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Exploratory Research – Types, Methods and Examples

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Exploratory Research

Definition:

Exploratory research is a type of research design that is used to investigate a research question when the researcher has limited knowledge or understanding of the topic or phenomenon under study.

The primary objective of exploratory research is to gain insights and gather preliminary information that can help the researcher better define the research problem and develop hypotheses or research questions for further investigation.

Exploratory Research Methods

There are several types of exploratory research, including:

Literature Review

This involves conducting a comprehensive review of existing published research, scholarly articles, and other relevant literature on the research topic or problem. It helps to identify the gaps in the existing knowledge and to develop new research questions or hypotheses.

Pilot Study

A pilot study is a small-scale preliminary study that helps the researcher to test research procedures, instruments, and data collection methods. This type of research can be useful in identifying any potential problems or issues with the research design and refining the research procedures for a larger-scale study.

This involves an in-depth analysis of a particular case or situation to gain insights into the underlying causes, processes, and dynamics of the issue under investigation. It can be used to develop a more comprehensive understanding of a complex problem, and to identify potential research questions or hypotheses.

Focus Groups

Focus groups involve a group discussion that is conducted to gather opinions, attitudes, and perceptions from a small group of individuals about a particular topic. This type of research can be useful in exploring the range of opinions and attitudes towards a topic, identifying common themes or patterns, and generating ideas for further research.

Expert Opinion

This involves consulting with experts or professionals in the field to gain their insights, expertise, and opinions on the research topic. This type of research can be useful in identifying the key issues and concerns related to the topic, and in generating ideas for further research.

Observational Research

Observational research involves gathering data by observing people, events, or phenomena in their natural settings to gain insights into behavior and interactions. This type of research can be useful in identifying patterns of behavior and interactions, and in generating hypotheses or research questions for further investigation.

Open-ended Surveys

Open-ended surveys allow respondents to provide detailed and unrestricted responses to questions, providing valuable insights into their attitudes, opinions, and perceptions. This type of research can be useful in identifying common themes or patterns, and in generating ideas for further research.

Data Analysis Methods

Exploratory Research Data Analysis Methods are as follows:

Content Analysis

This method involves analyzing text or other forms of data to identify common themes, patterns, and trends. It can be useful in identifying patterns in the data and developing hypotheses or research questions. For example, if the researcher is analyzing social media posts related to a particular topic, content analysis can help identify the most frequently used words, hashtags, and topics.

Thematic Analysis

This method involves identifying and analyzing patterns or themes in qualitative data such as interviews or focus groups. The researcher identifies recurring themes or patterns in the data and then categorizes them into different themes. This can be helpful in identifying common patterns or themes in the data and developing hypotheses or research questions. For example, a thematic analysis of interviews with healthcare professionals about patient care may identify themes related to communication, patient satisfaction, and quality of care.

Cluster Analysis

This method involves grouping data points into clusters based on their similarities or differences. It can be useful in identifying patterns in large datasets and grouping similar data points together. For example, if the researcher is analyzing customer data to identify different customer segments, cluster analysis can be used to group similar customers together based on their demographic, purchasing behavior, or preferences.

Network Analysis

This method involves analyzing the relationships and connections between data points. It can be useful in identifying patterns in complex datasets with many interrelated variables. For example, if the researcher is analyzing social network data, network analysis can help identify the most influential users and their connections to other users.

Grounded Theory

This method involves developing a theory or explanation based on the data collected during the exploratory research process. The researcher develops a theory or explanation that is grounded in the data, rather than relying on pre-existing theories or assumptions. This can be helpful in developing new theories or explanations that are supported by the data.

Applications of Exploratory Research

Exploratory research has many practical applications across various fields. Here are a few examples:

Marketing Research : In marketing research, exploratory research can be used to identify consumer needs, preferences, and behavior. It can also help businesses understand market trends and identify new market opportunities.
Product Development: In product development, exploratory research can be used to identify customer needs and preferences, as well as potential design flaws or issues. This can help companies improve their product offerings and develop new products that better meet customer needs.
Social Science Research: In social science research, exploratory research can be used to identify new areas of study, as well as develop new theories and hypotheses. It can also be used to identify potential research methods and approaches.
Healthcare Research : In healthcare research, exploratory research can be used to identify new treatments, therapies, and interventions. It can also be used to identify potential risk factors or causes of health problems.
Education Research: In education research, exploratory research can be used to identify new teaching methods and approaches, as well as identify potential areas of study for further research. It can also be used to identify potential barriers to learning or achievement.

Examples of Exploratory Research

Here are some more examples of exploratory research from different fields:

Social Science : A researcher wants to study the experience of being a refugee, but there is limited existing research on this topic. The researcher conducts exploratory research by conducting in-depth interviews with refugees to better understand their experiences, challenges, and needs.
Healthcare : A medical researcher wants to identify potential risk factors for a rare disease but there is limited information available. The researcher conducts exploratory research by reviewing medical records and interviewing patients and their families to identify potential risk factors.
Education : A teacher wants to develop a new teaching method to improve student engagement, but there is limited information on effective teaching methods. The teacher conducts exploratory research by reviewing existing literature and interviewing other teachers to identify potential approaches.
Technology : A software developer wants to develop a new app, but is unsure about the features that users would find most useful. The developer conducts exploratory research by conducting surveys and focus groups to identify user preferences and needs.
Environmental Science : An environmental scientist wants to study the impact of a new industrial plant on the surrounding environment, but there is limited existing research. The scientist conducts exploratory research by collecting and analyzing soil and water samples, and conducting interviews with residents to better understand the impact of the plant on the environment and the community.

How to Conduct Exploratory Research

Here are the general steps to conduct exploratory research:

Define the research problem: Identify the research problem or question that you want to explore. Be clear about the objective and scope of the research.
Review existing literature: Conduct a review of existing literature and research on the topic to identify what is already known and where gaps in knowledge exist.
Determine the research design : Decide on the appropriate research design, which will depend on the nature of the research problem and the available resources. Common exploratory research designs include case studies, focus groups, interviews, and surveys.
Collect data: Collect data using the chosen research design. This may involve conducting interviews, surveys, or observations, or collecting data from existing sources such as archives or databases.
Analyze data: Analyze the data collected using appropriate qualitative or quantitative techniques. This may include coding and categorizing qualitative data, or running descriptive statistics on quantitative data.
I nterpret and report findings: Interpret the findings of the analysis and report them in a way that is clear and understandable. The report should summarize the findings, discuss their implications, and make recommendations for further research or action.
Iterate : If necessary, refine the research question and repeat the process of data collection and analysis to further explore the topic.

When to use Exploratory Research

Exploratory research is appropriate in situations where there is limited existing knowledge or understanding of a topic, and where the goal is to generate insights and ideas that can guide further research. Here are some specific situations where exploratory research may be particularly useful:

New product development: When developing a new product, exploratory research can be used to identify consumer needs and preferences, as well as potential design flaws or issues.
Emerging technologies: When exploring emerging technologies, exploratory research can be used to identify potential uses and applications, as well as potential challenges or limitations.
Developing research hypotheses: When developing research hypotheses, exploratory research can be used to identify potential relationships or patterns that can be further explored through more rigorous research methods.
Understanding complex phenomena: When trying to understand complex phenomena, such as human behavior or societal trends, exploratory research can be used to identify underlying patterns or factors that may be influencing the phenomenon.
Developing research methods : When developing new research methods, exploratory research can be used to identify potential issues or limitations with existing methods, and to develop new methods that better capture the phenomena of interest.

Purpose of Exploratory Research

The purpose of exploratory research is to gain insights and understanding of a research problem or question where there is limited existing knowledge or understanding. The objective is to explore and generate ideas that can guide further research, rather than to test specific hypotheses or make definitive conclusions.

Exploratory research can be used to:

Identify new research questions: Exploratory research can help to identify new research questions and areas of inquiry, by providing initial insights and understanding of a topic.
Develop hypotheses: Exploratory research can help to develop hypotheses and testable propositions that can be further explored through more rigorous research methods.
Identify patterns and trends : Exploratory research can help to identify patterns and trends in data, which can be used to guide further research or decision-making.
Understand complex phenomena: Exploratory research can help to provide a deeper understanding of complex phenomena, such as human behavior or societal trends, by identifying underlying patterns or factors that may be influencing the phenomena.
Generate ideas: Exploratory research can help to generate new ideas and insights that can be used to guide further research, innovation, or decision-making.

Characteristics of Exploratory Research

The following are the main characteristics of exploratory research:

Flexible and open-ended : Exploratory research is characterized by its flexible and open-ended nature, which allows researchers to explore a wide range of ideas and perspectives without being constrained by specific research questions or hypotheses.
Qualitative in nature : Exploratory research typically relies on qualitative methods, such as in-depth interviews, focus groups, or observation, to gather rich and detailed data on the research problem.
Limited scope: Exploratory research is generally limited in scope, focusing on a specific research problem or question, rather than attempting to provide a comprehensive analysis of a broader phenomenon.
Preliminary in nature : Exploratory research is preliminary in nature, providing initial insights and understanding of a research problem, rather than testing specific hypotheses or making definitive conclusions.
I terative process : Exploratory research is often an iterative process, where the research design and methods may be refined and adjusted as new insights and understanding are gained.
I nductive approach : Exploratory research typically takes an inductive approach to data analysis, seeking to identify patterns and relationships in the data that can guide further research or hypothesis development.

Advantages of Exploratory Research

The following are some advantages of exploratory research:

Provides initial insights: Exploratory research is useful for providing initial insights and understanding of a research problem or question where there is limited existing knowledge or understanding. It can help to identify patterns, relationships, and potential hypotheses that can guide further research.
Flexible and adaptable : Exploratory research is flexible and adaptable, allowing researchers to adjust their methods and approach as they gain new insights and understanding of the research problem.
Qualitative methods : Exploratory research typically relies on qualitative methods, such as in-depth interviews, focus groups, and observation, which can provide rich and detailed data that is useful for gaining insights into complex phenomena.
Cost-effective : Exploratory research is often less costly than other research methods, such as large-scale surveys or experiments. It is typically conducted on a smaller scale, using fewer resources and participants.
Useful for hypothesis generation : Exploratory research can be useful for generating hypotheses and testable propositions that can be further explored through more rigorous research methods.
Provides a foundation for further research: Exploratory research can provide a foundation for further research by identifying potential research questions and areas of inquiry, as well as providing initial insights and understanding of the research problem.

Limitations of Exploratory Research

The following are some limitations of exploratory research:

Limited generalizability: Exploratory research is typically conducted on a small scale and uses non-random sampling techniques, which limits the generalizability of the findings to a broader population.
Subjective nature: Exploratory research relies on qualitative methods and is therefore subject to researcher bias and interpretation. The findings may be influenced by the researcher’s own perceptions, beliefs, and assumptions.
Lack of rigor: Exploratory research is often less rigorous than other research methods, such as experimental research, which can limit the validity and reliability of the findings.
Limited ability to test hypotheses: Exploratory research is not designed to test specific hypotheses, but rather to generate initial insights and understanding of a research problem. It may not be suitable for testing well-defined research questions or hypotheses.
Time-consuming : Exploratory research can be time-consuming and resource-intensive, particularly if the researcher needs to gather data from multiple sources or conduct multiple rounds of data collection.
Difficulty in interpretation: The open-ended nature of exploratory research can make it difficult to interpret the findings, particularly if the researcher is unable to identify clear patterns or relationships in the data.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Exploratory Research: Types & Characteristics

Consider a scenario where a juice bar owner feels that increasing the variety of juices will enable an increase in customers. However, he is not sure and needs more information. The owner intends to conduct exploratory research to find out; hence, he decides to do exploratory research to find out if expanding their juices selection will enable him to get more customers or if there is a better idea.

Another example of exploratory research is a podcast survey template that can be used to collect feedback about the podcast consumption metrics both from existing listeners as well as other podcast listeners that are currently not subscribed to this channel. This helps the author of the podcast create curated content that will gain a larger audience. Let’s explore this topic.

LEARN ABOUT: Research Process Steps

Content Index

Exploratory research: Definition

Primary research methods, secondary research methods, exploratory research: steps to conduct a research, characteristics of exploratory research, advantages of exploratory research, disadvantages of exploratory research, importance of exploratory research.

Exploratory research is defined as a research used to investigate a problem which is not clearly defined. It is conducted to have a better understanding of the existing research problem , but will not provide conclusive results. For such a research, a researcher starts with a general idea and uses this research as a medium to identify issues, that can be the focus for future research. An important aspect here is that the researcher should be willing to change his/her direction subject to the revelation of new data or insight. Such a research is usually carried out when the problem is at a preliminary stage. It is often referred to as grounded theory approach or interpretive research as it used to answer questions like what, why and how.

