explanatory research paper

What is explanatory research?

Last updated

12 June 2023

Reviewed by

Miroslav Damyanov

The search for knowledge and understanding never stops in the field of research. Researchers are always finding new techniques to help analyze and make sense of the world. Explanatory research is one such technique. It provides a new perspective on various areas of study.

So, what exactly is explanatory research? This article will provide an in-depth overview of everything you need to know about explanatory research and its purpose. You’ll also get to know the different types of explanatory research and how they’re conducted.

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• Explanatory research: definition

Explanatory research is a technique used to gain a deeper understanding of the underlying reasons for, causes of, and relationships behind a particular phenomenon that has yet to be extensively studied.

Researchers use this method to understand why and how a particular phenomenon occurs the way it does. Since there is limited information regarding the phenomenon being studied, it’s up to the researcher to develop fresh ideas and collect more data.

The results and conclusions drawn from explanatory research give researchers a deeper understanding and help predict future occurrences.

• Descriptive research vs. explanatory research

Descriptive research aims to define or summarize an event or population without explaining why it exists. It focuses on acquiring and conveying facts.

On the other hand, explanatory research aims to explain why a phenomenon occurs by working to understand the causes and correlations between variables.

Unlike descriptive research, which focuses on providing descriptions and characteristics of a given phenomenon, explanatory research goes a step further to explain different mechanisms and the reasons behind them. Explanatory research is never concerned with producing new knowledge or solving problems. Instead, it aims to explain why and how something happens.

• Exploratory research vs. explanatory research

Explanatory research explains why specific phenomena function as they do. Meanwhile, exploratory research examines and investigates an issue that is not clearly defined. Both methods are crucial for problem analysis.

Researchers use exploratory research at the outset to discover new ideas, concepts, and opportunities. Once exploratory research has identified a potential area of interest or problem, researchers employ explanatory research to delve further into the specific subject matter.

Researchers employ the explanatory research technique when they want to explain why and how something occurs in a certain way. Researchers who employ this approach usually have an outcome in mind, and carrying it out is their top priority.

• When to use explanatory research

Explanatory research may be helpful in the following situations:

When testing a theoretical model: explanatory research can help researchers develop a theory. It can provide sufficient evidence to validate or refine existing theories based on the available data.

When establishing causality: this research method can determine the cause-and-effect relationships between study variables and determine which variable influences the predicted outcome most. Explanatory research explores all the factors that lead to a certain outcome or phenomenon.

When making informed decisions: the results and conclusions drawn from explanatory research can provide a basis for informed decision-making. It can be helpful in different industries and sectors. For example, entrepreneurs in the business sector can use explanatory research to implement informed marketing strategies to increase sales and generate more revenue.

When addressing research gaps: a research gap is an unresolved problem or unanswered question due to inadequate research in that space. Researchers can use explanatory research to gather information about a certain phenomenon and fill research gaps. It also enables researchers to answer previously unanswered questions and explain different mechanisms that haven’t yet been studied.

When conducting program evaluation: researchers can also use the technique to determine the effectiveness of a particular program and identify all the factors that are likely to contribute to its success or failure.

• Types of explanatory research

Here are the different types of explanatory research:

Case study research: this method involves the in-depth analysis of a given individual, company, organization, or event. It allows researchers to study individuals or organizations that have faced the same situation. This way, they can determine what worked for them and what didn’t.

Experimental research: this involves manipulating independent variables and observing how they affect dependent variables. This method allows researchers to establish a cause-and-effect relationship between different variables.

Quasi-experimental research: this type of research is quite similar to experimental research, but it lacks complete control over variables. It’s best suited to situations where manipulating certain variables is difficult or impossible.

Correlational research: this involves identifying underlying relationships between two or more variables without manipulating them. It determines the strength and direction of the relationship between different variables.

Historical research: this method involves studying past events to gain a better understanding of their causes and effects. It’s mostly used in fields like history and sociology.

Survey research: this type of explanatory research involves collecting data using a set of structured questionnaires or interviews given to a representative sample of participants. It helps researchers gather information about individuals’ attitudes, opinions, and behaviors toward certain phenomena.

Observational research: this involves directly observing and recording people in their natural setting, like the home, the office, or a shop. By studying their actions, needs, and challenges, researchers can gain valuable insights into their behavior, preferences, and pain points. This results in explanatory conclusions.

• How to conduct explanatory research

Take the following steps when conducting explanatory research:

Develop the research question

The first step is to familiarize yourself with the topic you’re interested in and clearly articulate your specific goals. This will help you define the research question you want to answer or the problem you want to solve. Doing this will guide your research and ensure you collect the right data.

Formulate a hypothesis

The next step is to formulate a hypothesis that will address your expectations. Some researchers find that literature material has already covered their topic in the past. If this is the case with you, you can use such material as the main foundation of your hypothesis. However, if it doesn’t exist, you must formulate a hypothesis based on your own instincts or literature material on closely related topics.

Select the research type

Choose an appropriate research type based on your research questions, available resources, and timeline. Consider the level of control you need over the variables.

Next, design and develop instruments such as surveys, interview guides, or observation guidelines to gather relevant data.

Collect the data

Collecting data involves implementing the research instruments and gathering information from a representative sample of your target audience. Ensure proper data collection protocol, ethical considerations , and appropriate documentation for the data you collect.

Analyze the data

Once you have collected the data you need for your research, you’ll need to organize, code, and interpret it.

Use appropriate analytical methods, such as statistical analysis or thematic coding , to uncover patterns, relationships, and explanations that address your research goals and questions. You may have to suggest or conduct further research based on the results to elaborate on certain areas.

Communicate the results

Finally, communicate your results to relevant stakeholders , such as team members, clients, or other involved partners. Present your insights clearly and concisely through reports, slides, or visualizations. Provide actionable recommendations and avenues for future research.

• Examples of explanatory research

Here are some real-life examples of explanatory research:

Understanding what causes high crime rates in big cities

Law enforcement organizations use explanatory research to pinpoint what causes high crime rates in particular cities. They gather information about various influencing factors, such as gang involvement, drug misuse, family structures, and firearm availability.

They then use regression analysis to examine the data further to understand the factors contributing to the high crime rates.

Factors that influence students’ academic performance

Educators and stakeholders in the Department of Education use questionnaires and interviews to gather data on factors that affect academic performance. These factors include parental engagement, learning styles, motivation, teaching quality, and peer pressure.

The data is used to ascertain how these variables affect students’ academic performance.

Examining what causes economic disparity in certain areas

Researchers use correlational and experimental research approaches to gather information on variables like education levels, household income, and employment rates. They use the information to examine the causes of economic disparity in certain regions.

• Advantages of explanatory research

Here are some of the benefits you can expect from explanatory research:

Deeper understanding : the technique helps fill research gaps in previous studies by explaining the reasons, causes, and relationships behind particular behaviors or phenomena.

Competitive edge: by understanding the underlying factors that drive customer satisfaction and behavior, companies can create more engaging products and desirable services.

Predictable capabilities: it helps researchers and teams make predictions regarding certain phenomena like user behavior or future iterations of product features.

Informed decision-making: explanatory research generates insights that can help individuals make informed decisions in various sectors.

• Disadvantages of explanatory research

Explanatory research is a great approach for better understanding various phenomena, but it has some limitations.

It’s time-consuming: explanatory research can be a time-consuming process, requiring careful planning, data collection, analysis, and interpretation. The technique might extend your timeline.

It’s resource intensive: explanatory research often requires a significant allocation of resources, including financial, human, and technological. This could pose challenges for organizations with limited budgets or constraints.

You have limited control over real-world factors: this type of research often takes place in controlled environments. Researchers may find this limits their ability to capture real-world complexities and variables that influence a particular behavior or phenomenon.

Depth and breadth are difficult to balance : explanatory research mainly focuses on a narrow hypothesis, which can limit the scope of the research and prevent researchers from understanding a problem more broadly.

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• Explanatory Research: Types, Examples, Pros & Cons

Explanatory research is designed to do exactly what it sounds like: explain, and explore. You ask questions, learn about your target market, and develop hypotheses for testing in your study. This article will take you through some of the types of explanatory research and what they are used for.

What is Explanatory Research?

Explanatory research is defined as a strategy used for collecting data for the purpose of explaining a phenomenon. Because the phenomenon being studied began with a single piece of data, it is up to the researcher to collect more pieces of data. 

In other words, explanatory research is a method used to investigate a phenomenon (a situation worth studying) that had not been studied before or had not been well explained previously in a proper way. It is a process in which the purpose is to find out what would be a potential answer to the problem.

This method of research enables you to find out what does not work as well as what does and once you have found this information, you can take measures for developing better alternatives that would improve the process being studied. The goal of explanatory research is to answer the question “How,” and it is most often conducted by people who want to understand why something works the way it does, or why something happens as it does.

Read: How to Write a Problem Statement for your Research

By using this method, researchers are able to explain why something is happening and how it happens. In other words, explanatory research can be used to “explain” something, by providing the right context. This is usually done through the use of surveys and interviews.

Importance of Explanatory Research

Explanatory research helps researchers to better understand a subject, but it does not help them to predict what might happen in the future. Explanatory research is also known by other names, such as ex post facto (Latin for “after the fact”) and causal research.

The most important goal of explanatory research is to help understand a given phenomenon. This can be done through basic or applied research . 

Basic explanatory research, also known as pure or fundamental research, is conducted without any specific real-world application in mind. Applied explanatory research attempts to develop new knowledge that can be used to improve humans’ everyday lives. 

Read: How to Write a Thesis Statement for Your Research: Tips + Examples

For example, you might want to know why people buy certain products, why companies change their business processes, or what motivates people in the workplace. Explanatory research starts with a theory or hypothesis and then gathers evidence to prove or disprove the theory. 

Most explanatory research uses surveys to gather information from a pool of respondents . The results will then provide information about the target population as a whole.

Purpose of Explanatory Research

The purpose of explanatory research is to explore a topic and develop a deeper understanding of it so that it can be described or explained more fully. The researcher sets out with a specific question or hypothesis in mind, which will guide the data collection and analysis process.

Explanatory research can take any number of forms, from experimental studies in which researchers test a hypothesis by manipulating variables, to interviews and surveys that are used to gather insights from participants about their experiences. Explanatory research seeks neither to generate new knowledge nor solve a specific problem; rather it seeks to understand why something happens.

For example, imagine that you would like to know whether one’s age affects his or her ability to use a particular type of computer software. You develop the hypothesis that older people will have more difficulty using the software than younger people. 

In order to test your hypothesis and learn more about the relationship between age and software usage, you design and conduct an explanatory study.

Read: How to Write An Abstract For Research Papers: Tips & Examples

Characteristics of Explanatory Research

Explanatory research is used to explain something that has already happened but it doesn’t try to control anything, nor does it seek to predict what will happen. Instead, its aim is to understand what has happened when it comes to a certain phenomenon.

Here are some of the characteristics of explanatory research, they include:

• It is used when the researcher wants to explain the relationship between two variables that the researcher cannot manipulate. This means that the researcher must rely on secondary data instead to understand the variables.
• In explanatory research, the data is collected before the study begins and is usually collected by a different individual/organization than that of the researcher.
• Explanatory research does not involve random sampling or random allocation (the process of assigning subjects and participants to different study groups).

Types of Explanatory Research

Explanatory research generally focuses on the “why” questions. For example, a business might ask why customers aren’t buying their product or how they can improve their sales process. Types of explanatory research include:

1. Case studies: Case studies allow researchers to examine companies that experienced the same situation as them. This helps them understand what worked and what didn’t work for the other company.

 Explore: Formplus Customer Success Stories and Case Studies

2. Literature research: Literature research involves examining and reviewing existing academic literature on a topic related to your projects, such as a particular strategy or method. Literature research allows researchers to see how other people have discussed a similar problem and how they arrived at their conclusions.

3. Observations: Observations involve gathering information by observing events without interfering with them. They’re useful for gathering information about social interactions, such as who talks to whom on a subway platform or how people react to certain ads in public spaces, like billboards and bus shelters.

4. Pilot studies: Pilot studies are small versions of larger studies that help researchers prepare for larger studies by testing out methods, procedures, or instruments before using them in the final study design.

Read: Research Report: Definition, Types + [Writing Guide]

5. Focus groups: Focus groups involves gathering a group of people so participants can share opinions, instead of answering questions

Difference between Explanatory and Exploratory Research

Explanatory research is a type of research that answers the question “why.” It explains why something happens and it helps to understand what caused something to happen.

Explanatory research always has a clear objective in mind, and it’s all about the execution of that objective. Its main focus is to answer questions like “why?” and “how?”

Exploratory research on the other hand is a form of observational research, meaning that it involves observing and measuring what already exists. Exploratory research is also used when the researcher doesn’t know what they’re looking for. 

Its purpose is to help researchers better understand a subject so that they can develop a theory. It is not about drawing any conclusion but about learning more about the subject. 

Examples of Explanatory Research

Explanatory research will make it easier to find explanations for things that are difficult to understand. 

For example, if you’re trying to figure out why someone got sick, explanatory research can help you look at all of your options and figure out what happened.

