Explanatory Research Design

This type of research design is used when you have some information about the problem but it is not consistent or well-explored. Here, you need to answer the question ‘Why?’ and understand the topic in-depth, find out why the problem has occurred, and possibly, predict its development.

Explanatory research is usually a ‘cause-and-effect’ design because you can juxtapose the models, patterns, and trends that have already been researched with those that have not been discovered yet. Therefore, the research has a casual format.

Ensure not to confuse explanatory research with exploratory one when researching and writing a paper. This design is also preliminary but here we explore something that has been investigated to some extent, while exploratory design is used for something completely new and not discovered before.

Why Do We Need Explanatory Research?

We use this model when we look for the answers to the questions ‘Why?’ and ‘How?’. As other cause-and-effect studies, this format is useful at the very first stages of academic research. It directs the cause of the future investigations in the required pattern. Much data is available now for your question, but you need to define the relationships between different sets because they were not accurately studied.

With the help of explanatory research design, you will analyze the available data, formulate hypotheses, and learn how to continue your research in an appropriate way. Such research is also used when you want to find the relationships between different variables. Nevertheless, you are not going to come to the final points in your work with its help, and it is almost impossible to make persuasive conclusions on its basis.

Questions for Explanatory Research

These are mostly ‘Why?’ and ‘How?’ questions that can improve your understanding of the topic and add more clarity to it. The design can also lead to further research formats and give helpful tips on how to continue the research of this matter.

Look at the following examples of research questions for this design:

• ✔️ How can business process reengineering influence the quality of ultimate products?
• ✔️ How can you predict the basic values on the energy market if you know the current dividends and their growth rates?
• ✔️ Why can owning a pet improve the life quality of elderly people?
• ✔️ How does aspirin affect post-menopausal women suffering from cardiovascular disorders?
• ✔️ Why do unregulated manufacturing corporations cause more water pollution than regulated ones?

Collecting Data for Explanatory Research

When you have formulated the research question, it is time to pick out the most appropriate method for gathering data. You can use the following ones:

• focus groups and interviews
• experiments
• pilot studies
• literature reviews
• observations

The choice depends on your deadlines, questions, and budget capacity. If you can find the essentials for your further research, it is helpful. You can use a literature review in this case. Or you may prefer to compare some attitudes or opinions towards the subject matter. The format of a focus group or interview is appropriate here. Pilot studies and experiments require more time and costs. Though you can opt for them if you want to research your question in more depth.

Data Analysis in Explanatory Research

To let you make correct conclusions at the end of your research, your present design should be both correlated and causal. You already know that if two variables correlate, it does not mean that one of them is the cause of the other. When variables correlate, they can change simultaneously. However, if the link between them is causal, they may or may not change together depending on either direct or indirect nature of the link.

When there is causation, independent variables provoke changes in dependent variables but not vice versa. In fact, you can observe a direct cause-and-effect relationship.

Based on this characteristic, causal evidence requires certain criteria:

• non-spurious - there are no third variables hidden that can affect the result of research
• temporal - a cause always precedes an effect
• variation - you can observe systematic correlation between your independent and dependent variables

The last postulate means that correlation does not lead to causation but causation always means correlation. However, if you want to get to the reliable conclusions, you need to finish the entire experiment.

How to Conduct Explanatory Research Step-by-Step?

The most widely used research method to investigate the causal relationships between variables is an experiment. Though you can choose other methods, too. The format of explanatory research design will depend on this choice.

1. Research Question Development

The first step in explanatory research is understanding the topic you are going to deal with. Let’s suppose that you are interested in the preferences of packaging colors among the clients of supermarkets.

Supermarket managers have reported that the foods wrapped in red packaging attract more customers’ attention than the same products wrapped in blue ones. Your previous studies have shown that there is a strong relationship between the color of packaging and customers’ preferences. Now, you are interested in learning how color influences the food choice in the supermarket.

To continue, you want to do an experiment that can provide you with the answer to the following research question: How does color influence the customers’ choices of foods in the supermarket?

2. Hypothesis Formulation

Now, you should express your expectations in the form of a hypothesis. You can probably find a lot of literature on this subject matter, and you can use it as the basis for your hypothesis. Or the topic may not have been properly studied, and your hypothesis will be based on your assumptions or on the literature related to the topics that have some connection with yours.

For example, you expect that the color influences the senses of a person who chooses the products, and each color can evoke different feelings and emotions. So, you can formulate your expectations in two ways - a null hypothesis (H0) and alternative hypothesis (H1):

H0: Color does not have any direct influence on customers’ choices of products.

