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Explanatory Research – Types, Methods, Guide

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Explanatory Research

Definition :

Explanatory research is a type of research that aims to uncover the underlying causes and relationships between different variables. It seeks to explain why a particular phenomenon occurs and how it relates to other factors.

This type of research is typically used to test hypotheses or theories and to establish cause-and-effect relationships. Explanatory research often involves collecting data through surveys , experiments , or other empirical methods, and then analyzing that data to identify patterns and correlations. The results of explanatory research can provide a better understanding of the factors that contribute to a particular phenomenon and can help inform future research or policy decisions.

Types of Explanatory Research

There are several types of explanatory research, each with its own approach and focus. Some common types include:

Experimental Research

This involves manipulating one or more variables to observe the effect on other variables. It allows researchers to establish a cause-and-effect relationship between variables and is often used in natural and social sciences.

Quasi-experimental Research

This type of research is similar to experimental research but lacks full control over the variables. It is often used in situations where it is difficult or impossible to manipulate certain variables.

Correlational Research

This type of research aims to identify relationships between variables without manipulating them. It involves measuring and analyzing the strength and direction of the relationship between variables.

Case study Research

This involves an in-depth investigation of a specific case or situation. It is often used in social sciences and allows researchers to explore complex phenomena and contexts.

Historical Research

This involves the systematic study of past events and situations to understand their causes and effects. It is often used in fields such as history and sociology.

Survey Research

This involves collecting data from a sample of individuals through structured questionnaires or interviews. It allows researchers to investigate attitudes, behaviors, and opinions.

Explanatory Research Methods

There are several methods that can be used in explanatory research, depending on the research question and the type of data being collected. Some common methods include:

Experiments

In experimental research, researchers manipulate one or more variables to observe their effect on other variables. This allows them to establish a cause-and-effect relationship between the variables.

Surveys are used to collect data from a sample of individuals through structured questionnaires or interviews. This method can be used to investigate attitudes, behaviors, and opinions.

Correlational studies

This method aims to identify relationships between variables without manipulating them. It involves measuring and analyzing the strength and direction of the relationship between variables.

Case studies

Case studies involve an in-depth investigation of a specific case or situation. This method is often used in social sciences and allows researchers to explore complex phenomena and contexts.

Secondary Data Analysis

This method involves analyzing data that has already been collected by other researchers or organizations. It can be useful when primary data collection is not feasible or when additional data is needed to support research findings.

Data Analysis Methods

Explanatory research data analysis methods are used to explore the relationships between variables and to explain how they interact with each other. Here are some common data analysis methods used in explanatory research:

Correlation Analysis

Correlation analysis is used to identify the strength and direction of the relationship between two or more variables. This method is particularly useful when exploring the relationship between quantitative variables.

Regression Analysis

Regression analysis is used to identify the relationship between a dependent variable and one or more independent variables. This method is particularly useful when exploring the relationship between a dependent variable and several predictor variables.

Path Analysis

Path analysis is a method used to examine the direct and indirect relationships between variables. It is particularly useful when exploring complex relationships between variables.

Structural Equation Modeling (SEM)

SEM is a statistical method used to test and validate theoretical models of the relationships between variables. It is particularly useful when exploring complex models with multiple variables and relationships.

Factor Analysis

Factor analysis is used to identify underlying factors that contribute to the variation in a set of variables. This method is particularly useful when exploring relationships between multiple variables.

Content Analysis

Content analysis is used to analyze qualitative data by identifying themes and patterns in text, images, or other forms of data. This method is particularly useful when exploring the meaning and context of data.

Applications of Explanatory Research

The applications of explanatory research include:

• Social sciences: Explanatory research is commonly used in social sciences to investigate the causes and effects of social phenomena, such as the relationship between poverty and crime, or the impact of social policies on individuals or communities.
• Marketing : Explanatory research can be used in marketing to understand the reasons behind consumer behavior, such as why certain products are preferred over others or why customers choose to purchase from certain brands.
• Healthcare : Explanatory research can be used in healthcare to identify the factors that contribute to disease or illness, as well as the effectiveness of different treatments and interventions.
• Education : Explanatory research can be used in education to investigate the causes of academic achievement or failure, as well as the factors that influence teaching and learning processes.
• Business : Explanatory research can be used in business to understand the factors that contribute to the success or failure of different strategies, as well as the impact of external factors, such as economic or political changes, on business operations.
• Public policy: Explanatory research can be used in public policy to evaluate the effectiveness of policies and programs, as well as to identify the factors that contribute to social problems or inequalities.

Explanatory Research Question

An explanatory research question is a type of research question that seeks to explain the relationship between two or more variables, and to identify the underlying causes of that relationship. The goal of explanatory research is to test hypotheses or theories about the relationship between variables, and to gain a deeper understanding of complex phenomena.

Examples of explanatory research questions include:

• What is the relationship between sleep quality and academic performance among college students, and what factors contribute to this relationship?
• How do environmental factors, such as temperature and humidity, affect the spread of infectious diseases?
• What are the factors that contribute to the success or failure of small businesses in a particular industry, and how do these factors interact with each other?
• How do different teaching strategies impact student engagement and learning outcomes in the classroom?
• What is the relationship between social support and mental health outcomes among individuals with chronic illnesses, and how does this relationship vary across different populations?

Examples of Explanatory Research

Here are a few Real-Time Examples of explanatory research:

• Exploring the factors influencing customer loyalty: A business might conduct explanatory research to determine which factors, such as product quality, customer service, or price, have the greatest impact on customer loyalty. This research could involve collecting data through surveys, interviews, or other means and analyzing it using methods such as correlation or regression analysis.
• Understanding the causes of crime: Law enforcement agencies might conduct explanatory research to identify the factors that contribute to crime in a particular area. This research could involve collecting data on factors such as poverty, unemployment, drug use, and social inequality and analyzing it using methods such as regression analysis or structural equation modeling.
• Investigating the effectiveness of a new medical treatment: Medical researchers might conduct explanatory research to determine whether a new medical treatment is effective and which variables, such as dosage or patient age, are associated with its effectiveness. This research could involve conducting clinical trials and analyzing data using methods such as path analysis or SEM.
• Exploring the impact of social media on mental health : Researchers might conduct explanatory research to determine whether social media use has a positive or negative impact on mental health and which variables, such as frequency of use or type of social media, are associated with mental health outcomes. This research could involve collecting data through surveys or interviews and analyzing it using methods such as factor analysis or content analysis.

When to use Explanatory Research

Here are some situations where explanatory research might be appropriate:

• When exploring a new or complex phenomenon: Explanatory research can be used to understand the mechanisms of a new or complex phenomenon and to identify the variables that are most strongly associated with it.
• When testing a theoretical model: Explanatory research can be used to test a theoretical model of the relationships between variables and to validate or modify the model based on empirical data.
• When identifying the causal relationships between variables: Explanatory research can be used to identify the causal relationships between variables and to determine which variables have the greatest impact on the outcome of interest.
• When conducting program evaluation: Explanatory research can be used to evaluate the effectiveness of a program or intervention and to identify the factors that contribute to its success or failure.
• When making informed decisions: Explanatory research can be used to provide a basis for informed decision-making in business, government, or other contexts by identifying the factors that contribute to a particular outcome.

How to Conduct Explanatory Research

Here are the steps to conduct explanatory research:

• Identify the research problem: Clearly define the research question or problem you want to investigate. This should involve identifying the variables that you want to explore, and the potential relationships between them.
• Conduct a literature review: Review existing research on the topic to gain a deeper understanding of the variables and relationships you plan to explore. This can help you develop a hypothesis or research questions to guide your study.
• Develop a research design: Decide on the research design that best suits your study. This may involve collecting data through surveys, interviews, experiments, or observations.
• Collect and analyze data: Collect data from your selected sample and analyze it using appropriate statistical methods to identify any significant relationships between variables.
• Interpret findings: Interpret the results of your analysis in light of your research question or hypothesis. Identify any patterns or relationships between variables, and discuss the implications of your findings for the wider field of study.
• Draw conclusions: Draw conclusions based on your analysis and identify any areas for further research. Make recommendations for future research or policy based on your findings.

Purpose of Explanatory Research

The purpose of explanatory research is to identify and explain the relationships between different variables, as well as to determine the causes of those relationships. This type of research is often used to test hypotheses or theories, and to explore complex phenomena that are not well understood.

Explanatory research can help to answer questions such as “why” and “how” by providing a deeper understanding of the underlying causes and mechanisms of a particular phenomenon. For example, explanatory research can be used to determine the factors that contribute to a particular health condition, or to identify the reasons why certain marketing strategies are more effective than others.

The main purpose of explanatory research is to gain a deeper understanding of a particular phenomenon, with the goal of developing more effective solutions or interventions to address the problem. By identifying the underlying causes and mechanisms of a phenomenon, explanatory research can help to inform decision-making, policy development, and best practices in a wide range of fields, including healthcare, social sciences, business, and education

Advantages of Explanatory Research

Here are some advantages of explanatory research:

• Provides a deeper understanding: Explanatory research aims to uncover the underlying causes and mechanisms of a particular phenomenon, providing a deeper understanding of complex phenomena that is not possible with other research designs.
• Test hypotheses or theories: Explanatory research can be used to test hypotheses or theories by identifying the relationships between variables and determining the causes of those relationships.
• Provides insights for decision-making: Explanatory research can provide insights that can inform decision-making in a wide range of fields, from healthcare to business.
• Can lead to the development of effective solutions: By identifying the underlying causes of a problem, explanatory research can help to develop more effective solutions or interventions to address the problem.
• Can improve the validity of research: By identifying and controlling for potential confounding variables, explanatory research can improve the validity and reliability of research findings.
• Can be used in combination with other research designs : Explanatory research can be used in combination with other research designs, such as exploratory or descriptive research, to provide a more comprehensive understanding of a phenomenon.

Limitations of Explanatory Research

Here are some limitations of explanatory research:

• Limited generalizability: Explanatory research typically involves studying a specific sample, which can limit the generalizability of findings to other populations or settings.
• Time-consuming and resource-intensive: Explanatory research can be time-consuming and resource-intensive, particularly if it involves collecting and analyzing large amounts of data.
• Limited scope: Explanatory research is typically focused on a narrow research question or hypothesis, which can limit its scope in comparison to other research designs such as exploratory or descriptive research.
• Limited control over variables: Explanatory research can be limited by the researcher’s ability to control for all possible variables that may influence the relationship between variables of interest.
• Potential for bias: Explanatory research can be subject to various types of bias, such as selection bias, measurement bias, and recall bias, which can influence the validity of research findings.
• Ethical considerations: Explanatory research may involve the use of invasive or risky procedures, which can raise ethical concerns and require careful consideration of the potential risks and benefits of the study.

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Muhammad Hassan

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Types of Case Studies

There are several different types of case studies, as well as several types of subjects of case studies. We will investigate each type in this article.

Different Types of Case Studies

There are several types of case studies, each differing from each other based on the hypothesis and/or thesis to be proved. It is also possible for types of case studies to overlap each other.

Each of the following types of cases can be used in any field or discipline. Whether it is psychology, business or the arts, the type of case study can apply to any field.

Explanatory

The explanatory case study focuses on an explanation for a question or a phenomenon. Basically put, an explanatory case study is 1 + 1 = 2. The results are not up for interpretation.

A case study with a person or group would not be explanatory, as with humans, there will always be variables. There are always small variances that cannot be explained.

However, event case studies can be explanatory. For example, let's say a certain automobile has a series of crashes that are caused by faulty brakes. All of the crashes are a result of brakes not being effective on icy roads.