Types and methodologies of Exploratory research

While it may sound difficult to research something that has very little information about it, there are several methods which can help a researcher figure out the best research design, data collection methods and choice of subjects. There are two ways in which research can be conducted namely primary and secondary.. Under these two types, there are multiple methods which can used by a researcher. The data gathered from these research can be qualitative or quantitative . Some of the most widely used research designs include the following:

LEARN ABOUT: Best Data Collection Tools

Primary research is information gathered directly from the subject. It can be through a group of people or even an individual. Such a research can be carried out directly by the researcher himself or can employ a third party to conduct it on their behalf. Primary research is specifically carried out to explore a certain problem which requires an in-depth study.

Surveys/polls : Surveys /polls are used to gather information from a predefined group of respondents. It is one of the most important quantitative method. Various types of surveys or polls can be used to explore opinions, trends, etc. With the advancement in technology, surveys can now be sent online and can be very easy to access. For instance, use of a survey app through tablets, laptops or even mobile phones. This information is also available to the researcher in real time as well. Nowadays, most organizations offer short length surveys and rewards to respondents, in order to achieve higher response rates.

LEARN ABOUT: Live polls for Classroom Experience

For example: A survey is sent to a given set of audience to understand their opinions about the size of mobile phones when they purchase one. Based on such information organization can dig deeper into the topic and make business related decision.

Interviews: While you may get a lot of information from public sources, but sometimes an in person interview can give in-depth information on the subject being studied. Such a research is a qualitative research method . An interview with a subject matter expert can give you meaningful insights that a generalized public source won’t be able to provide. Interviews are carried out in person or on telephone which have open-ended questions to get meaningful information about the topic.

For example: An interview with an employee can give you more insights to find out the degree of job satisfaction, or an interview with a subject matter expert of quantum theory can give you in-depth information on that topic.

Focus groups: Focus group is yet another widely used method in exploratory research. In such a method a group of people is chosen and are allowed to express their insights on the topic that is being studied. Although, it is important to make sure that while choosing the individuals in a focus group they should have a common background and have comparable experiences.

For example: A focus group helps a research identify the opinions of consumers if they were to buy a phone. Such a research can help the researcher understand what the consumer value while buying a phone. It may be screen size, brand value or even the dimensions. Based on which the organization can understand what are consumer buying attitudes, consumer opinions, etc.

Observations: Observational research can be qualitative observation or quantitative observation . Such a research is done to observe a person and draw the finding from their reaction to certain parameters. In such a research, there is no direct interaction with the subject.

For example: An FMCG company wants to know how it’s consumer react to the new shape of their product. The researcher observes the customers first reaction and collects the data, which is then used to draw inferences from the collective information.

LEARN ABOUT: Causal Research

Secondary research is gathering information from previously published primary research. In such a research you gather information from sources likes case studies, magazines, newspapers, books, etc.

Online research: In today’s world, this is one of the fastest way to gather information on any topic. A lot of data is readily available on the internet and the researcher can download it whenever he needs it. An important aspect to be noted for such a research is the genuineness and authenticity of the source websites that the researcher is gathering the information from.

For example: A researcher needs to find out what is the percentage of people that prefer a specific brand phone. The researcher just enters the information he needs in a search engine and gets multiple links with related information and statistics.

Literature research : Literature research is one of the most inexpensive method used for discovering a hypothesis. There is tremendous amount of information available in libraries, online sources, or even commercial databases. Sources can include newspapers, magazines, books from library, documents from government agencies, specific topic related articles, literature, Annual reports, published statistics from research organizations and so on.

However, a few things have to be kept in mind while researching from these sources. Government agencies have authentic information but sometimes may come with a nominal cost. Also, research from educational institutions is generally overlooked, but in fact educational institutions carry out more number of research than any other entities.

Furthermore, commercial sources provide information on major topics like political agendas, demographics, financial information, market trends and information, etc.

For example: A company has low sales. It can be easily explored from available statistics and market literature if the problem is market related or organization related or if the topic being studied is regarding financial situation of the country, then research data can be accessed through government documents or commercial sources.

Case study research: Case study research can help a researcher with finding more information through carefully analyzing existing cases which have gone through a similar problem. Such exploratory data analysis are very important and critical especially in today’s business world. The researcher just needs to make sure he analyses the case carefully in regards to all the variables present in the previous case against his own case. It is very commonly used by business organizations or social sciences sector or even in the health sector.

LEARN ABOUT: Level of Analysis

For example: A particular orthopedic surgeon has the highest success rate for performing knee surgeries. A lot of other hospitals or doctors have taken up this case to understand and benchmark the method in which this surgeon does the procedure to increase their success rate.

Identify the problem : A researcher identifies the subject of research and the problem is addressed by carrying out multiple methods to answer the questions.
Create the hypothesis : When the researcher has found out that there are no prior studies and the problem is not precisely resolved, the researcher will create a hypothesis based on the questions obtained while identifying the problem.
Further research : Once the data has been obtained, the researcher will continue his study through descriptive investigation. Qualitative methods are used to further study the subject in detail and find out if the information is true or not.

LEARN ABOUT: Descriptive Analysis

They are not structured studies
It is usually low cost, interactive and open ended.
It will enable a researcher answer questions like what is the problem? What is the purpose of the study? And what topics could be studied?
To carry out exploratory research, generally there is no prior research done or the existing ones do not answer the problem precisely enough.
It is a time consuming research and it needs patience and has risks associated with it.
The researcher will have to go through all the information available for the particular study he is doing.
There are no set of rules to carry out the research per se, as they are flexible, broad and scattered.
The research needs to have importance or value. If the problem is not important in the industry the research carried out is ineffective.
The research should also have a few theories which can support its findings as that will make it easier for the researcher to assess it and move ahead in his study
Such a research usually produces qualitative data , however in certain cases quantitative data can be generalized for a larger sample through use of surveys and experiments.

LEARN ABOUT: Action Research

The researcher has a lot of flexibility and can adapt to changes as the research progresses.
It is usually low cost.
It helps lay the foundation of a research, which can lead to further research.
It enables the researcher understand at an early stage, if the topic is worth investing the time and resources and if it is worth pursuing.
It can assist other researchers to find out possible causes for the problem, which can be further studied in detail to find out, which of them is the most likely cause for the problem.
Even though it can point you in the right direction towards what is the answer, it is usually inconclusive.
The main disadvantage of exploratory research is that they provide qualitative data. Interpretation of such information can be judgmental and biased.
Most of the times, exploratory research involves a smaller sample , hence the results cannot be accurately interpreted for a generalized population.
Many a times, if the data is being collected through secondary research, then there is a chance of that data being old and is not updated.

LEARN ABOUT: Projective Techniques & Conformity Bias

Exploratory research is carried out when a topic needs to be understood in depth, especially if it hasn’t been done before. The goal of such a research is to explore the problem and around it and not actually derive a conclusion from it. Such kind of research will enable a researcher to set a strong foundation for exploring his ideas, choosing the right research design and finding variables that actually are important for the in-depth analysis . Most importantly, such a research can help organizations or researchers save up a lot of time and resources, as it will enable the researcher to know if it worth pursuing.

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Exploratory Research

Exploratory research, as the name implies, intends merely to explore the research questions and does not intend to offer final and conclusive solutions to existing problems. This type of research is usually conducted to study a problem that has not been clearly defined yet. Conducted in order to determine the nature of the problem, exploratory research is not intended to provide conclusive evidence, but helps us to have a better understanding of the problem.

When conducting exploratory research, the researcher ought to be willing to change his/her direction as a result of revelation of new data and new insights. [1] Accordingly, exploratory studies are often conducted using interpretive research methods and they answer to questions such as what, why and how.

Exploratory research design does not aim to provide the final and conclusive answers to the research questions, but merely explores the research topic with varying levels of depth. It has been noted that “exploratory research is the initial research, which forms the basis of more conclusive research. It can even help in determining the research design, sampling methodology and data collection method” [2] . Exploratory research “tends to tackle new problems on which little or no previous research has been done” [3] .

Unstructured interviews are the most popular primary data collection method with exploratory studies. Additionally, surveys , focus groups and observation methods can be used to collect primary data for this type of studies.

Examples of Exploratory Research Design

The following are some examples for studies with exploratory research design in business studies:

A study into the role of social networking sites as an effective marketing communication channel
An investigation into the ways of improvement of quality of customer services within hospitality sector in London
An assessment of the role of corporate social responsibility on consumer behaviour in pharmaceutical industry in the USA

Differences between Exploratory and Conclusive Research

The difference between exploratory and conclusive research is drawn by Sandhursen (2000) [4] in a way that exploratory studies result in a range of causes and alternative options for a solution of a specific problem, whereas, conclusive studies identify the final information that is the only solution to an existing research problem.

In other words, exploratory research design simply explores the research questions, leaving room for further researches, whereas conclusive research design is aimed to provide final findings for the research.

Moreover, it has been stated that “an exploratory study may not have as rigorous as methodology as it is used in conclusive studies, and sample sizes may be smaller. But it helps to do the exploratory study as methodically as possible, if it is going to be used for major decisions about the way we are going to conduct our next study” [5] (Nargundkar, 2003, p.41).

Exploratory studies usually create scope for future research and the future research may have a conclusive design. For example, ‘a study into the implications of COVID-19 pandemic into the global economy’ is an exploratory research. COVID-19 pandemic is a recent phenomenon and the study can generate an initial knowledge about economic implications of the phenomenon.

A follow-up study, building on the findings of this research ‘a study into the effects of COVID-19 pandemic on tourism revenues in Morocco’ is a causal conclusive research. The second research can produce research findings that can be of a practical use for decision making.

Advantages of Exploratory Research

Lower costs of conducting the study
Flexibility and adaptability to change
Exploratory research is effective in laying the groundwork that will lead to future studies.
Exploratory studies can potentially save time by determining at the earlier stages the types of research that are worth pursuing

Disadvantages of Exploratory Research

Inclusive nature of research findings
Exploratory studies generate qualitative information and interpretation of such type of information is subject to bias
These types of studies usually make use of a modest number of samples that may not adequately represent the target population. Accordingly, findings of exploratory research cannot be generalized to a wider population.
Findings of such type of studies are not usually useful in decision making in a practical level.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research designs. The e-book also explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy , research approach , methods of data collection , data analysis and sampling are explained in this e-book in simple words.

John Dudovskiy

[1] Source: Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th edition, Pearson Education Limited

[2] Singh, K. (2007) “Quantitative Social Research Methods” SAGE Publications, p.64

[3] Brown, R.B. (2006) “Doing Your Dissertation in Business and Management: The Reality of Research and Writing” Sage Publications, p.43

[4] Sandhusen, R.L. (2000) “Marketing” Barrons

[5] Nargundkar, R. (2008) “Marketing Research: Text and Cases” 3 rd edition, p.38

Exploratory Research: Definition, Types, Examples

Appinio Research · 12.10.2023 · 28min read

Are you ready to unlock the power of exploration in research? In this guide, we'll navigate the fascinating realm of exploratory research, demystifying its techniques and shedding light on its real-world applications.

Whether you're a seasoned researcher seeking to broaden your methodological toolkit or a novice embarking on your first research endeavor, this guide will equip you with the knowledge and insights to harness the full potential of exploratory research. Join us as we dive deep into the intricacies of understanding, planning, conducting, and reporting exploratory research, with real-life examples illuminating the way.

What is Exploratory Research?

Exploratory Research is an investigative method used in the early stages of a research project to delve into a topic when little to no existing knowledge or information is available. It is a dynamic and flexible approach aimed at gaining insights, uncovering trends, and generating initial hypotheses. The primary purposes of exploratory research are:

Understanding Complexity: Exploratory research helps researchers understand the intricate and multifaceted nature of a research topic, especially when the subject matter is not well-defined.
Idea Generation: It serves as a fertile ground for generating new ideas, hypotheses, and research questions that can guide more focused studies in the future.
Problem Identification: It helps identify research problems or gaps in existing knowledge, allowing researchers to refine their research objectives.
Decision Support: Exploratory research provides valuable information for making informed decisions about the direction and scope of a research project.

Importance of Exploratory Research

Exploratory research holds immense significance in the world of research and problem-solving for several reasons:

Risk Reduction: By exploring a topic before committing to a specific research path, exploratory research helps reduce the risk of pursuing unproductive or irrelevant research.
Informed Research: It lays the groundwork for subsequent phases of research, ensuring that subsequent studies are well-informed and more likely to yield meaningful results.
Creative Exploration: It encourages creative and open-minded exploration of topics, making it particularly useful when dealing with novel or emerging issues.
Adaptability: Exploratory research methods are adaptable and can be tailored to the unique characteristics of a research question or problem.

Types of Exploratory Research

Exploratory research encompasses various methodologies, each designed to suit specific research objectives and contexts. Let's explore these types in more detail:

Literature Review

Literature Review involves a systematic examination of existing research, publications, and sources related to a specific topic. It serves as a comprehensive exploration of the current state of knowledge.

Purpose: To identify existing theories, concepts, and gaps in the literature related to a research topic.
Methods: Researchers review academic papers, books, articles, and other scholarly sources. They synthesize and analyze the findings and theories presented in these sources.
Benefits: A literature review provides a solid foundation for understanding the historical context and key debates surrounding a topic. It helps researchers identify areas where further investigation is needed.