In this way, it is also used in order to determine whether or not something was caused by a person or an event. If a person was involved, you might want to consider looking at other people who may have been involved as well.

It can also be useful for determining whether or not the person who caused the problem has changed over time. This can be especially helpful when you’re dealing with a long-term relationship where there have been many changes.

Read: 21 Chrome Extensions for Academic Researchers in 2022

Let us assume a researcher wants to figure out what happened during an accident and how it happened. 

Explanatory research will try to understand if a person was driving while intoxicated, or if the person had been under the influence of alcohol or drugs at the time of their death. If they were not, then they may have had some other medical condition that caused them to pass away unexpectedly.

In the two examples, explanatory research wanted to answer the question of what happened and why did it happen.

Advantages of Explanatory Research

Here are some of the advantages of explanatory research:

• Explanatory research can explain how something happened
• It also helps to understand a cause of a phenomenon
• It is great in predicting what will happen in the future based on observations made today.
• It is also a great way to start your research if you are unfamiliar with the subject.

Disadvantages of Explanatory Research

Explanatory research is beneficial in many ways as listed above, but here are a few of the disadvantages of explanatory research.

1. Clarity on what is not known: The first disadvantage is that this kind of research is not always clear about what is and isn’t known. Which means it doesn’t always make the best use of existing information or knowledge.

You need to be specific about what you know already and how much more there might be left for future studies in order for this kind of research project to be useful at all times. This can help avoid wasting time by focusing on an issue that has already been studied enough without knowing it yet (or vice versa).

2. No clear hypothesis: Another disadvantage is that when designing experiments using this method there often isn’t any clear hypothesis about what will happen next which makes it impossible for scientists to predict

Explanatory research is taking a topic and explaining it thoroughly so that audiences have a better understanding of the topic in question. With explanatory research, having great explanations takes on more importance, so if you are a researcher in the social science field, you might want to put it to use.

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Explanatory Research: Definition, Types & Guide

There are many types of research, but today, we want to talk to you about one, in particular, that will give you a new perspective on your objects of study; for that, we have created this guide with everything you need to know about explanatory research . After all, w hat is the purpose of explanatory research?

What is Explanatory Research?

Explanatory research is a method developed to investigate a phenomenon that has not been studied or explained properly. Its main intention is to provide details about where to find a small amount of information.

With this method, the researcher gets a general idea and uses research as a tool to guide them quicker to the issues that we might address in the future. Its goal is to find the why and what of an object of study.

Explanatory research is responsible for finding the why of the events by establishing cause-effect relationships. Its results and conclusions constitute the deepest level of knowledge, according to author Fidias G. Arias. In this sense, explanatory studies can deal with the determination of causes (post-facto research) and effects ( experimental research ) through hypothesis testing.

Characteristics of Explanatory Research 

Among the most critical characteristics of explanatory research are:

• It allows for an increased understanding of a specific topic. Although it does not offer conclusive results, the researcher can find out why a phenomenon occurs.
• It uses secondary research as a source of information, such as literature or published articles, that are carefully chosen to have a broad and balanced understanding of the topic.
• It allows the researcher to have a broad understanding of the topic and refine subsequent research questions to augment the study’s conclusions.
• Researchers can distinguish the causes why phenomena arising during the research design process and anticipate changes.
• Explanatory research allows them to replicate studies to give them greater depth and gain new insights into the phenomenon.

Types of Explanatory Research

The most popular methods of explanatory research:

• Literature research: It is one of the fastest and least expensive means of determining the hypothesis of the phenomenon and collecting information. It involves searching for literature on the internet and in libraries. It can, of course, be in magazines, newspapers, commercial and academic articles.
• In-depth interview: The process involves talking to a knowledgeable person about the topic under investigation. The in-depth interview is used to take advantage of the information offered by people and their experience, whether they are professionals within or outside the organization.
• Focus groups: Focus groups consist of bringing together 8 to 12 people who have information about the phenomenon under study and organizing sessions to obtain from these people various data that will help the research.
• Case studies: This method allows researchers to deal with carefully selected cases. Case analysis allows the organization to observe companies that have faced the same issue and deal with it more efficiently.

Check out our library of QuestionPro Case Studies to learn more about how we help organizations conduct market research.

Importance of explanatory research

Explanatory research is conducted to help researchers study the research problem in greater depth and understand the phenomenon efficiently.

The primary use for explanatory research is problem-solving by finding the overlooked data that we had never investigated before. At the same time, it might not bring out conclusive data; it will allow us to understand the issue more efficiently.

In carrying out the research process, it is necessary to adapt to new findings and knowledge about the subject. Although it is impossible to conclude, it is possible to explore the variables with a high level of depth.

Explanatory research allows the researcher to become familiar with the topic to be examined and design theories to test them.

Explanatory Reseach Quick Guide

Explanatory research is a great method to use if you’re looking to understand why something is happening. Here’s a quick guide on how to conduct explanatory research:

• Clearly define your research question and objectives. This will help guide your research and ensure that you collect the right data.
• Choose your research methods. Explanatory research can be done using both qualitative and quantitative methods. Some popular methods include surveys, interviews, experiments, and observational studies.
• Collect and analyze your data. Once you’ve chosen your methods, it’s time to collect your data. Make sure to keep accurate records and organize your data so it’s easy to analyze.
• Draw conclusions and make recommendations. After analyzing your data, it’s time to draw conclusions and make recommendations based on your findings. Be sure to present your conclusions clearly and concisely and ensure your data supports them.
• Communicate your findings. Share your research findings with others, including your colleagues, stakeholders, or clients. Also, make sure to communicate your findings in a way that is easy for others to understand and act upon.

Remember that explanatory research is about understanding the relationship between variables, so be sure to keep that in mind when designing your research, collecting and analyzing your data, and communicating your findings.

Advantages and Conclusions

This method is precious for social research . It a llows researchers to find a phenomenon we did not study in depth. Although it does not conclude such a study, it helps to understand the problem efficiently. It’s essential to convey new data about a point of view on the study.

People who conduct explanatory research do so to study the interaction of the phenomenon in detail. Therefore, it is vital to have enough information to carry it out.

Finally, we invite you to refer to our market research guide . You can do incredible research and collect data free with our survey software . Get started now!

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Explanatory Research – Guide with Definition & Examples

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Explanatory research, a vital part of research methodology , is dedicated to providing a deep understanding of a phenomenon through the explanation of causal relationships among variables. Unlike exploratory research that seeks to generate new insights or ideas, explanatory research dives deeper to identify why and how certain situations occur. This methodology is often employed when there is a clear understanding of the problem but the reasons behind it remain obscure, thereby necessitating a comprehensive explanation.

Inhaltsverzeichnis

• 1 Explanatory Research – In a Nutshell
• 2 Definition: Explanatory Research
• 3 The usage of explanatory research
• 4 Explanatory research questions
• 5 Explanatory research: Data collection
• 6 Explanatory research: Data analysis
• 7 The 5 Steps of explanatory research with examples
• 8 Explanatory vs. exploratory research
• 9 Advantages vs. disadvantages

Explanatory Research – In a Nutshell

• Explanatory research is a cornerstone of other research.
• Without an explanatory study, your future research will be incomplete and inefficient.
• This research improves survey and study design and reduces unintended bias.

Definition: Explanatory Research

Explanatory research is a study method that investigates the causes of a phenomenon when only limited data is presented. It can help you better grasp a topic, determine why a phenomenon is happening, and forecast future events.

This research can be described as a “cause and effect” model, researching previously unexplored patterns and trends in current data. Consequently, it is sometimes considered a sort of causal research .

The usage of explanatory research

Explanatory research investigates how or why something happens. Therefore, this type of research is one of the first steps in the research process , serving as a beginning point for future work. Your topic may have data, but the causal relationship you’re interested in may not.

This research helps evaluate patterns and generate hypotheses for future work. An explanatory study can help you comprehend a variable’s relationship. However, don’t expect conclusive outcomes.

Explanatory research questions

This research answers “why” and “how” inquiries, resulting in a better knowledge of a previously unsolved topic or clarification for relevant future research.

• Why do bilingual individuals exhibit more risky behavior than monolingual individuals during commercial negotiations?
• How does a child’s capacity to resist gratification predict their future success?
• Why are adolescents more prone to litter in highly littered areas than in clean areas?

Explanatory research: Data collection

After deciding on your research subject, you have numerous alternatives for research and data collection methods.

The following are some of the most prevalent research methods:

• Literature reviews
• Interviews and focus groups
• Pilot studies

Explanatory research: Data analysis

Ensure that your explanatory research is conducted appropriately and that your analysis is causal and not merely correlative.

Correlated variables are merely linked: when one changes, so does the other . There is no direct or indirect causal relationship.

Causation means independent variable changes cause dependent variable changes. The link between variables is direct.

The requirements for causal evidence are:

• Temporal : Cause must precede effect.
• Variation : Independent and dependent variable intervention must be systematic.
• Non-spurious : Be sure no mitigating factors or third hidden variables contradict your results.

The 5 Steps of explanatory research with examples

The data collection approach determines your explanatory research design. In most circumstances, you’ll utilize an experiment to test causality. The steps are illustrated in the following.

Step 1 of explanatory research: Research question

The initial stage in the research is familiarizing yourself with the topic of interest to formulate a research question.

Suppose you are interested in adult language retention rates.

You’ve examined language retention in adoptees. People who learned a foreign language as infants had an easier time learning it again than those who weren’t exposed.

You want to know how language exposure affects long-term retention. You’re designing an experiment to answer this question: How does early language exposure affect language retention in adoptees?

Step 2 of explanatory research: Hypothesis

Next, set your expectations. In some circumstances, you can use relevant literature to build your hypothesis. In other cases, the topic isn’t well-studied; therefore, you must create your theory based on instincts or literature on distant themes.

You hypothesize that individuals exposed to a language in infancy for a shorter duration will be less likely to retain features of this language than adults exposed for a longer time.

You express your predictions in terms of the null (H 0 ) and alternative (H 1 ) hypotheses:

• H 0 : Infancy language exposure does not affect language retention in adopted adults.
• H 1 : Exposure to a language in infancy improves language retention in adult adoptees.

Step 3 of explanatory research: Methodology and data collection

Next, choose your data collecting and data analysis methodologies and document them. After meticulously planning your research, you can begin data collection.

To test a causal relationship, you run an experiment. You gather a group of adults adopted from Colombia and raised in the U.S.

You compare:

• 0-6-month-old Colombian adoptees.
• 6-12 month-old Colombian adoptees
• 12-18-month-old Colombian adoptees.
• Unexposed monolingual adults.

Using a three-stage research design, you administer two tests of their Spanish language skills during the study:

• Pre-test : Several language proficiency tests are administered to identify group variations before instruction.
• Intervention : You deliver eight hours of Spanish lessons to each group.
• Post-test : After the intervention, you administer multiple language proficiency tests to determine whether there are any differences between the groups.

Step 4 of explanatory research: Analysis and results

After data collection, assess and report results.

After experimenting, you examine the data and observe that:

• The pre-exposed adults demonstrated more excellent Spanish language skills than individuals who were not pre-exposed. The post-test reveals an even more significant disparity.
• Adults adopted between 12 and 18 months had higher Spanish competence than those adopted between 0 and 6 months or 6 and 12 months, but there was no difference between the latter two groups.

For significance, use a mixed ANOVA . ANOVA indicates that pre-test differences aren’t significant, while post-test differences are.

You report your findings following the criteria of your chosen citation style between the groups.

Step 5 of explanatory research: Interpretation and recommendation

Try to explain unexpected results as you interpret them. In most circumstances, you’ll need to provide recommendations for future research.

Your findings were per your expectations. Adopted individuals who were pre-exposed to a language in infancy for a longer time have preserved more of this knowledge than people who weren’t pre-exposed.

After the intervention, this difference becomes large.

You decide to do more research and suggest some topics:

• Replicate the study with a larger sample
• Study other mother tongues (e.g., Korean, Lingala, Arabic)
• Study other linguistic features, like accent nativeness.

Explanatory vs. exploratory research

Explanatory and exploratory research are often confused. Remember, exploratory research establishes the framework for explanatory research.

Many exploratory research inquiries begin with “what.” They are intended to guide future studies and typically lack definite conclusions. The research is frequently employed as the initial step in the research process to assist you in refining your study topic and ideas.

Explanatory research questions begin with “why” or “how.” They assist you in understanding why and how something happens.

Advantages vs. disadvantages

As with any other study methodology, this research involves trade-offs: while it offers a unique set of benefits, it also has major drawbacks.

What is explanatory research?

An explanatory study investigates how or why something happens with limited information. It helps you understand a topic.

Is explanatory research quantitative or qualitative?

The explanatory research model is a quantitative strategy used to examine a hypothesis by gathering evidence that either supports or contradicts it.

When should I use explanatory research?

Explanatory research aims to explain a phenomenon. Consequently, this form of research is frequently one of the initial steps of the research process, acting as a springboard for subsequent analysis.

