Explanatory Research – Designs, Methods and Examples

Explanatory research is a systematic approach used to investigate why or how a phenomenon occurs. It develops or tests explanations by examining relationships, mechanisms, and possible causes. It may use experiments, observational data, case studies, qualitative evidence, or mixed methods, but the strength of its conclusions depends on the design and assumptions used.

Introduction

Knowing that two variables are related does not necessarily explain why they are related. A university may observe that students who attend optional tutorials achieve higher grades, for example. That pattern does not reveal whether tutorials improve learning, whether highly motivated students are more likely to attend, or whether previous academic ability explains both attendance and achievement.

Explanatory research moves beyond documenting a pattern. It investigates the factors, processes, mechanisms, or causal relationships that could have produced the observed result.

This guide explains:

• What explanatory research means.
• How it differs from exploratory and descriptive research.
• Whether it is the same as causal research.
• Which qualitative, quantitative, and mixed methods can be used.
• How to formulate explanatory questions and hypotheses.
• How to design, analyze, and report a rigorous explanatory study.
• Which mistakes weaken explanatory conclusions.

Key takeaways

• Explanatory research asks why or how a phenomenon, relationship, or outcome occurs.
• An explanatory purpose does not automatically make a study causal.
• Experimental, quasi-experimental, observational, qualitative, and mixed-methods designs can all contribute to explanation.
• Correlation and statistical significance alone do not establish causation.
• A defensible explanation must address alternative explanations, measurement quality, temporal order, bias, and uncertainty.

What Is Explanatory Research?

Explanatory research is research designed to explain why a pattern, behaviour, relationship, event, or outcome occurs. It connects evidence to a theoretical or causal account rather than merely describing what has been observed.

A descriptive study might establish that employees working flexible schedules report greater job satisfaction. An explanatory study asks why that relationship exists.

Possible explanations include:

• Flexible schedules improve work–life balance.
• Employees experience greater autonomy.
• Flexible workers spend less time commuting.
• Supportive organizations are more likely to offer flexibility.
• Employees with certain occupations are more likely to receive flexible arrangements.

The explanatory researcher must decide which explanations are theoretically plausible and determine what evidence would support or weaken each one.

The purpose of explanatory research

The central purposes are to:

1. Explain an observed pattern or outcome.
2. Test a theory or theoretical proposition.
3. investigate a possible cause-and-effect relationship;
4. identify the mechanism through which an effect occurs;
5. evaluate competing explanations;
6. clarify why an intervention succeeds in one context but not another;
7. provide evidence that can improve policies, practices, or future studies.

Main characteristics

Explanatory research commonly has the following characteristics:

• It focuses on “why,” “how,” “through what process,” or “under what conditions.”
• It draws on existing knowledge or theory.
• It identifies an outcome that requires explanation.
• It proposes one or more explanatory factors.
• It considers alternative explanations.
• It examines relationships between variables, events, meanings, or processes.
• It usually produces a qualified explanation rather than absolute proof.
• It makes the strength and limits of the explanation explicit.

Not every explanatory study has all these features. Qualitative explanatory case studies, for example, may develop a mechanism-based explanation without estimating a numerical causal effect.

Is Explanatory Research the Same as Causal Research?

Explanatory research and causal research overlap, but they are not always identical. Causal research is a narrower form of explanatory research that attempts to determine whether changing one factor produces a change in another. Explanatory research may also examine mechanisms, interpretations, contexts, and theoretically plausible reasons without establishing a causal effect.

The term is used in at least three ways.

1. Explanation as a general research purpose

In its broadest sense, explanatory research develops an account of why or how something occurs. This account may be based on:

• Statistical relationships.
• Participant experiences.
• Organizational processes.
• Historical evidence.
• Comparative case evidence.
• Theoretical mechanisms.

2. Explanation as causal inference

In a narrower sense, explanatory research is treated as causal research. The objective is to estimate the effect of an exposure, treatment, policy, intervention, or condition on an outcome.

A causal question might be:

What is the effect of attending weekly tutoring sessions on first-year students’ final examination scores?

Answering this question requires more than demonstrating an association. The study must define the causal contrast, address confounding and selection, establish the correct time order, and justify the assumptions linking the data to the causal claim (Igelström et al., 2022).

