Portraying my Research Journey from Positivist to Post-Modernist. Journal of Kathmandu BernHardt College, 6(1), 140–157. https://doi.org/10.3126/jkbc.v6i1.72981 Babbie, E. R. (2021). The practice of social research (15th ed.). Cengage Learning. Bai, J., Wang, Y., Zheng, T., Guo, Y., Liu, X., & Song, Y. (2024). Advancing abductive reasoning in knowledge graphs through complex logical hypothesis generation. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1312–1329. Barroga, E., & Matanguihan, G. J. (2022). A practical guide to writing quantitative and qualitative research questions and hypotheses in scholarly articles. Journal of Korean Medical Science, 37(16), e121. https://doi.org/10.3346/jkms.2022.37.e121 Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press. Buriro, A., Ednut, N., & Khatoon, Z. (2021). Philosophical underpinning and phenomenology approach in social science research. Asia-Pacific – Annual Research Journal of Far East & South East Asia, 38, 237–254. https://doi.org/10.47781/asia-pacific.vol38.iss0.2526 Burnette, J. L. (2007). Operationalization. In Encyclopedia of Social Psychology. SAGE Publications, Inc. Chen, K., Ruan, D., Dan, Y., Wang, Y., Yan, S., Wu, X., Zhang, Y., Chen, Q., Zhou, J., He, L., Qi, B., Li, L., Guo, Q., Shi, X., & Zhang, W. (2025). A survey of inductive reasoning for large language models. In arXiv [cs.CL]. https://doi.org/10.48550/arXiv.2510.10182 Chiang, I.-C. A., Jhangiani, R. S., & Price, P. C. (2015). Reliability and validity of measurement. In Research Methods in Psychology – 2nd Canadian Edition. BCcampus Creswell, J.W. 2008. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. 3rd ed. Upper Saddle River, NJ: Pearson. Crupi, V. (2015). Inductive logic. Journal of Philosophical Logic, 44(6), 641–650. https://doi.org/10.1007/s10992-015-9348-8 Daud, A., Maspuroh, Fitriani, E. N., Alawiyah, N. Z., & Firjatullah, R. (2025). Knowledge, science and scientific knowledge. Bibliotheca: Journal of Philosophy, 1(1), 17–26. https://doi.org/10.61166/bibliotheca.v1i1.4 Deduction. (2021). In Encyclopedic Dictionary of Archaeology (pp. 371–371). Springer International Publishing. Department of Built Environment Studies & Technology, College of Built Environment, Universiti Teknologi MARA, Perak Branch, 32610, Seri Iskandar Campus, Malaysia, & Mohammad Ali, I. (2024). A guide for positivist research paradigm: From philosophy to methodology. Idealogy Journal, 9(2). https://doi.org/10.24191/idealogy.v9i2.596 Dominion Dominic, E. (2023). Linkages among ontology, epistemology, methodology and method in research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4508075 Durach, C. F., Kembro, J., & Wieland, A. (2017). A new paradigm for systematic literature reviews in supply chain management. Journal of Supply Chain Management, 53(4), 67-85. Dixon-Woods, M. (2011). Using framework-based synthesis for conducting reviews of qualitative studies. BMC Medicine, 9(1), 39. https://doi.org/10.1186/1741-7015-9-39 Djennad, B., & Djellouli, M. (2025). Probabilistic sampling in media and communication studies: Concept, procedures, and applications. EON, 6(1), 105–115. https://doi.org/10.56177/eon.6.1.2025.art.9 Economic scope. (2020). Publishing House Helvetica (Publications). Fischer, F. (1998). Beyond Empiricism: Policy Inquiry in Post positivist Perspective. Policy Studies Journal: The Journal of the Policy Studies Organization, 26(1), 129–146. https://doi.org/10.1111/j.1541-0072.1998.tb01929.x George, K. M. (2024). From methodological Authoritarianism to epistemic Realism: Multidisciplinary research paradigms and the post-modern turn. E-Journal of Humanities, Arts and Social Sciences, 2722–2733. https://doi.org/10.38159/ehass.20245163 Grass, K. (2024). The three logics of qualitative research: Epistemology, ontology, and methodology in political science. American Journal of Qualitative Research, 8(1), 42–56. https://doi.org/10.29333/ajqr/14083 Ghasemi, A., Hosseinpanah, F., Kashfi, K., & Bahadoran, Z. (2025). Research hypothesis: A brief history, central role in scientific inquiry, and characteristics. Addiction & Health, 17, 1623. https://doi.org/10.34172/ahj.1623 Hayes, B. K., & Heit, E. (2018). Inductive reasoning 2.0. Wiley Interdisciplinary Reviews. Cognitive Science, 9(3), e1459. https://doi.org/10.1002/wcs.1459 Kaluarachchi, K. A. D. P. (2025). Philosophical approaches to qualitative research (positivism, phenomenological approach & Critical social sciences). Sri Lanka Journal of Social Work, 8(2), 41–58. https://doi.org/10.4038/sljsw.v8i2.11 