Explanatory Research | Definition, Guide, & Examples

Explanatory research is a research method that explores why something occurs when limited information is available. It can help you increase your understanding of a given topic, ascertain how or why a particular phenomenon is occurring, and predict future occurrences.

Explanatory research can also be explained as a “cause and effect” model, investigating patterns and trends in existing data that haven’t been previously investigated. For this reason, it is often considered a type of causal research.

Note: Be careful not to confuse explanatory research with exploratory research, which is also preliminary in nature but instead explores a subject that hasn’t been studied in depth yet.

When to use explanatory research

Explanatory research is used to investigate how or why a phenomenon takes place. Therefore, this type of research is often one of the first stages in the research process, serving as a jumping-off point for future research. While there is often data available about your topic, it’s possible the particular causal relationship you are interested in has not been robustly studied.

Explanatory research helps you analyze these patterns, formulating hypotheses that can guide future endeavors. If you are seeking a more complete understanding of a relationship between variables, explanatory research is a great place to start. However, keep in mind that it will likely not yield conclusive results.

Example: Explanatory research 
You have been teaching statistics to undergraduate students during both the first and second semesters for several years in a row.

You analyzed their final grades and noticed that the students who take your course in the first semester always obtain higher grades than students who take the same course in the second semester.

You are interested in discovering what causes this pattern.

Explanatory research questions

Explanatory research answers “why” and “how” questions, leading to an improved understanding of a previously unresolved problem or providing clarity for related future research initiatives.

Here are a few examples:

  • Why do undergraduate students obtain higher average grades in the first semester than in the second semester?
  • How does marital status affect labor market participation?
  • Why do multilingual individuals show more risky behavior during business negotiations than monolingual individuals?
  • How does a child’s ability to delay immediate gratification predict success later in life?
  • Why are teens more likely to litter in a highly littered area than in a clean area?

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Explanatory research data collection

After choosing your research question, there is a variety of options for research and data collection methods to choose from.

A few of the most common research methods include:

The method you choose depends on several factors, including your timeline, budget, and the structure of your question. If there is already a body of research on your topic, a literature review is a great place to start. If you are interested in opinions and behavior, consider an interview or focus group format. If you have more time or funding available, an experiment or pilot study may be a good fit for you.

Explanatory research data analysis

In order to ensure you are conducting your explanatory research correctly, be sure your analysis is definitively causal in nature, and not just correlated.

Always remember the phrase “correlation doesn’t mean causation.” Correlated variables are merely associated with one another: when one variable changes, so does the other. However, this isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in the independent variable bring about changes in the dependent variable. In other words, there is a direct cause-and-effect relationship between variables.

Causal evidence must meet three criteria:

  1. Temporal: What you define as the “cause” must precede what you define as the “effect.”
  2. Variation: Intervention must be systematic between your independent variable and dependent variable.
  3. Non-spurious: Be careful that there are no mitigating factors or hidden third variables that confound your results.

Correlation doesn’t imply causation, but causation always implies correlation. In order to get conclusive causal results, you’ll need to conduct a full experimental design.

Step-by-step example of explanatory research

Your explanatory research design depends on the research method you choose to collect your data. In most cases, you’ll use an experiment to investigate potential causal relationships. We’ll walk you through the steps using an example.

Step 1: Develop the research question

The first step in conducting explanatory research is getting familiar with the topic you’re interested in, so that you can develop a research question.

Let’s say you’re interested in language retention rates in adults.

Example: Explanatory research question
You have previously studied language retention in adults who were adopted from abroad as children. You discovered that adults who were exposed to a foreign language as infants were better able to relearn the language than adults who were never exposed.

You are interested in finding out how the duration of exposure to language influences language retention ability later in life.

You want to set up an experiment to answer the following research question: How does the duration of exposure to a language in infancy influence language retention in adults who were adopted from abroad as children?

Step 2: Formulate a hypothesis

The next step is to address your expectations. In some cases, there is literature available on your subject or on a closely related topic that you can use as a foundation for your hypothesis. In other cases, the topic isn’t well studied, and you’ll have to develop your hypothesis based on your instincts or on existing literature on more distant topics.

Example: Explanatory research hypothesis
You expect that adults who have been exposed to a language in infancy for a shorter time are less likely to retain aspects of this language than adults who have been exposed for a longer period of time.

You phrase your expectations in terms of a null (H0) and alternative hypothesis (H1):

  • H0: The duration of exposure to a language in infancy does not influence language retention in adults who were adopted from abroad as children.
  • H1: The duration of exposure to a language in infancy has a positive effect on language retention in adults who were adopted from abroad as children.

Note: It is possible to add multiple hypotheses, but for this example we’ll continue with just one.

Step 3: Design your methodology and collect your data

Next, decide what data collection and data analysis methods you will use and write them up. After carefully designing your research, you can begin to collect your data.

