Explanatory Research: Methods, Design & Examples

Key Highlights

• Explanatory Research explains why or how a phenomenon occurs by examining relationships between variables and developing evidence-based explanations.
• It explores research purposes, characteristics, methods, designs, variables, hypotheses, and processes used to investigate relationships and possible causes.
• The approach can use surveys, experiments, case studies, observations, interviews, and statistical analysis depending on the research question.
• In this blog, you'll learn explanatory research concepts, practical examples, research methods, design, process, and differences from exploratory research.

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What is explanatory research?

Why did sales fall? Why are some students performing better than others? Why do customers leave? These are the kinds of questions explanatory research tries to answer. It looks beyond what happened and explores the reasons, relationships, and factors behind an outcome.

For example, if a university finds that students with lower attendance score fewer marks, researchers might examine whether attendance explains this difference while considering study time, previous grades, and course difficulty.

Explanatory research doesn't automatically prove cause and effect. The strength of the finding depends on the research design, data quality, and other factors considered during analysis.

Researchers may use surveys, experiments, interviews, observations, case studies, or existing data based on what the study needs.

What is explanatory research in research methodology?

Research methodology defines how a study is planned, conducted, and analysed. Explanatory research fits into this process when the goal is to understand why something happens or how different factors are related.

It isn't the same as experimental research. An experiment is one research design that can help test cause-and-effect relationships, while explanatory research can also use surveys, interviews, observations, case studies, or existing data.

For example, a researcher studying employee turnover might examine whether workload, job satisfaction, and management support are linked to employees' intention to leave.

The research method should match the question. If you want to understand customer drop-offs, interviews might reveal why they happen, while transaction data can show which factors are linked to those drop-offs.

Research approach	Main focus
Exploratory	Finding possible ideas or explanations
Descriptive	Understanding what is happening
Explanatory	Understanding why or how it happens
Experimental	Testing the effect of a changed factor

Good research methodology connects the question, evidence, method, and analysis.

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What is the purpose of explanatory research?

The main purpose of explanatory research is to understand why or how something happens. It looks beyond an observed result and examines the factors and relationships that might explain it.

For example, if a retailer notices fewer repeat purchases, researchers might examine delivery delays, pricing, product quality, or customer service to understand what could be driving the change.

It can help researchers:

• Examine relationships between variables
• Test a proposed explanation
• Identify alternative factors
• Provide direction for future research

The goal isn't to force one answer. It's to use evidence to understand what may be behind an observed outcome.

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What are the characteristics of explanatory research?

Explanatory research goes beyond asking what happened. It focuses on understanding why or how something happens and examines the factors that may explain the outcome.

Its key characteristics include:

• Focus on why and how: It investigates the reasons behind a situation or outcome.
• Examines relationships: It looks at how different variables are connected.
• Uses evidence: Findings are based on collected and analysed data, not assumptions.
• Uses hypotheses: Researchers may develop testable explanations about relationships between variables.
• Considers alternative explanations: It looks at other factors that could influence the findings.
• Uses flexible methods: Researchers can use quantitative, qualitative, or mixed methods based on the research question.

Key characteristics at a glance

Characteristic	What it focuses on
Why and how questions	Understanding reasons behind an outcome
Variables	Examining relationships between factors
Hypotheses	Testing proposed explanations
Evidence	Supporting findings with data
Analysis	Identifying patterns and relationships
Alternative explanations	Considering other factors that may influence results

These characteristics help researchers decide whether an explanatory approach fits their study and what kind of evidence they'll need.  

What variables are used in explanatory research?

Variables help researchers understand how different factors may be connected. The main types include independent, dependent, and control variables.

• Independent variable: The factor that may influence another factor.
• Dependent variable: The outcome being studied.
• Control variables: Other factors kept in check to make the results clearer.

For example, a study may examine whether study time affects exam scores. Study time is the independent variable, while exam scores are the dependent variable.

Other factors, such as attendance, previous grades, and course difficulty, could also affect scores. Researchers need to consider them so the findings aren't misleading.

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What methods are used in explanatory research?

There isn’t one fixed method for explanatory research. The choice depends on the research question, data available, and how deeply the researcher needs to investigate the outcome.

Method	Useful for	Example
Experiment	Testing effects	Testing a new teaching method
Correlation	Finding relationships	Income and education
Survey	Collecting large-scale responses	Factors linked to customer churn
Case study	Studying complex situations	Reasons behind a failed project
Observation	Studying real behaviour	Classroom participation
Interviews	Understanding personal experiences	Reasons employees leave

The method should match the question. A familiar method isn’t always the right one. 

What is an explanatory research design? 

An explanatory research design is the plan that connects the research question with the evidence needed to investigate it.

A good design identifies what will be studied, which variables matter, who or what will provide the data, how the information will be collected, and how the findings will be analysed.

The design also affects how confidently researchers can interpret their results. An experiment may provide stronger evidence for a causal relationship than a simple observational study, while an interview may provide richer insight into personal experiences.

An explanatory research design must therefore match the claim being investigated. If you want to establish whether one factor causes another, your design needs to address alternative explanations rather than simply identify an association.

What is the explanatory research process?

A clear process keeps the study focused. It also helps researchers avoid collecting large amounts of data without knowing what they want to explain.