3. Explanation in mixed-methods research

In an explanatory sequential mixed-methods design, researchers collect and analyze quantitative data first. They then conduct a qualitative phase to explain selected quantitative findings.

For example:

1. A survey finds that international students report lower use of counselling services.
2. Researchers interview selected students.
3. Interviews reveal that language concerns, cultural perceptions, appointment systems, and uncertainty about confidentiality influence service use.

This is a specific mixed-methods structure, not a general synonym for explanatory research (Fetters et al., 2013).

Explanatory vs Exploratory vs Descriptive Research

Research purpose	Main question	Starting knowledge	Typical outcome	Example
Exploratory	What might be happening?	Limited	Concepts, possible variables, preliminary questions	Exploring why students are beginning to use generative AI
Descriptive	What is happening, to whom, where, or how often?	Sufficient to define what will be measured	Frequencies, distributions, characteristics, or patterns	Measuring how many students use generative AI each week
Correlational	Are variables related?	Defined variables	Strength and direction of association	Testing whether AI use is associated with assignment grades
Explanatory	Why or how does the outcome occur?	A defined phenomenon and plausible explanations	Tested or developed explanation	Examining whether feedback quality explains the relationship between AI use and grades
Causal	What would happen to the outcome if the cause were changed?	Clearly specified intervention or exposure	Estimated causal effect	Estimating the effect of an AI-literacy workshop on assignment quality
Predictive	How accurately can an outcome be forecast?	Outcome and predictor data	Predictions for new observations	Predicting which students are at risk of failing

A study may combine purposes. A research project could begin by exploring student experiences, describe patterns in a survey, test explanatory hypotheses, and later evaluate an intervention.

Explanatory Research Designs and Methods

Explanatory research is a purpose rather than one fixed method. Researchers must choose a design capable of producing the kind of explanation they intend to claim.

1. Randomized experimental research

A randomized experiment assigns eligible participants or units to different conditions by chance. The researcher manipulates an independent variable and compares outcomes between groups.

Example: Students are randomly assigned to receive either conventional feedback or structured audio feedback. Their later writing performance is compared.

Random assignment helps make treatment groups comparable, reducing the influence of pre-existing differences. However, experiments may still be affected by attrition, noncompliance, measurement problems, spillovers, or limited generalizability.

Randomized trials should be transparently reported using an appropriate guideline, such as CONSORT 2025 where applicable.

2. Quasi-experimental research

Quasi-experiments estimate effects without conventional random assignment. They use a policy rule, timing difference, eligibility threshold, comparison group, or naturally occurring event to construct a credible comparison.

Common approaches include:

• Regression discontinuity.
• Interrupted time-series analysis.
• Difference-in-differences.
• Matched comparison groups.
• Instrumental-variable designs.
• Natural experiments.

Example: A scholarship is offered only to applicants scoring above a fixed eligibility threshold. Researchers compare students immediately above and below the threshold to estimate the scholarship’s effect on enrolment.

A quasi-experimental design can provide stronger causal evidence than an uncontrolled before-and-after comparison, but its assumptions must be defended.

3. Observational analytical research

Observational studies measure naturally occurring exposures rather than assigning them. Relevant designs include:

• Cohort studies.
• Case-control studies.
• Cross-sectional analytical studies.
• Longitudinal panel studies.
• Secondary-data studies.

Example: Researchers follow employees over three years to examine whether changes in remote-work frequency precede changes in job satisfaction.

Observational data can support causal inference under carefully stated assumptions, but simply adding control variables to a regression model does not eliminate every source of bias. Researchers should examine confounding, selection, measurement error, reverse causation, missing data, and model dependence.

4. Explanatory case-study research

An explanatory case study investigates how a result developed within its real-world context.

It may use:

• Interviews.
• Documents.
• Observations.
• Administrative records.
• Timelines.
• Process tracing.
• Pattern matching.
• Comparison of expected and observed events.

Example: A researcher studies why one hospital successfully implemented an electronic-record system while a similar hospital experienced severe disruption.

The objective is not necessarily to calculate an average causal effect. Instead, the researcher may reconstruct the sequence of events and identify mechanisms such as leadership support, staff participation, training quality, infrastructure, and local adaptation.

5. Comparative and historical explanatory research

Researchers can compare countries, institutions, communities, policies, or historical periods to explain differences in outcomes.