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• Methodology
• Open access
• Published: 06 March 2019

Understanding contexts: how explanatory theories can help

• Frank Davidoff   ORCID: orcid.org/0000-0001-7924-4005 1 , 2  

Implementation Science volume  14 , Article number:  23 ( 2019 ) Cite this article

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To rethink the nature and roles of context in ways that help improvers implement effective, sustained improvement interventions in healthcare quality and safety.

Critical analysis of existing concepts of context; synthesis of those concepts into a framework for the construction of explanatory theories of human environments, including healthcare systems.

Data sources

Published literature in improvement science, as well as in social, organization, and management sciences. Relevant content was sought by iteratively building searches from reference lists in relevant documents.

Scientific thought is represented in both causal and explanatory theories. Explanatory theories are multi-variable constructs used to make sense of complex events and situations; they include basic operating principles of explanation, most importantly: transferring new meaning to complex and confusing phenomena; separating out individual components of an event or situation; unifying the components into a coherent construct (model); and adapting that construct to fit its intended uses. Contexts of human activities can be usefully represented as explanatory theories of peoples’ environments; they are valuable to the extent they can be translated into practical changes in behaviors.

Healthcare systems are among the most complex human environments known. Although no single explanatory theory adequately represents those environments, multiple mature theories of human action, taken together, can usually make sense of them. Current mature theories of context include static models , universal-plus-variable models , activity theory and related models , and the FITT framework (Fit between Individuals, Tasks, and Technologies). Explanatory theories represent contexts most effectively when they include basic explanatory principles.

Conclusions

Healthcare systems can usefully be represented in explanatory theories. Improvement interventions in healthcare quality and safety are most likely to bring about intended and sustained changes when improvers use explanatory theories to align interventions with the host systems into which they are being introduced.

Peer Review reports

Introduction

Human contexts—defined in this commentary primarily as the meaning of human environments to the people who live and work in them—are major determinants of the effectiveness and generalizability of interventions to improve healthcare quality and safety [ 1 , 2 ]. Despite the importance of contex, much about it remains obscure, as do the specific mechanisms by which local contexts affect the implementation of improvement interventions. As a consequence, context is still sometimes vaguely referred to in scholarly work as “All those things in the situation which are relevant to meaning in some sense, but which I haven’t identified.”([ 2 ] p. 6).

Context plays an important role in both improvement science and implementation science; limited understanding of context therefore limits understanding of both the fundamental principles of improvement and the actions that put improvements into practice. Achieving deep understanding of context is a challenge that has baffled serious improvers, researchers, and scholars for years [ 2 ]. This difficulty [ 3 ] suggests that multiple complementary explanatory theories might prove more useful than any single theory in understanding both context in general and specific local contexts.

This commentary is intended as a complement to the SQUIRE guidelines for publication of work in quality improvement [ 4 ]. It explores the premise that explanatory theories of human environments can help improvers work flexibly from first principles rather than rigid formulas, and, as is true for good theories generally [ 6 ], can provide improvers with explicit reasons why particular interventions are likely to be effective in specific systems; it examines the nature of explanatory theories and the basic principles of explanation, considers the contributions of those principles to mature (i.e., fully-developed, refined) explanatory theories of complex human environments, and considers the nature of the data needed in constructing explanatory theories of local environments, and the methods used for gathering the requisite data. The commentary proposes, finally, that it is both appropriate and useful early in the planning of an improvement program, to create an explanatory theory of the local healthcare environment into which planned intervention is to be introduced, then use that theory in linking the intervention with that environment. The commentary also encourages improvers to reconsider and revise the initial explanatory theories from time to time as more is learned about the local environment during the improvement process.

Explanatory theories

Scientific thought is built primarily around two complementary mental constructs [ 5 ]: causal and explanatory theories. Explanatory theories are created to help to understand complex, confusing events and situations; they often also serve as sources of testable causal theories of events and situations.

Although explanatory theories are sometimes thought to play a less central role in science than causal theories [ 5 , 6 , 7 ], many explanatory theories— including the theory of evolution, the periodic table of the elements, and the structure of DNA—have proven uniquely helpful in understanding important phenomena in natural sciences. Political science is built largely around explanatory theories [ 7 ]; process flow diagrams and Pareto charts are among the explanatory theories that help understand events and situations in improvement science [ 8 ].

The concepts in this commentary were developed from the published literature in improvement science as well as the social, behavioral, organizational, improvement, and management sciences. Sources that proved especially important include Bate et al. [ 2 ] on the dynamic properties of context, Squires et al. [ 3 ] on the construction of explanatory theories, Braithwaite et al. [ 9 ] and Greenhalgh [ 10 ] on complexity, Nardi [ 11 ] and Greenhalgh et al. [ 12 ] on theories of human action, Vandenbroucke [ 5 ] and Clarke and Primo [ 7 ] on explanatory theory, and Pitt [ 13 ] on the fundamentals of explanation. Literature searches were built out from reference citations in these and related publications.

The author’s experience as editor of a major clinical journal ( Annals of Internal Medicine ), and as publications editor at the Institute for Healthcare Improvement (Cambridge, MA), also helped in constructing this commentary. Discussions in the improvement science development group at the Health Foundation (London, UK) and in the Standards for Quality Improvement Reporting Excellence (SQUIRE) leadership group [ 4 ] also contributed importantly to this effort.

The complexity and dynamism of human environments

The most salient properties of human environments are arguably their complexity [ 9 , 10 ] and their dynamic nature [ 2 ]. This commentary rests on the concept of “complex systems” summarized in Table  1 .

The degrees of complexity in human systems are usefully characterized in the following schema [ 14 ], in which the cooking of a specific dish is represented as simple . Challenges at this basic level are usually managed successfully by following explicit, straightforward recipes or protocols.

By comparison, sending a rocket to the moon is complicated for multiple psychological, social, and technical reasons. Successful management of complicated challenges often requires the use of dedicated management tools such as checklists (mainly to overcome the limitations of human memory) and protocols that map out contingency-dependent decision points (mainly to avoid oversimplification).

Finally, the challenge of raising a child can be seen as  complex , largely because it involves such a large number of variables, many of them poorly defined, which often leads to unpredictable outcomes, e.g., when the experience of raising one child successfully is of little use in raising the next.

Principles of explanation (sense-making)

Although a human event or situation can sometimes be explained adequately in terms of causal mechanisms, the inherent complexity and dynamic nature of events and situations usually requires explanations that go beyond causality and include descriptive explanatory principles [ 5 , 6 , 10 , 13 , 14 , 15 , 16 ]. Most importantly, those principles include transferring new meaning to the event or situation, establishing its familiarity and internal logic, separating out its individual components, unifying its components into a coherent mental construct or “gestalt”, and adapting the explanation to fit its intended uses.

Transferring (sharing) meaning

The classic human system for transferring or sharing meaning is, of course, language [ 17 ]: witness the substantial loss or distortion of its meaning that results when a word or phrase is taken out of context, and conversely the greater precision of a literature search that uses search terms embedded in linguistic contexts, when contrasted with a search that uses search terms lacking such embellishment [ 18 , 19 ]. (Salmon proposes that the transfer of information, energy or causal inference between processes is more meaningful than transfer between events [ 16 ].)

Familiarity

Familiarity, by itself, is neither necessary nor sufficient to make sense of an event or situation. But familiarity is nonetheless an important component of explanation, because a sense of familiarity provides a sense of understanding ([ 20 ], p. 52). Metaphor is often the chosen mechanism for transferring meaning from familiar things to those that are less familiar, a property that prompted Aristotle to comment that it is metaphor that most produces knowledge. The psychologist Julian Jaynes has argued that metaphor is not a “mere extra trick of language” but is rather “the very constitutive ground of language,” and that “it is by metaphor that language grows” ([ 20 ], pp. 48-9).

Explanation in natural sciences is usually considered adequate when its logic is clear, as when statement of a general law (a “regularity”) is coupled with statement of a specific antecedent condition. In physics, for example, a statement such as “All wave phenomena of a certain type satisfy the law of refraction, and light is a wave of that type” is accepted as a logical construct that meaningfully explains the refraction of light ([ 13 ] p. 10]).

Separating out and unifying components

By themselves, the individual components of an event or situation ordinarily have little if any inherent meaning. But the construct that results when those components are brought together to make a coherent whole (usually as a narrative, map, model, or mathematical expression) is uniquely helpful in making sense of that event or situation [ 4 , 21 , 22 ]. Important new meanings can emerge as well—often unexpectedly—from the resulting construct. For these reasons, some philosophers of science consider unification of a phenomenon’s individual components into a coherent whole as the main principle by which explanation renders a phenomenon understandable [ 4 , 5 , 21 , 22 ].

The sharing of meaning among a phenomenon’s individual components finds expression in catch-phrases such as the jigsaw puzzle effect , and “The whole is greater than the sum of its parts.” On a more grand scale, the theory of evolution is said to acquire its explanatory power when “an apparently modest allegiance to mere fact gathering” abruptly crystallizes into a “whole world view” [ 23 ].

Details of the mental process through which unification creates explanations unfortunately remain obscure. And curiously, even a highly coherent construct of an event or situation does not necessarily help understand whether its components are truly independent, whether the interactions among them are uni-directional or recursive, and which components (if any) are most important in determining its overall behavior. Moreover, craftspeople such as watchmakers and car mechanics understand that success in their work depends on their ability to separate out the components of the complex systems they are called on to assemble or repair (disaggregate them) at least as much as on their ability to understand how the components contribute collectively to an event or situation’s overall behavior (unify them). At least in theory, the explanatory principles of disaggregation and unification appear to contradict each other, but in practice, the two principles are often complementary. In managing a human system, for example, a leader’s ability to unify various groups’ individual modes of decision-making can complement his or her ability to distinguish those modes from one another [ 24 ].

Adapting explanations

Explanatory theories are arguably successful to the extent people can translate them into practical implementation behavior—e.g., manage the environments in which they live and work or predict the likelihood that a specific event will happen in the future ([ 16 ] p. 77). Not surprisingly, therefore, the explanatory theories people develop on their own to manage their personal environments differ substantially from the ones they develop collectively to further the work of the organizations in which they work. For similar reasons, personal and organization-related explanatory theories differ from those that outside researchers create to understand these various environments.

Personal contexts

Peoples’ intense, universal need to give meaning to “the brutal aboriginal flux” of their lived experience [ 1 ] suggests that humans can be defined as “reason-giving animals” [ 25 ]. They begin creating explanatory theories of their personal environments at an extremely early age [ 26 ], then extend and refine those theories as they and their personal environments change over time. Personal explanatory theories are usually implicit and poorly articulated; they can also be distorted, incomplete, or inappropriate since they frequently lack independent reality testing.

Organizational and professional contexts

Workers in organizations are called on to create explanatory models that make sense of the internal structure and function of those organizations, as well as of the external environments in which their organizations are embedded. Weick et al. describe this work as a creative, collaborative undertaking that involves “language, talk, and communication” and is “ongoing, subtle, swift, social, and easily taken for granted” [ 1 , 27 , 28 ]. Early in this sense-making process, workers in an organization “bracket” information (i.e., identify items they see as especially relevant to their particular situation), then name (label) those items, which stabilizes the streaming of their experience [ 1 ].

The way people in organizations envision events and situations also immediately begins their social and administrative work of organizing, because bracketing and labeling events predisposes them to find common ground and provides them with a set of cognitive categories, plus a typology of potential actions. (Bracketing central venous catheter infection and labeling it as primarily a social rather than a biological problem [ 29 ] played an important role in shaping an intervention that successfully lowers the infection rate [ 30 ].) Workers then use such newly defined contextual elements as they literally talk their organization-related explanatory theories into existence [ 1 ].

The sense-making process described above closely resembles the one that professionals in applied disciplines, together with their clients, use to make sense of the problem situations they are called on to manage ([ 31 ], pp. 267–83). More specifically, medical professionals will recognize its resemblance to the process by which they and their patients formulate the essential explanatory theories they know as diagnoses .

Mature explanatory theories of human environments

People initially sketch out rough explanatory theories of environments which usually involve basic principles of explanation, then subsequently broaden and refine these nascent constructs into more mature theories. Important examples of such mature explanatory theories include static theories , universal-plus-variable theories , activity theory and related general theories of human action , and the FITT framework (Fit between Individuals, Task, and Technology).

Static theories

Several research groups have developed explanatory theories of outstanding healthcare systems by selecting the components they judge to be most closely associated with certain systems’ ability to deliver exceptionally safe, high-quality care [ 32 , 33 , 34 , 35 , 36 ], then assembling those components into structured models. (A recent international effort is engaged in constructing a new and more meaningful theory of this type [ 3 ]).

The individual components identified in these theory-building exercises—buildings, equipment, leadership, geographic location, teaching status, financial and intellectual resources, and the like—are quite heterogeneous and the resulting constructs often pay little attention to functional relationships among the components or to the ways in which the process of care plays out over time for individual patients. Metaphorically speaking, then, explanatory theories such as these describe the anatomy of exceptional healthcare environments, but not their physiology ; that is, they are static , which could account for the limited ability of this type of explanatory theory to explain variation in the effectiveness of improvement interventions across different healthcare systems.