What kind of case study is explanatory? Think of an example of an explanatory case study that could be done today

When developing the case study, the researcher will explain the crash, and the detailed causes of the brake failure. They will investigate what actions caused the brakes to fail, and what actions could have been taken to prevent the failure.

Other car companies could then use this case study to better understand what makes brakes fail. When designing safer products, looking to past failures is an excellent way to ensure similar mistakes are not made.

The same can be said for other safety issues in cars. There was a time when cars did not have seatbelts. The process to get seatbelts required in all cars started with a case study! The same can be said about airbags and collapsible steering columns. They all began with a case study that lead to larger research, and eventual change.

Exploratory

An exploratory case study is usually the precursor to a formal, large-scale research project. The case study's goal is to prove that further investigation is necessary.

For example, an exploratory case study could be done on veterans coming home from active combat. Researchers are aware that these vets have PTSD, and are aware that the actions of war are what cause PTSD. Beyond that, they do not know if certain wartime activities are more likely to contribute to PTSD than others.

For an exploratory case study, the researcher could develop a study that certain war events are more likely to cause PTSD. Once that is demonstrated, a large-scale research project could be done to determine which events are most likely to cause PTSD.

Exploratory case studies are very popular in psychology and the social sciences. Psychologists are always looking for better ways to treat their patients, and exploratory studies allow them to research new ideas or theories.

Multiple-Case Studies or Collective Studies

Multiple case or collective studies use information from different studies to formulate the case for a new study. The use of past studies allows additional information without needing to spend more time and money on additional studies.

Using the PTSD issue again is an excellent example of a collective study. When studying what contributes most to wartime PTSD, a researcher could use case studies from different war. For instance, studies about PTSD in WW2 vets, Persian Gulf War vets, and Vietnam vets could provide an excellent sampling of which wartime activities are most likely to cause PTSD.

If a multiple case study on vets was done with vets from the Vietnam War, the Persian Gulf War, and the Iraq War, and it was determined the vets from Vietnam had much less PTSD, what could be inferred?

Furthermore, this type of study could uncover differences as well. For example, a researcher might find that veterans who serve in the Middle East are more likely to suffer a certain type of ailment. Or perhaps, that veterans who served with large platoons were more likely to suffer from PTSD than veterans who served in smaller platoons.

An intrinsic case study is the study of a case wherein the subject itself is the primary interest. The "Genie" case is an example of this. The study wasn't so much about psychology, but about Genie herself, and how her experiences shaped who she was.

Genie is the topic. Genie is what the researchers are interested in, and what their readers will be most interested in. When the researchers started the study, they didn't know what they would find.

They asked the question…"If a child is never introduced to language during the crucial first years of life, can they acquire language skills when they are older?" When they met Genie, they didn't know the answer to that question.

Instrumental

An instrumental case study uses a case to gain insights into a phenomenon. For example, a researcher interested in child obesity rates might set up a study with middle school students and an exercise program. In this case, the children and the exercise program are not the focus. The focus is learning the relationship between children and exercise, and why certain children become obese.

What is an example of an instrumental case study?

Focus on the results, not the topic!

Types of Subjects of Case Studies

There are generally five different types of case studies, and the subjects that they address. Every case study, whether explanatory or exploratory, or intrinsic or instrumental, fits into one of these five groups. These are:

Person – This type of study focuses on one particular individual. This case study would use several types of research to determine an outcome.

The best example of a person case is the "Genie" case study. Again, "Genie" was a 13-year-old girl who was discovered by social services in Los Angeles in 1970. Her father believed her to be mentally retarded, and therefore locked her in a room without any kind of stimulation. She was never nourished or cared for in any way. If she made a noise, she was beaten.

When "Genie" was discovered, child development specialists wanted to learn as much as possible about how her experiences contributed to her physical, emotional and mental health. They also wanted to learn about her language skills. She had no form of language when she was found, she only grunted. The study would determine whether or not she could learn language skills at the age of 13.

Since Genie was placed in a children's hospital, many different clinicians could observe her. In addition, researchers were able to interview the few people who did have contact with Genie and would be able to gather whatever background information was available.

This case study is still one of the most valuable in all of child development. Since it would be impossible to conduct this type of research with a healthy child, the information garnered from Genie's case is invaluable.

Group – This type of study focuses on a group of people. This could be a family, a group or friends, or even coworkers.

An example of this type of case study would be the uncontacted tribes of Indians in the Peruvian and Brazilian rainforest. These tribes have never had any modern contact. Therefore, there is a great interest to study them.

Scientists would be interested in just about every facet of their lives. How do they cook, how do they make clothing, how do they make tools and weapons. Also, doing psychological and emotional research would be interesting. However, because so few of these tribes exist, no one is contacting them for research. For now, all research is done observationally.

If a researcher wanted to study uncontacted Indian tribes, and could only observe the subjects, what type of observations should be made?

Location – This type of study focuses on a place, and how and why people use the place.

For example, many case studies have been done about Siberia, and the people who live there. Siberia is a cold and barren place in northern Russia, and it is considered the most difficult place to live in the world. Studying the location, and it's weather and people can help other people learn how to live with extreme weather and isolation.

Location studies can also be done on locations that are facing some kind of change. For example, a case study could be done on Alaska, and whether the state is seeing the effects of climate change.

Another type of study that could be done in Alaska is how the environment changes as population increases. Geographers and those interested in population growth often do these case studies.

Organization/Company – This type of study focuses on a business or an organization. This could include the people who work for the company, or an event that occurred at the organization.

An excellent example of this type of case study is Enron. Enron was one of the largest energy company's in the United States, when it was discovered that executives at the company were fraudulently reporting the company's accounting numbers.

Once the fraud was uncovered, investigators discovered willful and systematic corruption that caused the collapse of Enron, as well as their financial auditors, Arthur Andersen. The fraud was so severe that the top executives of the company were sentenced to prison.

This type of case study is used by accountants, auditors, financiers, as well as business students, in order to learn how such a large company could get away with committing such a serious case of corporate fraud for as long as they did. It can also be looked at from a psychological standpoint, as it is interesting to learn why the executives took the large risks that they took.

Most company or organization case studies are done for business purposes. In fact, in many business schools, such as Harvard Business School, students learn by the case method, which is the study of case studies. They learn how to solve business problems by studying the cases of businesses that either survived the same problem, or one that didn't survive the problem.

Event – This type of study focuses on an event, whether cultural or societal, and how it affects those that are affected by it. An example would be the Tylenol cyanide scandal. This event affected Johnson & Johnson, the parent company, as well as the public at large.

The case study would detail the events of the scandal, and more specifically, what management at Johnson & Johnson did to correct the problem. To this day, when a company experiences a large public relations scandal, they look to the Tylenol case study to learn how they managed to survive the scandal.

A very popular topic for case studies was the events of September 11 th . There were studies in almost all of the different types of research studies.

Obviously the event itself was a very popular topic. It was important to learn what lead up to the event, and how best to proven it from happening in the future. These studies are not only important to the U.S. government, but to other governments hoping to prevent terrorism in their countries.

Planning A Case Study

You have decided that you want to research and write a case study. Now what? In this section you will learn how to plan and organize a research case study.

Selecting a Case

The first step is to choose the subject, topic or case. You will want to choose a topic that is interesting to you, and a topic that would be of interest to your potential audience. Ideally you have a passion for the topic, as then you will better understand the issues surrounding the topic, and which resources would be most successful in the study.

You also must choose a topic that would be of interest to a large number of people. You want your case study to reach as large an audience as possible, and a topic that is of interest to just a few people will not have a very large reach. One of the goals of a case study is to reach as many people as possible.

Who is your audience?

Are you trying to reach the layperson? Or are you trying to reach other professionals in your field? Your audience will help determine the topic you choose.

If you are writing a case study that is looking for ways to lower rates of child obesity, who is your audience?

If you are writing a psychology case study, you must consider whether your audience will have the intellectual skills to understand the information in the case. Does your audience know the vocabulary of psychology? Do they understand the processes and structure of the field?

You want your audience to have as much general knowledge as possible. When it comes time to write the case study, you may have to spend some time defining and explaining terms that might be unfamiliar to the audience.

Lastly, when selecting a topic you do not want to choose a topic that is very old. Current topics are always the most interesting, so if your topic is more than 5-10 years old, you might want to consider a newer topic. If you choose an older topic, you must ask yourself what new and valuable information do you bring to the older topic, and is it relevant and necessary.

Determine Research Goals

What type of case study do you plan to do?

An illustrative case study will examine an unfamiliar case in order to help others understand it. For example, a case study of a veteran with PTSD can be used to help new therapists better understand what veterans experience.

An exploratory case study is a preliminary project that will be the precursor to a larger study in the future. For example, a case study could be done challenging the efficacy of different therapy methods for vets with PTSD. Once the study is complete, a larger study could be done on whichever method was most effective.

A critical instance case focuses on a unique case that doesn't have a predetermined purpose. For example, a vet with an incredibly severe case of PTSD could be studied to find ways to treat his condition.

Ethics are a large part of the case study process, and most case studies require ethical approval. This approval usually comes from the institution or department the researcher works for. Many universities and research institutions have ethics oversight departments. They will require you to prove that you will not harm your study subjects or participants.

This should be done even if the case study is on an older subject. Sometimes publishing new studies can cause harm to the original participants. Regardless of your personal feelings, it is essential the project is brought to the ethics department to ensure your project can proceed safely.

Developing the Case Study

Once you have your topic, it is time to start planning and developing the study. This process will be different depending on what type of case study you are planning to do. For thissection, we will assume a psychological case study, as most case studies are based on the psychological model.

Once you have the topic, it is time to ask yourself some questions. What question do you want to answer with the study?

For example, a researcher is considering a case study about PTSD in veterans. The topic is PTSD in veterans. What questions could be asked?

Do veterans from Middle Eastern wars suffer greater instances of PTSD?

Do younger soldiers have higher instances of PTSD?

Does the length of the tour effect the severity of PTSD?

Each of these questions is a viable question, and finding the answers, or the possible answers, would be helpful for both psychologists and veterans who suffer from PTSD.

Research Notebook

1. What is the background of the case study? Who requested the study to be done and why? What industry is the study in, and where will the study take place?

2. What is the problem that needs a solution? What is the situation, and what are the risks?

3. What questions are required to analyze the problem? What questions might the reader of the study have? What questions might colleagues have?

4. What tools are required to analyze the problem? Is data analysis necessary?

5. What is your current knowledge about the problem or situation? How much background information do you need to procure? How will you obtain this background info?

6. What other information do you need to know to successfully complete the study?

7. How do you plan to present the report? Will it be a simple written report, or will you add PowerPoint presentations or images or videos? When is the report due? Are you giving yourself enough time to complete the project?

The research notebook is the heart of the study. Other organizational methods can be utilized, such as Microsoft Excel, but a physical notebook should always be kept as well.

Planning the Research

The most important parts of the case study are:

1. The case study's questions

2. The study's propositions

3. How information and data will be analyzed

4. The logic behind the propositions

5. How the findings will be interpreted

The study's questions should be either a "how" or "why" question, and their definition is the researchers first job. These questions will help determine the study's goals.

Not every case study has a proposition. If you are doing an exploratory study, you will not have propositions. Instead, you will have a stated purpose, which will determine whether your study is successful, or not.

How the information will be analyzed will depend on what the topic is. This would vary depending on whether it was a person, group, or organization.