Example: A comparative study investigates why two cities with similar populations experienced different reductions in traffic fatalities after introducing road-safety policies.

The researcher might compare:

• Policy timing.
• Enforcement.
• Road design.
• Public transport.
• Political support.
• Implementation quality.
• Economic conditions.

Strong comparative research specifies why the selected cases provide useful evidence and avoids selecting cases only because they support the preferred explanation.

6. Qualitative explanatory research

Qualitative research can explain how participants interpret events, how processes unfold, and why actions make sense within a particular context.

Suitable approaches include:

• Explanatory case studies.
• Realist evaluation.
• Process tracing.
• Grounded-theory studies with an explanatory objective.
• Ethnographic explanation.
• Narrative and historical analysis.
• Mechanism-focused interviews.

Example: Interview data may explain why an apparently effective employee-wellness programme has low participation. Employees might regard participation as signalling weakness or fear that health information will not remain confidential.

Qualitative evidence can reveal a plausible mechanism that was not captured in a numerical dataset. It should not, however, be presented as proving a population-level causal effect unless the design supports that claim.

7. Mixed-methods explanatory research

Mixed methods combines quantitative and qualitative evidence to create a more complete explanation.

Explanatory sequential design

The usual sequence is:

QUAN → follow-up QUAL → integration

The qualitative phase is selected and designed in response to the quantitative results.

Researchers might follow up:

• Unexpected results.
• Outliers.
• Subgroup differences.
• Null findings.
• Contradictory patterns.
• Cases showing unusually strong or weak effects.

The final interpretation should integrate the two strands rather than report two disconnected studies.

Variables and Explanatory Logic

Independent and dependent variables

The independent variable is the proposed explanatory factor, exposure, treatment, or predictor.

The dependent variable is the outcome the researcher wants to explain.

Example:

• Independent variable: participation in peer tutoring.
• Dependent variable: examination performance.

These labels do not establish causality. A variable is not automatically a cause merely because it appears on the right-hand side of a regression equation.

Confounding variables

A confounder is a factor related to both the proposed cause and the outcome that can produce a misleading association.

Suppose motivated students are more likely to attend tutoring and more likely to study independently. Motivation may partly explain the association between tutoring and examination performance.

Researchers can address measured confounding through design or analysis, including:

• Randomization.
• Restriction.
• Matching.
• Stratification.
• Regression adjustment.
• Weighting.
• Standardization.
• Fixed-effects models.
• Appropriate quasi-experimental designs.

Unmeasured confounding may remain. Sensitivity analysis should be considered when causal conclusions depend on unverifiable assumptions.

Mediating variables

A mediator lies on a proposed pathway between the cause and outcome.

For example:

Tutoring → improved subject understanding → higher examination score

Subject understanding is the proposed mediator.

Mediation analysis asks how an effect occurs. Causal interpretations require stronger assumptions than merely placing variables into a sequence of regressions.

Moderating variables

A moderator changes the strength or direction of a relationship.

For example, tutoring may improve performance more strongly among students with low prior subject knowledge than among students who already possess advanced knowledge.

Prior knowledge is a moderator because it indicates for whom or under what conditions the relationship differs.

Directed acyclic graphs

A directed acyclic graph, or DAG, is a diagram showing assumed causal relationships among variables.

DAGs can help researchers:

• Make causal assumptions visible.
• Identify possible confounders.
• Avoid adjusting for inappropriate variables.
• Distinguish mediators from confounders.
• Plan which variables must be measured.
• Explain the analytical strategy.

A DAG does not discover the true causal structure automatically. It represents assumptions based on theory, evidence, timing, and subject knowledge.

Explanatory Research Questions

A good explanatory question identifies:

• The phenomenon or outcome.
• The proposed explanation or mechanism.
• The population or units.
• The context.
• Sometimes the period.

General templates

Effect-focused question

What is the effect of [X] on [Y] among [population] in [setting]?

Mechanism-focused question

Through what mechanisms does [X] influence [Y] among [population]?

Comparative question

Why does [Y] differ between [case A] and [case B]?

Conditional question

Under what conditions does [X] have a stronger or weaker relationship with [Y]?

Mixed-methods question

How do participant experiences help explain the observed relationship between [X] and [Y]?