Pilot Studies

Pilot Studies are small-scale research projects conducted before a full-scale study. They serve as test runs to assess the feasibility of research methods and data collection tools.

Purpose: To test research procedures, instruments, and methodologies in a controlled environment.
Methods: Researchers select a smaller sample and conduct data collection and analysis as if it were a full study.
Benefits: Pilot studies help identify potential problems, refine research designs, and improve the quality of data collection.

Case Studies

Case Studies involve an in-depth examination of a specific individual, group, organization, or event. They offer a holistic view of a particular phenomenon.

Purpose: To explore real-life contexts and understand complex, unique situations.
Methods: Researchers gather data through interviews, observations, and document analysis, providing rich, contextual insights.
Benefits: Case studies provide a deep understanding of specific instances, allowing researchers to extract valuable lessons or generate hypotheses for broader research.

Focus Groups

Focus Groups bring together a small group of participants to engage in open and structured discussions about a particular topic.

Purpose: To explore group dynamics, collective opinions, and shared perceptions on a specific subject.
Methods: Researchers facilitate group discussions with carefully designed questions, encouraging participants to express their thoughts and experiences.
Benefits: Focus groups reveal diverse perspectives, uncover latent issues, and provide qualitative data for further investigation.

In-depth Interviews

In-depth Interviews involve one-on-one conversations between a researcher and a participant, allowing for detailed exploration of experiences, opinions, and perceptions.

Purpose: To gain in-depth insights into individual perspectives and experiences.
Methods: Researchers use open-ended questions to guide interviews, creating a conversational and exploratory atmosphere.
Benefits: In-depth interviews provide rich, nuanced data and are well-suited for studying sensitive topics or personal experiences.

Observational Research

Observational Research entails the systematic observation and recording of behaviors, events, or phenomena in their natural settings.

Purpose: To understand behavior or phenomena as they naturally occur in their real-world context.
Methods: Researchers select settings, define variables, and record data through direct observations.
Benefits: Observational research captures authentic behavior and context, offering insights that might be missed in controlled environments.

Content Analysis

Content Analysis is a method for analyzing textual, visual, or audio content to uncover patterns, themes, or trends.

Purpose: To explore and understand the content and communication surrounding a particular topic or media.
Methods: Researchers define coding categories, code content based on these categories, and analyze the frequency and patterns of codes.
Benefits: Content analysis provides quantitative and qualitative insights into the content of documents, media, or communication channels.

These various types of exploratory research methods offer researchers a versatile toolkit for diving into the unknown and gaining valuable insights, setting the stage for further investigation and discovery.

How to Plan and Design Exploratory Research?

In the planning and design phase of exploratory research, careful consideration of key elements is crucial to ensure the research objectives are met effectively. Let's delve into these elements:

1. Research Objectives

Before embarking on exploratory research, it's essential to define clear and specific research objectives.

Purpose: Research objectives should clarify what you aim to achieve through your exploratory study. Are you looking to understand a phenomenon, generate hypotheses, identify research gaps, or explore new concepts?
Specificity: Objectives should be well-defined, leaving no room for ambiguity. They should guide your research process and serve as a benchmark for success.
Alignment: Ensure that your research objectives align with the broader goals of your research project and contribute to the generation of valuable insights.

2. Data Collection Methods

Selecting appropriate data collection methods is a critical step in planning exploratory research.

The choice of methods should align with your research objectives.

Method Suitability: Consider the nature of your research question. Qualitative methods like interviews and focus groups are ideal for exploring subjective experiences, while quantitative methods may be more suitable for gathering numerical data.
Data Sources: Identify the sources of data you will tap into, whether it's primary data (collected directly) or secondary data (existing sources).
Data Collection Tools: Determine the specific tools and instruments you will use for data collection. This may include interview guides, questionnaires, or observation protocols.

3. Sampling Techniques

Choosing the proper sampling techniques is crucial to ensure that your exploratory research represents the target population or context effectively.

Purposeful Sampling: When using qualitative methods like interviews and focus groups, purposeful or selective sampling helps identify participants who can provide valuable insights based on specific criteria, such as expertise or experience.
Random Sampling: If your exploratory research involves quantitative data collection, consider random sampling methods to ensure that your sample is representative of the larger population.
Snowball Sampling: In cases where it's challenging to identify participants through traditional methods, snowball sampling allows initial participants to refer others, creating a chain of referrals.

4. Data Analysis Approaches

Determining the data analysis approaches is essential for making sense of the information collected during exploratory research.

Qualitative Data Analysis: For qualitative data, approaches like thematic analysis , content analysis, or narrative analysis help identify patterns, themes, and trends within the data.
Quantitative Data Analysis: If you have quantitative data, statistical analysis and data visualization techniques can reveal trends, correlations, and significant findings.
Mixed-Methods Analysis: In cases where both qualitative and quantitative data are collected, a mixed-methods analysis approach can provide a more comprehensive understanding.

5. Ethical Considerations

Ethical considerations are paramount in exploratory research, as they ensure the well-being and rights of participants are protected.

Informed Consent: Obtain informed consent from participants, explaining the purpose of the research, their role, and the potential risks and benefits. Consent forms should be clear and voluntary.
Privacy and Anonymity: Protect the confidentiality and anonymity of participants by avoiding disclosing personal or sensitive information without explicit consent.
Data Security: Safeguard research data to prevent unauthorized access or breaches of confidentiality.
Conflict of Interest: Disclose any conflicts of interest or potential biases that may affect the research process or findings.
Compliance: Adhere to ethical guidelines and regulations established by relevant institutions or governing bodies, such as institutional review boards (IRBs).

With a well-planned approach that includes clearly defined research objectives, appropriate data collection methods, thoughtful sampling techniques, robust data analysis approaches, and ethical considerations, you can set the stage for a successful exploratory research endeavor.

How to Conduct Exploratory Research?

In this section, we will delve into the practical aspects of conducting exploratory research, which involves data collection and analysis. These steps are vital to uncover insights and generate hypotheses. Let's explore each component in detail:

Data Collection

Effective data collection is the cornerstone of exploratory research. Here are various methods you can use to collect data:

1. Literature Review Process

Literature review is the process of systematically searching, reviewing, and summarizing existing academic literature related to your research topic. This step is crucial as it provides a foundation for understanding the current state of knowledge and identifying research gaps:

Identify Relevant Sources: Begin by searching for relevant academic papers, books, articles, and reports. Online databases like PubMed, Google Scholar, and academic library catalogs are excellent resources.
Synthesize Information: Summarize the essential findings and ideas from the sources you've collected. Create a literature review matrix or summary to organize your results and identify common themes.
Identify Research Gaps: As you review the literature, pay attention to areas where there's a lack of research or conflicting findings. These gaps can inform your exploratory research objectives.

2. Conducting Pilot Studies

Pilot studies are small-scale research projects designed to test and refine your research methods and instruments. They provide valuable insights and help identify potential issues before embarking on a full-scale study.

Define Objectives: Clearly define the objectives of your pilot study. What specific aspects of your research design are you testing? What do you hope to learn from the pilot?
Select Sample: Choose a small, representative sample for your pilot study. This sample should reflect your target population as closely as possible.
Collect Data: Implement your research methods on the selected sample. Pay close attention to any challenges or issues that arise during data collection.
Analyze Results: After collecting data, analyze the results. Look for any anomalies or unexpected findings that may require adjustments to your research design.

3. Running Case Studies

Case studies involve in-depth investigations into specific individuals, groups, organizations, or events. They provide rich, contextual data.

Select a Case: Choose a relevant case that aligns with your research objectives. Consider cases that offer unique insights or perspectives on your topic.
Gather Data: Collect data through a combination of interviews, observations, and document analysis. Triangulate your data sources for a comprehensive view.
Analyze Data: Analyze the collected data to identify patterns, themes, and insights. Use coding or thematic analysis to categorize information.

4. Organizing Focus Groups

Focus groups bring together a small group of participants to engage in open and structured discussions about a particular topic.

Recruit Participants: Recruit a diverse group of participants who can provide valuable insights into your research questions. Ensure that the group dynamics are conducive to open discussion.
Design Questions: Prepare a set of open-ended questions that guide the discussion. Encourage participants to share their perspectives and experiences.
Conduct the Session: Facilitate the focus group session, making sure everyone has an opportunity to speak. Take detailed notes and consider using audio or video recording.
Analyze Findings: Transcribe and analyze the focus group discussions. Look for common themes, opinions, and areas of agreement or disagreement among participants.

5. Performing In-depth Interviews

In-depth interviews involve one-on-one conversations between a researcher and a participant, allowing for detailed exploration of experiences, opinions, and perceptions.

Prepare Interview Guide: Develop a structured interview guide with open-ended questions that align with your research objectives. The guide provides a framework for the interview.
Select Participants: Choose participants who can offer in-depth insights into your research questions. Establish rapport and build trust during the interviews.
Conduct Interviews: Conduct one-on-one interviews, following the interview guide but allowing for flexibility to explore unexpected topics. Encourage participants to share their thoughts and experiences.
Transcribe and Analyze: Transcribe the interviews and analyze the responses. Look for common themes, patterns, and noteworthy quotes that support your research objectives.

6. Observational Research Techniques

Observational research involves the systematic observation and recording of behaviors, events, or phenomena in their natural settings.

Select the Setting: Choose a setting that allows for unobtrusive observation of the behavior or phenomena you're studying. Ensure that your presence does not influence the behavior.
Define Variables: Clearly define the behaviors or phenomena you're observing. Create an observation checklist or coding scheme to record data systematically.
Record Data: Systematically record your observations, either in real-time or through video/audio recordings. Be objective and avoid making interpretations during the observation.
Analyze Data: After data collection, analyze the recorded observations to identify patterns, trends, and any noteworthy behaviors. Consider interrater reliability if multiple observers are involved.

7. Content Analysis Methods

Content analysis is a method for systematically analyzing textual, visual, or audio content to uncover patterns, themes, or trends.

Define Coding Categories: Determine the coding categories or themes that align with your research objectives. Create a coding scheme that can be applied consistently.
Code Content: Apply the coding scheme to the content you're analyzing. This may involve categorizing text passages, images, or audio segments based on predefined criteria.
Record and Analyze Data: Record the coded data systematically and analyze it to identify patterns, trends, or recurring themes. Consider using software tools to assist in content analysis.

Data Analysis

After collecting data through the various methods, it's essential to analyze it effectively to extract meaningful insights:

1. Qualitative Data Analysis

Qualitative data analysis involves the examination of non-numeric data, such as text, interviews, and observations.

Data Coding: Begin by coding the qualitative data, which involves categorizing information into themes or codes. This step helps organize the data for analysis.
Thematic Analysis: Conduct thematic analysis to identify recurring themes, patterns, and trends within the data. Look for connections and relationships between themes.
Constant Comparison: Use constant comparison, where you compare new data with existing codes and themes to refine your understanding of the data.
Interpretation: Interpret the qualitative data in the context of your research objectives. Explore the implications of your findings and consider how they contribute to your research goals.

2. Quantitative Data Analysis

Quantitative data analysis involves the examination of numerical data gathered through surveys, experiments, or other structured methods.

Data Cleaning: Begin by cleaning the data and addressing any missing values, outliers, or inconsistencies. Ensure that the data is in a usable format for analysis.
Descriptive Analysis : Perform descriptive analysis to summarize the main characteristics of the data. This includes calculating measures like mean, median, and standard deviation.
Inferential Analysis: If applicable, conduct inferential analysis to test hypotheses or determine relationships between variables. Common statistical tests include t-tests, ANOVA, and regression analysis.
Data Visualization: Create visual representations of your quantitative data using charts, graphs, and tables to illustrate key findings.

3. Identifying Patterns and Themes

Across both qualitative and quantitative data analysis, the process of identifying patterns and themes is essential.

Pattern Recognition: Look for recurring patterns, trends, or regularities in the data. These patterns may be related to your research objectives or unexpected discoveries.
Theme Identification: In qualitative data analysis, identify themes or categories that emerge from the data. Themes represent commonalities in participants' responses or behaviors.
Cross-Referencing Data: Compare findings from different data collection methods (e.g., interviews, surveys) to triangulate your results and gain a more comprehensive understanding.
Iterative Process: Data analysis is often an iterative process. You may revisit and refine your analysis as you uncover new insights or refine your research questions.

By effectively collecting and analyzing data, you can extract meaningful insights, identify trends, and generate hypotheses that will guide your exploratory research and inform future research endeavors.

How to Report and Present Exploratory Research Findings?

Effectively reporting and presenting exploratory research findings is vital to communicate insights and guide future actions. Let's explore the components of this phase in more detail.

Structure of Research Reports

Creating a well-structured research report ensures that your exploratory findings are communicated clearly and effectively.