When setting up your research, you will want to follow case study protocol. The protocol should have the following sections:

1. An overview of the case study, including the objectives, topic and issues.

2. Procedures for gathering information and conducting interviews.

3. Questions that will be asked during interviews and data collection.

4. A guide for the final case study report.

When deciding upon which research methods to use, these are the most important:

1. Documents and archival records

2. Interviews

3. Direct observations

4. Indirect observations, or observations of subjects

5. Physical artifacts and tools

Documents could include almost anything, including letters, memos, newspaper articles, Internet articles, other case studies, or any other document germane to the study.

Archival records can include military and service records, company or business records, survey data or census information.

Research Strategy

Before beginning the study you want a clear research strategy. Your best chance at success will be if you use an outline that describes how you will gather your data and how you will answer your research questions.

The researcher should create a list with four or five bullet points that need answers. Consider the approaches for these questions, and the different perspectives you could take.

The researcher should then choose at least two data sources (ideally more). These sources could include interviews, Internet research, and fieldwork or report collection. The more data sources used, the better the quality of the final data.

The researcher then must formulate interview questions that will result in detailed and in-depth answers that will help meet the research goals. A list of 15-20 questions is a good start, but these can and will change as the process flows.

Planning Interviews

The interview process is one of the most important parts of the case study process. But before this can begin, it is imperative the researcher gets informed consent from the subjects.

The process of informed consent means the subject understands their role in the study, and that their story will be used in the case study. You will want to have each subject complete a consent form.

The researcher must explain what the study is trying to achieve, and how their contribution will help the study. If necessary, assure the subject that their information will remain private if requested, and they do not need to use their real name if they are not comfortable with that. Pseudonyms are commonly used in case studies.

Informed Consent

The process by which permission is granted before beginning medical or psychological research

A fictitious name used to hide ones identity

It is important the researcher is clear regarding the expectations of the study participation. For example, are they comfortable on camera? Do they mind if their photo is used in the final written study.

Interviews are one of the most important sources of information for case studies. There are several types of interviews. They are:

Open-ended – This type of interview has the interviewer and subject talking to each other about the subject. The interviewer asks questions, and the subject answers them. But the subject can elaborate and add information whenever they see fit.

A researcher might meet with a subject multiple times, and use the open-ended method. This can be a great way to gain insight into events. However, the researcher mustn't rely solely on the information from the one subject, and be sure to have multiple sources.

Focused – This type of interview is used when the subject is interviewed for a short period of time, and answers a set of questions. This type of interview could be used to verify information learned in an open-ended interview with another subject. Focused interviews are normally done to confirm information, not to gain new information.

Structured – Structured interviews are similar to surveys. These are usually used when collecting data for large groups, like neighborhoods. The questions are decided before hand, and the expected answers are usually simple.

When conducting interviews, the answers are obviously important. But just as important are the observations that can be made. This is one of the reasons in-person interviews are preferable over phone interviews, or Internet or mail surveys.

Ideally, when conducing in-person interviews, more than one researcher should be present. This allows one researcher to focus on observing while the other is interviewing. This is particularly important when interviewing large groups of people.

The researcher must understand going into the case study that the information gained from the interviews might not be valuable. It is possible that once the interviews are completed, the information gained is not relevant.

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Home Market Research

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: 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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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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Case Study Methods and Examples

By Janet Salmons, PhD Manager, Sage Research Methods Community

What is Case Study Methodology ?

Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it. Or, case study can refer to a type of academic writing that typically delves into a problem, process, or situation.

Case study methodology can entail the study of one or more "cases," that could be described as instances, examples, or settings where the problem or phenomenon can be examined. The researcher is tasked with defining the parameters of the case, that is, what is included and excluded. This process is called bounding the case , or setting boundaries.

Case study can be combined with other methodologies, such as ethnography, grounded theory, or phenomenology. In such studies the research on the case uses another framework to further define the study and refine the approach.

Case study is also described as a method, given particular approaches used to collect and analyze data. Case study research is conducted by almost every social science discipline: business, education, sociology, psychology. Case study research, with its reliance on multiple sources, is also a natural choice for researchers interested in trans-, inter-, or cross-disciplinary studies.

The Encyclopedia of case study research provides an overview:

The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case.

It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed methods because this they use either more than one form of data within a research paradigm, or more than one form of data from different paradigms.

A case study inquiry could include multiple types of data:

multiple forms of quantitative data sources, such as Big Data + a survey

multiple forms of qualitative data sources, such as interviews + observations + documents

multiple forms of quantitative and qualitative data sources, such as surveys + interviews

Case study methodology can be used to achieve different research purposes.

Robert Yin , methodologist most associated with case study research, differentiates between descriptive , exploratory and explanatory case studies:

Descriptive : A case study whose purpose is to describe a phenomenon. Explanatory : A case study whose purpose is to explain how or why some condition came to be, or why some sequence of events occurred or did not occur. Exploratory: A case study whose purpose is to identify the research questions or procedures to be used in a subsequent study.

Robert Yin’s book is a comprehensive guide for case study researchers!

You can read the preface and Chapter 1 of Yin's book here . See the open-access articles below for some published examples of qualitative, quantitative, and mixed methods case study research.

Mills, A. J., Durepos, G., & Wiebe, E. (2010).  Encyclopedia of case study research (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412957397

Yin, R. K. (2018). Case study research and applications (6th ed.). Thousand Oaks: SAGE Publications.

Open-Access Articles Using Case Study Methodology

As you can see from this collection, case study methods are used in qualitative, quantitative and mixed methods research.

Ang, C.-S., Lee, K.-F., & Dipolog-Ubanan, G. F. (2019). Determinants of First-Year Student Identity and Satisfaction in Higher Education: A Quantitative Case Study. SAGE Open. https://doi.org/10.1177/2158244019846689

Abstract. First-year undergraduates’ expectations and experience of university and student engagement variables were investigated to determine how these perceptions influence their student identity and overall course satisfaction. Data collected from 554 first-year undergraduates at a large private university were analyzed. Participants were given the adapted version of the Melbourne Centre for the Study of Higher Education Survey to self-report their learning experience and engagement in the university community. The results showed that, in general, the students’ reasons of pursuing tertiary education were to open the door to career opportunities and skill development. Moreover, students’ views on their learning and university engagement were at the moderate level. In relation to student identity and overall student satisfaction, it is encouraging to state that their perceptions of studentship and course satisfaction were rather positive. After controlling for demographics, student engagement appeared to explain more variance in student identity, whereas students’ expectations and experience explained greater variance in students’ overall course satisfaction. Implications for practice, limitations, and recommendation of this study are addressed.

Baker, A. J. (2017). Algorithms to Assess Music Cities: Case Study—Melbourne as a Music Capital. SAGE Open. https://doi.org/10.1177/2158244017691801

Abstract. The global  Mastering of a Music City  report in 2015 notes that the concept of music cities has penetrated the global political vernacular because it delivers “significant economic, employment, cultural and social benefits.” This article highlights that no empirical study has combined all these values and offers a relevant and comprehensive definition of a music city. Drawing on industry research,1 the article assesses how mathematical flowcharts, such as Algorithm A (Economics), Algorithm B (Four T’s creative index), and Algorithm C (Heritage), have contributed to the definition of a music city. Taking Melbourne as a case study, it illustrates how Algorithms A and B are used as disputed evidence about whether the city is touted as Australia’s music capital. The article connects the three algorithms to an academic framework from musicology, urban studies, cultural economics, and sociology, and proposes a benchmark Algorithm D (Music Cities definition), which offers a more holistic assessment of music activity in any urban context. The article concludes by arguing that Algorithm D offers a much-needed definition of what comprises a music city because it builds on the popular political economy focus and includes the social importance of space and cultural practices.

Brown, K., & Mondon, A. (2020). Populism, the media, and the mainstreaming of the far right: The Guardian’s coverage of populism as a case study. Politics. https://doi.org/10.1177/0263395720955036

Abstract. Populism seems to define our current political age. The term is splashed across the headlines, brandished in political speeches and commentaries, and applied extensively in numerous academic publications and conferences. This pervasive usage, or populist hype, has serious implications for our understanding of the meaning of populism itself and for our interpretation of the phenomena to which it is applied. In particular, we argue that its common conflation with far-right politics, as well as its breadth of application to other phenomena, has contributed to the mainstreaming of the far right in three main ways: (1) agenda-setting power and deflection, (2) euphemisation and trivialisation, and (3) amplification. Through a mixed-methods approach to discourse analysis, this article uses  The Guardian  newspaper as a case study to explore the development of the populist hype and the detrimental effects of the logics that it has pushed in public discourse.

Droy, L. T., Goodwin, J., & O’Connor, H. (2020). Methodological Uncertainty and Multi-Strategy Analysis: Case Study of the Long-Term Effects of Government Sponsored Youth Training on Occupational Mobility. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 147–148(1–2), 200–230. https://doi.org/10.1177/0759106320939893

Abstract. Sociological practitioners often face considerable methodological uncertainty when undertaking a quantitative analysis. This methodological uncertainty encompasses both data construction (e.g. defining variables) and analysis (e.g. selecting and specifying a modelling procedure). Methodological uncertainty can lead to results that are fragile and arbitrary. Yet, many practitioners may be unaware of the potential scale of methodological uncertainty in quantitative analysis, and the recent emergence of techniques for addressing it. Recent proposals for ‘multi-strategy’ approaches seek to identify and manage methodological uncertainty in quantitative analysis. We present a case-study of a multi-strategy analysis, applied to the problem of estimating the long-term impact of 1980s UK government-sponsored youth training. We use this case study to further highlight the problem of cumulative methodological fragilities in applied quantitative sociology and to discuss and help develop multi-strategy analysis as a tool to address them.

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward Developing a Framework for Conducting Case Study Research .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406918817954

Abstract. This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many researchers commented on the methodological issues of the case study research from their point of view thus, presenting a comprehensive framework was missing. We try representing a general framework with methodological and analytical perspective to design, develop, and conduct case study research. To test the coverage of our framework, we have analyzed articles in three major journals related to the management of technology and innovation to approve our framework. This study represents a general structure to guide, design, and fulfill a case study research with levels and steps necessary for researchers to use in their research.

Lai, D., & Roccu, R. (2019). Case study research and critical IR: the case for the extended case methodology. International Relations , 33 (1), 67-87. https://doi.org/10.1177/0047117818818243

Abstract. Discussions on case study methodology in International Relations (IR) have historically been dominated by positivist and neopositivist approaches. However, these are problematic for critical IR research, pointing to the need for a non-positivist case study methodology. To address this issue, this article introduces and adapts the extended case methodology as a critical, reflexivist approach to case study research, whereby the case is constructed through a dynamic interaction with theory, rather than selected, and knowledge is produced through extensions rather than generalisation. Insofar as it seeks to study the world in complex and non-linear terms, take context and positionality seriously, and generate explicitly political and emancipatory knowledge, the extended case methodology is consistent with the ontological and epistemological commitments of several critical IR approaches. Its potential is illustrated in the final part of the article with reference to researching the socioeconomic dimension of transitional justice in Bosnia and Herzegovina.

Lynch, R., Young, J. C., Boakye-Achampong, S., Jowaisas, C., Sam, J., & Norlander, B. (2020). Benefits of crowdsourcing for libraries: A case study from Africa . IFLA Journal. https://doi.org/10.1177/0340035220944940

Abstract. Many libraries in the Global South do not collect comprehensive data about themselves, which creates challenges in terms of local and international visibility. Crowdsourcing is an effective tool that engages the public to collect missing data, and it has proven to be particularly valuable in countries where governments collect little public data. Whereas crowdsourcing is often used within fields that have high levels of development funding, such as health, the authors believe that this approach would have many benefits for the library field as well. They present qualitative and quantitative evidence from 23 African countries involved in a crowdsourcing project to map libraries. The authors find benefits in terms of increased connections between stakeholders, capacity-building, and increased local visibility. These findings demonstrate the potential of crowdsourced approaches for tasks such as mapping to benefit libraries and similarly positioned institutions in the Global South in multifaceted ways.