Examples

• How does formative feedback influence undergraduate students’ academic writing performance?
• Why do some small businesses adopt cybersecurity practices while similar businesses do not?
• Does access to green space reduce self-reported stress among urban residents?
• Through what mechanisms does remote work influence employee retention?
• Why did the same education policy produce different outcomes across two regions?
• Under what organizational conditions does AI-assisted decision support improve productivity?

Explanatory Hypotheses

A hypothesis should be specific, theoretically justified, and testable with the selected design.

Association hypothesis

H1: Greater participation in peer tutoring is associated with higher examination scores among first-year biology students.

This wording does not claim causality.

Causal hypothesis

H1: Offering structured peer tutoring increases first-year biology students’ examination scores compared with not offering the programme.

This claim requires a design capable of supporting the intervention contrast.

Mediation hypothesis

H1: The effect of peer tutoring on examination performance is partly mediated by improved subject understanding.

Moderation hypothesis

H1: The effect of peer tutoring on examination performance is stronger among students with lower baseline subject knowledge.

How to Conduct Explanatory Research

Step 1: Define the outcome requiring explanation

Begin with a clear phenomenon rather than a broad topic.

Too broad:

Why do students succeed?

More focused:

Why do first-year biology students who attend peer-tutoring sessions achieve higher examination scores?

Step 2: Review theory and existing evidence

Identify:

• What is already known.
• Which explanations have been proposed.
• Which findings are inconsistent.
• Which variables have been measured.
• Which mechanisms remain uncertain.
• Which designs have already been used.
• What your study can add.

The literature review should guide the explanatory model rather than serve only as background.

Step 3: Draw a conceptual or causal model

Map the proposed relationships among:

• Exposure or intervention.
• Outcome.
• Confounders.
• Mediators.
• Moderators.
• Competing explanations.
• Contextual influences.

A simple conceptual model may be sufficient for a qualitative study. A causal DAG may be more appropriate for an observational effect-estimation study.

Step 4: Formulate the research question and hypothesis

Ensure that the wording matches the intended claim.

Use “associated with” when the study estimates an association.

Use causal wording such as “effect,” “impact,” or “causes” only when the design and assumptions justify causal interpretation.

Step 5: Operationalize the concepts

Define how each concept will be observed or measured.

For example, “academic engagement” could be operationalized through:

• Class attendance.
• Learning-platform activity.
• Assignment completion.
• A validated engagement scale.
• Interview evidence about participation.

Poor operationalization can undermine an otherwise sophisticated analysis.

Step 6: Choose the design

Ask:

• Can the proposed cause be randomized?
• Is a policy threshold or implementation date available?
• Is longitudinal data needed to establish temporal order?
• Are comparison groups sufficiently similar?
• Does the question concern average effects, mechanisms, meaning, or context?
• Would qualitative evidence explain the process?
• Would mixed methods provide value beyond either method alone?

Step 7: Develop the sampling strategy

Quantitative studies may require:

• A clearly defined target population.
• Probability or defensible nonprobability sampling.
• A sample-size or power calculation.
• Adequate representation of important subgroups.

Qualitative studies may use purposive, theoretical, maximum-variation, critical-case, or criterion-based sampling.

In explanatory sequential mixed methods, the qualitative sample should be deliberately connected to the quantitative findings.

Step 8: Collect data ethically and systematically

The researcher should address:

• Informed consent.
• Privacy and confidentiality.
• Data security.
• Risks to participants.
• Conflicts of interest.
• Researcher positionality where relevant.
• Institutional or ethics-review requirements.
• Treatment of vulnerable groups.
• Secondary use of administrative or digital data.

Step 9: Analyze the evidence

The analysis should match the question and design.

Possible methods include:

• Regression analysis.
• Analysis of variance.
• Multilevel modelling.
• Structural equation modelling.
• Mediation and moderation analysis.
• Time-series analysis.
• Difference-in-differences.
• Regression discontinuity.
• Matching and weighting.
• Thematic analysis.
• Process tracing.
• Pattern matching.
• Comparative case analysis.
• Mixed-methods joint displays.

Step 10: Evaluate alternative explanations

Ask:

• Could reverse causation explain the result?
• Could an omitted variable explain both X and Y?
• Did participant selection bias the comparison?
• Was the outcome measured differently across groups?
• Did a simultaneous event affect the result?
• Are conclusions sensitive to model specification?
• Do contradictory cases weaken the proposed mechanism?
• Is there evidence that should have appeared if the explanation were true?