Khabibullah, M., Alimin, A., & Sholahuddin, G. M. I. (2025). Beyond the paradigm wars: Pragmatism as a meta-framework for integrating philosophical traditions in mixed methods research. Qomaruna, 2(2), 110–125. https://doi.org/10.62048/qjms.v2i2.81 Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. The European Journal of General Practice, 24(1), 120–124. https://doi.org/10.1080/13814788.2017.1375092 Kouam Arthur William, F. (2024). Interpretivism or constructivism: Navigating research paradigms in social science research. International Journal of Research Publications, 143(1). https://doi.org/10.47119/ijrp1001431220246122 Kyngäs, H., Kääriäinen, M., & Elo, S. (2020). The trustworthiness of content analysis. In The Application of Content Analysis in Nursing Science Research (pp. 41–48). Springer International Publishing. Lawani, A. (2021). Critical realism: what you should know and how to apply it. Qualitative Research Journal, 21(3), 320–333. https://doi.org/10.1108/qrj-08-2020-0101 Lim, W. M. (2025). What is quantitative research? An overview and guidelines. Australasian Marketing Journal (AMJ), 33(3), 325–348. https://doi.org/10.1177/14413582241264622 Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13(6), 522–525. https://doi.org/10.1093/fampra/13.6.522 Martela, F. (2015). Fallible inquiry with ethical ends-in-view: A pragmatist philosophy of science for organizational research. Organization Studies, 36(4), 537–563. https://doi.org/10.1177/0170840614559257 Mazur, L. B. (2020). The epistemic imperialism of science. Reinvigorating early critiques of scientism. Frontiers in Psychology, 11, 609823. https://doi.org/10.3389/fpsyg.2020.609823 McChesney, K., & Aldridge, J. (2019). Weaving an interpretivist stance throughout mixed methods research. International Journal of Research & Method in Education, 42(3), 225–238. https://doi.org/10.1080/1743727x.2019.1590811 McLeod, S. (2024, November 8). Reliability vs validity in research. Simply Psychology. https://www.simplypsychology.org/reliability-or-validity.html Misra, D. P., Gasparyan, A. Y., Zimba, O., Yessirkepov, M., Agarwal, V., & Kitas, G. D. (2021). Formulating hypotheses for different study designs. Journal of Korean Medical Science, 36(50), e338. https://doi.org/10.3346/jkms.2021.36.e338 Morrow, S. L. (2005). Quality and trustworthiness in qualitative research in counseling psychology. Journal of Counseling Psychology, 52(2), 250–260. https://doi.org/10.1037/0022-0167.52.2.250 Mohajan, H. K. (2020). Quantitative research: A successful investigation in natural and social sciences. Journal of Economic Development Environment and People, 9(4). https://doi.org/10.26458/jedep.v9i4.679 Mr, M. I. F., Anam, A. M., Wiratmoko, D., Yaacob, N. H., & Alwiyah, N. (2025). Positivism and Ibn Khaldun’s thought: bridging objectivity and social dynamics. Harmoni Sosial Jurnal Pendidikan IPS, 12(1), 26–36. https://doi.org/10.21831/hsjpi.v12i1.83847 Mwange, A., Joseph, D. B. A., Matoka, P. D. W., Sampa, D. B. A., & Shiyunga, D. (2023). Research methodology and design: Types, methods, tools, tests and analysis. Research Journal of Finance and Accounting, 14(14), 1-11. Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231205789 National Academies of Sciences, Engineering, Medicine, Policy, Global Affairs, Committee on Science, Engineering, Medicine, Public Policy, … Replicability. (2019). Replicability. Washington, D.C., DC: National Academies Press. Neuman, W. L. (2011). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson Education. Ngwenya, S., & Boshoff, N. (2018). Valorisation: The case of the faculty of applied sciences at the national university of science and technology, Zimbabwe. South African Journal of Higher Education, 32(2). https://doi.org/10.20853/32-2-2468 Niure, D. P., & Sapkota, M. (2025). Philosophical assumptions across paradigms: Implications for educational research. ILAM इलम, 21(1), 36–46. https://doi.org/10.3126/ilam.v21i1.75654 Olmsted, J. (2024). Research Reliability and Validity: Why do they matter? Journal of Dental Hygiene, 98(6), 53–57. https://jdh.adha.org/content/98/6/53 Park, L. S.