Explanatory research is a type of research that aims to provide a deeper understanding of a phenomenon when there is limited existing information. The primary goal of explanatory research is to investigate the reasons behind the occurrence of a particular event or situation.
By examining how and why something happens, explanatory research helps researchers to enhance their knowledge of the subject matter and make predictions about future occurrences.
One way to understand explanatory research is to view it as a “cause and effect” model. This approach involves analyzing existing data to identify patterns and trends that have not been previously explored.
When to use explanatory research
Explanatory research is a research approach that focuses on exploring the underlying reasons or mechanisms behind a particular phenomenon. The primary purpose of explanatory research is to understand how or why something occurs, rather than simply describing what is happening. This type of research is often conducted in the early stages of the research process, serving as a foundation for further investigation.
In many cases, explanatory research is initiated when there is existing data or information about a topic, but the specific causal relationship of interest has not been thoroughly examined. By analyzing patterns and trends in the available data, researchers can begin to formulate hypotheses about the potential causes or factors that contribute to the phenomenon under study.
Example: Explanatory research
Explanatory research on the relationship between social media use and mental health outcomes in teenagers.
Suppose you are a researcher interested in understanding how social media use may be impacting the mental health of teenagers. While there is existing data on both social media usage patterns and mental health outcomes among this age group, the specific causal relationship between the two has not been thoroughly investigated.
You begin by conducting a literature review to gather background information and identify potential variables of interest. You find that previous studies have suggested associations between high levels of social media use and increased rates of anxiety, depression, and low self-esteem among teenagers. However, the exact nature of this relationship and the underlying mechanisms are not well understood.
Explanatory research questions
Explanatory research questions are a key component of explanatory research, which aims to uncover the underlying reasons, mechanisms, or processes behind a particular phenomenon. These questions often focus on “how” or “why” something happens, and seek to explore the relationships between different variables that may contribute to the phenomenon of interest.
Here are some examples of explanatory research questions:
- How does parental involvement in education affect student academic performance?
- Why do some individuals experience more severe symptoms of post-traumatic stress disorder (PTSD) than others?
- What factors contribute to the high turnover rates in the tech industry?
- How do social media algorithms influence political polarization among users?
- Why do certain communities have higher rates of obesity compared to others?
Explanatory research data collection
Explanatory research data collection involves gathering information to help answer your research questions and test your hypotheses about the underlying causes or mechanisms behind a phenomenon. As with other types of research, explanatory research can involve both primary and secondary data collection methods.
Primary data collection methods:
- Surveys: Surveys allow you to gather quantitative data from a large sample of participants. In explanatory research, surveys can be used to collect information on variables that may be related to the phenomenon of interest.
- Interviews: In-depth interviews with key informants or participants can provide rich, qualitative data on the topic under study. Interviews allow you to explore individual experiences, perceptions, and opinions in greater detai.
- Focus groups: Focus groups involve guided discussions with small groups of participants who share common characteristics or experiences relevant to the research topic. These discussions can yield insights into group dynamics, shared perceptions, and potential explanations for the phenomenon of interest.
- Observations: Direct observations of behaviors, events, or interactions can provide valuable data for explanatory research. .
Secondary data collection methods:
- Existing datasets: You can use existing datasets, such as government statistics, survey data, or research databases, to explore relationships between variables and test hypotheses. This can be a cost-effective way to conduct explanatory research, as long as the data is relevant and reliable.
- Literature review: A thorough review of existing literature on the topic can help you identify key variables, theories, and potential explanations that have been proposed by other researchers. This can inform your own research questions and hypotheses, and provide context for your findings.
When collecting data for explanatory research, it’s important to use a combination of methods to gather a comprehensive and balanced picture of the phenomenon under study. This can help to strengthen the validity and reliability of your findings, and allow for a more nuanced understanding of the complex factors at play.
Explanatory research data analysis
When conducting explanatory research, it’s crucial to ensure that your analysis is truly causal and not merely correlational. Remember, correlation does not necessarily imply causation. Correlated variables are simply associated with each other, meaning when one changes, the other does too. However, this doesn’t always indicate a direct or indirect causal relationship.
To establish causation, you must demonstrate that changes in the independent variable directly lead to changes in the dependent variable. Causal evidence must satisfy three criteria: temporal (the cause precedes the effect), variation (systematic intervention between variables), and non-spurious (no confounding factors or hidden variables).
While correlation doesn’t imply causation, causation always implies correlation. To obtain definitive causal results, a full experimental design is necessary.
Step-by-step example of explanatory research
The design of your explanatory research will be largely influenced by the research method you select for data collection. In many instances, experiments are the preferred choice for investigating potential causal relationships. To illustrate the process of conducting explanatory research using an experiment, let’s consider a step-by-step example.
Step 1: Develop the research question
When conducting explanatory research, the first step is to develop a clear and focused research question that addresses the potential causal relationship between variables. This involves familiarizing yourself with the topic of interest and identifying a specific aspect that you want to investigate further.
Let’s say you’re interested in the effects of sleep duration on academic performance in college students.
Example: Explanatory research question
You have previously studied the importance of sleep for overall health and well-being. You discovered that insufficient sleep can lead to various health issues and cognitive impairments.
You are interested in finding out how sleep duration specifically influences academic performance in college students.
You want to set up an experiment to answer the following research question: How does sleep duration affect the academic performance of college students?