Step 11: Integrate and interpret the results

Separate:

• What the data directly show.
• What the analysis estimates.
• What the interpretation assumes.
• What remains uncertain.

Do not transform a weak association into a strong causal conclusion in the discussion section.

Step 12: Report transparently

Report:

• The research question.
• The theoretical model.
• The design and its justification.
• Sampling and recruitment.
• Measures.
• Data exclusions and missing data.
• Analytical decisions.
• Assumptions.
• Alternative explanations.
• Sensitivity analyses.
• Limitations.
• Data, code, and materials availability where appropriate.

Data-Collection Methods

Method	Explanatory contribution	Important limitation
Experiment	Estimates effects under controlled assignment	Artificial settings or limited generalizability
Survey	Measures proposed variables across a sample	Common-method bias and weak temporal order in cross-sectional surveys
Longitudinal study	Shows whether the proposed cause precedes the outcome	Attrition and time-varying confounding
Interview	Reveals mechanisms, reasoning, and context	Retrospective accounts may be incomplete
Observation	Captures processes and behaviour in context	Observer influence and interpretive subjectivity
Case study	Integrates multiple evidence sources	Limited statistical generalization
Administrative data	Provides large-scale or longitudinal records	Variables were collected for another purpose
Document analysis	Reconstructs policies, decisions, and historical processes	Records may be incomplete or strategically produced
Digital trace data	Captures detailed behaviour over time	Privacy, selection, platform, and measurement concerns

Data Analysis in Explanatory Research

Regression analysis

A basic model may be written as:

Yᵢ = β₀ + β₁Xᵢ + β₂Cᵢ + εᵢ

Where:

• Y is the outcome.
• X is the proposed explanatory variable.
• C represents measured covariates.
• β₁ is the estimated relationship between X and Y after accounting for variables included in C.
• ε represents unexplained variation.

The coefficient β₁ is not automatically causal. A causal interpretation depends on design, measurement, model specification, selection processes, and assumptions about unmeasured confounding.

Qualitative analysis

Qualitative explanatory analysis may focus on:

• Event sequences.
• Decisions and responses.
• Mechanisms.
• Contextual conditions.
• Contradictory evidence.
• Differences between cases.
• Participants’ explanations.
• Links between theoretical expectations and observed processes.

Researchers should provide enough evidence for readers to understand how interpretations were developed.

Mixed-methods integration

Mixed-methods integration can occur by:

• Connecting one sample to another.
• Building the qualitative phase from quantitative results.
• Merging datasets.
• Comparing findings.
• Creating joint displays.
• Developing an integrated conclusion or meta-inference.

Merely including a survey and interviews does not make a study meaningfully mixed methods. The two forms of evidence must inform one another.

Complete Explanatory Research Example

Research problem

A university observes that students who attend optional peer-tutoring sessions receive higher examination scores.

Descriptive finding

Tutoring participants have an average examination score of 74, while nonparticipants have an average score of 66.

This difference does not yet explain why the groups differ.

Explanatory question

What is the effect of peer-tutoring participation on first-year biology examination performance, and through what mechanisms might tutoring influence achievement?

Possible explanations

1. Tutoring improves subject understanding.
2. Tutoring improves study routines.
3. Motivated students are more likely to attend.
4. Students with greater available time are more likely to attend.
5. Tutors provide information about assessment expectations.
6. Social belonging improves persistence.

Proposed design

Suppose places are limited and eligible students are randomly offered access to the programme.

The researchers could:

1. Randomly offer tutoring access.
2. Measure baseline knowledge and motivation.
3. Track attendance.
4. Measure subject understanding during the semester.
5. Compare examination outcomes by assigned offer.
6. Interview a purposive sample of participants and nonparticipants.
7. Integrate the estimated programme effect with evidence about mechanisms and implementation.

Quantitative analysis

The primary analysis estimates the effect of being offered tutoring. Secondary analyses examine actual attendance and possible mediation through subject understanding.

Qualitative phase

Interviews explore:

• Why students accepted or declined.
• Which tutoring activities were useful.
• Whether tutoring changed study practices.
• Whether students experienced barriers.
• Whether the programme operated differently across tutors.