Mason, W., Morris, K., Webb, C., Daniels, B., Featherstone, B., Bywaters, P., Mirza, N., Hooper, J., Brady, G., Bunting, L., & Scourfield, J. (2020). Toward Full Integration of Quantitative and Qualitative Methods in Case Study Research: Insights From Investigating Child Welfare Inequalities. Journal of Mixed Methods Research, 14 (2), 164-183. https://doi.org/10.1177/1558689819857972

Abstract. Delineation of the full integration of quantitative and qualitative methods throughout all stages of multisite mixed methods case study projects remains a gap in the methodological literature. This article offers advances to the field of mixed methods by detailing the application and integration of mixed methods throughout all stages of one such project; a study of child welfare inequalities. By offering a critical discussion of site selection and the management of confirmatory, expansionary and discordant data, this article contributes to the limited body of mixed methods exemplars specific to this field. We propose that our mixed methods approach provided distinctive insights into a complex social problem, offering expanded understandings of the relationship between poverty, child abuse, and neglect.

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406919862424

Abstract. Qualitative case study methodology enables researchers to conduct an in-depth exploration of intricate phenomena within some specific context. By keeping in mind research students, this article presents a systematic step-by-step guide to conduct a case study in the business discipline. Research students belonging to said discipline face issues in terms of clarity, selection, and operationalization of qualitative case study while doing their final dissertation. These issues often lead to confusion, wastage of valuable time, and wrong decisions that affect the overall outcome of the research. This article presents a checklist comprised of four phases, that is, foundation phase, prefield phase, field phase, and reporting phase. The objective of this article is to provide novice researchers with practical application of this checklist by linking all its four phases with the authors’ experiences and learning from recently conducted in-depth multiple case studies in the organizations of New Zealand. Rather than discussing case study in general, a targeted step-by-step plan with real-time research examples to conduct a case study is given.

VanWynsberghe, R., & Khan, S. (2007). Redefining Case Study. International Journal of Qualitative Methods, 80–94. https://doi.org/10.1177/160940690700600208

Abstract. In this paper the authors propose a more precise and encompassing definition of case study than is usually found. They support their definition by clarifying that case study is neither a method nor a methodology nor a research design as suggested by others. They use a case study prototype of their own design to propose common properties of case study and demonstrate how these properties support their definition. Next, they present several living myths about case study and refute them in relation to their definition. Finally, they discuss the interplay between the terms case study and unit of analysis to further delineate their definition of case study. The target audiences for this paper include case study researchers, research design and methods instructors, and graduate students interested in case study research.

More Sage Research Methods Community Posts about Case Study Research

Use research cases as the basis for individual or team activities that build skills.

Find an 10-step process for using research cases to teach methods with learning activities for individual students, teams, or small groups. (Or use the approach yourself!)

How do you decide which methodology fits your study? In this dialogue Linda Bloomberg and Janet Boberg explain the importance of a strategic approach to qualitative research design that stresses alignment with the purpose of the study.

Case study methods are used by researchers in many disciplines. Here are some open-access articles about multimodal qualitative or mixed methods designs that include both qualitative and quantitative elements.

Case study methodology is both unique, and uniquely confusing. It is unique given one characteristic: case studies draw from more than one data source.

What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of multidisciplinary examples.

Find discussion of case studies and published examples.

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Understanding contexts: how explanatory theories can help

Frank davidoff.

1 Lexington, USA

2 Geisel School of Medicine, Dartmouth College, Hanover, NH 03755 USA

Associated Data

Not applicable, because this analysis involves no original research data.

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.

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 – 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 .

Distinctive properties of complex systems (adapted from references [ 9 , 10 , 14 ])

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 – 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 – 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.

Use of explanatory principles in constructing an electronic decision-support system to improve postoperative care (adapted from references [ 5 , 6 , 13 , 41 ])

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 – 11 , 29 , 31 , 36 – 38 , 41 – 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 ).

Characteristics of data that contribute meaningfully to explanatory theories of human environments (adapted from references [ 9 , 42 – 48 ])

Methods for collecting and analyzing data that help to plan, implement, and evaluate the impact of improvement interventions (adapted from references [ 31 , 42 , 47 , 48 ])

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 – 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.

Acknowledgements

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.

Acronym for Standards for Quality Improvement Reporting Excellence.

No funding was received for this work.

Availability of data and materials

Author’s contributions.

The author gathered all the reference material, drafted the initial versions of the paper and all subsequent revisions, and takes responsibility for the entire content of the article.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Competing interests.

The author declares no competing interests.

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Explanatory Case Study Design—A Clarification

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Megan Anne Simons, Jenny Ziviani, Explanatory Case Study Design—A Clarification, Journal of Burn Care & Research , Volume 32, Issue 1, January-February 2011, Page e14, https://doi.org/10.1097/BCR.0b013e3182033569

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For the purpose of clarity for the readership, we wish to address the description of the explanatory case study design (ECSD) as a qualitative research method in the invited critique to our original article. 1 Yin, 2 the primary source for ECSD, described case study as suitable when the number of variables of interest exceeds the number of data points (i.e., participants). He has positioned the case study as a stand-alone method, in which the collection of both quantitative and qualitative data is appropriate. 3 We agree that within a qualitative research paradigm, the sample size of seven participants 1 would likely be insufficient to satisfy the sampling strategy. However, ECSD is “driven to theory.” 3 , p. 1212 The use of multiple case studies (as in the original study) is deemed the equivalent of multiple experiments. 2 Generalization from the case studies is accomplished using replication logic derived from theoretical propositions (hypotheses) or theories about the case. Results are considered even more potent when two or more cases support the same theory but not an equally plausible, rival theory. 2 The problem of generalizing from case studies is the same as generalizing from experiments—where hypotheses and theory are the vehicles for generalization. 3 With this point of clarification, we acknowledge that the findings from the original study are limited to children with lower injury severity (when measured as %TBSA) within a shortened timeframe postburn injury (6 months).

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Case Studies

Case studies are a popular research method in business area. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

According to its design, case studies in business research can be divided into three categories: explanatory, descriptive and exploratory.

Explanatory case studies aim to answer ‘how’ or ’why’ questions with little control on behalf of researcher over occurrence of events. This type of case studies focus on phenomena within the contexts of real-life situations. Example: “An investigation into the reasons of the global financial and economic crisis of 2008 – 2010.”

Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Studies in business research belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena. Example: “Impact of increasing levels of multiculturalism on marketing practices: A case study of McDonald’s Indonesia.”

Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc. Example: “A study into differences of leadership practices between private and public sector organizations in Atlanta, USA.”

Advantages of case study method include data collection and analysis within the context of phenomenon, integration of qualitative and quantitative data in data analysis, and the ability to capture complexities of real-life situations so that the phenomenon can be studied in greater levels of depth. Case studies do have certain disadvantages that may include lack of rigor, challenges associated with data analysis and very little basis for generalizations of findings and conclusions.

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• Exploratory Research | Definition, Guide, & Examples

Exploratory Research | Definition, Guide, & Examples

Published on 6 May 2022 by Tegan George . Revised on 20 January 2023.

Exploratory research is a methodology approach that investigates topics and research questions that have not previously been studied in depth.

Exploratory research is often qualitative in nature. However, a study with a large sample conducted in an exploratory manner can be quantitative as well. It is also often referred to as interpretive research or a grounded theory approach due to its flexible and open-ended nature.

Table of contents

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

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use this type of research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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Exploratory research questions are designed to help you understand more about a particular topic of interest. They can help you connect ideas to understand the groundwork of your analysis without adding any preconceived notions or assumptions yet.

Here are some examples:

• What effect does using a digital notebook have on the attention span of primary schoolers?
• What factors influence mental health in undergraduates?
• What outcomes are associated with an authoritative parenting style?
• In what ways does the presence of a non-native accent affect intelligibility?
• How can the use of a grocery delivery service reduce food waste in single-person households?

Collecting information on a previously unexplored topic can be challenging. Exploratory research can help you narrow down your topic and formulate a clear hypothesis , as well as giving you the ‘lay of the land’ on your topic.

Data collection using exploratory research is often divided into primary and secondary research methods, with data analysis following the same model.

Primary research

In primary research, your data is collected directly from primary sources : your participants. There is a variety of ways to collect primary data.

Some examples include:

• Survey methodology: Sending a survey out to the student body asking them if they would eat vegan meals
• Focus groups: Compiling groups of 8–10 students and discussing what they think of vegan options for dining hall food
• Interviews: Interviewing students entering and exiting the dining hall, asking if they would eat vegan meals

Secondary research

In secondary research, your data is collected from preexisting primary research, such as experiments or surveys.

Some other examples include:

• Case studies : Health of an all-vegan diet
• Literature reviews : Preexisting research about students’ eating habits and how they have changed over time
• Online polls, surveys, blog posts, or interviews; social media: Have other universities done something similar?

For some subjects, it’s possible to use large- n government data, such as the decennial census or yearly American Community Survey (ACS) open-source data.

How you proceed with your exploratory research design depends on the research method you choose to collect your data. In most cases, you will follow five steps.

We’ll walk you through the steps using the following example.

Therefore, you would like to focus on improving intelligibility instead of reducing the learner’s accent.

Step 1: Identify your problem

The first step in conducting exploratory research is identifying what the problem is and whether this type of research is the right avenue for you to pursue. Remember that exploratory research is most advantageous when you are investigating a previously unexplored problem.

Step 2: Hypothesise a solution

The next step is to come up with a solution to the problem you’re investigating. Formulate a hypothetical statement to guide your research.

Step 3. Design your methodology

Next, conceptualise your data collection and data analysis methods and write them up in a research design.

Step 4: Collect and analyse data

Next, you proceed with collecting and analysing your data so you can determine whether your preliminary results are in line with your hypothesis.

In most types of research, you should formulate your hypotheses a priori and refrain from changing them due to the increased risk of Type I errors and data integrity issues. However, in exploratory research, you are allowed to change your hypothesis based on your findings, since you are exploring a previously unexplained phenomenon that could have many explanations.

Step 5: Avenues for future research

Decide if you would like to continue studying your topic. If so, it is likely that you will need to change to another type of research. As exploratory research is often qualitative in nature, you may need to conduct quantitative research with a larger sample size to achieve more generalisable results.

It can be easy to confuse exploratory research with explanatory research. To understand the relationship, it can help to remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research investigates research questions that have not been studied in depth. The preliminary results often lay the groundwork for future analysis.

Explanatory research questions tend to start with ‘why’ or ‘how’, and the goal is to explain why or how a previously studied phenomenon takes place.

Like any other research design , exploratory research has its trade-offs: it provides a unique set of benefits but also comes with downsides.

• It can be very helpful in narrowing down a challenging or nebulous problem that has not been previously studied.
• It can serve as a great guide for future research, whether your own or another researcher’s. With new and challenging research problems, adding to the body of research in the early stages can be very fulfilling.
• It is very flexible, cost-effective, and open-ended. You are free to proceed however you think is best.