Universal-plus-variable models

Working from detailed on-site observations in high-performing healthcare systems, Bate et al. [ 2 , 37 ] have constructed a generalized explanatory theory of such systems. Their experience is reflected in their comment that “although research has provided an abundance of data on key success factors in QI efforts, very little was previously known about how these combine and interact with each other in the improvement process over time.” They comment further that the context of a healthcare system is “a process; dynamic, fluid, and constantly moving, not lumpen, material, or static,” and that “it is the dynamic and ongoing interaction between [the domains of an environment] rather than any one of them individually or independently, that accounts for the effectiveness of a QI intervention,” as well as for “the striking variation between similar QI interventions in different places” ([ 2 ]p. 11).

These investigators then refine and sharpen the focus of their emerging explanatory theory by postulating that a healthcare system’s ability to deliver outstanding care lies in the combination of the two major components— universals and variables —that characterize an organization’s local situation. More specifically, they identify the challenges inherent in several distinct areas—physical/technological, emotional, educational, cultural and political, and structural—as the universals in all healthcare organizations; they also characterize the actions that individual workers and groups take in response to those challenges as differing both within and across organizations to the point where those actions and the possible combinations among them can be assumed to be “practically innumerable” ([ 37 ], p. 168), i.e., they are the variables .

The resulting universal-plus-variable explanatory theory of human contexts gains plausibility from its affinity with other established cognitive systems in which people represent the complex meanings that matter to them. The best known and arguably most important of such systems is of course language [ 17 ]; people produce language by embedding differing strings of individual words (the variables) in a relatively small number of stable grammatical structures (the universals). They then use the resulting construct to create a virtually unlimited number of statements that are meaningful to others, even though many of those statements have not been seen or heard previously.

Music provides another illuminating example of a meaningful universal-plus-variable explanatory theory [ 39 ]. Composers in each musical tradition embed differing arrays of tones (variables) in a limited set of stable, widely recognized harmonic constructs (universals). One critic has elegantly captured this explanatory theory of music (or at least of Western music) in his pithy comment that “Mozart used the same B-flat as everyone else.”

Activity theory and related models of human action

The universal-plus-variable explanatory theory of contexts also resonates with several earlier mature explanatory theories of human action, including Activity Theory and related models [ 11 ]. Some of these action theories are now seen as especially useful in understanding the interaction between people and computer systems [ 12 ]. In these theories, it is precisely the ongoing bi-directional interaction between static human environments and the dynamic needs, interests, and experiences people bring to encounters with those environments that creates most of the contexts (meanings) of human life. For example, context is understood as follows in Activity Theory as an overarching, albeit secondary, consideration: “[W]hat takes place in an activity system composed of object, actions, and operations, is the context… [C]ontext is not an outer container or shell inside of which people behave in certain ways.” Context in these theories is thus “both internal to people…and at the same time, external to people” [ 11 ], i.e., as an integrated whole. This unifying perspective invalidates “simplistic explanations that divide internal and external, and schemes that see context as external to people.”

The FITT framework (Fit between Individuals, Tasks, and Technologies)

Developed largely to explain the adoption of information and communication technologies (IT) [ 40 ], the FITT framework clearly distinguishes an organization’s established and widely recognized tasks and technologies from its workers’ shifting dynamic behaviors [ 5 , 12 , 40 , 41 ] (Table  2 ), and in that respect, it resembles other universal-plus-variable explanatory theories of human activity.

As noted elsewhere [ 6 ], the FITT framework has been used to guide the successful implementation of an innovative electronic order system for post-operative surgical care [ 41 ]. Researchers in that study explicitly used the FITT framework to help them interweave their new electronic system with the healthcare environment in which they implemented it.

The nature of data needed to construct explanatory theories of healthcare environments

Adequate understanding of human environments requires that explanatory theories take the enormous complexity of those environments appropriately into account. Although complexity of this magnitude can be a cause for despair among improvers and researchers, the statistician George Box’s pungent comment that “All theories are wrong, but some are illuminating and useful” offers reassurance that creating explanatory theories of human environments, including healthcare systems, is likely on balance to be worth the effort.

Data used to create meaningful explanatory theories of human environments

Creating explanatory theories of human environments that help implement successful improvement interventions apparently requires open-ended, multi-level data on working relationships in organizations [ 1 , 9 , 10 , 11 , 29 , 31 , 36 , 37 , 38 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. Research groups are now laboring to clarify the essential nature of such data (Table  3 ), while also obtaining insights into effective techniques for collecting and analyzing those data (Table  4 ).

It is important to note in this connection that improvement interventions reach their full potential more successfully when their implementation builds on the complexity of the systems they intend to change than when they underestimate or ignore that complexity [ 9 ]. Even documenting that a healthcare system has “a long way to go” to achieve specific solutions within each of the six universal challenge area (in contrast to being either “some way there” or “already there”) can help improvers pinpoint current gaps and opportunities in that system’s quality and safety, and facilitate productive discussions on their future improvement efforts (Cf. Codebook for Quality Improvement Practice, for example) ([ 37 ], p., 177).

In like fashion, answering a question regarding organizational complexity (e.g., “How did this practice miss a diagnosis?”) can be more effective in changing system performance than obtaining answering a narrowly focused question such as “How did an individual practitioner miss a diagnosis?”) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 ].

Traditional scientific methods will undoubtedly continue over time to help understand human environments, including environments that are as complex and dynamic as healthcare systems. At the same time, the difficulty of understanding those environments in the concepts and language of sciences suggests that explanatory theories of those environments will be more meaningful when they include contributions from the arts and humanities.

An important, and intriguing, painting by the Belgian surrealist René Magritte hints at the potential of such an ecumenical approach. In this work, Magritte apparently tries to represent the complex, emotionally freighted world of tobacco use by juxtaposing the image of a tobacco pipe with a written comment: “Ceci n’est pas une pipe” (“This is not a pipe”). The resulting cognitive dissonance suggests the artist’s intent is to increase the painting’s impact by cautioning his viewers that “This is only the image of a pipe, not the actual object; don’t confuse the two,” and encouraging them not to mistake the part for the whole (a pipe is, after all, only one small part of tobacco smoking).

But he does not stop there: in his effort to jolt viewers toward even deeper and more precise awareness of tobacco use, Magritte resorts to a particularly unorthodox representation of the pernicious habit, when he flatly asserts that “a pipe actually isn’t a pipe,” his surrogate for a paradoxical characterization of tobacco use in terms of what it is not . Examples of this startling apophatic (i.e., reverse) way to represent complex, confusing realities are now appearing in the literature of improvement science, as in “wake-up calls” telling us that  neither a checklist of infection control measures [ 49 ] nor a surgical safety checklist  [ 50 ], by itself, is an improvement intervention (the unstated subtext being that successful, sustained improvement absolutely requires explicit, extensive coordination, and tight linkage, between the intervention and the environment in which it is being implemented).

In articulating her explanatory theory of the world of falconry , the scholar and writer Helen Macdonald also turns, as follows, to this paradoxical, inverse way of understanding the deeper meaning of a complex human environment [ 51 ]:

“[T]here is a world of things out there – rocks and trees and grass and all the things that crawl and run and fly. They are all things in themselves, but we make them sensible to us by giving them meanings that shore up our own views of the world. In my time [living with and training my goshawk] Mabel I’ve learned how you feel more human once you have known, even in your imagination, what it is likely to be not”.

This commentary considers evidence that reinforces the crucial reality that the healthcare systems in which improvement programs take place—or, more specifically, the values and character of those systems—are at least as important in improving care as the specifics of the improvement interventions themselves. This obvious but often underappreciated reality environmental feature argues strongly for the development of sophisticated, nuanced understanding of those environments early in the implementation of improvement programs, and consistent application of that understanding during the improvement process. Realistically, understanding a human environment—especially one as complex and dynamic as a healthcare system—is an arduous, demanding undertaking, which further underscores the value of building a basic set of context-related initiatives into the implementation of any sizeable healthcare improvement program. These initiatives might include the following:

As early as possible in planning the program, create an explanatory theory of the host environment that incorporates the basic principles of explanation, especially unification of the environment’s major components;

If possible, involve social scientists, as well as professionals from humanities (e.g., creative writers, reporters, historians, graphic artists and the like) in the development of that explanatory theory;

Use that explanatory theory in coordinating and linking the intervention with the host environment;

Explore the use of established mature explanatory theories, individually or collectively, in making sense of the local host environment;

Assess the relative importance of the environment’s major components as determinants of its nature and behavior; its successes and failures;

From time to time, review the most current version of the explanatory theory and revise it if necessary as more is learned about the host environment and about the interaction between environment and intervention

To avoid creating jitter and instability in the program, resist unnecessary tinkering with the makeup and application of the explanatory theory;

Make explicit efforts to assure that all members of the improvement team are familiar with the major components of the host environment, and understand how those components fit/work together;

Adapt the focus, comprehensiveness, organization, and level of detail of the explanatory theory of the host environment, to make it as useful as possible for its most important users.

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The author gratefully acknowledges useful comments of Paul Batalden, Trisha Greenhalgh, Mary Dixon-Woods, Lucian Leape, Tom Sheridan, Cyrus Hopkins, and Judith Singer on earlier versions of this article.

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• Explanatory Research | Definition, Guide, & Examples

Explanatory Research | Definition, Guide & Examples

Published on 7 May 2022 by Tegan George and Julia Merkus. Revised on 20 January 2023.

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 .

Table of contents

When to use explanatory research, explanatory research questions, explanatory research data collection, explanatory research data analysis, step-by-step example of explanatory research, explanatory vs exploratory research, advantages and disadvantages of exploratory research, frequently asked questions about 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 analyse 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.

You analysed 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.

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Explanatory research answers ‘why’ and ‘what’ 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 labour market participation?
• Why do multilingual individuals show more risky behaviour during business negotiations than monolingual individuals?
• How does a child’s ability to delay immediate gratification predict success later in life?
• Why are teenagers more likely to litter in a highly littered area than in a clean area?

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:

• Literature reviews
• Interviews and focus groups
• Pilot studies
• Observations
• Experiments

The method you choose depends on several factors, including your timeline, your 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 behaviour, 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.

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 imply 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:

• Temporal : What you define as the ’cause’ must precede what you define as the ‘effect’.
• Variation : Intervention must be systematic between your independent variable and dependent variable.
• 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 .

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.

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

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.

• H 0 : The duration of exposure to a language in infancy does not influence language retention in adults who were adopted from abroad as children.
• H 1 : 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.

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.

• 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:

• Pretest : 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.
• Posttest : 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, and proficiency in other languages.

Step 4: Analyse your data and report results

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

• 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 posttest.
• 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 pretest, but they are significant for the posttest.

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.

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

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 utilised 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.

Like any other research design , exploratory 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 utilise pre-existing resources to guide your research before committing to heavier analyses.

Disadvantages

• 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.

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.

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.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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How to Write an Explanatory Essay: Comprehensive Guide with Examples

What Is an Explanatory Essay: Definition

Have you ever been tasked with explaining a complex topic to someone without prior knowledge? It can be challenging to break down complex ideas into simple terms that are easy to understand. That's where explanatory writing comes in! An explanatory essay, also known as an expository essay, is a type of academic writing that aims to explain a particular topic or concept clearly and concisely. These essays are often used in academic settings but can also be found in newspapers, magazines, and online publications.

For example, if you were asked to explain how a car engine works, you would need to provide a step-by-step explanation of the different parts of the engine and how they work together to make the car move. Or, if you were asked to explain the process of photosynthesis, you would need to explain how plants use sunlight, water, and carbon dioxide to create energy.

When wondering - 'what is an explanatory essay?', remember that the goal of an explanatory paper is to provide the reader with a better understanding of the topic at hand. Unlike an opinion essay , this type of paper does not argue for or against a particular viewpoint but rather presents information neutrally and objectively. By the end of the essay, the reader should clearly understand the topic and be able to explain it to others in their own words.

Also, there is no set number of paragraphs in an explanatory essay, as it can vary depending on the length and complexity of the topic. However, when wondering - 'how many paragraphs in an explanatory essay?', know that a typical example of explanatory writing will have an introduction, body paragraphs, and a conclusion.

However, some essays may have more or fewer body paragraphs, depending on the topic and the writer's preference. Ultimately, an explanatory essay format aims to provide a clear and thorough explanation of the topic, using as many paragraphs as necessary.

20 Interesting Explanatory Essay Topics 

Now that we have defined what is explanatory essay, the next step is choosing a good explanatory topic. A well-chosen topic is interesting and relevant to your audience while also being something you are knowledgeable about and can provide valuable insights on. By selecting a topic that is too broad or too narrow, you run the risk of either overwhelming your audience with too much information or failing to provide enough substance to fully explain the topic. Additionally, choosing a topic that is too controversial or biased can lead to difficulty in presenting information objectively and neutrally. By choosing a good explanatory topic, you can ensure that your essay is well-informed, engaging, and effective in communicating your ideas to your audience.