Interpretation

A defensible conclusion might be:

Students offered peer tutoring achieved higher examination scores than students receiving usual support. Interview and process evidence suggests that repeated practice and timely clarification of misconceptions were important mechanisms. However, the study was conducted in one department, and the effect may depend on tutor quality and programme implementation.

This conclusion separates the estimated effect, the proposed mechanism, and the limits of generalization.

Examples Across Disciplines

Education

• Why does formative assessment improve learning in some classrooms but not others?
• Does attendance monitoring reduce student absenteeism?
• How does teacher feedback influence academic self-efficacy?

Psychology

• Does sleep restriction impair working-memory performance?
• Does perceived social support mediate the relationship between stress and wellbeing?
• Under what conditions does social comparison reduce self-esteem?

Public health

• Why do vaccination rates differ between neighbouring communities?
• Does a reminder intervention increase clinic attendance?
• Through what mechanisms does neighbourhood deprivation influence health outcomes?

Business and management

• Does flexible work improve employee retention?
• Why do similar digital-transformation projects produce different outcomes?
• Does personalized advertising increase purchasing behaviour?

Sociology

• How does neighbourhood segregation influence educational opportunity?
• Why do levels of institutional trust differ among demographic groups?
• Under what conditions does online participation lead to offline collective action?

Environmental research

• Does access to public transport reduce household car use?
• Why do communities respond differently to flood-risk warnings?
• How does environmental regulation influence industrial emissions?

Information systems

• Why do employees resist a newly introduced information system?
• Does cybersecurity training reduce unsafe password behaviour?
• How does perceived usefulness influence continued software adoption?

Advantages of Explanatory Research

It goes beyond description

Explanatory research investigates the reasons behind an observed result rather than stopping at frequencies or associations.

It tests and refines theory

Evidence can support, modify, restrict, or challenge a proposed theoretical explanation.

It informs interventions and decisions

Understanding why an outcome occurs can help decision-makers target relevant mechanisms instead of responding only to symptoms.

It evaluates alternative explanations

A well-designed explanatory study asks what else could have produced the finding.

It can integrate multiple methods

Numerical patterns, participant experiences, implementation evidence, and contextual information can contribute to a richer explanation.

It supports cumulative knowledge

Clearly stated mechanisms and assumptions can be tested in other populations, settings, or periods.

Limitations of Explanatory Research

Causal identification is difficult

Many variables cannot be randomized, and observational explanations may depend on strong assumptions.

Explanations can be context-dependent

A mechanism operating in one country, institution, or population may not operate identically elsewhere.

Important variables may be unmeasured

Administrative or secondary datasets often omit motivation, informal practices, social relationships, or implementation quality.

Explanatory models can oversimplify

Social and behavioural outcomes may result from interacting individual, organizational, institutional, and historical forces.

Mechanisms are difficult to establish

A variable may statistically mediate an association without being the true causal mechanism.

Strong designs can require substantial resources

Longitudinal studies, experiments, comparative case studies, and integrated mixed-methods projects can be expensive and time-consuming.

Researcher assumptions influence the explanation

Theory selection, variable definitions, coding choices, case selection, and model specification affect the conclusions.

Common Mistakes

Mistake 1: Treating correlation as causation

A relationship between two variables may result from confounding, reverse causation, selection, measurement error, or chance.

Mistake 2: Calling every regression study explanatory

Regression is an analytical technique. Its inclusion does not establish the purpose or validity of the research design.

Mistake 3: Controlling for every available variable

Some variables are mediators or colliders rather than confounders. Indiscriminate adjustment can introduce rather than remove bias.

Mistake 4: Using causal language with cross-sectional data

When cause and outcome are measured simultaneously, temporal order is often uncertain.

Mistake 5: Ignoring competing explanations

An explanation is more credible when the study states and investigates plausible alternatives.

Mistake 6: Confusing explanatory research with explanatory sequential design

The first is a broad purpose. The second is a particular quantitative-to-qualitative mixed-methods structure.

Mistake 7: Reporting only p-values

Researchers should report effect estimates, uncertainty intervals, substantive importance, assumptions, data quality, and limitations.

Mistake 8: Selecting a method before defining the question

The design should follow the research question and intended claim, not the software or dataset that happens to be available.