The process usually starts with a research problem and ends with an evidence-based explanation. Researchers first define what they want to understand, identify relevant variables, and develop a research question or hypothesis.

Next, they choose a suitable research design and collect the required data. Once the data is available, they analyse relationships and look for patterns that could explain the outcome.

The final step is interpretation. Researchers consider whether the findings support the proposed explanation, whether other factors could be involved, and what the results mean for future research.

What are explanatory research questions?

A strong research question gives the entire study direction. Explanatory questions usually focus on why or how a phenomenon occurs rather than simply asking what exists.

For example, "What percentage of employees work remotely?" is mainly descriptive.

"How does remote work affect employee satisfaction?" is explanatory because it examines a relationship that needs investigation.

Good questions are focused, answerable, and connected to measurable or observable factors. They shouldn't contain several unrelated problems at once.

Here are a few examples.

• Why do customers abandon their shopping carts?
• How does delivery time affect customer satisfaction?
• Why do some employees leave an organisation within their first year?
• How does class attendance relate to academic performance?
• What factors explain differences in treatment outcomes?

What is a hypothesis in explanatory research?

A hypothesis is a testable statement about an expected relationship between variables.

For example, a researcher studying employee turnover might propose that higher workload is associated with a greater intention to leave.

A useful hypothesis identifies the factors being examined and makes a relationship clear. It should also be possible to collect evidence that supports or challenges the proposed relationship.

Hypotheses aren't mandatory for every explanatory study. Qualitative research may begin with open questions and develop explanations from participant data rather than testing a predetermined hypothesis.

Read: Types of Research Design: Key elements, Characteristics and More! 

Explanatory research Examples

Explanatory research becomes easier to understand when you look at the question behind the study. Each example starts with an observed outcome and then investigates the factors that might explain it.

Area	Research situation	Factors examined
Business	Employee turnover is increasing.	Workload, management support, salary satisfaction, and career growth
Marketing	Customers add products to carts but don't complete purchases.	Delivery charges, pricing, payment options, and page experience
Education	Students show different academic performance.	Attendance, study habits, learning resources, and previous grades
Healthcare	Patients have different recovery times after similar treatments.	Age, health conditions, treatment adherence, and follow-up care
Social science	Crime rates vary across urban areas.	Employment, education, income, population density, and community services

How does explanatory research establish cause and effect?

A relationship between two variables doesn't prove that one caused the other. Other factors may be influencing the result.

For example, people who exercise more may report better health. But diet, age, income, and existing health conditions could also affect those results.

To support a causal claim, researchers look at:

• Whether the cause happened before the outcome
• Whether the variables are actually related
• Whether other possible explanations have been considered
• Whether the research design supports causal conclusions

Experiments can provide stronger causal evidence because researchers control or change a factor and observe the outcome.

Observational studies can show useful relationships, but they need more caution when explaining cause and effect.

What is the difference between explanatory and exploratory research?

Exploratory research is useful when a problem isn't clearly understood and researchers need to discover possible ideas, factors, or questions.

Explanatory research starts with a more defined problem and investigates relationships or possible reasons behind an observed phenomenon.

Factor	Exploratory research	Explanatory research
Main purpose	Discover possibilities	Explain relationships
Typical questions	What might be happening?	Why or how is it happening?
Problem definition	Often less developed	Usually more defined
Hypothesis	Often developed during or after exploration	May be tested from the beginning
Outcome	New ideas or research questions	Evidence-based explanation

A researcher might begin with exploratory interviews to discover why customers are dissatisfied. The findings could then inform an explanatory study that tests whether specific factors are associated with customer churn.

Also read: Types of Research Design: Key elements, Characteristics and More! 

Advantages and Limitations of Explanatory Research

Explanatory research helps researchers understand relationships, test possible explanations, and explore why an outcome occurs.

However, the findings depend on the quality of the study. Poor sampling, missing variables, or biased data can weaken the results. A relationship also doesn't automatically prove causation, especially in observational studies.

Findings from one organisation, school, or location may not apply elsewhere. Researchers should consider these limits before drawing conclusions.

The key advantages include:

• Helps explain why or how a phenomenon occurs
• Examines relationships between variables
• Supports hypothesis testing
• Can guide future research
• Provides deeper insight into observed patterns

The main limitations include:

• Causal claims depend on research design
• Confounding variables can affect findings
• Data quality can limit explanations
• Results may not apply to every population
• Some research designs require substantial time and resources

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When should you use explanatory research?

Use explanatory research when you want to understand why something happens or how different factors are connected.

It's a good choice when:

• You have a clear research problem.
• You need to examine relationships between variables.
• You want to test a possible explanation.
• Describing the situation isn't enough.
• You need to explore possible causes.

If the problem is still unclear, exploratory research may be a better starting point. If you only need to describe facts or patterns, descriptive research may fit better.

Conclusion

Explanatory Research is useful when a simple description isn't enough. It helps researchers investigate why or how something happens by examining relationships, testing explanations, and interpreting evidence carefully.

The strongest studies start with a focused question, identify relevant variables, select an appropriate design, and remain honest about what the evidence can show.

Once you understand that connection, the choice of method becomes much clearer. You're not just collecting data. You're using it to build a defensible explanation.

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