Step 2: Formulate a hypothesis
Next, you should formulate a hypothesis based on existing literature on the topic or closely related topics. If the topic is not well-studied, you may need to develop a hypothesis based on your instincts or existing literature on more distant topics. Your hypothesis should be stated in terms of a null (H0) and alternative hypothesis (H1), with the null hypothesis typically stating that there is no relationship between the variables and the alternative hypothesis proposing a specific relationship.
Example: Explanatory research hypothesis
You expect that college students who consistently get the recommended amount of sleep (7-9 hours per night) will have better academic performance compared to those who sleep less than the recommended amount.
You phrase your expectations in terms of a null (H0) and alternative hypothesis (H1):
- H0: Sleep duration does not influence the academic performance of college students.
- H1: College students who consistently sleep the recommended amount (7-9 hours per night) will have better academic performance compared to those who sleep less than the recommended amount.
Step 3: Design your methodology and collect your data
Once you have your research question and hypothesis, you need to design your methodology and collect your data. This involves deciding on the appropriate data collection and analysis methods, writing up a detailed research design, and carefully planning and executing the data collection process while controlling for potential confounding variables.
Example: Data collection and data analysis methods
You conduct an experiment with a group of college students from various majors and year levels.
You compare:
- Students who consistently sleep 7-9 hours per night (recommended sleep duration group)
- Students who consistently sleep less than 7 hours per night (sleep-deprived group)
During the study, you assess their academic performance using three measures:
- Grade point average (GPA) for the current semester
- Scores on a standardized test
- Self-reported academic satisfaction and perceived performance
You control for confounding variables such as age, gender, major, and study habits.
You choose an independent samples t-test to compare the academic performance measures between the two groups.
Step 4: Analyze your data and report results
After data collection is complete, you should analyze the data using appropriate statistical techniques and report the results in a clear and concise manner, following the guidelines of the chosen citation style. Make sure to include relevant descriptive statistics, inferential statistics, and effect sizes, and use tables and figures to present the results visually, if appropriate.
Example: Results
The analysis reveals that:
- The recommended sleep duration group had significantly higher GPAs compared to the sleep-deprived group.
- The recommended sleep duration group scored significantly higher on the standardized test compared to the sleep-deprived group.
- The recommended sleep duration group reported significantly higher academic satisfaction and perceived performance compared to the sleep-deprived group.
You report the results following the appropriate citation style guidelines.
Step 5: Interpret your results and provide suggestions for future research
Finally, interpret the results in the context of your research question and hypothesis, discuss whether the results support or refute your hypothesis, provide explanations for unexpected results, acknowledge the limitations of your study, and suggest directions for future research that could build upon your findings.
Example: Interpretation and future research ideas
Your results support your hypothesis. College students who consistently sleep the recommended amount have better academic performance across various measures compared to those who are sleep-deprived.
However, you acknowledge that the study has limitations, such as relying on self-reported sleep duration and not controlling for all possible confounding variables.
You propose the following ideas for future research:
- Conduct a longitudinal study to assess the long-term effects of sleep duration on academic performance
- Investigate the underlying mechanisms by which sleep influences cognitive function and academic performance
- Explore the effectiveness of interventions designed to improve sleep habits among college students and their impact on academic outcomes
Explanatory vs. exploratory research
Explanatory and exploratory research are two distinct approaches to investigating phenomena, each with its own goals, methods, and outcomes. While both types of research seek to expand our understanding of a topic, they differ in their focus, the nature of the research questions they address, and the level of prior knowledge required.
| Characteristic | Explanatory Research | Exploratory Research |
| Goal | To identify and explain causal relationships between variables | To gain insights, discover new ideas, and generate hypotheses for future research |
| Research Questions | Focused on “why” and “how” questions; tests hypotheses and theories | Broad, open-ended questions; explores a topic or phenomenon |
| Prior Knowledge | Requires a solid foundation of prior knowledge about the topic | Conducted when there is little or no prior knowledge about a topic |
| Hypotheses | Develops specific, testable hypotheses based on existing knowledge | Generates tentative hypotheses and identifies potential variables of interest |
| Methods | Uses structured, deductive methods to collect and analyze data (e.g., experiments, surveys) | Uses flexible, inductive methods to collect and analyze data (e.g., interviews, focus groups, observations) |
| Data | Primarily quantitative data; seeks to measure and control variables | Often qualitative data; seeks to gather rich, detailed information |
| Results | Provides conclusive evidence for causal relationships; supports or refutes existing theories | Generates tentative findings and insights that require further investigation; identifies patterns, themes, and avenues for future research |
| Outcome | Informs decision-making and practice; contributes to theory development | Guides the development of new research questions and hypotheses; lays the foundation for future explanatory research |
Advantages and disadvantages of explanatory research
Explanatory research, like any research approach, has its advantages and disadvantages. Understanding these pros and cons can help researchers determine when explanatory research is the most appropriate method for their study and how to address potential limitations.
| Advantages | Disadvantages |
| Provides a deep understanding of causal relationships | Can be complex and time-consuming |
| Allows for the testing of hypotheses and theories | Requires a substantial amount of prior knowledge |
| Can establish cause-and-effect relationships | May oversimplify complex phenomena |
| Helps predict future outcomes based on identified causal mechanisms | Limited in its ability to explore new or unexpected findings |
| Contributes to the development and refinement of theories | Potential for researcher bias in hypothesis formulation and testing |
| Offers conclusive results that can inform decision-making and practice | May not capture the full context or subjective experiences of participants |
| Can be generalized to larger populations if based on representative samples | Ethical concerns may arise when manipulating variables or withholding interventions |