Modern Research Tools and Practices

Literature and reference management

Researchers can use bibliographic databases, citation managers, and systematic search records to document how relevant evidence was identified.

Useful functions include:

• Duplicate removal.
• Citation organization.
• Search-history documentation.
• Annotation.
• Reference-format checking.
• Linking claims to original sources.

Statistical and qualitative software

Quantitative analyses may be conducted using R, Python, Stata, SPSS, SAS, or specialist causal-inference software.

Qualitative researchers may use NVivo, ATLAS.ti, MAXQDA, spreadsheets, or reproducible coding frameworks.

Software does not determine the quality of the explanation. The design, data, assumptions, and interpretation remain more important than the platform.

Causal diagrams

DAG software can help researchers formalize assumptions, identify adjustment sets, and communicate causal models.

The diagram should be developed from substantive knowledge. An automatically generated graph should not be accepted as a verified causal structure without expert evaluation.

Preregistration and reproducibility

Depending on the field and design, researchers can preregister:

• Research questions.
• Hypotheses.
• Primary outcomes.
• Exclusion rules.
• Statistical models.
• Subgroup analyses.
• Qualitative sampling and analytical commitments.
• Procedures for integrating mixed-methods findings.

Preregistration does not eliminate bias, but it helps readers distinguish planned analyses from later exploratory decisions.

Reproducible projects may also provide:

• Analysis code.
• Data dictionaries.
• Survey instruments.
• Interview guides.
• Coding frameworks.
• Version histories.
• De-identified data where ethical and legal conditions permit.

Artificial Intelligence in Explanatory Research

AI tools may assist with:

• Generating search terms.
• Screening titles under human supervision.
• Summarizing notes.
• Drafting code that is independently tested.
• Suggesting alternative model specifications.
• Transcribing or organizing qualitative material where privacy permits.
• Improving grammar and readability.

AI should not be treated as an authoritative methodological source. It can generate incorrect claims, nonexistent references, unsuitable code, biased classifications, or unjustified causal interpretations.

Researchers should:

1. Verify every reference against the original source.
2. Test all AI-generated code.
3. Retain a record of consequential AI use.
4. Follow institutional, funder, and journal policies.
5. Avoid uploading confidential data or manuscripts to systems without suitable protections.
6. Disclose AI assistance where required.
7. Keep human researchers accountable for every conclusion.

Current ICMJE guidance states that AI tools should not be listed as authors, that humans remain responsible for accuracy and originality, and that relevant AI use should be disclosed (ICMJE, 2026).

How to Report Explanatory Research

The reporting guideline should match the actual study design.

• Use CONSORT 2025 for applicable randomized trials.
• Use STROBE for cohort, case-control, and cross-sectional observational studies.
• Use COREQ for qualitative interview and focus-group studies where applicable.
• Use an appropriate mixed-methods reporting framework when integrating quantitative and qualitative evidence.
• Use field-specific extensions when available.

A reporting guideline improves transparency but does not repair a poorly designed study.

Methodology justification template

This study adopts an explanatory research purpose because it seeks to examine why [outcome or pattern] occurs rather than merely describing its prevalence or characteristics. A [design] was selected to investigate the relationship between [explanatory factor] and [outcome] while addressing [main validity issue]. Data will be collected using [methods] and analyzed through [analysis]. Conclusions will be limited to [association, mechanism, or causal effect] supported by the design and stated assumptions.

Noncausal conclusion template

The results indicate that X was associated with Y after adjustment for the measured covariates. Because the study was observational and [main limitation], the finding should not be interpreted as definitive evidence that X caused Y.

Causal conclusion template

Under the stated assumptions, the analysis estimates that changing X from [comparison condition] to [intervention condition] would change Y by approximately [estimate]. The interpretation remains subject to [attrition, noncompliance, interference, residual confounding, or other limitation].

Conclusion

Explanatory research investigates why or how an observed phenomenon occurs. It can test causal effects, develop mechanism-based accounts, explain differences between cases, or use qualitative evidence to interpret quantitative findings.

The label “explanatory” alone does not determine the strength of the conclusion. A credible explanatory study aligns its question, theory, design, measurement, analysis, and wording; evaluates alternative explanations; and states clearly what remains uncertain.