Here are 20 creative explanatory essay topics by our admission essay service to consider:

• How does the human brain process emotions?
• The benefits and drawbacks of remote work.
• The science behind climate change and its effects.
• The history and evolution of hip-hop music.
• The impact of social media on mental health.
• The benefits of learning a second language.
• The process of how a computer operates.
• The causes and effects of bullying in schools.
• The impact of technology on modern education.
• The reasons for the decline of bee populations and their importance to the ecosystem.
• The effects of caffeine on the human body.
• The process of how vaccines work to prevent disease.
• The impact of video games on youth behavior and development.
• The reasons for the gender pay gap and how to close it.
• The benefits and drawbacks of renewable energy sources.
• The history and cultural significance of tattoos.
• The causes and effects of income inequality in society.
• The process of how a book is published.
• The impact of social media on political discourse.
• The benefits and drawbacks of the gig economy.

How to Start an Explanatory Essay: Important Steps

Starting an explanatory essay can be challenging, especially if you are unsure where to begin. However, by following a few simple steps, you can effectively kick-start your writing process and produce a clear and concise essay. Here are some tips and examples from our term paper writing services on how to start an explanatory essay:

• Choose an engaging topic : Your topic should be interesting, relevant, and meaningful to your audience. For example, if you're writing about climate change, you might focus on a specific aspect of the issue, such as the effects of rising sea levels on coastal communities.
• Conduct research : Gather as much information as possible on your topic. This may involve reading scholarly articles, conducting interviews, or analyzing data. For example, if you're writing about the benefits of mindfulness meditation, you might research the psychological and physical benefits of the practice.
• Develop an outline : Creating an outline will help you logically organize your explanatory essay structure. For example, you might organize your essay on the benefits of mindfulness meditation by discussing its effects on mental health, physical health, and productivity.
• Provide clear explanations: When writing an explanatory article, it's important to explain complex concepts clearly and concisely. Use simple language and avoid technical jargon. For example, if you're explaining the process of photosynthesis, you might use diagrams and visual aids to help illustrate your points.
• Use evidence to support your claims : Use evidence from reputable sources to support your claims and arguments. This will help to build credibility and persuade your readers. For example, if you're writing about the benefits of exercise, you might cite studies that demonstrate its positive effects on mental health and cognitive function.

By following these tips and examples, you can effectively start your expository essays and produce a well-structured, informative, and engaging piece of writing.

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Explanatory Essay Outline

As mentioned above, it's important to create an explanatory essay outline to effectively organize your ideas and ensure that your essay is well-structured and easy to follow. An outline helps you organize your thoughts and ideas logically and systematically, ensuring that you cover all the key points related to your topic. It also helps you identify gaps in your research or argument and allows you to easily revise and edit your essay. In this way, an outline can greatly improve the overall quality and effectiveness of your explanatory essay.

Explanatory Essay Introduction

Here are some tips from our ' do my homework ' service to create a good explanatory essay introduction that effectively engages your readers and sets the stage for the entire essay:

• Start with a hook: Begin your introduction with an attention-grabbing statement or question that draws your readers in. For example, you might start your essay on the benefits of exercise with a statistic on how many Americans suffer from obesity.
• Provide context: Give your readers some background information on the topic you'll be discussing. This helps to set the stage and ensures that your readers understand the importance of the topic. For example, you might explain the rise of obesity rates in the United States over the past few decades.
• State your thesis: A good explanatory thesis example should be clear, concise, and focused. It should state the main argument or point of your essay. For example, you might state, ' Regular exercise is crucial to maintaining a healthy weight and reducing the risk of chronic diseases.'
• Preview your main points: Give your readers an idea of what to expect in the body of your essay by previewing your main points. For example, you might explain that you'll be discussing the benefits of exercise for mental health, physical health, and longevity.
• Keep it concise: Your introduction should be brief and to the point. Avoid getting bogged down in too much detail or providing too much background information. A good rule of thumb is to keep your introduction to one or two paragraphs.

The Body Paragraphs

By following the following tips, you can create well-organized, evidence-based explanation essay body paragraphs that effectively support your thesis statement.

• Use credible sources: When providing evidence to support your arguments, use credible sources such as peer-reviewed academic journals or reputable news outlets. For example, if you're writing about the benefits of a plant-based diet, you might cite a study published in the Journal of the American Medical Association.
• Organize your paragraphs logically: Each body paragraph should focus on a specific aspect or argument related to your topic. Organize your paragraphs logically so that each one builds on the previous one. For example, if you're writing about the causes of climate change, you might organize your paragraphs to focus on human activity, natural causes, and the effects of climate change.
• Use transitional phrases: Use transitional phrases to help your readers follow the flow of your ideas. For example, you might use phrases such as 'in addition,' 'furthermore,' or 'on the other hand' to indicate a shift in your argument.
• Provide analysis: Don't just present evidence; provide analysis and interpretation of the evidence. For example, if you're writing about the benefits of early childhood education, you might analyze the long-term effects on academic achievement and future earnings.
• Summarize your main points: End each body paragraph with a sentence that summarizes the main point or argument you've made. This helps to reinforce your thesis statement and keep your essay organized. For example, you might end a paragraph on the benefits of exercise by stating, 'Regular exercise has been shown to improve mental and physical health, making it a crucial aspect of a healthy lifestyle.'

Explanatory Essay Conclusion

Here are some unique tips on how to write an explanatory essay conclusion that leaves a lasting impression on your readers.

• Offer a solution or recommendation: Instead of summarizing your main points, offer suggestions based on the information you've presented. This can help to make your essay more impactful and leave a lasting impression on your readers. For example, if you're writing about the effects of pollution on the environment, you might recommend using more eco-friendly products or investing in renewable energy sources.
• Emphasize the importance of your topic: Use your concluding statement to emphasize the importance of your topic and why it's relevant to your readers. This can help to inspire action or change. For example, suppose you're writing about the benefits of volunteering. In that case, you might emphasize how volunteering helps others and has personal benefits such as improved mental health and a sense of purpose.
• End with a powerful quote or statement: End your explanatory essay conclusion with a powerful quote or statement that reinforces your main point or leaves a lasting impression on your readers. For example, if you're writing about the importance of education, you might end your essay with a quote from Nelson Mandela, such as, 'Education is the most powerful weapon which you can use to change the world.'

Explanatory Essay Example

Here is an example of an explanatory essay:

Explanatory Essay Example:

Importance of Basketball

Final Thoughts

Now you understand whats an explanatory essay. However, if you're still feeling overwhelmed or unsure about writing an explanatory essay, don't worry. Our team of experienced writers is here to provide you with top-notch academic assistance tailored to your specific needs. Whether you need to explain what is an appendix in your definition essay or rewrite essay in five paragraphs, we've got you covered! With our professional help, you can ensure that your essay is well-researched, well-written, and meets all the academic requirements.

And if you'd rather have a professional craft flawless explanatory essay examples, know that our friendly team is dedicated to helping you succeed in your academic pursuits. So why not take the stress out of writing and let us help you achieve the academic success you deserve? Contact us today with your ' write paper for me ' request, and we will support you every step of the way.

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The potential of working hypotheses for deductive exploratory research

Mattia casula.

1 Department of Political and Social Sciences, University of Bologna, Strada Maggiore 45, 40125 Bologna, Italy

Nandhini Rangarajan

2 Texas State University, San Marcos, TX USA

Patricia Shields

While hypotheses frame explanatory studies and provide guidance for measurement and statistical tests, deductive, exploratory research does not have a framing device like the hypothesis. To this purpose, this article examines the landscape of deductive, exploratory research and offers the working hypothesis as a flexible, useful framework that can guide and bring coherence across the steps in the research process. The working hypothesis conceptual framework is introduced, placed in a philosophical context, defined, and applied to public administration and comparative public policy. Doing so, this article explains: the philosophical underpinning of exploratory, deductive research; how the working hypothesis informs the methodologies and evidence collection of deductive, explorative research; the nature of micro-conceptual frameworks for deductive exploratory research; and, how the working hypothesis informs data analysis when exploratory research is deductive.

Introduction

Exploratory research is generally considered to be inductive and qualitative (Stebbins 2001 ). Exploratory qualitative studies adopting an inductive approach do not lend themselves to a priori theorizing and building upon prior bodies of knowledge (Reiter 2013 ; Bryman 2004 as cited in Pearse 2019 ). Juxtaposed against quantitative studies that employ deductive confirmatory approaches, exploratory qualitative research is often criticized for lack of methodological rigor and tentativeness in results (Thomas and Magilvy 2011 ). This paper focuses on the neglected topic of deductive, exploratory research and proposes working hypotheses as a useful framework for these studies.

To emphasize that certain types of applied research lend themselves more easily to deductive approaches, to address the downsides of exploratory qualitative research, and to ensure qualitative rigor in exploratory research, a significant body of work on deductive qualitative approaches has emerged (see for example, Gilgun 2005 , 2015 ; Hyde 2000 ; Pearse 2019 ). According to Gilgun ( 2015 , p. 3) the use of conceptual frameworks derived from comprehensive reviews of literature and a priori theorizing were common practices in qualitative research prior to the publication of Glaser and Strauss’s ( 1967 ) The Discovery of Grounded Theory . Gilgun ( 2015 ) coined the terms Deductive Qualitative Analysis (DQA) to arrive at some sort of “middle-ground” such that the benefits of a priori theorizing (structure) and allowing room for new theory to emerge (flexibility) are reaped simultaneously. According to Gilgun ( 2015 , p. 14) “in DQA, the initial conceptual framework and hypotheses are preliminary. The purpose of DQA is to come up with a better theory than researchers had constructed at the outset (Gilgun 2005 , 2009 ). Indeed, the production of new, more useful hypotheses is the goal of DQA”.

DQA provides greater level of structure for both the experienced and novice qualitative researcher (see for example Pearse 2019 ; Gilgun 2005 ). According to Gilgun ( 2015 , p. 4) “conceptual frameworks are the sources of hypotheses and sensitizing concepts”. Sensitizing concepts frame the exploratory research process and guide the researcher’s data collection and reporting efforts. Pearse ( 2019 ) discusses the usefulness for deductive thematic analysis and pattern matching to help guide DQA in business research. Gilgun ( 2005 ) discusses the usefulness of DQA for family research.

Given these rationales for DQA in exploratory research, the overarching purpose of this paper is to contribute to that growing corpus of work on deductive qualitative research. This paper is specifically aimed at guiding novice researchers and student scholars to the working hypothesis as a useful a priori framing tool. The applicability of the working hypothesis as a tool that provides more structure during the design and implementation phases of exploratory research is discussed in detail. Examples of research projects in public administration that use the working hypothesis as a framing tool for deductive exploratory research are provided.

In the next section, we introduce the three types of research purposes. Second, we examine the nature of the exploratory research purpose. Third, we provide a definition of working hypothesis. Fourth, we explore the philosophical roots of methodology to see where exploratory research fits. Fifth, we connect the discussion to the dominant research approaches (quantitative, qualitative and mixed methods) to see where deductive exploratory research fits. Sixth, we examine the nature of theory and the role of the hypothesis in theory. We contrast formal hypotheses and working hypotheses. Seven, we provide examples of student and scholarly work that illustrates how working hypotheses are developed and operationalized. Lastly, this paper synthesizes previous discussion with concluding remarks.

Three types of research purposes

The literature identifies three basic types of research purposes—explanation, description and exploration (Babbie 2007 ; Adler and Clark 2008 ; Strydom 2013 ; Shields and Whetsell 2017 ). Research purposes are similar to research questions; however, they focus on project goals or aims instead of questions.

Explanatory research answers the “why” question (Babbie 2007 , pp. 89–90), by explaining “why things are the way they are”, and by looking “for causes and reasons” (Adler and Clark 2008 , p. 14). Explanatory research is closely tied to hypothesis testing. Theory is tested using deductive reasoning, which goes from the general to the specific (Hyde 2000 , p. 83). Hypotheses provide a frame for explanatory research connecting the research purpose to other parts of the research process (variable construction, choice of data, statistical tests). They help provide alignment or coherence across stages in the research process and provide ways to critique the strengths and weakness of the study. For example, were the hypotheses grounded in the appropriate arguments and evidence in the literature? Are the concepts imbedded in the hypotheses appropriately measured? Was the best statistical test used? When the analysis is complete (hypothesis is tested), the results generally answer the research question (the evidence supported or failed to support the hypothesis) (Shields and Rangarajan 2013 ).

Descriptive research addresses the “What” question and is not primarily concerned with causes (Strydom 2013 ; Shields and Tajalli 2006 ). It lies at the “midpoint of the knowledge continuum” (Grinnell 2001 , p. 248) between exploration and explanation. Descriptive research is used in both quantitative and qualitative research. A field researcher might want to “have a more highly developed idea of social phenomena” (Strydom 2013 , p. 154) and develop thick descriptions using inductive logic. In science, categorization and classification systems such as the periodic table of chemistry or the taxonomies of biology inform descriptive research. These baseline classification systems are a type of theorizing and allow researchers to answer questions like “what kind” of plants and animals inhabit a forest. The answer to this question would usually be displayed in graphs and frequency distributions. This is also the data presentation system used in the social sciences (Ritchie and Lewis 2003 ; Strydom 2013 ). For example, if a scholar asked, what are the needs of homeless people? A quantitative approach would include a survey that incorporated a “needs” classification system (preferably based on a literature review). The data would be displayed as frequency distributions or as charts. Description can also be guided by inductive reasoning, which draws “inferences from specific observable phenomena to general rules or knowledge expansion” (Worster 2013 , p. 448). Theory and hypotheses are generated using inductive reasoning, which begins with data and the intention of making sense of it by theorizing. Inductive descriptive approaches would use a qualitative, naturalistic design (open ended interview questions with the homeless population). The data could provide a thick description of the homeless context. For deductive descriptive research, categories, serve a purpose similar to hypotheses for explanatory research. If developed with thought and a connection to the literature, categories can serve as a framework that inform measurement, link to data collection mechanisms and to data analysis. Like hypotheses they can provide horizontal coherence across the steps in the research process.