-C., & Peter, S. (2022). Application of critical realism in social work research: Methodological considerations. Aotearoa New Zealand Social Work, 34(2), 55–66. https://doi.org/10.11157/anzswj-vol34iss2id932 Pathak, K. P., & Thapaliya, S. (2022). Some philosophical paradigms and their implications in health research: A critical analysis. International Research Journal of MMC, 3(3), 9–17. https://doi.org/10.3126/irjmmc.v3i3.48627 Patino, C. M., & Ferreira, J. C. (2018). Internal and external validity: can you apply research study results to your patients? Jornal Brasileiro de Pneumologia: Publicacao Oficial Da Sociedade Brasileira de Pneumologia e Tisilogia, 44(3), 183. https://doi.org/10.1590/S1806-37562018000000164 Pearson, A., White, H., Bath-Hextall, F., Salmond, S., Apostolo, J., & Kirkpatrick, P. (2015). A mixed-methods approach to systematic reviews. International Journal of Evidence-Based Healthcare, 13(3), 121–131. https://doi.org/10.1097/XEB.0000000000000052 Pratiwi, U., Karneli, Y., & Marsidin, S. (2024). Pemahaman Mendasar tentang Hakekat Ilmu dalam Tinjauan Filsafat: Ontologi, Epistemologi, dan Aksiologi. Jurnal Pendidikan Siber Nusantara, 2(2), 74–80. https://doi.org/10.38035/jpsn.v2i2.170 QuestionPro Collaborators. (2023, August 17). Reliability vs. Validity in research: Types & examples. QuestionPro. https://www.questionpro.com/blog/reliability-vs-validity-in-research/ Rauteda, K. R. (2025). Quantitative research in education: Philosophy, uses and limitations. Journal of Multidisciplinary Research and Development, 2(1), 1–11. https://doi.org/10.56916/jmrd.v2i1.993 Rubin, M. (2017). When does HARKing hurt? Identifying when different types of undisclosed post hoc hypothesizing harm scientific progress. Review of General Psychology: Journal of Division 1, of the American Psychological Association, 21(4), 308–320. https://doi.org/10.1037/gpr0000128 Sauer, P. C., & Seuring, S. (2023). How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions. Review of Managerial Science, 17(5), 1899–1933. https://doi.org/10.1007/s11846-023-00668-3 Sagi, G. (2021). Logic as a methodological discipline. Synthese, 199(3–4), 9725–9749. https://doi.org/10.1007/s11229-021-03223-3 Scientific knowledge: Methodology and technology. (2019). Ushynsky University. Scotland, J. (2012). Exploring the philosophical underpinnings of research: Relating ontology and epistemology to the methodology and methods of the scientific, interpretive, and critical research paradigms. English Language Teaching, 5(9). https://doi.org/10.5539/elt.v5n9p9 Swanson, E. (2014). Validity, reliability, and the questionable role of psychometrics in plastic surgery. Plastic and Reconstructive Surgery. Global Open, 2(6), e161. https://doi.org/10.1097/GOX.0000000000000103 Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. doi:10.5116/ijme.4dfb.8dfd Thompson, W. H., & Skau, S. (2023). On the scope of scientific hypotheses. Royal Society Open Science, 10(8), 230607. https://doi.org/10.1098/rsos.230607 Turner, D. P. (2020). Sampling methods in research design. Headache, 60(1), 8–12. https://doi.org/10.1111/head.13707 van der Waldt, G. (2020). Constructing conceptual frameworks in social science research. The Journal for Transdisciplinary Research in Southern Africa, 16(1). https://doi.org/10.4102/td.v16i1.758 Varpio, L., Paradis, E., Uijtdehaage, S., & Young, M. (2020). The distinctions between theory, theoretical framework, and conceptual framework. Academic Medicine: Journal of the Association of American Medical Colleges, 95(7), 989–994. https://doi.org/10.1097/ACM.0000000000003075 Wati, R. (2024). Analyzing and viewing the development of construction of the philosophical view of positivism. Journal of World Science, 3(8), 906–913. https://doi.org/10.58344/jws.v3i8.689 Yam, J. H., & Taufik, R. (2021). Hipotesis Penelitian Kuantitatif. Perspektif : Jurnal Ilmu Administrasi, 3(2), 96–102. https://doi.org/10.33592/perspektif.v3i2.1540 Yao, Q. (2024). Concepts and reasoning: A conceptual review and analysis of logical issues in empirical social science research. Integrative Psychological & Behavioral Science, 58(2), 502–530. https://doi.org/10.1007/s12124-023-09792-x Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences: European Journal of Education. European Journal of Education, 48(2), 311–325. https://doi.org/10.1111/ejed.12014 Yvonne Feilzer, M. (2010). Doing mixed methods research pragmatically: Implications for the rediscovery of pragmatism as a research paradigm. Journal of Mixed Methods Research, 4(1), 6–16. https://doi.org/10.1177/1558689809349691 Zhang, T. (2023). Critical realism: A critical evaluation. Social Epistemology, 37(1), 15–29. https://doi.org/10.1080/02691728.2022.2080127 Zrineh, A., Al-Usta, M., & Alwawi, A. (2026). Sampling methods and sample size determination in clinical research: An educational review. Journal of General and Family Medicine, 27(1), e70096. https://doi.org/10.1002/jgf2.70096