Title Page: Begin with a title page that includes the title of the report, your name, affiliation, and the date of publication.
Executive Summary: Provide a concise summary of the research objectives, methods, key findings, and recommendations. This section should be informative yet brief.
Table of Contents: Include a table of contents to help readers navigate through the report easily.
Introduction: Introduce the research topic, objectives, and the importance of exploratory research in addressing your research questions.
Methodology: Describe the methods used for data collection, including sampling techniques, data analysis approaches, and ethical considerations.
Findings: Present your research findings, organized by research method (e.g., literature review, pilot study, focus groups, interviews, etc.).
Discussion: Interpret your findings, discuss their implications, and relate them to your research objectives. Consider addressing any limitations.
Recommendations: Offer recommendations based on your exploratory research. What actions or further research should be pursued?
Conclusion: Summarize the key points of your study, emphasizing its significance.
Appendices: Include any supplementary materials, such as interview transcripts, survey questionnaires, or additional data.
References: Cite all the sources you referenced in your report using a consistent citation style (e.g., APA, MLA).

Visualizing Data

Effective data visualization enhances the understanding of your exploratory findings.

Tables: Organize data in tabular format for easy comparison.
Charts and Graphs: Use bar charts, line graphs, pie charts, or scatter plots to represent quantitative data.
Infographics: Create visual summaries of key findings using infographics.
Images and Visuals: Include relevant images, photographs, or screenshots to illustrate points.

Interpreting Results

Interpreting your exploratory research results involves:

Contextualizing Findings: Explain the significance of your findings within the broader context of your research objectives.
Discussing Implications: Consider the practical implications of your findings. How do they impact the research area or field?
Addressing Limitations: Acknowledge any limitations or constraints in your study, such as sample size or data collection challenges.
Comparing with Hypotheses: If applicable, compare your findings with any initial hypotheses you may have developed during the exploratory phase.
Suggesting Future Research: Identify areas where further research is needed, building upon the insights gained in your exploratory study.

Making Recommendations

Based on your exploratory research, provide actionable recommendations.

Practical Steps: Offer specific actions or decisions that can be made based on your findings.
Policy Recommendations: If relevant, suggest changes or improvements to policies or practices.
Further Research: Highlight areas where more in-depth research is required to build upon your exploratory findings.
Implementation Plan: Outline a plan for implementing the recommendations, if applicable.

Remember that the clarity of your report and the persuasiveness of your recommendations are crucial in making your exploratory research valuable to your audience. Effective communication ensures that your insights lead to informed decisions and further exploration in your field of study.

Exploratory Research Advantages and Limitations

Exploratory research offers valuable insights into various aspects of a research topic, but it also comes with its own set of advantages and limitations. Understanding these factors is essential for making informed decisions about using exploratory research in your projects. Let's explore both sides of the coin.

Exploratory Research Advantages

Insight Generation: Exploratory research excels at discovering the unknown. It allows you to explore and uncover new phenomena, trends, or perspectives that may have been previously unknown or overlooked.
Hypothesis Generation: By investigating a topic with an open mind, you can generate hypotheses and research questions that can guide more focused research in the future. These initial hypotheses can serve as a valuable starting point.
Flexibility: Exploratory research is well-suited for complex and multifaceted topics where a structured approach may not be appropriate. It provides the flexibility to adapt to evolving research objectives.
Qualitative Understanding: Methods like interviews, focus groups, and content analysis provide rich qualitative data. This qualitative understanding is crucial for exploring nuances and complexities in human experiences and behaviors.
Contextual Understanding: Exploratory research often takes place in real-world contexts. Case studies and observational research, for example, allow you to understand how phenomena operate in their natural environments, providing valuable context.
Pilot Testing: Exploratory research, including pilot studies, helps in refining research methodologies and instruments. By uncovering potential issues early on, it reduces errors in subsequent studies.

Exploratory Research Limitations

Lack of Generalizability: Exploratory research often uses small, non-representative samples. This makes it challenging to generalize findings to larger populations or broader contexts. The insights gained may be specific to the participants or conditions involved.
Subjectivity: The qualitative nature of many exploratory research methods can introduce subjectivity in data analysis and interpretation. Researchers' biases and perspectives may influence the findings.
Time and Resource Intensive: Some exploratory research methods, such as in-depth interviews or case studies, can be time-consuming and resource-intensive. This can limit the scalability of exploratory studies.
Limited Quantitative Data: If your research requires precise numerical data, exploratory research may not be sufficient. It primarily focuses on qualitative insights and quantitative data may be limited in scope.
Potential for Bias: The choice of research methods and participants can introduce bias into your findings. For example, purposive sampling in qualitative research may inadvertently select participants with similar perspectives.
Incomplete Picture: Exploratory research may provide an insufficient or preliminary picture of a topic. It often requires further investigation for validation and a more comprehensive understanding.
Ethical Challenges: The open-ended nature of exploratory research can raise ethical challenges, especially in sensitive research areas. Ensuring participant consent and privacy is essential.

Understanding these advantages and limitations is crucial for researchers to make informed decisions about when and how to apply exploratory research methods. It's essential to carefully consider these factors in the context of your research objectives and the specific challenges and opportunities presented by your research topic.

Exploratory Research Examples

Exploratory research is a versatile approach employed across various fields to gain insights, uncover trends, and generate hypotheses. Let's explore real-world examples of how different exploratory research methods have been applied effectively:

1. Real-Life Case Studies

Facebook's emotional contagion study.

Background: In 2014, Facebook conducted a controversial exploratory research study to investigate emotional contagion. The study involved manipulating the content that appeared in users' newsfeeds to measure how emotional content impacted their own posts.

Method: Facebook used large-scale data analysis to conduct this study, which involved over 689,000 users. They manipulated the visibility of positive and negative posts to examine whether emotional states could be influenced online.

Findings: The study found that when users saw fewer positive posts in their newsfeeds, they tended to post fewer positive updates themselves, and vice versa for negative posts. This research sparked discussions about ethical considerations in online experimentation and the power of social media platforms to influence emotions.

Harvard Business School's Airbnb Case Study

Background: Harvard Business School conducted an exploratory case study on Airbnb, a disruptive platform in the hospitality industry. The goal was to understand how Airbnb disrupted traditional lodging markets and its impact on the hotel industry.

Method: Researchers collected data from various sources, including interviews with Airbnb hosts, surveys of travelers, and publicly available data on Airbnb listings and hotel occupancy rates. They analyzed the data to identify trends and insights.

Findings: The study found that Airbnb significantly impacted the hotel industry by offering unique, affordable, and personalized lodging options. It also highlighted challenges such as regulatory issues and concerns about safety and quality control.

2. Focus Groups

Apple's product development.

Background: Apple Inc. frequently conducts exploratory research through focus groups to gather insights and opinions from potential users before launching new products or features.

Method: Apple assembles small groups of potential users and conducts moderated discussions. Participants are encouraged to share their thoughts, preferences, and concerns about prototypes or concepts.

Findings: Apple gains valuable feedback about user preferences and pain points by engaging with focus groups. For example, before launching the Apple Watch, focus groups provided insights into desired features like health tracking and customization.

Political Campaign Strategy

Background: In politics, campaign teams often use focus groups to explore voters' opinions, reactions to candidates, and key campaign issues.

Method: Focus groups consist of a diverse set of voters who engage in discussions about campaign messages, policies, and candidate attributes. Campaign teams use these insights to tailor their strategies.

Findings: Focus groups help political campaigns understand which messages resonate with different voter demographics. For instance, a focus group may reveal that a candidate's stance on a specific policy particularly appeals to a specific age group, influencing campaign messaging.

3. Content Analysis

Climate change discourse in media.

Background: Exploratory content analysis has been employed to study media coverage of climate change. Researchers aim to understand how different media outlets frame climate change issues.

Method: Researchers collect articles and news reports from various sources and then analyze the content to identify recurring themes, framing, and the use of language. This helps determine how climate change is portrayed in the media.

Findings: Content analysis has revealed that media outlets may use different frames when discussing climate change, such as "economic impact," "environmental consequences," or "scientific consensus." These frames can influence public perception and policy discussions.

Social Media Sentiment Analysis

Background: Companies and organizations use content analysis of social media posts to gauge public sentiment and gather insights into customer opinions and preferences.

Method: Automated tools are used to collect and analyze social media posts, comments, and mentions related to a specific brand, product, or topic. Natural language processing techniques identify sentiment (positive, negative, neutral) and key themes.

Findings: By analyzing social media content, companies can identify customer complaints, emerging trends, or public sentiment shifts in real time. For example, a restaurant chain may use sentiment analysis to track customer reactions to new menu items.

4. Observational Research

Supermarket shopping behavior.

Background: Observational research is frequently used in the retail industry to understand consumer behavior. One example is studying how shoppers navigate supermarkets.

Method: Researchers observe shoppers in a supermarket, noting their paths through the store, product choices, and interactions with displays. This data helps retailers optimize store layouts and product placement.

Findings: Observational research has shown that shoppers tend to follow predictable patterns in supermarkets, such as starting with fresh produce. Retailers use this data to design store layouts that encourage specific shopping behaviors and maximize sales.

Child Development Studies

Background: Observational research is crucial in child development studies to understand how children learn and develop through their interactions with the environment.

Method: Researchers use video recordings or live observations to document children's behaviors in various settings, such as classrooms or homes. They analyze these observations to identify developmental milestones and learning patterns.

Findings: Observational research in child development has contributed to our understanding of how children acquire language, social skills, and cognitive abilities. For example, it has revealed how peer interactions influence language development in preschoolers.

These real-world examples illustrate the diverse applications of exploratory research methods, from understanding user preferences for tech giants like Apple to analyzing media discourse on critical issues like climate change. Exploratory research empowers organizations and researchers with valuable insights that inform decision-making and shape future research directions.

Exploratory research is a dynamic tool that opens doors to discovery. It helps us uncover hidden insights, generate fresh ideas, and better understand the world around us. By delving into the unknown and embracing its flexibility, we can embark on journeys of exploration that enrich our knowledge and inform future endeavors.

So, whether you're exploring uncharted territories in academia, industry, or any field, remember that the spirit of curiosity and the methods of exploratory research can be your compass. With the right strategies and ethical considerations, you'll not only navigate the challenges but also uncover the treasures of knowledge that await. As you embark on your own exploratory research adventures, may you find answers to your questions, ignite new inquiries, and, above all, revel in the joy of discovery.

How to Conduct Exploratory Research in Minutes?

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Exploratory Research: What are its Method & Examples?

Research is a continuous process that needs improvement as time goes by, and as such is non-exhaustive. Although, a lot of researchers working on novel projects, most researchers work on existing theories or formulations and build on them.

Researchers may decide to work on a problem that has not been studied very clearly to establish priorities, develop operational definitions and improving the final research design. This type of research is what is called exploratory research.

What is Exploratory Research

Exploratory research is the process of investigating a problem that has not been studied or thoroughly investigated in the past . Exploratory type of research is usually conducted to have a better understanding of the existing problem, but usually doesn’t lead to a conclusive result.

Researchers use exploratory research when trying to gain familiarity with an existing phenomenon and acquire new insight into it to form a more precise problem. It begins based on a general idea and the outcomes of the research are used to find out related issues with the topic of the research.

In exploratory research, the process of the research varies according to the finding of new data or insight. Also referred to as interpretative research or grounded theory approach, the outcomes of this research provide answers to questions like what, how and why.

Characteristics of Exploratory Research

Exploratory research is inexpensive, highly interactive and open-ended in nature.
There is usually no prior relevant information available from past researchers.
It has no predefined structure.
It answers questions like how and why aiding the researcher to acquire more information about the research.
The absence of relevant information from past research means the researcher will spend a lot of time studying materials in detail. Therefore, spending so much time conducting exploratory research.
Since there is no standard for carrying out exploratory research, it is usually flexible and scattered.
There must a few theories which can verify your outcome.
Researchers cannot form a conclusion based on exploratory research.
The research problem must be important and valuable
Exploratory research mostly deals with qualitative data.

Exploratory Research Methods

There are several exploratory research methods available for data gathering and research. However, exploratory research has been classified into two main methods, namely the primary and secondary research methods . The process of conducting research tends to be more difficult when dealing with a problem that hasn’t been deeply investigated before.

Primary Research Methods

In primary research methods , data is collected directly from the subject of investigation. The subject, in this case, maybe a group of people or an individual.

It doesn’t matter whether the data is collected by the researcher himself or through a third party, the main purpose of the research should be fulfilled. The purpose of conducting this research is to collect information about the problem which requires in-depth analysis.

Some of the primary research methods used in exploratory research include:

Observations

In this primary research method, the researcher does not come in close contact with the subject. Rather, the subject is being watched from afar. Subject observation can be done in two ways.

The first is that the subject is aware that he/she is being observed while the second way is that the subject is not aware of it. The latter method is said to gather fairer data because the subject may behave differently when he/she is aware that (s)he is being watched.

Surveys are used to collect data from a predefined subject(s). It can be used collected to study trends, opinions, and behaviour of a group of people.