Table  1 demonstrated these connections for deductive, descriptive and explanatory research. The arrow at the top emphasizes the horizontal or across the research process view we emphasize. This article makes the case that the working hypothesis can serve the same purpose as the hypothesis for deductive, explanatory research and categories for deductive descriptive research. The cells for exploratory research are filled in with question marks.

Table 1

Connecting research purpose and frameworks for deductive inquiry

The remainder of this paper focuses on exploratory research and the answers to questions found in the table:

• What is the philosophical underpinning of exploratory, deductive research?
• What is the Micro-conceptual framework for deductive exploratory research? [ As is clear from the article title we introduce the working hypothesis as the answer .]
• How does the working hypothesis inform the methodologies and evidence collection of deductive exploratory research?
• How does the working hypothesis inform data analysis of deductive exploratory research?

The nature of exploratory research purpose

Explorers enter the unknown to discover something new. The process can be fraught with struggle and surprises. Effective explorers creatively resolve unexpected problems. While we typically think of explorers as pioneers or mountain climbers, exploration is very much linked to the experience and intention of the explorer. Babies explore as they take their first steps. The exploratory purpose resonates with these insights. Exploratory research, like reconnaissance, is a type of inquiry that is in the preliminary or early stages (Babbie 2007 ). It is associated with discovery, creativity and serendipity (Stebbins 2001 ). But the person doing the discovery, also defines the activity or claims the act of exploration. It “typically occurs when a researcher examines a new interest or when the subject of study itself is relatively new” (Babbie 2007 , p. 88). Hence, exploration has an open character that emphasizes “flexibility, pragmatism, and the particular, biographically specific interests of an investigator” (Maanen et al. 2001 , p. v). These three purposes form a type of hierarchy. An area of inquiry is initially explored . This early work lays the ground for, description which in turn becomes the basis for explanation . Quantitative, explanatory studies dominate contemporary high impact journals (Twining et al. 2017 ).

Stebbins ( 2001 ) makes the point that exploration is often seen as something like a poor stepsister to confirmatory or hypothesis testing research. He has a problem with this because we live in a changing world and what is settled today will very likely be unsettled in the near future and in need of exploration. Further, exploratory research “generates initial insights into the nature of an issue and develops questions to be investigated by more extensive studies” (Marlow 2005 , p. 334). Exploration is widely applicable because all research topics were once “new.” Further, all research topics have the possibility of “innovation” or ongoing “newness”. Exploratory research may be appropriate to establish whether a phenomenon exists (Strydom 2013 ). The point here, of course, is that the exploratory purpose is far from trivial.

Stebbins’ Exploratory Research in the Social Sciences ( 2001 ), is the only book devoted to the nature of exploratory research as a form of social science inquiry. He views it as a “broad-ranging, purposive, systematic prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life” (p. 3). It is science conducted in a way distinct from confirmation. According to Stebbins ( 2001 , p. 6) the goal is discovery of potential generalizations, which can become future hypotheses and eventually theories that emerge from the data. He focuses on inductive logic (which stimulates creativity) and qualitative methods. He does not want exploratory research limited to the restrictive formulas and models he finds in confirmatory research. He links exploratory research to Glaser and Strauss’s ( 1967 ) flexible, immersive, Grounded Theory. Strydom’s ( 2013 ) analysis of contemporary social work research methods books echoes Stebbins’ ( 2001 ) position. Stebbins’s book is an important contribution, but it limits the potential scope of this flexible and versatile research purpose. If we accepted his conclusion, we would delete the “Exploratory” row from Table  1 .

Note that explanatory research can yield new questions, which lead to exploration. Inquiry is a process where inductive and deductive activities can occur simultaneously or in a back and forth manner, particularly as the literature is reviewed and the research design emerges. 1 Strict typologies such as explanation, description and exploration or inductive/deductive can obscures these larger connections and processes. We draw insight from Dewey’s ( 1896 ) vision of inquiry as depicted in his seminal “Reflex Arc” article. He notes that “stimulus” and “response” like other dualities (inductive/deductive) exist within a larger unifying system. Yet the terms have value. “We need not abandon terms like stimulus and response, so long as we remember that they are attached to events based upon their function in a wider dynamic context, one that includes interests and aims” (Hildebrand 2008 , p. 16). So too, in methodology typologies such as deductive/inductive capture useful distinctions with practical value and are widely used in the methodology literature.

We argue that there is a role for exploratory, deductive, and confirmatory research. We maintain all types of research logics and methods should be in the toolbox of exploratory research. First, as stated above, it makes no sense on its face to identify an extremely flexible purpose that is idiosyncratic to the researcher and then basically restrict its use to qualitative, inductive, non-confirmatory methods. Second, Stebbins’s ( 2001 ) work focused on social science ignoring the policy sciences. Exploratory research can be ideal for immediate practical problems faced by policy makers, who could find a framework of some kind useful. Third, deductive, exploratory research is more intentionally connected to previous research. Some kind of initial framing device is located or designed using the literature. This may be very important for new scholars who are developing research skills and exploring their field and profession. Stebbins’s insights are most pertinent for experienced scholars. Fourth, frameworks and deductive logic are useful for comparative work because some degree of consistency across cases is built into the design.

As we have seen, the hypotheses of explanatory and categories of descriptive research are the dominate frames of social science and policy science. We certainly concur that neither of these frames makes a lot of sense for exploratory research. They would tend to tie it down. We see the problem as a missing framework or missing way to frame deductive, exploratory research in the methodology literature. Inductive exploratory research would not work for many case studies that are trying to use evidence to make an argument. What exploratory deductive case studies need is a framework that incorporates flexibility. This is even more true for comparative case studies. A framework of this sort could be usefully applied to policy research (Casula 2020a ), particularly evaluative policy research, and applied research generally. We propose the Working Hypothesis as a flexible conceptual framework and as a useful tool for doing exploratory studies. It can be used as an evaluative criterion particularly for process evaluation and is useful for student research because students can develop theorizing skills using the literature.

Table  1 included a column specifying the philosophical basis for each research purpose. Shifting gears to the philosophical underpinning of methodology provides useful additional context for examination of deductive, exploratory research.

What is a working hypothesis

The working hypothesis is first and foremost a hypothesis or a statement of expectation that is tested in action. The term “working” suggest that these hypotheses are subject to change, are provisional and the possibility of finding contradictory evidence is real. In addition, a “working” hypothesis is active, it is a tool in an ongoing process of inquiry. If one begins with a research question, the working hypothesis could be viewed as a statement or group of statements that answer the question. It “works” to move purposeful inquiry forward. “Working” also implies some sort of community, mostly we work together in relationship to achieve some goal.

Working Hypothesis is a term found in earlier literature. Indeed, both pioneering pragmatists, John Dewey and George Herbert Mead use the term working hypothesis in important nineteenth century works. For both Dewey and Mead, the notion of a working hypothesis has a self-evident quality and it is applied in a big picture context. 2

Most notably, Dewey ( 1896 ), in one of his most pivotal early works (“Reflex Arc”), used “working hypothesis” to describe a key concept in psychology. “The idea of the reflex arc has upon the whole come nearer to meeting this demand for a general working hypothesis than any other single concept (Italics added)” (p. 357). The notion of a working hypothesis was developed more fully 42 years later, in Logic the Theory of Inquiry , where Dewey developed the notion of a working hypothesis that operated on a smaller scale. He defines working hypotheses as a “provisional, working means of advancing investigation” (Dewey 1938 , pp. 142). Dewey’s definition suggests that working hypotheses would be useful toward the beginning of a research project (e.g., exploratory research).

Mead ( 1899 ) used working hypothesis in a title of an American Journal of Sociology article “The Working Hypothesis and Social Reform” (italics added). He notes that a scientist’s foresight goes beyond testing a hypothesis.

Given its success, he may restate his world from this standpoint and get the basis for further investigation that again always takes the form of a problem. The solution of this problem is found over again in the possibility of fitting his hypothetical proposition into the whole within which it arises. And he must recognize that this statement is only a working hypothesis at the best, i.e., he knows that further investigation will show that the former statement of his world is only provisionally true, and must be false from the standpoint of a larger knowledge, as every partial truth is necessarily false over against the fuller knowledge which he will gain later (Mead 1899 , p. 370).

Cronbach ( 1975 ) developed a notion of working hypothesis consistent with inductive reasoning, but for him, the working hypothesis is a product or result of naturalistic inquiry. He makes the case that naturalistic inquiry is highly context dependent and therefore results or seeming generalizations that may come from a study and should be viewed as “working hypotheses”, which “are tentative both for the situation in which they first uncovered and for other situations” (as cited in Gobo 2008 , p. 196).

A quick Google scholar search using the term “working hypothesis” show that it is widely used in twentieth and twenty-first century science, particularly in titles. In these articles, the working hypothesis is treated as a conceptual tool that furthers investigation in its early or transitioning phases. We could find no explicit links to exploratory research. The exploratory nature of the problem is expressed implicitly. Terms such as “speculative” (Habib 2000 , p. 2391) or “rapidly evolving field” (Prater et al. 2007 , p. 1141) capture the exploratory nature of the study. The authors might describe how a topic is “new” or reference “change”. “As a working hypothesis, the picture is only new, however, in its interpretation” (Milnes 1974 , p. 1731). In a study of soil genesis, Arnold ( 1965 , p. 718) notes “Sequential models, formulated as working hypotheses, are subject to further investigation and change”. Any 2020 article dealing with COVID-19 and respiratory distress would be preliminary almost by definition (Ciceri et al. 2020 ).

Philosophical roots of methodology

According to Kaplan ( 1964 , p. 23) “the aim of methodology is to help us understand, in the broadest sense not the products of scientific inquiry but the process itself”. Methods contain philosophical principles that distinguish them from other “human enterprises and interests” (Kaplan 1964 , p. 23). Contemporary research methodology is generally classified as quantitative, qualitative and mixed methods. Leading scholars of methodology have associated each with a philosophical underpinning—positivism (or post-positivism), interpretivism or constructivist and pragmatism, respectively (Guba 1987 ; Guba and Lincoln 1981 ; Schrag 1992 ; Stebbins 2001 ; Mackenzi and Knipe 2006 ; Atieno 2009 ; Levers 2013 ; Morgan 2007 ; O’Connor et al. 2008 ; Johnson and Onwuegbuzie 2004 ; Twining et al. 2017 ). This section summarizes how the literature often describes these philosophies and informs contemporary methodology and its literature.

Positivism and its more contemporary version, post-positivism, maintains an objectivist ontology or assumes an objective reality, which can be uncovered (Levers 2013 ; Twining et al. 2017 ). 3 Time and context free generalizations are possible and “real causes of social scientific outcomes can be determined reliably and validly (Johnson and Onwuegbunzie 2004 , p. 14). Further, “explanation of the social world is possible through a logical reduction of social phenomena to physical terms”. It uses an empiricist epistemology which “implies testability against observation, experimentation, or comparison” (Whetsell and Shields 2015 , pp. 420–421). Correspondence theory, a tenet of positivism, asserts that “to each concept there corresponds a set of operations involved in its scientific use” (Kaplan 1964 , p. 40).

The interpretivist, constructivists or post-modernist approach is a reaction to positivism. It uses a relativist ontology and a subjectivist epistemology (Levers 2013 ). In this world of multiple realities, context free generalities are impossible as is the separation of facts and values. Causality, explanation, prediction, experimentation depend on assumptions about the correspondence between concepts and reality, which in the absence of an objective reality is impossible. Empirical research can yield “contextualized emergent understanding rather than the creation of testable theoretical structures” (O’Connor et al. 2008 , p. 30). The distinctively different world views of positivist/post positivist and interpretivist philosophy is at the core of many controversies in methodology, social and policy science literature (Casula 2020b ).

With its focus on dissolving dualisms, pragmatism steps outside the objective/subjective debate. Instead, it asks, “what difference would it make to us if the statement were true” (Kaplan 1964 , p. 42). Its epistemology is connected to purposeful inquiry. Pragmatism has a “transformative, experimental notion of inquiry” anchored in pluralism and a focus on constructing conceptual and practical tools to resolve “problematic situations” (Shields 1998 ; Shields and Rangarajan 2013 ). Exploration and working hypotheses are most comfortably situated within the pragmatic philosophical perspective.