Example: Data collection and data analysis methods
You decide to conduct an experiment, since you’re interested in testing a causal relationship. You gather a group of adults who were adopted from Colombia but have lived in the United States since the time of their adoption.

You compare:

  • Adults who were adopted from Colombia between 0 and 6 months of age.
  • Adults who were adopted from Colombia between 6 and 12 months of age.
  • Adults who were adopted from Colombia between 12 and 18 months of age.
  • Monolingual adults who have not been exposed to a different language.

During the study, you test their Spanish language proficiency twice in a research design that has three stages:

  • Pre-test: You conduct several language proficiency tests to establish any differences between groups pre-intervention.
  • Intervention: You provide all groups with 8 hours of Spanish class.
  • Post-test: You again conduct several language proficiency tests to establish any differences between groups post-intervention.

You made sure to control for any confounding variables, such as age, gender, proficiency in other languages, etc.

Since you have chosen a between-subjects variable (different exposure duration) and a within-subjects variable (pre-test vs. post-test), you decide to conduct a mixed ANOVA.

Step 4: Analyze your data and report results

After data collection is complete, proceed to analyze your data and report the results.

Example: Results
After conducting the experiment, you explore the data.

You notice that:

  • The pre-exposed adults showed higher language proficiency in Spanish than those who had not been pre-exposed. The difference is even greater for the post-test.
  • The adults who were adopted between 12 and 18 months of age had a higher Spanish language proficiency level than those who were adopted between 0 and 6 months or 6 and 12 months of age, but there was no difference found between the latter two groups.

To determine whether these differences are significant, you conduct a mixed ANOVA. The ANOVA shows that all differences are not significant for the pre-test, but they are significant for the post-test.

You report your results in accordance with the guidelines from the citation style you use (e.g., APA).

Step 5: Interpret your results and provide suggestions for future research

As you interpret the results, try to come up with explanations for the results that you did not expect. In most cases, you want to provide suggestions for future research.

Example: Interpretation and future research ideas
Your results were in line with your expectations. Adopted adults who were pre-exposed to a language in infancy for a longer period of time have retained more of this knowledge than adopted adults who were pre-exposed for a shorter duration and adults who weren’t pre-exposed at all.

However, this difference is only significant after the intervention (the Spanish class.)

You decide it’s worth it to further research the matter, and propose a few additional research ideas:

  • Replicate the study with a larger sample
  • Replicate the study for other maternal languages (e.g. Korean, Lingala, Arabic)
  • Replicate the study for other language aspects, such as nativeness of the accent

Explanatory vs. exploratory research

It can be easy to confuse explanatory research with exploratory research. If you’re in doubt about the relationship between exploratory and explanatory research, just remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research questions often begin with “what”. They are designed to guide future research and do not usually have conclusive results. Exploratory research is often utilized as a first step in your research process, to help you focus your research question and fine-tune your hypotheses.

Explanatory research questions often start with “why” or “how”. They help you study why and how a previously studied phenomenon takes place.

Advantages and disadvantages of explanatory research

Like any other research design, explanatory research has its trade-offs: while it provides a unique set of benefits, it also has significant downsides:


  • It gives more meaning to previous research. It helps fill in the gaps in existing analyses and provides information on the reasons behind phenomena.
  • It is very flexible and often replicable, since the internal validity tends to be high when done correctly.
  • As you can often use secondary research, explanatory research is often very cost- and time-effective, allowing you to utilize pre-existing resources to guide your research prior to committing to heavier analyses.


  • While explanatory research does help you solidify your theories and hypotheses, it usually lacks conclusive results.
  • Results can be biased or inadmissible to a larger body of work and are not generally externally valid. You will likely have to conduct more robust (often quantitative) research later to bolster any possible findings gleaned from explanatory research.
  • Coincidences can be mistaken for causal relationships, and it can sometimes be challenging to ascertain which is the causal variable and which is the effect.

Frequently asked questions about explanatory research

What is explanatory research?

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

What’s the difference between exploratory and explanatory research?

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

When should I use explanatory research?

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process, serving as a jumping-off point for future research.

What’s the difference between quantitative and qualitative methods?

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses. Qualitative methods allow you to explore concepts and experiences in more detail.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

This Scribbr article

George, T. & Merkus, J. (August 17, 2022). Explanatory Research | Definition, Guide, & Examples. Scribbr. Retrieved October 20, 2022, from https://www.scribbr.com/methodology/explanatory-research/

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Tegan George

Tegan is an American based in Amsterdam, with master's degrees in political science and education administration. While she is definitely a political scientist at heart, her experience working at universities led to a passion for making social science topics more approachable and exciting to students. A well-designed natural experiment is her favorite type of research, but she also loves qualitative methods of all varieties.