Online form builders like Formplus have made it easier to conduct surveys online and reach diverse demography of participants from all over the world. Although, rarely in use these days, researchers can also conduct offline surveys.

Although more stressful and time-consuming than others, the interview technique is the best in terms of collecting detailed and correct data. Interviews can be conducted in person, via phone call or video call.

Interviews can also be recorded by the researcher in case he/she needs to go back to it and confirm specific information.

Focus Groups

Focus group is often used by researchers when trying to collect data from a group of people with similar characteristics. The research can be done using any of the three methods explained above.

For example, a focus group of fresh graduates may be investigated on how they spend their time.

Secondary Research Methods

Secondary research method uses existing resources on the subject under study. Existing sources like newspapers, magazines, articles, papers, etc. are what researchers conduct for exploratory research.

All the resources used must be cited in publications. Some of the secondary research methods used in exploratory research include:

Literature research is the process of conducting old resources like publications, textbooks, articles, magazines, etc. All this information can be gathered in both sift copy and hard copy documents.

For example, an undergraduate student conducting his/her final project research will need to conduct textbooks, publications, papers, articles, etc.

Online Sources

With the advent of technology, this research has gained much popularity among millennials. Online research sources are the cheapest and easiest method of research.

With access to the internet and a personal computer or mobile phone, a researcher can browse through as many resources as possible. They can also be downloaded for further use in the future.

The setback of this method is the difficulty of combing through the many online resources to find genuine information. Researchers face the possibility of ending up with incorrect data because false information may be difficult to identify.

A researcher might find relevant information on the problem under study by studying existing cases. For example, a mathematician trying to formulate a model to solve the queuing problem in an airport may conduct existing research in similar areas.

A case study could be research that solved the queuing problem in a shopping mall. This research will be studied and modified to suit that of the airport queuing problem.

A researcher may decide to get more creative by using informal sources like email newsletter subscription, RSS feeds, google alerts, google trends or even design a bot that combs through the large repository of data online.

How To Conduct Exploratory Research

Step 1 – identify the problem.

This is a common starting point for all types of research. Here, the researcher identifies the purpose of the research by answering the “what question”.For example, when investigating a crime scene, the FBI needs to first identify what happened. Was it theft, murder or a case of child abuse?

Step 2 – Create the hypothesis

After identifying the problem, the researcher goes ahead to check whether there have been prior investigations regarding the subject matter. But when the researcher realizes that there are no previous investigations, he/she arrives at a hypothesis based on the questions obtained while identifying the problem.

If you are investigating a crime scene, an autopsy will be performed on the dead body to answer how he/she was killed. Questions like, Was he in a gang?, Fighting over a business deal? or very rich? will answer the question of why he was killed.

With this information, the investigator can arrive at a hypothesis.

Step 3 – Conduct further research

To conduct further research, the researcher needs to first obtain relevant data that will assist in the research process. Some of the methods of collecting data include interviews, surveys, online sources, etc.

Once the data has been collected, the researcher will continue the investigation through descriptive methods. This process uses qualitative data.

Examples of Exploratory Research

In this section, we shall be considering three examples of exploratory research and will be going through the research process as explained above.

Exploratory Research Example on Murder Investigation

A fresh or inconclusive murder case will be investigated using exploratory research because it has not been investigated clearly in the past. To gain a better understanding of how exploratory research is used to conduct a murder investigation, let us review this popular crime movie titled Murder on the Orient Express .

Adapted from Agatha Christie’s novel , we see in this movie that the first thing detective Hercule Poirot did was to identify the problem which is the murder of Ratchett. After that was the question of how he was killed.

The how consists of the murder weapon, how it was used, the time he was murdered, etc. The last piece of information the detective needed to nail the culprit was the why.

By discovering why Ratchett was murdered, the detective can easily arrive at a hypothesis on who the murderer is. In the search for why the detective used a primary research approach to collect relevant data that will aid the investigation.

When an avalanche stops the Orient Express dead in its tracks, the world’s greatest detective–Hercule Poirot–arrives to interrogate all passengers and search for clues before the killer can strike again. After a series of interviews, the detective was able to arrive at a hypothesis on who the killer was.

The results of any criminal investigation will remain a hypothesis until tried under a court which will either confirm or nullify the hypothesis. The evidence acquired during the investigation is what will assist the court in making a decision.

Exploratory Research Example on Product Research

Organizations conduct two major research when working on a new product or service. The first one is conducted before developing the product while the second one is conducted after product development.

Our focus will be on the exploratory research conducted after product development. For tech products, it is called the beta testing stage of product development.

If a new feature is added to an existing app, for example, product researchers will want to investigate whether the feature will be well received among the users. If the feature added to the app is something that is already in existence, then the research is not exploratory.

For example, if telegram adds a status feature to its app, the beta research stage of the app is not exploratory. This is because this feature is something that is already in existence, and they can easily get enough information from WhatsApp.

However, if it is a new feature like the Snapchat filters when they just came out, the research is explanatory. In this case, exploratory research is carried out using a focus group of beta testers.

Trend Analysis

A good example of trend analysis research is studying the relationship between an increased rate of charity and crime rate in a community. Will giving food, clothes, etc. to the people in a community help decrease the rate at which people steal?

This exploratory research may be conducted through observations. A sample crime laden community will be given charity for a certain period, while the crime rate during this period will be observed.

This kind of research is better carried out when the subject is not aware they are under observation. An alternative to this approach is using the case study method.

Although this research may not have been done in this specific community, something similar may have been done in the past. If that is the case, the research can be easily carried out by investigating the case study to get relevant information.

This will make the research process easier and a hypothesis easier to come by.

How to use Formplus for Exploratory Surveys

Start creating exploratory surveys with Formplus in three easy steps.

Step 1: Register or Sign up

Visit www.formpl.us on your desktop or mobile device.
Sign up through your Email, Google or Facebook in less than 30 seconds.

Step 2: Create Your Exploratory Survey

We will be creating a product research exploratory survey in this guide. Consider a software company that just added some new features to their app. The app is currently in the beta testing stage and they are taking an exploratory survey to get feedback from the beta testers.

Radio Choice Multiple Choice Question

Click on the Choice Options section of the form builder menu.
Create Radio multiple choice questions by clicking on the radio tab.
Edit the question with your preferred stem and choice options.

Open-Ended Question

Click on the Inputs section of the form builder menu.
Create a short text open-ended question by clicking on the Short Text tab.

Edit the label and placeholder text as preferred.

Checkbox Multiple Choice Question

Create a checkbox multiple choice question by clicking on the radio tab.

Matrix Rating Multiple Choice Question

Click on the Ratings section of the form builder menu.
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Edit the table as preferred.

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Step 3: Customise and Share

This is the final stage where you customise your form and start sharing with respondents.

Feel free to customise your forms as you please. You can also add logic in the settings before sharing.

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Advantages of Exploratory Research

Exploratory research is inexpensive to perform, especially when using the second method for research.
Exploratory does not have a standard process and as such is very flexible.
Information gathered from exploratory research is very useful as it helps lay the foundation for future research.
It gives researchers more insight into the problem under study.
Researchers don’t have to waste time conducting irrelevant research when using an exploratory approach. It helps the researcher if the topic is worth investigating at an early stage.

Disadvantages of Exploratory Research

It produces an inconclusive result.
Exploratory research provides qualitative data, which may be difficult to interpret. The interpretation of qualitative data may be bias and/or judgemental.
Many of the data collected through secondary sources may be old and outdated.
If collected through online sources, the researcher may be prone to collecting false information.
Exploratory research mostly involves a smaller sample whose results may be incorrect for a larger population.

Conclusion

Research is built on the incredible inquisitive and resourceful minds of researchers and the urge to solve problems. This stems from the child-like tendency to frequently ask questions like what, why, and how—a trademark of exploratory research.

Exploratory research is one of the three main objectives of market research, with the other two being descriptive research and causal research. It is commonly used for various applied research projects.

Applied research is often exploratory because there is a need for flexibility in approaching the problem. Also, there are often data limitations and a need to decide within a short period.

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Explanatory Research: Types, Examples, Pros & Cons

This article will take you through some of the types of explanatory research and what they are used for.

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Chapter 3: Developing a Research Question

3.2 Exploration, Description, Explanation

As you can see, there is much to think about and many decisions to be made as you begin to define your research question and your research project. Something else you will need to consider in the early stages is whether your research will be exploratory, descriptive, or explanatory. Each of these types of research has a different aim or purpose, consequently, how you design your research project will be determined in part by this decision. In the following paragraphs we will look at these three types of research.

Exploratory research

Researchers conducting exploratory research are typically at the early stages of examining their topics. These sorts of projects are usually conducted when a researcher wants to test the feasibility of conducting a more extensive study; he or she wants to figure out the lay of the land with respect to the particular topic. Perhaps very little prior research has been conducted on this subject. If this is the case, a researcher may wish to do some exploratory work to learn what method to use in collecting data, how best to approach research participants, or even what sorts of questions are reasonable to ask. A researcher wanting to simply satisfy his or her own curiosity about a topic could also conduct exploratory research. Conducting exploratory research on a topic is often a necessary first step, both to satisfy researcher curiosity about the subject and to better understand the phenomenon and the research participants in order to design a larger, subsequent study. See Table 2.1 for examples.

Descriptive research

Sometimes the goal of research is to describe or define a particular phenomenon. In this case, descriptive research would be an appropriate strategy. A descriptive may, for example, aim to describe a pattern. For example, researchers often collect information to describe something for the benefit of the general public. Market researchers rely on descriptive research to tell them what consumers think of their products. In fact, descriptive research has many useful applications, and you probably rely on findings from descriptive research without even being aware that that is what you are doing. See Table 3.1 for examples.

Explanatory research

The third type of research, explanatory research, seeks to answer “why” questions. In this case, the researcher is trying to identify the causes and effects of whatever phenomenon is being studied. An explanatory study of college students’ addictions to their electronic gadgets, for example, might aim to understand why students become addicted. Does it have anything to do with their family histories? Does it have anything to do with their other extracurricular hobbies and activities? Does it have anything to do with the people with whom they spend their time? An explanatory study could answer these kinds of questions. See Table 3.1 for examples.

Table 3.1 Exploratory, descriptive and explanatory research differences (Adapted from Adjei, n.d.).

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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.

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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A guide to exploratory research design

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9 March 2023

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Jean Kaluza

Knowledge is power, especially when designing a new product or improving an existing one. You may have questions like who will use your product. What niche market needs this product? How will customers respond to the product? Where does the product need improving?

Analyze exploratory research

Finds answers to questions asked in your exploratory research faster when you analyze it in Dovetail

What is exploratory research?

When you're blazing a trail for a new concept, you need questions answered and problems solved. Exploratory research will help you better understand the problems and offer solutions you could focus on to transform the idea into reality.

What is an exploratory research design example?

When you have an idea about a new product, you're excited about the prospect that customers will be lining up at the door to purchase it. Before spending money on design and development, determine if customers will love it as much as you do.

You will want to conduct exploratory research to determine how people will respond to your product. Your data may show that your potential customers have a different opinion than you expected. Once you receive the data, your perception of how to proceed with the product's design will become more apparent.

Methods and types of exploratory research

Understanding the methods of exploratory research and how to reach potential customers can provide valuable data for product conception. There are two primary methods of conducting exploratory research: primary research and secondary research.

Primary research

Primary research involves direct interactions with your customer base. This could include conducting surveys , hosting focus groups , or one-on-one interviews.

Primary research aims to gather first-hand information about your customers' needs, preferences, and opinions. You can gain valuable insights into their behaviors and decision-making processes by interacting directly with your target audience.

Secondary research

Secondary research involves gathering information that others have already collected. This could include conducting online searches, reviewing industry reports, or visiting the library to read books and journals. Secondary research aims to gather information that can help you better understand your target market and industry trends.

Exploratory research data collection

Gathering data about a new subject can be difficult. But exploratory research can make it easier by helping you focus on a specific topic and creating a clear hypothesis and problem statement. It also gives you an overview of the subject.

Exploratory research involves two types of data collection methods: primary and secondary research. Both methods follow the same model for data analysis.

Primary research methods

This research method involves communicating with people in different ways to gather information, including:

Observations

Interviews

Focus groups

You might have your product's models, drawings, or prototypes ready for testing. Then, you can gather a target sample group to interact with it. By observing their interactions and listening to their questions, answers, and comments, you can identify necessary changes to the product. This process will also give you insights into how customers will respond to it when it launches.

Exploratory research questions

Once you establish which primary research method you will use, tailor those methods to retrieve data that will answer questions about moving forward with your product.

These questions can include the following:

Who will get the most benefit from using the product?

What features of the product will customers most likely use or not use?

Is the product easy to use or too complicated?

How can the product be improved?

Secondary research methods

This research method is limited in providing a detailed understanding of product performance among potential customers. Nevertheless, it can help you explore whether similar concepts have been tried before and their success rates. To gather such data, you can refer to these sources:

Case studies

Existing literature

Online sources

Characteristics of exploratory research

When exploring what type of data you require for your project, consider the characteristics of exploratory research. Check whether the following features align with your project's needs.