Research approaches

Empirical investigation relies on three types of methodology—quantitative, qualitative and mixed methods.

Quantitative methods

Quantitative methods uses deductive logic and formal hypotheses or models to explain, predict, and eventually establish causation (Hyde 2000 ; Kaplan 1964 ; Johnson and Onwuegbunzie 2004 ; Morgan 2007 ). 4 The correspondence between the conceptual and empirical world make measures possible. Measurement assigns numbers to objects, events or situations and allows for standardization and subtle discrimination. It also allows researchers to draw on the power of mathematics and statistics (Kaplan 1964 , pp. 172–174). Using the power of inferential statistics, quantitative research employs research designs, which eliminate competing hypotheses. It is high in external validity or the ability to generalize to the whole. The research results are relatively independent of the researcher (Johnson & Onwuegbunzie 2004 ).

Quantitative methods depend on the quality of measurement and a priori conceptualization, and adherence to the underlying assumptions of inferential statistics. Critics charge that hypotheses and frameworks needlessly constrain inquiry (Johnson and Onwuegbunzie 2004 , p. 19). Hypothesis testing quantitative methods support the explanatory purpose.

Qualitative methods

Qualitative researchers who embrace the post-modern, interpretivist view, 5 question everything about the nature of quantitative methods (Willis et al. 2007 ). Rejecting the possibility of objectivity, correspondence between ideas and measures, and the constraints of a priori theorizing they focus on “unique impressions and understandings of events rather than to generalize the findings” (Kolb 2012 , p. 85). Characteristics of traditional qualitative research include “induction, discovery, exploration, theory/hypothesis generation and the researcher as the primary ‘instrument’ of data collection” (Johnson and Onwuegbunzie 2004 , p. 18). It also concerns itself with forming “unique impressions and understandings of events rather than to generalize findings” (Kolb 2012 , p. 85). The data of qualitative methods are generated via interviews, direct observation, focus groups and analysis of written records or artifacts.

Qualitative methods provide for understanding and “description of people’s personal experiences of phenomena”. They enable descriptions of detailed “phenomena as they are situated and embedded in local contexts.” Researchers use naturalistic settings to “study dynamic processes” and explore how participants interpret experiences. Qualitative methods have an inherent flexibility, allowing researchers to respond to changes in the research setting. They are particularly good at narrowing to the particular and on the flipside have limited external validity (Johnson and Onwuegbunzie 2004 , p. 20). Instead of specifying a suitable sample size to draw conclusions, qualitative research uses the notion of saturation (Morse 1995 ).

Saturation is used in grounded theory—a widely used and respected form of qualitative research, and a well-known interpretivist qualitative research method. Introduced by Glaser and Strauss ( 1967 ), this “grounded on observation” (Patten and Newhart 2000 , p. 27) methodology, focuses on “the creation of emergent understanding” (O’Connor et al. 2008 , p. 30). It uses the Constant Comparative method, whereby researchers develop theory from data as they code and analyze at the same time. Data collection, coding and analysis along with theoretical sampling are systematically combined to generate theory (Kolb 2012 , p. 83). The qualitative methods discussed here support exploratory research.

A close look at the two philosophies and assumptions of quantitative and qualitative research suggests two contradictory world views. The literature has labeled these contradictory views the Incompatibility Theory, which sets up a quantitative versus qualitative tension similar to the seeming separation of art and science or fact and values (Smith 1983a , b ; Guba 1987 ; Smith and Heshusius 1986 ; Howe 1988 ). The incompatibility theory does not make sense in practice. Yin ( 1981 , 1992 , 2011 , 2017 ), a prominent case study scholar, showcases a deductive research methodology that crosses boundaries using both quantaitive and qualitative evidence when appropriate.

Mixed methods

Turning the “Incompatibility Theory” on its head, Mixed Methods research “combines elements of qualitative and quantitative research approaches … for the broad purposes of breadth and depth of understanding and corroboration” (Johnson et al. 2007 , p. 123). It does this by partnering with philosophical pragmatism. 6 Pragmatism is productive because “it offers an immediate and useful middle position philosophically and methodologically; it offers a practical and outcome-oriented method of inquiry that is based on action and leads, iteratively, to further action and the elimination of doubt; it offers a method for selecting methodological mixes that can help researchers better answer many of their research questions” (Johnson and Onwuegbunzie 2004 , p. 17). What is theory for the pragmatist “any theoretical model is for the pragmatist, nothing more than a framework through which problems are perceived and subsequently organized ” (Hothersall 2019 , p. 5).

Brendel ( 2009 ) constructed a simple framework to capture the core elements of pragmatism. Brendel’s four “p”’s—practical, pluralism, participatory and provisional help to show the relevance of pragmatism to mixed methods. Pragmatism is purposeful and concerned with the practical consequences. The pluralism of pragmatism overcomes quantitative/qualitative dualism. Instead, it allows for multiple perspectives (including positivism and interpretivism) and, thus, gets around the incompatibility problem. Inquiry should be participatory or inclusive of the many views of participants, hence, it is consistent with multiple realities and is also tied to the common concern of a problematic situation. Finally, all inquiry is provisional . This is compatible with experimental methods, hypothesis testing and consistent with the back and forth of inductive and deductive reasoning. Mixed methods support exploratory research.

Advocates of mixed methods research note that it overcomes the weaknesses and employs the strengths of quantitative and qualitative methods. Quantitative methods provide precision. The pictures and narrative of qualitative techniques add meaning to the numbers. Quantitative analysis can provide a big picture, establish relationships and its results have great generalizability. On the other hand, the “why” behind the explanation is often missing and can be filled in through in-depth interviews. A deeper and more satisfying explanation is possible. Mixed-methods brings the benefits of triangulation or multiple sources of evidence that converge to support a conclusion. It can entertain a “broader and more complete range of research questions” (Johnson and Onwuegbunzie 2004 , p. 21) and can move between inductive and deductive methods. Case studies use multiple forms of evidence and are a natural context for mixed methods.

One thing that seems to be missing from mixed method literature and explicit design is a place for conceptual frameworks. For example, Heyvaert et al. ( 2013 ) examined nine mixed methods studies and found an explicit framework in only two studies (transformative and pragmatic) (p. 663).

Theory and hypotheses: where is and what is theory?

Theory is key to deductive research. In essence, empirical deductive methods test theory. Hence, we shift our attention to theory and the role and functions of the hypotheses in theory. Oppenheim and Putnam ( 1958 ) note that “by a ‘theory’ (in the widest sense) we mean any hypothesis, generalization or law (whether deterministic or statistical) or any conjunction of these” (p. 25). Van Evera ( 1997 ) uses a similar and more complex definition “theories are general statements that describe and explain the causes of effects of classes of phenomena. They are composed of causal laws or hypotheses, explanations, and antecedent conditions” (p. 8). Sutton and Staw ( 1995 , p. 376) in a highly cited article “What Theory is Not” assert the that hypotheses should contain logical arguments for “why” the hypothesis is expected. Hypotheses need an underlying causal argument before they can be considered theory. The point of this discussion is not to define theory but to establish the importance of hypotheses in theory.

Explanatory research is implicitly relational (A explains B). The hypotheses of explanatory research lay bare these relationships. Popular definitions of hypotheses capture this relational component. For example, the Cambridge Dictionary defines a hypothesis a “an idea or explanation for something that is based on known facts but has not yet been proven”. Vocabulary.Com’s definition emphasizes explanation, a hypothesis is “an idea or explanation that you then test through study and experimentation”. According to Wikipedia a hypothesis is “a proposed explanation for a phenomenon”. Other definitions remove the relational or explanatory reference. The Oxford English Dictionary defines a hypothesis as a “supposition or conjecture put forth to account for known facts.” Science Buddies defines a hypothesis as a “tentative, testable answer to a scientific question”. According to the Longman Dictionary the hypothesis is “an idea that can be tested to see if it is true or not”. The Urban Dictionary states a hypothesis is “a prediction or educated-guess based on current evidence that is yet be tested”. We argue that the hypotheses of exploratory research— working hypothesis — are not bound by relational expectations. It is this flexibility that distinguishes the working hypothesis.

Sutton and Staw (1995) maintain that hypotheses “serve as crucial bridges between theory and data, making explicit how the variables and relationships that follow from a logical argument will be operationalized” (p. 376, italics added). The highly rated journal, Computers and Education , Twining et al. ( 2017 ) created guidelines for qualitative research as a way to improve soundness and rigor. They identified the lack of alignment between theoretical stance and methodology as a common problem in qualitative research. In addition, they identified a lack of alignment between methodology, design, instruments of data collection and analysis. The authors created a guidance summary, which emphasized the need to enhance coherence throughout elements of research design (Twining et al. 2017 p. 12). Perhaps the bridging function of the hypothesis mentioned by Sutton and Staw (1995) is obscured and often missing in qualitative methods. Working hypotheses can be a tool to overcome this problem.

For reasons, similar to those used by mixed methods scholars, we look to classical pragmatism and the ideas of John Dewey to inform our discussion of theory and working hypotheses. Dewey ( 1938 ) treats theory as a tool of empirical inquiry and uses a map metaphor (p. 136). Theory is like a map that helps a traveler navigate the terrain—and should be judged by its usefulness. “There is no expectation that a map is a true representation of reality. Rather, it is a representation that allows a traveler to reach a destination (achieve a purpose). Hence, theories should be judged by how well they help resolve the problem or achieve a purpose ” (Shields and Rangarajan 2013 , p. 23). Note that we explicitly link theory to the research purpose. Theory is never treated as an unimpeachable Truth, rather it is a helpful tool that organizes inquiry connecting data and problem. Dewey’s approach also expands the definition of theory to include abstractions (categories) outside of causation and explanation. The micro-conceptual frameworks 7 introduced in Table  1 are a type of theory. We define conceptual frameworks as the “way the ideas are organized to achieve the project’s purpose” (Shields and Rangarajan 2013 p. 24). Micro-conceptual frameworks do this at the very close to the data level of analysis. Micro-conceptual frameworks can direct operationalization and ways to assess measurement or evidence at the individual research study level. Again, the research purpose plays a pivotal role in the functioning of theory (Shields and Tajalli 2006 ).

Working hypothesis: methods and data analysis

We move on to answer the remaining questions in the Table  1 . We have established that exploratory research is extremely flexible and idiosyncratic. Given this, we will proceed with a few examples and draw out lessons for developing an exploratory purpose, building a framework and from there identifying data collection techniques and the logics of hypotheses testing and analysis. Early on we noted the value of the Working Hypothesis framework for student empirical research and applied research. The next section uses a masters level student’s work to illustrate the usefulness of working hypotheses as a way to incorporate the literature and structure inquiry. This graduate student was also a mature professional with a research question that emerged from his job and is thus an example of applied research.

Master of Public Administration student, Swift ( 2010 ) worked for a public agency and was responsible for that agency’s sexual harassment training. The agency needed to evaluate its training but had never done so before. He also had never attempted a significant empirical research project. Both of these conditions suggest exploration as a possible approach. He was interested in evaluating the training program and hence the project had a normative sense. Given his job, he already knew a lot about the problem of sexual harassment and sexual harassment training. What he did not know much about was doing empirical research, reviewing the literature or building a framework to evaluate the training (working hypotheses). He wanted a framework that was flexible and comprehensive. In his research, he discovered Lundvall’s ( 2006 ) knowledge taxonomy summarized with four simple ways of knowing ( Know - what, Know - how, Know - why, Know - who ). He asked whether his agency’s training provided the participants with these kinds of knowledge? Lundvall’s categories of knowing became the basis of his working hypotheses. Lundvall’s knowledge taxonomy is well suited for working hypotheses because it is so simple and is easy to understand intuitively. It can also be tailored to the unique problematic situation of the researcher. Swift ( 2010 , pp. 38–39) developed four basic working hypotheses:

• WH1: Capital Metro provides adequate know - what knowledge in its sexual harassment training
• WH2: Capital Metro provides adequate know - how knowledge in its sexual harassment training
• WH3: Capital Metro provides adequate know - why knowledge in its sexual harassment training
• WH4: Capital Metro provides adequate know - who knowledge in its sexual harassment training

From here he needed to determine what would determine the different kinds of knowledge. For example, what constitutes “know what” knowledge for sexual harassment training. This is where his knowledge and experience working in the field as well as the literature come into play. According to Lundvall et al. ( 1988 , p. 12) “know what” knowledge is about facts and raw information. Swift ( 2010 ) learned through the literature that laws and rules were the basis for the mandated sexual harassment training. He read about specific anti-discrimination laws and the subsequent rules and regulations derived from the laws. These laws and rules used specific definitions and were enacted within a historical context. Laws, rules, definitions and history became the “facts” of Know-What knowledge for his working hypothesis. To make this clear, he created sub-hypotheses that explicitly took these into account. See how Swift ( 2010 , p. 38) constructed the sub-hypotheses below. Each sub-hypothesis was defended using material from the literature (Swift 2010 , pp. 22–26). The sub-hypotheses can also be easily tied to evidence. For example, he could document that the training covered anti-discrimination laws.