Disadvantages

• It usually lacks conclusive results, and results can be biased or subjective due to a lack of preexisting knowledge on your topic.
• It’s typically not externally valid and generalisable, and it suffers from many of the challenges of qualitative research .
• Since you are not operating within an existing research paradigm, this type of research can be very labour-intensive.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

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• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Spatial response of urban land use intensity to ecological networks: a case study of Xi'an Metropolitan Region, China

• Research Article
• Published: 15 May 2024

Cite this article

• Yaotao Xu 1 ,
• Peng Li   ORCID: orcid.org/0000-0003-1795-6466 1 , 2 ,
• Jinjin Pan 1 ,
• Nibing Gong 1 ,
• Zixuan Yan 1 ,
• Junfang Cui 3 &
• Binhua Zhao 1  

In the face of the persistent degradation of ecological environments and fragmentation of ecological networks brought about by rapid urbanization, this study focuses on examining the interaction between urban land use intensity and ecological networks in the Xi'an Metropolitan Region (XAMR), China, and their impact on ecological equilibrium and sustainable development. By comprehensively evaluating the changes in land use intensity in XAMR from 2010 to 2020, the aim is to underscore the pivotal role of ecological networks in maintaining urban ecological balance and promoting sustainable development. The findings indicate a transition in land use intensity in the XAMR from low to high concentration, reflecting an intensification in land resource utilization during urbanization. However, the establishment and management of ecological networks can significantly enhance urban ecological security and biodiversity. Notably, this research identified crucial ecological corridors and source areas, augmenting the connectivity of urban green infrastructure and providing vital support for urban biodiversity. Additionally, a significant finding of this study is the spatial spillover effects generated by socioeconomic factors such as the proportion of tertiary and secondary industries and per capita GDP through the ecological network, which have profound impacts on land use intensity in the surrounding areas. These insights offer a novel understanding of the complex interactions within urban ecosystems, emphasizing the importance of incorporating ecological network construction in urban planning. Overall, through a comprehensive analysis of the relationship between the ecological network and land use intensity in the XAMR, this study proposes new directions for urban ecosystem management and land use planning, highlighting the significance of scientific ecological network planning and management in achieving long-term sustainable development in urbanization processes.

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This work was supported by the National Natural Science Foundation of China (Nos. U214320057, U2040208, 42007070 and 42277359), Key Laboratory Project of Shaanxi Provincial Education Department grant number 20JS100, and Shaanxi Province Innovation Capability Support Plan in 2022 grant number 2022KJXX-86.

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State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, 710048, China

Yaotao Xu, Peng Li, Jinjin Pan, Nibing Gong, Zixuan Yan & Binhua Zhao

Key Laboratory National Forestry Administration On Ecological Hydrology and Disaster Prevention in Arid Regions, Xi’an University of Technology, Xi’an, 710048, China

Key Laboratory of Mountain Surface Biological Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, China Acadmey of Sciences, Chengdu, 610041, China

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Yaotao Xu: data analysis and article writing, manuscript revision. Peng Li: ideas contribution and providing valuable suggestions, manuscript revision. Jinjin Pan: providing valuable suggestions and comments for the manuscript. Nibing Gong: providing valuable suggestions and comments for the manuscript. Zixuan Yan: data collection and collation. Junfang Cui: data collection and collation. Binhua Zhao: guidance in cartography. All authors contributed critically to the drafts and gave final approval for publication.

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Xu, Y., Li, P., Pan, J. et al. Spatial response of urban land use intensity to ecological networks: a case study of Xi'an Metropolitan Region, China. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33562-w

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Biological response to Przewalski’s horse reintroduction in native desert grasslands: a case study on the spatial analysis of ticks

• Yu Zhang 1   na1 ,
• Jiawei Liu 1   na1 ,
• Ke Zhang 2 ,
• Anqi Wang 1 ,
• Duishan Sailikebieke 3 ,
• Zexin Zhang 4 ,
• Tegen Ao 5 ,
• Liping Yan 1 ,
• Dong Zhang 1 ,
• Kai Li 1 &
• Heqing Huang 6  

BMC Ecology and Evolution volume  24 , Article number:  61 ( 2024 ) Cite this article

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Reintroduction represents an effective strategy for the conservation of endangered wildlife, yet it might inadvertently impact the native ecosystems. This investigation assesses the impact of reintroducing endangered Przewalski's horses into the desert grassland ecosystem of the Kalamaili Nature Reserve (KNR), particularly its effect on the spatial distribution of ticks. In a 25 km 2 core area of Przewalski's horse distribution, we set up 441 tick sampling sites across diverse habitats, including water sources, donkey trails, and grasslands, recording horse feces and characteristics to analyze the occurrence rate of ticks. Additionally, we gathered the data of 669 fresh feces of horses. To evaluate the spatial dynamics between these feces and ticks, we used methods such as Fixed Kernel Estimation (FKE), Moran’s I spatial autocorrelation index, and Generalized Linear Models (GLM).

The dominant species of ticks collected in the core area were adult Hyalomma asiaticum (91.36%). Their occurrence rate was higher near donkey trails (65.99%) and water sources (55.81%), particularly in areas with the fresh feces of Przewalski's horses. The ticks’ three risk areas, as defined by FKE, showed significant overlap and positive correlation with the distribution of Przewalski's horses, with respective overlap rates being 90.25% in high risk, 33.79% in medium risk, and 23.09% in low risk areas. Moran's I analysis revealed a clustering trend of the fresh feces of Przewalski's horses in these areas. The GLM confirmed a positive correlation between the distribution of H. asiaticum and the presence of horse fresh feces, alongside a negative correlation with the proximity to water sources and donkey trails.

Conclusions

This study reveals the strong spatial correlation between Przewalski's horses and H. asiaticum in desert grasslands, underlining the need to consider interspecific interactions in wildlife reintroductions. The findings are crucial for shaping effective strategies of wildlife conservation and maintaining ecological balance.

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Desert grassland ecosystems are a critical component of terrestrial ecosystems, harboring unique communities of flora and fauna adapted to extreme arid conditions [ 1 , 2 ]. Nonetheless, the ungulates in these ecosystems, serving as pivotal species for maintaining ecological balance, are confronting a series of challenges including habitat loss and climate change [ 3 , 4 ]. In this context, endangered species reintroduction stands as one of the effective measures for the restoration and preservation of desert grassland ecosystems, as well as for augmenting their biodiversity [ 2 , 5 , 6 ]. The successful reintroduction of the Przewalski’s horse into the Kalamaili Nature Reserve (KNR) in Xinjiang, China, serves as a quintessential example of this practice [ 7 , 8 ]. The KNR is located at the southern edge of the Junggar Basin in northwestern China, characterized by an arid climate and infrequent rainfall, leading to the formation of a unique Central Asian continental desert grassland biota [ 8 , 9 ]. Since 2001, the population of Przewalski's horses in the region has experienced substantial growth, escalating from 27 to 230 by the year 2019 [ 8 , 9 , 10 ]. This resurgence of this population in the KNR provides us with a unique opportunity to explore the responses of related species following the reintroduction of large ungulates into their native ecosystems.

However, as an introduced species, the reintroduction of Przewalski's horses could potentially give rise to some unforeseen ecological consequences, especially regarding the impact on parasite dynamics [ 10 , 11 , 12 ]. In response, a specialized team undertakes annual monitoring of parasitic diseases within the Przewalski’s horses [ 10 , 12 , 13 , 14 ]. It was discovered that the Przewalski's horses in the KNR are heavily infected with the obligate parasite, Gasterophilus spp. [ 10 , 14 ], with infection intensity more than double that found in the sympatric Mongolian wild ass ( E. hemionus ) [ 13 ]. The research team initially aimed to study the transmission patterns of the myiasis disease by tracking and examining the feces of Przewalski's horses, as the larvae of these Gasterophilus spp. tend to burrow into the soil within 5 min after the horse's defecation [ 10 , 11 , 12 , 13 , 14 ]. However, in recent investigations, we inadvertently found that ticks frequently appeared near the feces of the Przewalski's horses, which sparked our interest in studying the spatial distribution relationship between Przewalski's horses’ feces and ticks. Additionally, during the peak season of tick activity in spring and summer [ 15 , 16 ], we incidentally discovered the Hyalomma asiaticum (Acari: Ixodidae) parasitizing on the abdomen of deceased Przewalski's horses (Fig. S1), and also fortuitously encountered naturally detached and engorged adult H. asiaticum near the Przewalski's horse dung piles (Fig. S2). These observations not only corroborate the hypothesis that Przewalski's horses could serve as potential new hosts for H. asiaticum but also provide important clues into the interactions between hosts and parasites in the KNR.

H. asiaticum , a tick prevalent in arid desert regions, poses a direct threat to host animals through biting, leading to conditions such as inflammation and anemia [ 17 , 18 , 19 , 20 ]. It also plays a crucial role in the transmission of various diseases, including Crimean-Congo Hemorrhagic Fever (also known as Xinjiang Hemorrhagic Fever locally) and Rickettsial diseases, among others [ 21 , 22 ]. Being a three-host parasitic tick, H. asiaticum progresses through a life cycle consisting of four stages: egg, larva, nymph, and adult [ 15 ]. Parasitism occurs in the stages after the egg, with immature H. asiaticum ticks predominantly parasitizing small rodents, and adults typically attaching to large ungulates such as horses, cattle, and sheep [ 23 , 24 , 25 , 26 , 27 ]. Ticks generally employ two strategies to seek hosts: the ambush strategy and the hunter strategy. In the ambush strategy, ticks usually position themselves on the top of plants or rocks, waiting for hosts to pass by. Conversely, in the hunter strategy, ticks move through the environment, actively seeking out hosts by detecting carbon dioxide released by the hosts and following trails of the hosts’ excreta [ 28 , 29 ]. H. asiaticum are typical hunter type ticks, as they conceal themselves in the surrounding environment and actively wait to attack passing hosts [ 15 , 28 , 30 ]. Notably, the adult H. asiaticum in the unfed state, while actively seeking host animals, not only demonstrate a strong preference towards large ungulate hosts but also amplify the risk of disease transmission during this process [ 15 , 18 ]. Hence, understanding and monitoring the distribution of ticks at this specific life stage is paramount for the efficacious prevention and management of the pervasive transmission of tick-borne diseases in the KNR.

The water sources in arid and desert regions, along with their adjacent areas, are vital habitats for wildlife [ 31 , 32 ]. In the KNR, the Przewalski's horses exhibit unique seasonal behaviors. In the spring and summer, they frequently travel along designated paths, known as 'donkey trails' [ 11 ], to reach water sources. They tend to congregate around these water sources and the adjacent grasslands, showing a pronounced propensity to cluster more notably than other ungulate species such as the Mongolian wild ass ( E. hemionus ) and the Goose-throated gazelle ( Gazella subgutturosa ) [ 7 , 11 , 33 ]. This behavioral pattern does not only highlight the attraction of water sources for Przewalski’s horses but also presents potential opportunities for the local parasite populations to spread [ 11 , 12 ]. Although there is a notable correlation between the spatial distribution of wildlife and parasites [ 11 , 12 , 29 , 34 , 35 ], studies specifically examined the relationship between the distribution of Przewalski’s horses and parasites are comparatively limited [ 11 , 12 ]. A previous study revealed that areas within a 300 m radius surrounding water sources, nortably high density of Przewalski's horse feces were observed [ 11 ]. Furthermore, there is a positive correlation between the density of Przewalski's horse feces and the spatial distribution of the Gasterophilus spp. [ 11 , 12 ]. This further confirms the significance of water sources and their surrounding areas as principal locales for the interaction between Przewalski's horses and potential parasites. Moreover, a prior report indicated that before the reintroduction of Przewalski's horses into the KNR, large-scale tick infestations were not observed [ 16 ]. However, this historical absence of infestations does not rule out the potential for these horses to experience health challenges when reintroduced, as they may lack natural immunity or the ability to adapt to local parasites [ 11 , 12 , 13 ]. For instance, a decade after elk ( Cervus canadensis ) were reintroduced to southeastern Kentucky, USA, the distribution of local ticks became more widespread [ 36 ]. Similarly, in Japan, as populations of sika deer ( C. nippon ) and wild boar ( Sus scrofa ) increased, the distribution of the Haemaphysalis ticks changed, potentially increasing the transmission of the pathogen causing tick-borne diseases [ 37 ]. These findings highlight the importance of monitoring changes in local ticks distribution following reintroduction of wildlife [ 38 , 39 ]. Therefore, this study focused on the Przewalski's horses and the ticks, conducted systematic surveys of typical habitats for horses in the KNR, including water sources, grasslands, and donkey trails. It incorporates the activity patterns of Przewalski's horses to thoroughly examine the spatial distribution relationship between host and parasite. The study aims to analyze the risk areas of ticks in the core distribution areas of Przewalski's horses. This study will provide a comprehensive assessment of whether reintroduced animals have expanded the distribution range of parasites, thereby offering a new perspective on understanding the synergistic adaptation of reintroduced species with related species in their new environment.