Difficult to quantify

It’s extremely difficult to quantify unstructured data. This data type does not typically contain common variables to compare corresponding data points to. However, quantitative data points can be pulled if studies are conducted with a large enough sample size. It just takes significantly longer to analyze. Still, unstructured data is more valuable because it's open-ended qualitative feedback that will help direct your project.

Low-cost, interactive, open-ended

Taking the time to budget for exploratory research has excellent cost-saving significance. The cost of designing and developing a product that may not do well on the market can be higher than what you spend when doing exploratory research.

And the research doesn't have to stop after one survey or one focus group. You can continue this type of interactive research with your target group or customer base throughout all phases of product development. This includes the design, manufacturing, market introduction, and customer experience phases.

Time-consuming

Although it is time-consuming to perform exploratory research, this is nothing compared to the time you could waste producing a product that the public might not receive well. Take the time to construct exploratory research designs that will reap high-quality data with steps that include:

Addressing the problems that you will need to solve

Identifying the target sample group

Designing the data collection format

Collecting the data

Categorizing the data into useful information

Incorporating the information into the design process

Depending on how extensive your target sample group is and what formats you use to collect the data, this also may impact how long it takes to get the information you need.

For example, a survey format may take less time than an interview structure. And if you're surveying 15,000 people rather than just 1,000, this can take a while to receive and examine the results.

When to use exploratory research

Exploratory research can be used not only for product design issues but also to determine the ideal market target and improve customer experience with your product or service.

For example, suppose your business has a website or app. In that case, you can use exploratory research to determine user experience when customers use them.

How to conduct exploratory research

In conducting exploratory research, here are the steps you can follow:

Step 1: Identify your problem

Regarding product design, the first step is identifying what obstacles, challenges, or motivations your product will solve for your customers to become viable in the market.

Step 2: Hypothesize a solution

Conducting secondary research on products similar to yours can provide valuable insights that can help you develop a successful solution. By examining the launch and performance of these products, you can generate hypotheses about what may work for your own product.

You may want to add features to your product that were considered successful or remove features that weren't.

Step 3: Design your methodology or process

Next, determine at what points and how you want to collect feedback on your product as you design and iterate it. Perhaps, surveys adequately produce the data you need at the conceptual phase, and running a focus group could be better before the alpha release.

The processes and methodology depend on your resources, team strengths, and at which points in the development process you need direction the most.

Step 4: Collect and analyze data

Analyzing the data collected is how we make our findings actionable. Techniques such as content analysis , thematic analysis , or grounded theory help identify patterns and themes in the data.

If we identify a theme where potential customers are consistently choosing our competitor over us, it may indicate a specific feature that they prefer. To address this, we should conduct further exploration and analysis to determine the reason for this preference. Based on our findings, we may need to build and design similar features to better compete with our rivals.

Step 5: Avenues for future research

If the research that you did helps the design process of your product, you now have a proven avenue for future research in product design, manufacturing, market introduction, and customer experiences for your business.

Advantages of exploratory research

Exploratory research provides significant cost-effectiveness and time-savings on projects. If a project is unsuccessful because you did not conduct exploratory research, it will lead to much more cost and time expenditures in the future. And once you have a proven exploratory research process established, it will be easier to do further research when needed.

Challenges of exploratory research

When doing exploratory research, flexibility is key. If you're unwilling to be open to the results, bias can factor into data interpretation, rendering the data useless. Also, if you haphazardly assemble a quick study with a small sample, the sample size may not represent the target audience.

The extra effort of exploratory research is worth it

Now that you know the significance of exploratory research and its impact on successful product development and customer experience , it's time to initiate your exploratory research design. And to organize your exploratory research efforts, find a platform that helps you store customer research , feedback, and insights all in one place.

What is exploratory research vs. descriptive?

Exploratory research studies unexamined topics or problems. Descriptive research describes the characteristics of a subject to compare and contrast with other subjects observed in the same study.

Which exploratory research is the quickest and least costly?

Secondary research methods are the quickest and less costly. However, they do not offer comprehensive or specified information that will help develop a product design. Primary research methods can be more expensive than secondary ones but still possible to conduct on a budget.

Which type of research design takes the longest?

Primary research takes the longest because of the necessary steps to collect the information you need. It also depends on how wide of a net you cast to collect the data. The more people involved in surveys, focus groups, and interviews, the more time it will take to extract and analyze the data.

What is the sample size of exploratory research?

The sample size is the number of people participating in your exploratory research design. The sample size should be representative of the target audience for your product.

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Exploratory Research: A Guide to Unlocking Insightful Data

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You can’t just develop a new product without understanding the need or interest for it in the market. So how do begin with such research? Which research should you even conduct?

This brings us to the topic of exploratory research. Exploratory research helps us gain an understanding of a topic, defines the variables of the problem, and establishes a basis for a more specific research question.

Read the article to learn what exploratory research is, its characteristics, & the methods used to perform it.

What is Exploratory Research?

Exploratory research investigates problems that are not clearly defined. It is conducted to gain insight into the existing problem, however, exploratory research does not provide a conclusive answer to these problems.

A researcher starts with an idea that is general in nature and uses this as a means to recognize issues that can become the focus of future research. An important feature of exploratory research is that the researcher should keep an open mind and be willing to change the direction of their research as they collect more and more insightful data.

Exploratory research uses the grounded theory approach, also known as interpretive research. It aims to answer questions such as: “What is happening?” “Why is this happening?” “How is this happening?”

For example, if a researcher wants to know how the target audience of their app perceives a particular filter, they can first find out which section uses their app. Then proceeding to find out which filters are most used, why they are used, and decide whether adding an additional filter similar to the existing ones will be a good idea.

Read how Voxco helped Walmart conduct customer research and expand their online Grocery Pick-Up services.

What are the characteristics of exploratory research.

Now that we have defined exploratory research, it is important to be familiar with its attributes. Exploratory research has several features that researchers need to learn to understand when to use it.

The following are the characteristics of exploratory research:

1. They are not structured in nature.

2. Exploratory research design is interactive, open-ended, and usually accessible within the budget of the organization.

3. It helps researchers uncover answers to questions such as: what is the problem being studied? What is the need for this study? What topics should be included in the study?

4. It is time-consuming and thus requires patience and persistence on the part of the researcher.

5. Exploratory research is broad, flexible, and adaptive in nature.

6. The researcher needs to go through all the information and data collected through the research.

7. Exploratory research needs to have an important cost or value; if not, it is ineffective.

8. The researcher should have some theories that will help in supporting the findings uncovered during the exploratory research.

9. Exploratory research generally produces qualitative data.

10. In some instances, where the study sample is large, and data is collected through surveys and experimentation, explorative research can be quantitative.

Now, that we have cataloged the characteristics, the question is how to go about collecting the data for your exploratory research. The following section explains the two methods you can use to conduct your research.

What are the Types of Exploratory Research?

Carrying out research on something that one has limited information about sounds and feels difficult. However, several methodologies can help you decide the best research design, how to collect data, and the variables to study.

There are two main methods of conducting exploratory research – primary research and secondary research . Under these two broad types, various methods can be used depending on the nature of your study.

The data can be of quantitative or qualitative nature. Let’s look at each of the research methods in detail.

1. Primary Research Methods

In the primary research , the information is collected directly from the respondents. This data can be collected from a group of people or just an individual. It is usually done to explore a problem that needs in-depth analysis.

A) Surveys:

Surveys or polls can gather large amounts of data, usually from a predetermined group of respondents. They are one of the most popular quantitative research methods. Surveys or polls are used in exploratory research to explore the opinions, trends, or beliefs of the target population.

Surveys can now be conducted online and thus be made more accessible, thanks to technology! Nowadays, organizations have started offering shorter surveys and rewards to the respondents who fill them to increase the response rates and gain more insights. Short surveys can be sent to respondents through text messages right after they make a purchase and are asked to fill it for a coupon/discount in return, so organizations can understand their views on the product under study.

Voxco lets you conduct omnichannel surveys to gather insightful market research data anywhere, anytime.

B) Focus Groups:

Another widely used methodology in exploratory research is focus groups. In this method, a group of respondents is chosen and asked to express their opinions on the topic of interest. One important consideration when making a focus group is choosing people with a common background and similar experiences to get unified and consistent data.

An example of a focus group would be when a researcher wants to explore what qualities consumers value when buying a laptop. This could be the display quality, battery life, brand value, or color. The researcher can make a focus group of people who buy laptops regularly and understand the dynamics a consumer considers when buying electronic devices.

A wide range of online survey tool functionalities to create interactive surveys in seconds.

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C) Observation:

Observational research can be quantitative or qualitative. It involves observing an individual and making inferences from their reactions to certain variables.

This research does not require direct interaction with the participants. For instance, a researcher can simply record the observations of how people react at the launch of a new product.

D) Interviews:

Surveys provide huge amounts of information in a relatively short period of time, but an interview with one person can provide in-depth information that can otherwise be overlooked in surveys. Interviews are a methodology for collecting data for qualitative research.

You can conduct the interview face-to-face or even on the telephone. For example, an interview with an employee about their job satisfaction can offer valuable insights that would otherwise go unnoticed in the closed-ended questions asked in a survey.

2. Secondary Research Methods:

In secondary research, information is gathered from primary research that has been published before. For instance, gathering information from case studies, newspapers, online blogs or websites, or government sources.

A) Online Resources:

The quickest way to find information on any topic is through the internet. A huge amount of data is available on the internet that you can download and use whenever you need it. One important factor to consider when acquiring data online is to check the authenticity of the sources provided by the websites.

For example, a researcher can find out the number of people using a preferred brand of clothing through a poll conducted by an independent website online.

B) Literature review:

Reviewing the existing literature on a particular topic from online sources, libraries or commercial databases is the most inexpensive method of collecting data. The information in these sources can help a researcher discover a hypothesis that they can test.

Here, sources can include information provided by newspapers, research journals, books, government documents, annual reports published by organizations, etc. However, the authenticity of the sources needs to be considered and examined.

Government sources can provide authentic data but may require you to pay a nominal price to acquire it. Research agencies also produce data that you can acquire at a nominal cost, and this data tends to be quantitative in nature.

C) Case studies:

Another way researchers can gather information for their exploratory research design is by carefully analyzing the cases that have been through a similar problem the researcher wishes to study. These cases are important and critical in the business world, especially.

The researcher should be cautious in reviewing and analyzing a case that is similar to the variables of concern in the present study. This methodology is commonly used in the health sector, social sciences, and business organizations.

For example; let’s assume that a researcher is interested in understanding how to effectively solve the problems of turnover in organizations. While exploring, he came across an organization that had high rates of turnover and was able to solve the problem by the end of the year. The researcher can study this case in detail and come up with methods that increase the chances of success for this organization.

[Related read: Primary Vs. Secondary Research ]

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What are the Steps to Conduct Exploratory Research?

Let’s explore the practical aspect of how you can conduct exploratory research from design to data analysis. Follow the steps as per your research requirements to uncover insights and validate your research question.

1. Identifying the problem area –

The very first step is for the researcher to identify the area of research and the problem can be addressed by finding out ways to solve it.

2. Creating a hypothesis –

If the researcher is aiming to solve a problem for which there are no prior studies or the problem has not been resolved efficiently in previous research, then the researcher creates his/her own problem statement. This problem statement, also called a hypothesis, will be based on the questions that the researcher came up with while identifying the area of concern.

3. Determining data collection methods –

While planning your research design, it is important to select the proper data collection methods. In this blog, we have explored the various methods of data collection so you can determine which method aligns with your objective.

Consider the nature of your research goal and identify the source of data you want to explore. Determine the data collection tools you need, which may include an online survey tool or a phone survey tool.

4. Choosing sampling method –

In order to ensure your research findings represent the target population, you need to choose the appropriate sampling method or leverage a market research panel. This step will help you gather data from the audience who have knowledge or experience about the subject, thus allowing you to gather relevant and accurate insights.

5. Analyzing data and identifying patterns –

Leverage a survey software that enables you to store and analyze data seamlessly. Conduct quantitative data analysis, text, and sentiment analysis to identify patterns, reveal trends, and discover key findings. By utilizing a robust tool, you can unveil meaningful insights to guide your future research.

6. Advancing future research –

Once the data for the current problem has been obtained, the researcher will continue the study through a descriptive investigation. Generally, qualitative methods are used for a detailed study of the data to find out if the information gathered through exploratory research is true or not.

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When to Use Exploratory Research?

Exploratory research design helps you investigate a subject that is vague, new, or poorly understood. Often referred to as grounded theory research, the insights help strategies the foundation of future research.

1. Define a vague topic:

This research design is an ideal choice when you have a poorly defined research problem. The exploratory method helps you gain clarity on the subject before you dive deeper.

2. Explore unexplored topics:

The research method helps brands delve into emerging or new markets with limited prior data. It helps identify variables, trends, and characteristics.