WH1: Capital Metro provides adequate know - what knowledge in its sexual Harassment training

• WH1a: The sexual harassment training includes information on anti-discrimination laws (Title VII).
• WH1b: The sexual harassment training includes information on key definitions.
• WH1c: The sexual harassment training includes information on Capital Metro’s Equal Employment Opportunity and Harassment policy.
• WH1d: Capital Metro provides training on sexual harassment history.

Know-How knowledge refers to the ability to do something and involves skills (Lundvall and Johnson 1994 , p. 12). It is a kind of expertise in action. The literature and his experience allowed James Smith to identify skills such as how to file a claim or how to document incidents of sexual harassment as important “know-how” knowledge that should be included in sexual harassment training. Again, these were depicted as sub-hypotheses.

WH2: Capital Metro provides adequate know - how knowledge in its sexual Harassment training

• WH2a: Training is provided on how to file and report a claim of harassment
• WH2b: Training is provided on how to document sexual harassment situations.
• WH2c: Training is provided on how to investigate sexual harassment complaints.
• WH2d: Training is provided on how to follow additional harassment policy procedures protocol

Note that the working hypotheses do not specify a relationship but rather are simple declarative sentences. If “know-how” knowledge was found in the sexual harassment training, he would be able to find evidence that participants learned about how to file a claim (WH2a). The working hypothesis provides the bridge between theory and data that Sutton and Staw (1995) found missing in exploratory work. The sub-hypotheses are designed to be refined enough that the researchers would know what to look for and tailor their hunt for evidence. Figure  1 captures the generic sub-hypothesis design.

A Common structure used in the development of working hypotheses

When expected evidence is linked to the sub-hypotheses, data, framework and research purpose are aligned. This can be laid out in a planning document that operationalizes the data collection in something akin to an architect’s blueprint. This is where the scholar explicitly develops the alignment between purpose, framework and method (Shields and Rangarajan 2013 ; Shields et al. 2019b ).

Table  2 operationalizes Swift’s working hypotheses (and sub-hypotheses). The table provide clues as to what kind of evidence is needed to determine whether the hypotheses are supported. In this case, Smith used interviews with participants and trainers as well as a review of program documents. Column one repeats the sub-hypothesis, column two specifies the data collection method (here interviews with participants/managers and review of program documents) and column three specifies the unique questions that focus the investigation. For example, the interview questions are provided. In the less precise world of qualitative data, evidence supporting a hypothesis could have varying degrees of strength. This too can be specified.

Table 2

Operationalization of the working hypotheses: an example

For Swift’s example, neither the statistics of explanatory research nor the open-ended questions of interpretivist, inductive exploratory research is used. The deductive logic of inquiry here is somewhat intuitive and similar to a detective (Ulriksen and Dadalauri 2016 ). It is also a logic used in international law (Worster 2013 ). It should be noted that the working hypothesis and the corresponding data collection protocol does not stop inquiry and fieldwork outside the framework. The interviews could reveal an unexpected problem with Smith’s training program. The framework provides a very loose and perhaps useful ways to identify and make sense of the data that does not fit the expectations. Researchers using working hypotheses should be sensitive to interesting findings that fall outside their framework. These could be used in future studies, to refine theory or even in this case provide suggestions to improve sexual harassment training. The sensitizing concepts mentioned by Gilgun ( 2015 ) are free to emerge and should be encouraged.

Something akin to working hypotheses are hidden in plain sight in the professional literature. Take for example Kerry Crawford’s ( 2017 ) book Wartime Sexual Violence. Here she explores how basic changes in the way “advocates and decision makers think about and discuss conflict-related sexual violence” (p. 2). She focused on a subsequent shift from silence to action. The shift occurred as wartime sexual violence was reframed as a “weapon of war”. The new frame captured the attention of powerful members of the security community who demanded, initiated, and paid for institutional and policy change. Crawford ( 2017 ) examines the legacy of this key reframing. She develops a six-stage model of potential international responses to incidents of wartime violence. This model is fairly easily converted to working hypotheses and sub-hypotheses. Table  3 shows her model as a set of (non-relational) working hypotheses. She applied this model as a way to gather evidence among cases (e.g., the US response to sexual violence in the Democratic Republic of the Congo) to show the official level of response to sexual violence. Each case study chapter examined evidence to establish whether the case fit the pattern formalized in the working hypotheses. The framework was very useful in her comparative context. The framework allowed for consistent comparative analysis across cases. Her analysis of the three cases went well beyond the material covered in the framework. She freely incorporated useful inductively informed data in her analysis and discussion. The framework, however, allowed for alignment within and across cases.

Table 3

Example illustrating a set of working hypotheses as a framework for comparative case studies

Source : Adaptation from Table 1.1 of Crawford’s ( 2017 ) book Wartime Sexual Violence

In this article we argued that the exploratory research is also well suited for deductive approaches. By examining the landscape of deductive, exploratory research, we proposed the working hypothesis as a flexible conceptual framework and a useful tool for doing exploratory studies. It has the potential to guide and bring coherence across the steps in the research process. After presenting the nature of exploratory research purpose and how it differs from two types of research purposes identified in the literature—explanation, and description. We focused on answering four different questions in order to show the link between micro-conceptual frameworks and research purposes in a deductive setting. The answers to the four questions are summarized in Table  4 .

Table 4

Linking micro-conceptual frameworks and research purposes in deductive research

Firstly, we argued that working hypothesis and exploration are situated within the pragmatic philosophical perspective. Pragmatism allows for pluralism in theory and data collection techniques, which is compatible with the flexible exploratory purpose. Secondly, after introducing and discussing the four core elements of pragmatism (practical, pluralism, participatory, and provisional), we explained how the working hypothesis informs the methodologies and evidence collection of deductive exploratory research through a presentation of the benefits of triangulation provided by mixed methods research. Thirdly, as is clear from the article title, we introduced the working hypothesis as the micro-conceptual framework for deductive explorative research. We argued that the hypotheses of explorative research, which we call working hypotheses are distinguished from those of the explanatory research, since they do not require a relational component and are not bound by relational expectations. A working hypothesis is extremely flexible and idiosyncratic, and it could be viewed as a statement or group of statements of expectations tested in action depending on the research question. Using examples, we concluded by explaining how working hypotheses inform data collection and analysis for deductive exploratory research.

Crawford’s ( 2017 ) example showed how the structure of working hypotheses provide a framework for comparative case studies. Her criteria for analysis were specified ahead of time and used to frame each case. Thus, her comparisons were systemized across cases. Further, the framework ensured a connection between the data analysis and the literature review. Yet the flexible, working nature of the hypotheses allowed for unexpected findings to be discovered.

The evidence required to test working hypotheses is directed by the research purpose and potentially includes both quantitative and qualitative sources. Thus, all types of evidence, including quantitative methods should be part of the toolbox of deductive, explorative research. We show how the working hypotheses, as a flexible exploratory framework, resolves many seeming dualisms pervasive in the research methods literature.

To conclude, this article has provided an in-depth examination of working hypotheses taking into account philosophical questions and the larger formal research methods literature. By discussing working hypotheses as applied, theoretical tools, we demonstrated that working hypotheses fill a unique niche in the methods literature, since they provide a way to enhance alignment in deductive, explorative studies.

Acknowledgements

The authors contributed equally to this work. The authors would like to thank Quality & Quantity’ s editors and the anonymous reviewers for their valuable advice and comments on previous versions of this paper.

Open access funding provided by Alma Mater Studiorum - Università di Bologna within the CRUI-CARE Agreement. There are no funders to report for this submission.

Compliance with ethical standards

No potential conflict of interest was reported by the author.

1 In practice, quantitative scholars often run multivariate analysis on data bases to find out if there are correlations. Hypotheses are tested because the statistical software does the math, not because the scholar has an a priori, relational expectation (hypothesis) well-grounded in the literature and supported by cogent arguments. Hunches are just fine. This is clearly an inductive approach to research and part of the large process of inquiry.

2 In 1958 , Philosophers of Science, Oppenheim and Putnam use the notion of Working Hypothesis in their title “Unity of Science as Working Hypothesis.” They too, use it as a big picture concept, “unity of science in this sense, can be fully realized constitutes an over-arching meta-scientific hypothesis, which enables one to see a unity in scientific activities that might otherwise appear disconnected or unrelated” (p. 4).

3 It should be noted that the positivism described in the research methods literature does not resemble philosophical positivism as developed by philosophers like Comte (Whetsell and Shields 2015 ). In the research methods literature “positivism means different things to different people….The term has long been emptied of any precise denotation …and is sometimes affixed to positions actually opposed to those espoused by the philosophers from whom the name derives” (Schrag 1992 , p. 5). For purposes of this paper, we are capturing a few essential ways positivism is presented in the research methods literature. This helps us to position the “working hypothesis” and “exploratory” research within the larger context in contemporary research methods. We are not arguing that the positivism presented here is anything more. The incompatibility theory discussed later, is an outgrowth of this research methods literature…

4 It should be noted that quantitative researchers often use inductive reasoning. They do this with existing data sets when they run correlations or regression analysis as a way to find relationships. They ask, what does the data tell us?

5 Qualitative researchers are also associated with phenomenology, hermeneutics, naturalistic inquiry and constructivism.

6 See Feilzer ( 2010 ), Howe ( 1988 ), Johnson and Onwuegbunzie ( 2004 ), Morgan ( 2007 ), Onwuegbuzie and Leech ( 2005 ), Biddle and Schafft ( 2015 ).

7 The term conceptual framework is applicable in a broad context (see Ravitch and Riggan 2012 ). The micro-conceptual framework narrows to the specific study and informs data collection (Shields and Rangarajan 2013 ; Shields et al. 2019a ) .

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Exploratory Vs Explanatory Research

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Exploratory research and explanatory research are two fundamental types of research studies, and they have different objectives, approaches, and outcomes.

Exploratory Research

Exploratory research is usually conducted when the researcher is trying to gain a deeper understanding of a particular phenomenon, situation, or problem. The primary purpose of exploratory research is to explore and generate ideas, hypotheses, and theories about a topic or issue that is not well understood. The researcher typically uses qualitative research methods, such as in-depth interviews, focus groups, or observational studies, to collect data. The data collected in exploratory research is usually descriptive and helps the researcher to identify patterns and trends, generate hypotheses, and develop a deeper understanding of the research problem. Exploratory research is usually the first step in a larger research project, and its results are used to guide the design of subsequent studies.

Explanatory Research

Explanatory research , on the other hand, is conducted when the researcher is trying to explain the relationship between variables or to test hypotheses that have been generated through exploratory research. The primary purpose of explanatory research is to explain why and how things happen. The researcher typically uses quantitative research methods, such as surveys or experiments, to collect data. The data collected in explanatory research is usually analyzed statistically to test hypotheses and to establish cause-and-effect relationships between variables.

Differences Between Exploratory and Explanatory Research

In summary, exploratory research is used to gain a deeper understanding of a research problem, while explanatory research is used to explain the relationship between variables or to test hypotheses. Both types of research are important and complement each other in the research process. Exploratory research is usually the first step in a larger research project, while explanatory research is conducted after exploratory research to test hypotheses and to establish cause-and-effect relationships between variables.

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Introductions, Body Paragraphs, and Conclusions for Exploratory Papers

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Many paper assignments call for you to establish a position and defend that position with an effective argument. However, some assignments are not argumentative, but rather, they are exploratory. Exploratory essays ask questions and gather information that may answer these questions. However, the main point of the exploratory or inquiry essay is not to find definite answers. The main point is to conduct inquiry into a topic, gather information, and share that information with readers.

Introductions for Exploratory Essays

The introduction is the broad beginning of the paper that answers three important questions:

• What is this?
• Why am I reading it?
• What do you want me to do?

You should answer these questions in an exploratory essay by doing the following:

• Set the context – provide general information about the main idea, explaining the situation so the reader can make sense of the topic and the questions you will ask
• State why the main idea is important – tell the reader why they should care and keep reading. Your goal is to create a compelling, clear, and educational essay people will want to read and act upon
• State your research question – compose a question or two that clearly communicate what you want to discover and why you are interested in the topic. An overview of the types of sources you explored might follow your research question.

If your inquiry paper is long, you may want to forecast how you explored your topic by outlining the structure of your paper, the sources you considered, and the information you found in these sources. Your forecast could read something like this:

In order to explore my topic and try to answer my research question, I began with news sources. I then conducted research in scholarly sources, such as peer-reviewed journals. Lastly, I conducted an interview with a primary source. All these sources gave me a better understanding of my topic, and even though I was not able to fully answer my research questions, I learned a lot and narrowed my subject for the next paper assignment, the problem-solution report.

For this OWL resource, the example exploratory process investigates a local problem to gather more information so that eventually a solution may be suggested.

Identify a problem facing your University (institution, students, faculty, staff) or the local area and conduct exploratory research to find out as much as you can on the following:

• Causes of the problem and other contributing factors
• People/institutions involved in the situation: decision makers and stakeholders
• Possible solutions to the problem.

You do not have to argue for a solution to the problem at this point. The point of the exploratory essay is to ask an inquiry question and find out as much as you can to try to answer your question. Then write about your inquiry and findings.

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