Research area

The KNR (88°30’ ~ 90°03’E, 40°36’ ~ 46°00’N) is located in the Junggar Basin of Xinjiang, China (Fig.  1 ), and is a typical arid and semi-arid desert grassland. The reserve has scarce water resources, with an annual precipitation of only about 159 mm [ 10 , 40 ]. The composition of plant communities is relatively simple, featuring an average coverage of 20 to 30%, primarily consisting of xerophytic shrubs and herbs, such as Ceratoides spp., Tamarix spp., Haloxylon spp., Anabasis spp., and Reaumuria spp. [ 40 ]. Among them, the Hong Liu, Xiao, No.6 are key water sources within the region [ 11 , 12 ]. Przewalski’s horses primarily depend on these water sources for their distribution, whereas other wildlife, including the E. hemionus and the G. subgutturosa , have a broad distribution throughout the entire reserve [ 7 , 12 , 31 , 40 ]. Based on this context, this study was conducted during the season when is the peak of H. asiaticum activity from April to June 2021 [ 20 , 41 ], focusing primarily on the spatial utilization patterns of Przewalski’s horses and their impact on the distribution of ticks. The research was centered around three water sources, designating a 25 km 2 area as the study area, which also constitutes the core habitat of Przewalski’s horses (Fig.  1 ).

Tick sampling sites in Kalamaili Nature Reserve (KNR). Note: The left section delineates the three types of study habitats, namely, donkey trails, water sources, and grasslands; the center displays the distribution map of the sampling sites of ticks; the right section represents the study area

Research method

Ticks sampling sites’ survey.

Tick sampling sites were set up across three types of typical habitats: water sources, donkey trails, and grasslands, and the specific sampling method is shown in Fig. S3. In each habitat, based on the frequency of Przewalski’s horses’ activities, three types of dung piles were randomly selected for tick collection: stallion feces, non-stallion feces and no feces (Fig. S4). Stallion feces are generally higher than the feces of non-stallions because stallions repeatedly defecate in the same location, forming larger mounds. Conversely, non-stallion defecation behavior tends to be more random, usually occurring just once at various locations, resulting in smaller dung piles.

Arid desert regions are mainly covered with small shrubs. In such environments, ticks typically adopt an active waiting and attacking strategy for survival [ 28 , 30 ].

Therefore, to ensure consistency in the sampling process, we limited time at each tick sampling site to 5 min, employing the 'waiting for ticks' method as referenced by Yu et al. [ 16 ]. The specific procedure involved shaking the ground with sticks in areas with Tamarix spp. and Haloxylon spp. to attract and collect non-engorged ticks [ 16 , 28 , 30 ]. Concurrently, each sampling site was centered within a defined area of 2 m × 2 m, thereby maintaining an effective sampling area of 4 m 2 . Using the “Create Buffer” and “Create Fishnet Tool” in ArcGIS 10.3, buffer zones were delineated, extending 100 m from the three water sources perimeters and 10 m along the donkey trails. Additionally, the grassland area was segmented into a grid pattern, each cell measuring 500 m × 500 m. The collected data were stored in KML format and subsequently imported into a GPS device (eTrex309x, Garmin Ltd., Olathe) to precisely locate the sampling sites for efficient field collection. Furthermore, at the sampling sites where dung piles were present, we conducted detailed measurements of each dung pile’s characteristics. The dimensions of the dung piles (length × width × height in cm) were recorded using a tape measure. The moisture content of these dung piles was assessed using a soil moisture meter (PR-ECTH-SC-37DC, Pruisen brand Ltd., China) with an accuracy of ± 2% RH. Based on these readings, moisture levels were classified into two categories: low moisture (0 ~ 15% RH) and high moisture (15 ~ 30%RH). This classification approach is based on previous experience and is consistent with the fecal moisture classification standards found in the literature [ 42 , 43 ].

The classification and identification of the ticks, along with the tallying of their numbers and other pertinent details, were carried out in the laboratory. Using a SZ51 stereomicroscope equipped with LED lighting / SZ2-ILST (Olympus corporation, Tokyo, Japan), we scrutinized the distinguishing features of the ticks, which encompassed the dorsal surface, ventral surface, eye, basis capituli, scutum, porose area, spiracle plate, marginal groove, and genital groove [ 44 , 45 , 46 ].

Przewalski’s horses’ spatial distribution survey

The distribution and density of wildlife feces are reliable indicators of spatial utilization in designated areas [ 40 , 47 , 48 ]. For data collection, we tracked Przewalski’s horses, and recorded the GPS coordinates of fresh feces after the horses had departed from the area. Our survey encompassed 8 herds, comprising a total of 60 reintroduced horses [ 12 ]. The study area included frequently visited water sources and grasslands in the KNR, enveloping the same 20km 2 area also surveyed for ticks [ 12 ].

Data analysis

The occurrence rate of h. asiaticum under different conditions.

Formula ( 1 ), \({P}_{ij}\) represents the occurrence rate of ticks under different conditions, \({N}_{ij}\) is the number of sampling sites with ticks under different conditions, \({M}_{ij}\) is the total number of sampling sites under different conditions. \(i\) is water source, donkey trail, grassland, and \(j\) is stallion-feces, non-stallion-feces and no-feces.

Analysis of H. asiaticum occurrence rate and Przewalski’s Horse Dung Parameters

Given the observed higher occurrence rates of H. asiaticum near the dung piles of Przewalski’s horses, this study investigates the influence of the dung piles’ physical characteristics (size and moisture) on the occurrence rate of the H. asiaticum . Dung pile size parameters include three key dimensions: pile height (cm), bottom area (cm 2 ), and volume (cm 3 ). Concurrently, the moisture content of the dung piles is categorized into two levels: low moisture (0 ~ 15% RH) and high moisture (15 ~ 30%RH). For maintaining consistency and ensuring comparability among variables, the variables were standardized using the Z-score function from the R package scale. The relationship between the number of the H. asiaticum and dung pile size was analyzed through Pearson correlation analysis. A correlation coefficient (r) approaching 1 indicates a stronger correlation. T-tests were used to compare the size differences of dung piles with and without the presence of the H. asiaticum , using p  < 0.05*, p  < 0.01**, and p  < 0.001*** as thresholds to ascertain the levels of statistical significance. By calculating the occurrence rates of the H. asiaticum at different moisture levels, this study visually presents the results using bar graphs. The plotting was conducted utilizing the ‘ggplot2’ package in R.

Risk area analysis of the H. asiaticum

Based on the analysis results from the occurrence rates of the H. asiaticum , this study selected the GPS locations where the H. asiaticum was found. Additionally, by referencing to the GPS locations of Przewalski’s horses fresh dung piles reported in Zhang et al. [ 12 ], a comprehensive assessment of the spatial distribution relationship between the H. asiaticum and the horse fresh feces. These data underwent preprocessing using the R packages ‘sp’, ‘sf’, and ‘tidyverse’, which included data cleaning and standardization to ensure accuracy and consistency. The R package ‘adehabitatHR’ was utilized to calculate the 95% Minimum Convex Polygon (MCP) for the fresh feces of Przewalski’s horses, establishing their distribution within the 25 km 2 study area (Formula 2 ). The definition of risk areas is based on Fixed Kernel Estimation (FKE) to categorize the distribution of ticks into high, medium, and low risk levels. Specifically, 50% FKE is used to define high risk areas, 75% FKE for medium risk areas, and 95% FKE for low risk areas (Formula 3 ). This classification reflects the occurrence rate of H. asiaticum within the study area. The Intersect module in the Arctoolbox of ArcGIS 10.3 software was employed to evaluate the spatial overlap between the distribution of Przewalski’s horses and the three risk areas of the H. asiaticum .

MCP calculation formula:

Formula ( 2 ), S is the area, and \({x}_{i}\) and \({y}_{i}\) are the latitude and longitude.

FKE calculation formula:

Formula ( 3 ), n represents the number of ticks, h is the bandwidth, and \({dist}_{i}\) represents the distance between the point i and the geographical coordinates.

Spatial autocorrelation analysis

Drawing from the findings of the risk area analysis of the H. asiaticum , this study conducted a bivariate spatial autocorrelation analysis of the spatial distribution of horse fresh feces within the three risk areas. We employed the nearest neighbor method to create spatial weight matrices and used these matrices to calculate the Cross Moran’s I index for each area [ 49 , 50 ]. This index aims to evaluate the degree of clustering between the horse fresh feces and the risk distribution areas of the H. asiaticum . Specifically, the Moran’s I value approaching 1 indicates a high level of clustering of horse fresh feces in the area, while values close to 0 suggest a weaker spatial association with horse fresh feces.

Kernel density estimation and multiscale correlation analysis

The spatial density of the Przewalski’s horse fresh feces and the H. asiaticum was calculated using Kernel Density Estimation (KDE) in the Spatial Analyst module of ArcGIS 10.3 (Formula 4 ). In this study, a uniform pixel value of 20 and a search radius of 500 m [ 12 ], were configured to precisely evaluate the correlation between the Przewalski’s horse fresh feces and the H. asiaticum across multiple scales. Five scales, 100 m, 250 m, 500 m, 750 m, and 1000 m (Fig. S5), were selected for conducting Pearson correlation analysis. We used ArcGIS 10.3 to construct grids corresponding to the five scales and to generate a central representative point for each grid. Based on the KDE layers of the H. asiaticum and Przewalski’s horse fresh feces, we extracted the raster attribute values for each point at every scale. The three risk areas of tick distribution were used as a backdrop for intersection processing. Ultimately, the ‘cor’ function in R was applied to perform Pearson correlation analysis between the attribute values of the H. asiaticum and the fresh feces from Przewalski’s horses at each scale within each risk area.

KDE calculation formula:

In formula ( 4 ), R represents the search radius, \({pop}_{i}\) represents the number of ticks or horses’ fresh feces at point i, and \({dist}_{i}\) represents the distance between the point i and the geographical coordinates.

Generalized linear model analysis

Based on the results of multiscale correlation analysis, this study selected the 100 m scale and used the Generalized Linear Model (GLM) to accurately evaluate the impact of Przewalski’s horses and external environmental factors on the H. asiaticum . Using the nearest neighbor analysis tool in ArcGIS 10.3, we conducted nearest neighbor analysis on the three water sources and donkey trails. This analysis facilitated the identification of two crucial parameters: the nearest distance to water sources and the nearest distance to donkey trails. Before constructing the GLM, to ensure the model's accuracy and stability, we conducted multicollinearity diagnostics using the Variance Inflation Factor (VIF). The VIF for each explanatory variable was calculated using the 'car' package in R. If VIF ≥ 5, variables were excluded from the model. By constructing a GLM model with the ‘glm’ function in R, we set the attribute values of the H. asiaticum at the 100 m scale as the response variable, while considering the attribute values of fresh feces of Przewalski’s horses, the nearest distance to water sources, and the nearest distance to donkey trails as explanatory variables.