3. Conduct market research:

Brands can utilize exploratory research to gauge market trends, customer preferences, behaviors, and needs. You can use the feedback to guide your marketing strategies and product/service developments.

4. Study diverse population:

The research method is valuable in gathering knowledge on diverse cultural groups. It can help you understand the nuances of different cultures, behaviors, needs, and more.

Advantages of Exploratory Research

Exploratory research provides the researcher an opportunity to keep an open mind and explore the variables affecting their area of interest. Some of the advantages of exploratory research are:

It allows researchers to be flexible and change their stance on the problem being studied as the research progresses.
It is cost-effective.
It lays a foundation and structure for future research.
It can help researchers find out the causes of the problem being studied, which can be elaborated on in future studies.
It allows you to adapt the method of data collection as required by the research.

Now that we have listed the benefits, we can’t forget the limitations. It is important to learn about both before you jump into the research mode.

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Limitation of Exploratory Research

Exploratory research is not without its limitations.

The research findings are usually inconclusive.
Some of the data collected can be biased or subjective as it is mostly qualitative in nature.
Since exploratory research has a smaller sample size, there is hesitancy in generalizing the findings to the whole population.
If data is collected through secondary sources, there is a chance that the data will be old or outdated.

Wrapping up;

Exploratory research helps you form the foundation of your research project. It lays down the groundwork for a research question you can explore in the future. Exploratory research design is best used when you need insights into a problem or phenomenon before you begin to conduct further research.

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What is Exploratory Research and how to use it?

Welcome to the world of exploratory research, a method designed to shine a light on the unknown and guide you through the unexplored. As a UX Researcher , I absolutely LOVE doing discovery research. I’ve used it to find product market fit at a startup where we were looking for our first 100 users! I’ve even used it for existing products in the market to research our competitors to find out what features we were lacking.

In this article, I will go over what exactly is exploratory research (discovery research) and how you can conduct it and specifically which research methods you can use to do it.

What is exploratory research?

Instead of aiming for clear answers, exploratory research helps you gather hints and ideas that can later be used to dig deeper. It’s about asking open-ended questions, the kind that lets people speak their minds freely. An example of a puzzling and unclear problem could look something like this: ‘Who are our primary users and what are their goals?”.

When can you use exploratory research?

First, when you’re facing a problem that’s new and you’re not sure where to start . For instance, say you’ve just come up with a groundbreaking idea for a product that nobody has seen before. You can use exploratory research to peek into people’s minds and understand what they think about your idea, and even get a sense of how much they might be willing to pay for it. It’s like getting a glimpse into the future before you take the big leap.

Second, when you’re dealing with something totally new, like a brand-new product . For example, consider a situation where you’ve invented a smart gadget that can translate your pet’s sounds into understandable words. Since this kind of product has never been on the market before, exploratory research comes to the rescue. You can use it to explore how pet owners feel about this idea, what concerns they might have, and whether they find it valuable enough to invest in.

Lastly, if you’re not sure what to test , exploratory research is like a map that guides you to form predictions and guesses. For example, let’s say you’re working for a fitness company and you want to launch a new workout program. Instead of jumping straight into designing the program and hoping it resonates with your audience. You might conduct interviews or surveys to learn about people’s current fitness routines, their preferences, and their pain points.

6 Reasons Why You Need to Know How To Do Exploratory Research

Here are some compelling reasons why mastering the art of exploratory research is essential:

1. Unraveling the Unseen:

Sometimes, problems are like hidden treasures waiting to be discovered. Exploratory research helps you unearth these gems by allowing you to venture into uncharted territories, uncovering dimensions you might never have imagined.

2. Minimizing Costs, Maximizing Interaction:

Exploratory research doesn’t require a hefty budget or elaborate setups. It’s the art of simplicity – interactive interviews, open-ended questions, and candid discussions. You engage directly with participants, creating a valuable dialogue that enriches your understanding.

3. The Puzzle of “What”:

While explanatory research focuses on the “why,” exploratory research delves into the “what.” It helps you identify what the problem is, outline its contours, and set the stage for future investigations.

4. It can be both Qualitative and Quantitative:

While exploratory research leans towards qualitative methods, it’s flexible enough to incorporate quantitative data when needed. It’s a versatile tool that adapts to your research needs, ensuring you gather insights from all angles .

5. The Starting Line of Knowledge:

Exploratory research is done usually where no other UX research has been done. It is the first step in the research process and precedes explanatory research. Since you start with no research, you will have to come up with a few hypotheses to test.

6. Open-Ended Exploration:

There are no rulebooks in exploratory research. It’s a blank canvas where you choose the colors, techniques, and brushes that best suit your research landscape. Flexibility and an open mind are your guiding principles.

In the realm of exploratory research, you’re not just a researcher – you’re an adventurer, an investigator, and a visionary. It’s the foundation upon which you build your understanding, the initial strokes that give shape to your masterpiece of knowledge.

Exploratory Research: Types and Methodologies

there are two types of research that can be done when doing exploratory research, they are primary and secondary research

In this section we will discuss two types of exploratory research as well as the types of research methods you can use to conduct exploratory research:

Primary Research

Primary research is the firsthand explosion of discovery. It’s the thrilling journey where you gather fresh data straight from the source, whether through surveys, interviews, or experiments. It is usually carried out by a UX researcher, start up founder or really anyone looking to dig deeper into the unknown. If you are the one carrying out the research read my article on the 15 best user testing tools & usability testing tools of 2023 .

Focus groups

A focus group is where people have been specifically chosen based on a set criteria, for example a focus group could consist of people who bought a specific brand of laptops in the last 6 months, to take part in group discussions consisting of 5 to 10 people led by a person moderating the group.

Surveys are a quantitative research method used in the exploratory research stage to gather data quickly and cheaply. You are not going to be able to go in depth into ‘why’ the problem is happening but you will surely be able to understand ‘what’ is happening within your problem space. For example, you could use a survey to understand people’s opinions about the different brands of mobile phones they purchase.

Secondary Research

Also known as desk research, secondary research is like diving into a treasure trove of existing information. It’s all about mining data from sources like books, articles, reports, and studies that others have already conducted. It helps you grasp the landscape before you set out on your own journey. You scour through existing data to uncover patterns, trends, and insights. This groundwork informs your direction and primes you for deeper investigations.

Desk research

Doing research online is always my go-to whenever I start exploratory research. There is so much information that has already been researched. Through online research, you dive into a world of articles, databases, and reports, extracting valuable insights that others have uncovered.

Literature Review

Literature research is an intellectual expedition through written works to uncover insights, trends, and established knowledge on a specific topic. It involves scouring books, academic articles, reports, and scholarly journals. To conduct effective literature research, analyze and synthesize the gathered information to identify patterns, gaps, and prevailing viewpoints. This method equips you with a solid foundation before embarking on your own research journey. It’s like drawing from the collective wisdom of predecessors to enrich your understanding and insights.

How To Conduct Exploratory Research?

Here are the 12 steps I take to conduct exploratory research:

Step 1: Define Your Objective

Clearly outline the goal of your exploratory research. What do you aim to explore or understand better? It could be a new problem, a potential opportunity, or an emerging trend.

Step 2: Formulate Research Questions

Craft open-ended questions that align with your objective. These questions should guide your exploration and help you gather relevant insights. For instance, if you’re researching consumer preferences for a new product, your questions could revolve around their needs, preferences, and pain points.

Step 3: Choose Data Collection Methods

Select appropriate methods to gather data. Common methods include interviews, focus groups, surveys, and observation. Depending on your objective, choose methods that allow you to gather qualitative and diverse perspectives .

Step 4: Identify Participants

These individuals should possess insights relevant to your research objective. Whether it’s customers, experts, or stakeholders, ensure they represent a variety of viewpoints.

Step 5: Conduct Data Collection

If you’re conducting interviews or focus groups, guide conversations based on your research questions. If surveys are your approach, design them to gather the required insights.

Step 6: Record and Document

Thoroughly document your data. Record interviews, transcribe discussions, or collate survey responses. This ensures you don’t miss any valuable insights during analysis.

Step 7: Analyze the Data

Look for patterns, trends, and recurring themes. This process might involve coding qualitative data, quantifying survey responses, or categorizing information.

Step 8: Refine Your Hypotheses

Based on the insights gained from your data analysis, refine or adjust your hypotheses. Remember, exploratory research allows for flexibility in hypothesis formulation.

Step 9: Synthesize Findings

Organize insights, observations, and patterns in a way that addresses your research questions and supports your refined hypotheses.

Step 10: Draw Conclusions

What insights have you gained? How do they shed light on your research objective? Keep in mind that exploratory research might not provide definitive answers, but it should offer valuable insights.

Step 11: Determine Next Steps

Reflect on your exploratory research’s outcomes. Do your findings warrant further investigation? Are there specific areas that require more focused research? Decide if additional research steps are necessary.

Step 12: Communicate Your Findings

Share your exploratory research findings with relevant stakeholders. This could be through presentations, reports, or discussions. Highlight the insights you’ve gained and the potential implications for future actions or decisions.

By following this step-by-step approach, you’ll be able to carry out exploratory research like a professional.

What Are The Advantages and disadvantages of exploratory research?

Advantages of exploratory research.

It uncovers new and surprising insights, revealing things you didn’t know before.
You can adjust your research as you go, adapting to changing circumstances or new discoveries.
It captures a wide range of perspectives and experiences, giving you a fuller understanding of the topic.
It helps you generate ideas for future research and guides you towards more focused studies.
You get quick initial insights, even when you have limited time or resources.

Disadvantages of Exploratory Research

The findings might be influenced by personal opinions or biases, making it less objective.
It’s hard to apply the findings to a larger group or different situation since it often involves a small sample.
You won’t get lots of numbers and statistics, as it focuses more on understanding experiences and opinions.
It can take more time and effort due to continuous adjustments and refinements.
With so much information, it can be tough to sort out the most important findings.

While the advantages of exploratory research, such as generating fresh insights and adapting to evolving scenarios, are undeniable, it’s crucial to be mindful of potential drawbacks, like subjectivity and limited generalization.

In conclusion, the process of exploratory research empowers you to adeptly define objectives, craft hypotheses, embrace flexible methodologies, and decipher insightful data, leading you on a transformative journey of discovery that unveils new dimensions and perspectives.

Whether you’re a seasoned researcher seeking innovative approaches or a curious newcomer eager to delve into the realm of exploratory research, this guide furnishes you with the knowledge to deftly navigate the intricacies of this approach.

What is exploratory research design?

Exploratory research design is an investigative approach used to delve into new and unfamiliar topics, aiming to uncover insights, patterns, and relationships. It involves flexible methods like interviews, focus groups, and observations to gather qualitative data.

What is the purpose of exploratory research?

The purpose of exploratory research is to explore and understand a subject when little prior information is available. It aids in identifying potential problems, generating hypotheses, and refining research questions for more focused studies. Exploratory research sets the stage for deeper investigations, helping researchers grasp nuances and complexities.

Is exploratory research qualitative or quantitative?

Exploratory research is primarily qualitative in nature. It focuses on gathering subjective insights, opinions, and experiences. Through methods like interviews and discussions, it uncovers diverse perspectives and in-depth understanding, emphasizing quality over quantity.

When exploratory research is used?

Exploratory research is used in various scenarios:

When a problem is new and poorly understood.
To generate initial hypotheses and research questions.
Before conducting larger-scale quantitative studies.
To explore emerging trends or phenomena.
When there’s a need for a diverse range of perspectives.
In situations where little or no existing data exists.

UX Consultant

UX Consultant | UX Design Mentor

Roxanne Rosewood, is an accomplished UX designer and researcher with five years of experience. Drawing from her professional expertise in the field, she shares her valuable insights on UX design, UX research, UX writing, and UI design on her blog TheRoxannePerspective.com where she provides a wealth of knowledge and expertise in these areas.

Roxanne’s dedication extends beyond UX design and research, as she also serves as a mentor, guiding and supporting aspiring UX professionals.

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The Use of the Exploratory Sequential Approach in Mixed-Method Research: A Case of Contextual Top Leadership Interventions in Construction H&S

Siphiwe gogo.

1 Postgraduate School of Engineering Management, University of Johannesburg, Cnr Kingsway & University Roads, Auckland Park, Johannesburg 2092, South Africa

Innocent Musonda

2 Department of Construction Management and Quantity Surveying, University of Johannesburg, Cnr Kingsway & University Roads, Auckland Park, Johannesburg 2092, South Africa; az.ca.ju@adnosumi

Associated Data

The data supporting the reported results can be received upon reasonable request, in accordance with the data policy of the University of Johannesburg and the prevailing legislation on data sharing.