H1: Color has a substantial effect on the food choices of most customers in the supermarkets because it evokes senses and emotions.

You can also use some other hypotheses - the research design can have several of them. Though here we will continue with only one.

3. Methodology Choice and Data Collection

Now, you need to choose the methods for data collection and analysis. When your research has a straightforward design, you can start collecting the data.

For instance, you have chosen to conduct an experiment because you are interested in close psychological relationships between the color and choice of foods. You announce the experiment and a group of volunteers responds to your call. It consists of the people who usually shop for food at the supermarket.

Then, you need to compare:

• color preferences between blue and red in people aged 25-35
• color preferences between blue and red in people aged 35-45
• color preferences between blue and red in people aged 45-55
• color preferences between blue and red in people aged 55-65
• color preferences in people of all ages who do not pay much attention to the color of packaging while buying food

Your research design will have three stages:

1. Pre-test - you ask all participants to define their favorite colors in clothing, interior, pictures, and in other spheres of everyday use.
2. Intervention - you provide your participants with a set of products wrapped in packaging of blue and red and ask them to make their choice.
3. Post-test - you ask everybody to explain their choices of products and colors of packaging.

Be sure to control all confounding variables, such as the social status of a participant, their budget capacity, preferences for a certain brand, etc.

4. Data Analysis and Reporting Results

You have finished collecting the data. Now it’s time for its analysis and reporting the results. The results of exploring the data show that:

• ✔️ The participants who reported red or blue as their favorite colors in the pre-test have chosen the food packaging of their favorite color and explained their choice in this way in the post-test.
• ✔️ The participants aged 45-65 did not pay much attention to the color of food packages and gave preferences to their favorite brands.
• ✔️ The participants of any age who reported that they do not care about the color of packaging have chosen red packaging explaining it by a more attractive look.

Therefore, you have not observed the significant differences between people of all age groups because most of them chose red packaging intuitively. ANOVA helped you see that no age or practical aim differences have influenced the food packaging choice because most people from all groups chose a red color. As their answers in the post-test demonstrated, the choice was intuitive and it was not related to any reasoning.

You then have to report the results by following the guidelines of the citation style (APA, MLA, etc.) that was assigned to you before the beginning of the project.

5. Interpretation of Results and Suggestions for Further Research

When you interpret your results, start with the explanations of those of them you have not expected. That will lead you to suggesting the topics and aims for future research.

For example, the results of this experiment correspond to your suggestion that colors evoke senses and emotions, so they help make a choice of food packaging. Obviously, red color evokes more emotions, so most people choose red packaging.

Nevertheless, you expected that the difference will be more apparent in different age groups. Though, the results show that neither age nor brand preferences influenced much the choice of red color.

Therefore, you decide to continue this research to test a few other ideas:

• ✔️ the study in a bigger sample
• ✔️ the study for other colors (green, yellow, orange, etc.)
• ✔️ the study of people’s choices of clothing depending on the colors

Comparison of Explanatory and Exploratory Research Designs

You can confuse explanatory and exploratory research designs easily because they are pretty similar. However, remember that exploratory research always comes before explanatory one and makes the basis for it.

Most exploratory research questions can start with ‘What?’ and they set the aims for further research. The results of such research are not conclusive at all. Exploratory research is usually considered as the first step of the overall research process. It helps concentrate on the research question(s) and formulate hypotheses.

Explanatory research answers the questions starting with ‘How?’ and ‘Why?’. Their answers cover the cause-result relationships and explain the phenomenon under research consistently.

Pros and Cons of Explanatory Research Design

There are different research designs used in academic work, and each of them has its advantages and disadvantages.

Pros✔️	Cons❌
✔️ It adds more detail to the previous research and fills the gaps in completed analysis explaining the reasons and outcomes hidden behind the subject matter.	❌ The research does not provide conclusive results despite its ability to support theories and hypotheses.
✔️ Its internal validity is pretty high if the research is completed correctly because you can always adjust its aims and purposes and respond to new challenges.	❌ The results can be inapplicable to larger parts of work because they may be biased, so you need to conduct additional quantitative research to support the explanatory findings.
✔️ Its internal validity is pretty high if the research is completed correctly because you can always adjust its aims and purposes and respond to new challenges.	❌ There may be some coincidences that you can mistakenly take for causal relationships, so you need to be attentive while defining a causal variable and the effect.

Final Thoughts

Now you have a detailed explanation of what explanatory research design is and how to use it properly in your academic work. This design works perfectly for any field of studies but it is especially appropriate for social sciences, marketing, and psychology.

When you know how to implement the main principles of explanatory research design, it will be much easier for you to make plans for your future academic career and continue your research successfully.