The ticks sampling sites survey results

During the peak period of ticks, this study encompassed 441sampling sites. Among these, the 219 sampling sites (49.66%) found unfed state adult ticks. A cumulative count of 301 host-seeking ticks was collected, with 275 being H. asiaticum (91.36%),identified across all 219 sites. In addition, 14 Dcrmacentor nuttalli were collected from 4 sampling sites (4.65%), and 12 Rhipicephalus microplus were found across 3 sampling sites (3.99%). The detailed statistics regarding species identification and distribution are presented in Table S1.

The occurrence rate of H. asiaticum

Given that H. asiaticum is the predominant species in this region and is present at all tick sampling sites, this study conducted an in-depth analysis of the spatial distribution of the H. asiaticum . The occurrence rates of the H. asiaticum under different habitats are as follows: donkey trail (65.99%) > water source (55.81%) > grassland (39.04%). Moreover, regions with a high frequency of Przewalski’s horse distribution also exhibit elevated occurrence rates of H. asiaticum , characterized by the following sequence: stallion-feces (70.06%) > non-stallion-feces (51.67%) > no-feces (25.97%). In the following sites, the occurrence rate of H. asiaticum exceeded 50%: stallion feces at water source (77.27%) > stallion feces at donkey trail (72.88%) > non-stallion feces at grassland (53.41%) > stallion feces at grassland (51.85%) > non-stallion feces at donkey trail (50.00%) (Table  1 ).

H. asiaticum occurrence rate and Przewalski’s horse dung parameters

Pearson correlation analysis demonstrated a positive correlation between the size of Przewalski’s horse feces and the number of the H. asiaticum . The correlation coefficients are as follows: fecal height ( r (287)  = 0.401, P  < 0.001), fecal bottom area ( r (287)  = 0.328, P  < 0.001), fecal volume ( r (287)  = 0.369, P  < 0.001). The T-test analysis revealed that the H. asiaticum prefer to conceal themselves near larger piles of Przewalski’s horse feces (Fig.  2 ). Specifically, stallion feces showed significant differences in the height (t (167)  = 3.659, P  < 0.001), bottom area (t (167)  = 2.752, P  < 0.001), and volume (t (167)  = 3.846, P  < 0.001), while non-stallion-feces only showed significant difference in height (t (120)  = 3.000, P  < 0.01).

The T-test analysis of Przewalski's horses fecal size differences in presence and absence of H. asiaticum

The study found that the fresher the Przewalski’s horse feces, the higher the occurrence rate of the H. asiaticum , and the occurrence rates of ticks near stallion feces is generally higher than near non-stallion feces (Fig.  3 ). The occurrence rates of the H. asiaticum was distributed as follows: high humidity of stallion feces (94.29%) > high humidity of non-stallion feces (77.27%) > low humidity of stallion feces (63.64%) > low humidity of non-stallion feces (45.92%).

The correlation between humidity of Przewalski's horse feces and the occurrence rate of H. asiaticum

Three risk area of H. asiaticum

This analysis used the data from 219 sampling sites of ticks and the 669 locations of Przewalski's horse fresh feces. (Fig.  4 a, b). The area covered by fresh feces from Przewalski's horses in the survey region was determined to be 14.31 km 2 , as calculated by the 95% MCP method (Fig.  4 a). We categorized the risk areas for H. asiaticum into high, medium, and low categories based on 50% FKE, 75% FKE, and 95% FKE thresholds. Subsequent analysis revealed a positive correlation between the risk levels of ticks and the overlap rate with the distribution of Przewalski's horse (Table  2 ). Specifically, the high-risk area for H. asiaticum was 8.00 km 2 , of which 90.25% overlapped with the distribution of Przewalski's horses, covering an area of 7.22 km 2 (Fig.  4 b, Table  2 ); the medium-risk area was 7.04 km 2 , with 33.79% overlap rate, overlap area covering 4.80 km 2 (Fig.  4 b, Table  2 ); and the low-risk area was 9.96 km 2 , with 23.09% overlap rate, overlap area covering 2.30 km 2 (Fig.  4 b, Table  2 ).

Distribution of Przewalski’s horse fresh feces and H. asiaticum . a Distribution area of Przewalski’s horse fresh feces. b Three risk areas of H. asiaticum . c Kernel Density Estimation (KDE) distribution of fresh feces from Przewalski’s horses. d KDE distribution of H. asiaticum

Spatial autocorrelation index Moran's I of H. asiaticum and Przewalski's horse fresh feces

In the three designated risk areas, we observed that the distribution of H. asiaticum and horse fresh feces exhibited an extremely high degree of spatial autocorrelation, with Moran's I values approaching 1 (Table  3 ). The Z-score values further substantiated the statistical significance of this aggregation pattern, thereby greatly diminishing the probability of a random distribution ( P  < 0.001). In the high-risk area, Moran's I value was 0.998 (Z = 16.499, P  < 0.001) and similarly, in the medium-risk area, Moran's I was also 0.998 (Z = 8.968, P  < 0.001). This indicates a very strong spatial clustering of H. asiaticum and horse fresh feces in both high and medium-risk areas. In the low-risk area, the Moran's I value was slightly lower at 0.994 (Z = 8.335, P  < 0.001), still indicating a significant spatial aggregation trend of H. asiaticum and horse fresh feces (Table  3 ).

Kernel density estimation and multiscale correlation analysis of H. asiaticum and Przewalski's horse fresh feces

The KDE analysis indicated that the distribution of fresh feces from Przewalski's horses is predominantly concentrated to the north of major water sources, including HongLiu and No. 6 (Fig.  4 c). Similarly, H. asiaticum were also predominantly found in the areas near these water sources (Fig.  4 d). To quantify the correlation between H. asiaticum and horse fresh feces at different scales, this study conducted Pearson correlation analysis at distances at distances of 100 m, 250 m, 500 m, 750 m, and 1000 m (Table  4 ). The specific distribution sites at these five scales are presented in Fig. S5. At the 100 m scale, a positive correlated was observed across all risk areas, with the strength of the correlation diminishing in the order of high, medium, and low. This suggests that the presence of horse fresh feces may be more closely correlation with the distribution of H. asiaticum at smaller spatial scales. However, at larger scales such as 500 m, 750 m, and 1000 m, the variability in correlations indicates that factors other than horse fresh feces could be influencing the distribution of H. asiaticum , particularly noted in the negative correlations observed in medium-risk areas at 500 m and 1000 m scales (Table  4 ).

Comprehensive analysis using generalized linear model

In this study, we used a GLM to unveil the significant correlations between the distribution of H. asiaticum and three pivotal factors: the distribution of the horse fresh feces; nearest distance to water sources; nearest distance to donkey trails (Table  5 ). The multicollinearity analysis results show that the VIF for the horse fresh feces is 1.134, for the nearest distance to water sources is 1.048, and for the nearest distance to donkey trails is 1.143. The VIF values of these three variables are close to 1, indicating almost no linear correlation between them, making them suitable as explanatory variables in the GLM. The intercept of the GLM is 13.536 (T = 44.170, P  < 0.001). Notably, the distribution of fresh feces from Przewalski’s horses showed a significant positive correlation with the distribution of H. asiaticum (T = 27.980, P  < 0.001), suggesting that accumulations of horse feces can attract the ticks. Conversely, the proximity to water sources exhibited a negative correlation with the tick distribution (T = -14.940, P  < 0.001), similar to the proximity to donkey trails (T = -25.630, P  < 0.001), indicating that closer proximity to water sources and donkey trails increases the likelihood of encountering H. asiaticum (Table  5 ).

This study aims to delve into the impact of the reintroduction of Przewalski’s horses on the parasites in the desert grassland ecosystem of the KNR, with a focus on analyzing the influence of their distribution patterns on H. asiaticum . The reintroduction of Przewalski’s horses, an endangered species, not only represents a crucial measure for biodiversity conservation but also significantly affects the structure of the local parasitic community, which has been confirmed by previous studies [ 10 , 11 , 12 ]. Although the primary intent of reintroduction is to protect or restore ecosystems, it may result in unintended consequences [ 38 , 39 , 51 ]. This study is the first to identify a positive correlation between the distribution of Przewalski’s horse feces and the spatial distribution patterns of the H. asiaticum , providing new insights into the mechanisms by which the reintroduction of Przewalski’s horses impacts the parasites in desert grassland ecosystems. This finding is consistent with studies from other regions regarding the relationship between reintroduced species and ticks. For example, in Kentucky, reintroduced elk have turned into hosts for various ticks, potentially expanding their distribution [ 36 ]. Similarly, in Japan, an increase in the population of wildlife has coincided with an increase of ticks [ 37 ]. Therefore, this study points out that even introducing animals for conservation purposes could inadvertently facilitate the spread of ticks and their pathogen carriers [ 36 , 37 ].

The occurrence rates of the H. asiaticum and its relationship with Przewalski’s horse feces constitute the central focus of this study. Previous research has revealed that host feces significantly impact the spatial distribution of parasites [ 12 , 52 , 53 ], evidenced by the parasites’ inclination to gather in the areas proximate to the host’s feces [ 11 , 12 , 15 ]. In light of this context, and considering the tendency of Przewalski’s horses in the KNR to congregate in family units [ 7 , 12 , 54 ], as well as the proactive host-seeking behavior of H. asiaticum [ 15 ], this study posits that zones where Przewalski’s horses frequently defecate are more likely to encounter ticks in an unfed state. This hypothesis was supported by field data indicating that the H. asiaticum was found significantly more frequently in proximity to the Przewalski’s horse feces than in areas without feces. Subsequent analysis elucidated a positive correlation between the size and freshness of the horse feces and the occurrence rates of the H. asiaticum . This phenomenon suggests that Przewalski’s horse feces may provide an important survival environment for the H. asiaticum [ 55 , 56 ]. Significantly, this spatial association is not limited to the H. asiaticum , other studies have also shown that zones of frequent defecation by Przewalski’s horses are considered high-risk for obligate parasites such as horse stomach flies [ 10 , 11 , 12 ]. These findings highlight the necessity of integrating a comprehensive consideration of the multifaceted impacts of reintroducing endangered species on other species within local ecosystems when formulating biodiversity conservation strategies and ecosystem management measures.

In ecological research, animal feces are considered a valuable ecological indicator for discerning their behavioral patterns and spatial distribution [ 47 , 48 ]. Previous studies have confirmed a substantial correlation between the distribution of ungulates and ticks, as exemplified by the findings of Qviller et al. [ 47 ], who discovered a tight spatial association between the distribution of the red deer and the Ixodes ricinis in Norway. Similarly, Schulze et al. [ 57 ] observed a close relationship between the distribution of the white-tailed deer and the Amblyomus ticks in America. However, due to the numerous challenges of field surveys and the limitations in data collection, this study prefers to use fresh feces as an indirect indicator of Przewalski's horses’ activity range. The H. asiaticum , an ectoparasite that actively seeks hosts based on host scent [ 15 , 28 , 30 ], examining the distribution of Przewalski's horse fecal feces can better illustrate the spatial correlation between the host animals and ticks. Furthermore, initial investigations in this study found a relatively high prevalence of ticks near fresh feces of Przewalski's horses, confirming that data on fresh feces from synchronized surveys can serve as an indirect indicator of the spatial association between Przewalski's horses and the H. asiaticum.

Przewalski's horses have a social structure based on family groups, typically consisting of one stallion, three to four mares, and their foals [ 9 , 58 ]. Different genders of Przewalski's horses may display unique activity patterns and territorial marking behaviors, leading to their feces containing different chemical information [ 32 , 43 , 59 ], which could affect their attractiveness to ticks and influence tick distribution. In this study, fecal samples were differentiated by observing that stallions and non-stallions defecate in different locations. Specifically, mares and foals tend to defecate randomly on grasslands, whereas stallions prefer to repeatedly defecate in the central areas of their family group’s territory [ 54 , 58 ]. The results showed a higher probability of ticks near stallion feces than feces of the non-stallions, with the larger feces showing a higher occurrence rate of ticks. This suggests that the spatial distribution of ticks in this region may be related to the volume of Przewalski's horse feces and their gender-specific behaviors.

This finding provides a basis for future research on how gender difference in Przewalski's horses could influence tick distribution. Moreover, the widespread E. hemionus and G. subgutturosa in the reserve exhibit a more dispersed distribution due to their lower dependency on water and higher alertness, leading to the observation of typically drier feces [ 60 , 61 ], which are less suitable for tick spatial distribution studies. According to Zhang et al. [ 12 ] and Huang et al. [ 11 ], fresh feces of these species are rare in our study area, thus their direct impact on the study results is limited. This supports the study's focus on Przewalski's horses, an endangered reintroduced species, and largely excludes the impact of other local wildlife in the study area on the spatial distribution of ticks.

Before the reintroduction of the Przewalski’s horses, the KNR had not shown a high correlation between the distribution of ungulates and ticks [ 16 ]. Research suggests that post-reintroduction, the increased density of host feces from Przewalski's horses may have amplified the spread and reproduction of parasite populations, thereby accelerated the transmission patterns of parasites and potentially altered the ecological chain relationships between local hosts and parasites [ 12 ]. Therefore, this research focuses on analyzing the distribution relationship between the Przewalski's horses and the H. asiaticum , and it quantifies the risk levels of H. asiaticum in the surveyed area. The MCP algorithm was deployed to estimate the distribution area of Przewalski's horses. Although a 100% MCP can include all GPS sites, it may overestimate the area due to extreme points [ 62 ]. Consequently, a 95% MCP was chosen to exclude the impact of extreme points. This method is in line with methods previously employed by researchers to determine the home range of Przewalski's horses [ 7 , 62 ]. The FKE method enhances the MCP by smoothing the distribution area calculation through correcting extreme points [ 12 , 63 ] For this study, 50% FKE, 75% FKE, and 95% FKE were utilized as thresholds to delineate high, medium, and low-risk areas, respectively, in order to quantify the risk areas of the H. asiaticum . Through analysis of the overlap rates, it was observed that areas with the fresh feces form the Przewalski's horses overlap with high-risk areas for the H. asiaticum by more than 90%. This suggests that Przewalski's horses are consistently exposed to tick-infested environments within their activity areas. Moreover, high-risk areas frequently correspond with areas where Przewalski's horses are commonly active and defecate, which aligns with our conclusions drawn from spatial autocorrelation analysis. These methods of analysis approaches allow us to reveal the spatial relationship between the distribution of the H. asiaticum and horse fresh feces, indicating that the tick distribution is not random but closely related to the spatial distribution of horse fresh feces.

The water sources and grasslands are crucial factors for the survival of ungulate species in arid and desert regions [ 31 , 40 ]. The strong reliance of Przewalski’s horses on these water sources often directs their frequent use of donkey trails near these areas, consequently resulting in more severe occurrences of the H. asiaticum in such areas [ 11 , 12 , 31 ]. Grassland areas, being vital habitats for wild ungulates, may exhibit a comparatively lower presence of ticks. This phenomenon could be due to Przewalski’s horses lingering and being active over extended periods in these areas [ 32 ], leading to a higher number of the H. asiaticum successfully attaching to the horses. Therefore, by focusing the analysis on three factors associated with a higher occurrence rate of ticks — the presence of fresh Przewalski's horse feces, proximity to water sources, and closeness to trails used by donkeys — we can more effectively assess the impact of hosts and ticks distribution. To address potential multicollinearity among these variables, VIF values were calculated and found to be below 5, signifying an absence of significant multicollinearity. This indicates that these factors can serve as independent explanatory variables in the model. This study employed a combination of KDE and GLM to comprehensively analyze the spatial association between fresh feces of Przewalski’s horses and the H. asiaticum across various habitats. The KDE method revealed a significant spatial correlation between the H. asiaticum and fresh feces of Przewalski’s horses within a 100 m scale, conforming to the established criteria for categorizing areas into high, medium, and low risk levels [ 47 , 63 , 64 , 65 ]. The GLM analysis further indicated that the distribution of the H. asiaticum is positively correlated with the presence of fresh feces from Przewalski’s horses. Moreover, it revealed a negative correlation with the proximity to water sources and donkey trails. This implies that the higher the abundance of fresh feces from Przewalski’s horses, and the closer the proximity to water sources and donkey trails, the higher the probability of the distribution of the H. asiaticum . This not only intensifies the threat of the H. asiaticum in these regions but also exposes other wildlife species, including the Mongolian wild ass and the goose-throated gazelle, which visit these water sources to drink, to increased risks of parasitic infestations [ 31 , 33 ]. Due to the unique digestive system of Przewalski’s horse, it is highly dependent on water sources during the spring and summer tick peak period [ 10 , 16 , 33 ]. Long-term gathering near water sources may lead to the risk of infestation with the H. asiaticum in these areas, which will affect the success rate of its reintroduction process and aggravate the expansion of tick-borne diseases in the desert steppe ecosystem [ 12 , 31 , 33 ].

The reintroduction of wild animals often results in them feeling unfamiliar with their post-release environment, leading to more limited spatial movement [ 8 , 9 , 51 , 66 ]. As the reintroduction process in the KNR region advances, the increasing activity range of Przewalski’s horses may result in the spread of parasites to wider areas, posing a potential threat to the health of other local species [ 7 , 10 , 12 ]. Furthermore, due to the reintroduced species may lack sufficient resistance to endemic parasites, which could lead to more severe infestations compared to other species [ 51 ]. This study using the reintroduction of Przewalski’s horses in the KNR as a case study, provides insights for scholars and managers. It emphasizes that when Przewalski’s horses establish a stable presence in an area, it invariably affects the local parasitic situation [ 11 , 12 ]. While Przewalski's horses fall victim to parasitic infections, their presence also transforms the area into the high-risk areas for parasites [ 12 ]. The purpose of reintroduction is to protect endangered species or associated species to maintain their population levels. However, concentrating solely on population growth without considering the stability of the entire ecosystem can disturb the local ecology. In extreme cases, this may impact the health and survival activities of wildlife in the entire region [ 51 , 67 , 68 , 69 ]. Therefore, through this study, we suggest that relevant departments, when formulating reintroduction policies, should comprehensively consider and assess the impact of new animal introductions on ecosystems. They should implement measures to mitigate these potential problems, such as setting tick traps in the animals’ active areas to monitor and manage parasite transmission and creating additional water sources to decrease the animals’ density. These measures could indirectly control parasite spread, improve living conditions for the Przewalski's horses, and reduce potential risk of spillover and spillback of zoonotic diseases.

With the reintroduction of the endangered Przewalski's horses to the KNR, there has been a sustained increase in both its population size and the frequency of their activities within its habitat. This developing trend of species reintroduction, aimed at maintaining biodiversity, may potentially disturb the established ecological balance between hosts and parasites in local ecosystems. Additionally, it could inadvertently lead to the introduction of other non-native species or disease vectors, further complicating ecological interactions. This study has found that the spatial utilization characteristics of Przewalski's horses in this region would have a significant impact on the distribution of the H. asiaticum . The horses' marked reliance on particular habitats, like water sources and donkey trails, leads to a heightened density of the H. asiaticum . This not only escalates the risk of parasitic infestation risk for the horses themselves but also presents a potential threat to other wildlife reliant on the same water sources. Additionally, the potential absence of immunity to local parasites in the reintroduced horses could intensify parasitic concerns within the reintroduction areas. Consequently, systematic planning in wildlife conservation and consideration of the complex effects of reintroduced animals on existing ecosystems are vital in maintaining ecological balance and protecting biodiversity. Through comprehensive management and prevention strategies, reintroduction can be ensured as a beneficial approach for biodiversity conservation and ecological restoration, rather than becoming a new threat to ecosystem stability.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its additional information files.

Abbreviations

Kalamaili Nature Reserve

Minimum Convex Polygon

Fixed Kernel Estimation

Kernel Density Estimation

Variance Inflation Factor

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Acknowledgements

We would like to thank the department of the Kalamaili Nature Reserve for the logistical support in our experimental facilities.

This work was supported by the Investigation of natural protected areas and scientific investigation of potential areas of National Parks in Xinjiang (2021xjkk1201), and the Parasite Control Project of the Forestry and Grassland Bureau of Xinjiang (2024-HXFWBH-LK-01).

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Yu Zhang and Jiawei Liu contributed equally to this work.

Authors and Affiliations

School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China

Yu Zhang, Jiawei Liu, Anqi Wang, Liping Yan, Dong Zhang & Kai Li

Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining, China

Xinjiang Fuyun County Kizillike Township Agricultural Development Center, Altay, China

Duishan Sailikebieke

Tongliao Forestry Pest Control Station, Tongliao, China

Zexin Zhang

Tongliao Control and Quarantine Station of Forest Pest, Tongliao, China

Chongqing Academy of Environmental Science, Chongqing, China

Heqing Huang

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YZ and KZ: Responsible for data analysis, writing, and manuscript modification. JWL and AQW: Engaged in data analysis and manuscript writing. DS, ZXZ, TGA, LPY, and DZ: Contributed to data collection and reviewed the manuscript. KL and HQH: Checked and reviewed the manuscript. All authors reviewed the manuscript, and all authors declare that they have no competing interests.

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Correspondence to Kai Li or Heqing Huang .

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The study was performed in strict accordance with the relevant guidelines and regulations regarding animal welfare. All experimental protocols were approved by the Ethic and Animal Welfare Committee, Beijing Forestry University. In addition, we obtained the permissions for conducting research within the Kalamaili Nature Reserve from the reserve's administrative department. These permissions authorized our research team to conduct field studies and investigations related to Przewalski's horse parasites in the reserve, ensuring our adherence to both ethical standards and local conservation policies.

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Supplementary Information

12862_2024_2252_moesm1_esm.pdf.

Additional file 1: Fig. S1 The pictures of the H. asiaticum bites on the abdomen of Przewalski's horses in Kalamaili Nature Reserve (KNR).pdf

12862_2024_2252_MOESM2_ESM.pdf

Additional file 2: Fig. S2 Accidental discovery of naturally detached engorged female H. asiaticum near stallion feces on donkey trails.pdf

Additional file 3: Fig. S3 The Method of tick sampling sites under three habitat types.pdf

12862_2024_2252_moesm4_esm.pdf.

Additional file 4: Fig. S4 Three types of activity traces of Przewalski's horses: stallion feces, non-stallion feces, no feces.pdf

Additional file 5: Fig. S5 Distribution of grids and points at five different scales.pdf

Additional file 6: table s1 the statistics of ticks’ identification and distribution at sampling sites.xlsx, rights and permissions.

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Zhang, Y., Liu, J., Zhang, K. et al. Biological response to Przewalski’s horse reintroduction in native desert grasslands: a case study on the spatial analysis of ticks. BMC Ecol Evo 24 , 61 (2024). https://doi.org/10.1186/s12862-024-02252-z

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• Reintroduction ecology
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• Hyalomma asiaticum
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