Quality and rigour remain central to the methodological process in research. The use of qualitative and quantitative methods in a single study was justified here against using a single method; the empirical output from the literature review should direct the current worldview and, subsequently, the methodologies applied in research. It is critical to gather contextual behavioural data from subject matter experts—this helps establish context and confirm the hypotheses arising from the literature, which leads to the refinement of the theory’s applicability for developing a conceptual model. This paper identified the top leaders in construction organisations as subject matter experts. Nine semi-structured interviews were conducted, representing the South African construction industry grading. The output of the refined hypothesis was followed by a survey that targeted n = 182 multi-level senior leaders to gather further perspectives and validate the conceptual model. The outcome resulting from the rigorous validation process adopted—the analysis process, which included Spearman rank correlation, ordinal logistic regression and multinomial generalised linear modelling—demonstrated that the lack of H&S commitment in top leadership persists, despite high awareness of the cruciality of H&S in their organisations. Contextual competence, exaggerated by the local setting, is one source of this deficiency. This paper provides guidelines for using the exploratory sequential approach in mixed-method research to effectively deal with contextual issues based on non-parametric modelling data in top leadership H&S interventions.

1. Introduction

Edmonds and Kennedy [ 1 ] defined the exploratory sequential technique as a progressive strategy that is used anytime that quantitative (QUAN) results are augmented by qualitative (QUAL) data. As a result, quantitative data analyses and explains the QUAL results in succession. The exploratory sequential technique is distinct from the explanatory sequential technique because it explores a concept before validating it, allowing for greater versatility in discovering novel ideas offered by the QUAL approach [ 2 ]. Numerous projects characterised by novel instrument creation choose this method as it enables the scholar to construct the instrument using QUAL information and afterwards verify it quantitatively [ 1 , 3 ]. As the sort of information generated by the first phase is uncertain—including whether it will emerge in a deterministic or non-parametric framework—and because the first phase is undertaken on a limited sample size, even though saturation would be achieved, the development of a new measurement instrument will be required. This is undertaken to handle the complexities of the resultant model characteristics because the contextual setting of top leadership is uncertain of the H&S culture’s consequences. Categorical data enables a greater level of precision and unambiguity [ 4 ]. Hence, it is advisable to perform validation or tests on the QUAN part of the model [ 3 , 5 ].

One of the distinct advantages of using an exploratory sequential approach is described by Heesen et al. [ 6 ] as a method that comparatively provides more robust validity. First, according to Flick, the interview-based QUAL methodological technique is suitable for resolving unresolved issues and developing and extending ideas based on such discoveries [ 7 ]. Interviews generate extensive data that allows subdomains of ideas to be studied. Furthermore, interviews are a direct data-collecting approach that is optimum for understanding issues’ complexity and depth. These collected ideas stemming from the rich data collected are used to reinforce the hypothesis [ 8 ]. When referring to the survey QUAN methodological approach, Bajpai [ 9 ] asserts that primary sources of data provide multitudes of benefits; it is noted that primary findings are frequently pertinent to the research objectives since they are collected on an individual basis. Applying both QUAL and QUAL approaches to single research works offers a more significant opportunity to establish more insight into the study subject, whilst a higher degree of validity and accuracy are achieved compared to applying a single approach [ 10 , 11 ].

This paper presents an interpretative, exploratory sequential methodology established on contextualism/a pragmatic worldview. Therefore, it is critical to establish the basis for this worldview as a start, to create a platform for the type of knowledge approach that this paper has adopted.

2.1. Establishing the Worldview

According to Crotty [ 12 ], a worldview or ontology is how the world is interpreted as existing. Research indicates the difficulty caused by the environmental context in the infrastructural development initiative in South Africa—particularly the necessary competency in upper-echelon leaders to lead a high-performance culture in organisational H&S [ 13 ]. In research associated with this, failure to select an appropriate tool in the beginning further increases methodological difficulties and causes severe confusion, leading to worthless study outputs [ 14 , 15 ]. Dumrak et al. [ 16 ] and Marle and Vidal [ 17 ] emphasise the complications brought by context by pointing to the extreme intricacies of major construction projects compared to smaller projects.

Accordingly, in a literature review, this study has applied an approach promoting contextualisation, according to Pepper [ 18 ]. This theoretical paradigm is therefore applied systematically throughout the whole study. Perception may be classified into four theoretical aspects: formism, mechanism, organicism, and contextualism, according to the orientation to cognition by Pepper [ 18 ]. Contextualism is a theoretical paradigm that presupposes a definitive understanding of a phenomenon categorisation occurs once it is placed in its main context [ 18 ]. As a result, it is logical for this paper to be aligned to a worldview that demonstrates the effects of the national and economic sectoral environment on the capacity of top leadership to change H&S culture and influence H&S results.

Zikmund [ 19 ] views contextualism as pragmatism, asserting that pragmatism as a philosophy is based on behaviour, circumstances, and outcomes rather than past conditions. It is supported by a paradigm focused on what constitutes logic and how to resolve issues.

2.2. Epistemology

True perception is seldom universal but rather illative, interpretative, and speculative. The criterion by which existence is measured is mostly pragmatic [ 3 , 14 , 20 ]. Crotty [ 12 ] describes epistemology as a conceptual viewpoint followed by a logical position that informs methodology and thus brings purpose to a technique that specifies the study’s logic and variables selection. The respondent (knower) and the individual cognitive bias (the known) in the H&S leadership commitment, in the context defined by the worldview, are the criteria in this case. As Morris [ 21 ] suggests, this “known” knowledge acts as a precursor to the efficacy of the interpretative paradigm, which is based on the notion that all knowledge is contextual. The interpretative method is concerned with perceiving nature via one’s subjective impressions. These rely on a mutual engagement between the researcher and the issues and use perception procedures (rather than QUAN) such as interviews. This approach backs up the notion that substance is formed via first-hand opinion; it believes that forecasts are difficult to come by. Per this concept, individuals have free will, aspirations, emotions, and thinking [ 11 , 22 ]. By conducting interviews with subject matter experts—the top leaders—and by also conducting surveys with the top management team (TMT), this study fits well within the interpretative scientific logic concerning the nature of knowledge—hence the adoption of the exploratory sequential technique for gathering and analysing the required data.

2.3. Research Design

Research design is a thorough description of the steps that must be followed during the data gathering and analysis to produce a satisfactory answer to research questions [ 5 , 23 ]. Additionally, research design may be defined as the overarching principle that the study will adhere to for the many components of the study to be applied logically and succinctly, assisting the scholar in reaching an ideal outcome [ 24 ]. Figure 1 shows the research design for this paper.

An external file that holds a picture, illustration, etc.
Object name is ijerph-19-07276-g001.jpg

Research design outline (Adapted from Zikmund et al. [ 19 ]).

To accomplish a best-fit conceptual model, which is one of the outputs of this paper, the procedure for adopting the conceptual model’s hypotheses, description, and assessment requirements follow Elangovan and Rajendran’s [ 25 ] seven-step rigorous conceptual modelling framework and integrate an acclimation of Zikmund et al.’s [ 19 ] scientific approach. The background knowledge was derived from the literature review’s output and synthesised into eight hypotheses, forming a typology. Gogo and Musonda [ 26 ] state that this typology forms the basis for the background input for the methodology section described in this paper. The literature demonstrated that leadership is implicitly and explicitly related to H&S outcomes. The eight hypotheses developed from the literature review showed that leadership positions are challenging and require greater comprehension to ensure a decrease in the rates of injury to workers. These hypotheses are all anchored on the actions emanating from top leadership commitment in the South African construction industry.

2.4. Conceptual Typology

The functional content measure for the typology in Table 1 is the synthesis of the project’s eight propositions and how these contribute to one another and the study’s four core principles, namely: top leadership participation, regional cultural background, H&S culture and H&S performance. The H&S competency creation and audit framework contribute to top leadership engagement in the typology. In contrast, external influences such as regional culture and the business field are used as the backdrop for top leadership engagement. Table 1 shows the interactions between the model variables.

Functional content measures for the typology.

Source: Gogo and Musonda [ 26 ].

The contextualism approach to philosophy is reinforced by the national culture and construction industry for this typology. The description of conceptual restrictions by Babbie [ 27 ], which range between Meta (South Africa), Macro (Construction sector), Meso (Top organisational leadership) and Micro (Leadership commitment), is applied consistently in the typology [ 26 ].

3.1. Data Collection Approaches

The initial phase of the data collection and analysis was the interview stage. It was characterised by non-probability purposive sampling to establish a theory based on the conceptual model and hypothesis. This was the QUAL phase, where the nine interviews were conducted. The second phase, QUAN, comprised the pilot stage of the survey study, where non-probability convenience sampling was applied to 10% of the target sample as a pilot study to establish the tool’s validity. The third stage, characterised by random probability sampling, used the developed survey questionnaire to gather perspectives on top leadership commitment to H&S by applying QUAN.

3.2. Population and Sampling of the QUAL Study

Patten and Newhart [ 28 ] describe a population in research as the people, things, wildlife and vegetation involved in the research. Typically, a sample is drawn to represent the population [ 29 ]. This paper considered the target population as the top leaders of the construction organisations in South Africa at all nine levels of the CIDB.

Representation, subject-matter expertise and thematic saturation are critical for determining sample sizes [ 30 ]. For qualitative studies, Guest et al. [ 31 ] describe the adequacy of a sample size as reaching saturation at 6 to 12—where 30 has been defined as the upper limit. To reach QUAL saturation in this paper, a sample of n = 9 was aimed for. Additionally, in the QUAL study, subject-matter expertise was ensured by targeting top organisational leaders for their primary prowess in the upper-echelon leadership of contractors and, in particular, their H&S business liability, as per Galvin [ 32 ]. Although the target sample for reaching QUAL saturation is small, it still has to fulfil the representation criteria to ensure that the population is well represented [ 29 , 32 ]. In the case of the QUAL study, this representation is achieved by first ensuring that all nine levels of the CIDB are included, and then secondly, by applying a non-probability, purposive sampling method. Non-probability purposive sampling creates a direct method for targeting a subject-matter expert based on defined criteria (CIDB grade, position in company, legal appointment in company and more) [ 31 ].

3.3. Population and Sampling of the QUAN Study

Consistency is key to quantitative studies [ 33 ]. Accordingly, comprehensive insight into a specific phenomenon is validated by many respondents, demonstrating consistency in supporting a defined proposition. Cooper and Schindler [ 2 ] posit that of the many variables that define a sample size, the size of the population, uncertainty, variance and confidence interval are among the most influential.

In the QUAN study of this paper, a pilot survey that targeted n = 18 respondents was achieved by non-probability convenience sampling. The main survey applied random probability sampling to n = 180 respondents. Non-probability convenience sampling was selected for its versatility and to limit the selection of multiple members in the same group—thus ensuring full representation in a smaller sample size. On the other hand, random probability sampling was selected because the likelihood of each member being selected is known—thus ensuring greater participation in the larger sample size [ 22 , 33 , 34 ]. This is particularly useful when targeting multi-level respondents, as was the case in this paper, to ensure that perspectives in top leadership commitment to H&S are gathered from all levels of the upper-echelon construction organisations’ leadership.

3.4. Interview Data Collection Procedure

To ensure clarity in the keynote and the spheres of the enquiry and assessment, Arksey and Knight [ 35 ] support the idea of two interviewers; however, this view is refuted by Whiting [ 36 ], who posits that the use of a single interview conductor is sufficient. Whiting [ 36 ] substantiates this assertion by further providing robust interview guidelines. This paper uses a single interviewer following Whiting [ 36 ].

For this study, the design of the semi-structured interview questionnaire followed the protocols and guidelines by Scheele and Groeben [ 37 ], Graneheim and Lundman [ 38 ] and Whiting [ 36 ], which emphasise that questions should be based on the reviewed literature. Accordingly, all interview questions are based on a conceptual typology proposed by Gogo and Musonda [ 26 ], which depicts how each leadership aspect relates to each respondent’s H&S aptitude for each contextual determinant. This allows the researcher to examine how the model depicts leadership commitment related to H&S in its full context by accurately representing the respondent [ 39 ].

Furthermore, these guidelines included the setting, which in the case of this paper was the respondent’s office—or online in case of constraints for a physical meeting. The respondents were also provided with a short description of the research, and the purpose of the interview was explained clearly. The divisions of the interview questionnaire were explained, and the entire meeting session was kept aligned with the ethical boundaries set beforehand. The interviews were verbal while incorporating the probing techniques shown in Table 2 to reach a clear response. The respondent’s answers were recorded verbatim on both tape and interview answer sheets by the interviewer. Recording the interview answers verbatim offers a robust method for data collection [ 3 ].

Techniques for probing which can be used during interviews.

Source: Adopted from Whiting [ 36 ].

3.5. Survey Data Collection Procedure

Bajpai [ 9 ] posits that a comprehensive review consists of primary and secondary data. The secondary data collected is an input for the survey method for the primary data collection. It is important to mention that while primary data is typically gathered on a case-by-case basis, it generally is closely tied to the research aims and questions [ 9 ]. According to Cooper and Schindler [ 2 ], there are various methods for collecting primary data, but surveys are the most robust method for quantitative data collection. Employing primary data for analysis has numerous advantages, but it also has certain limitations. First, it requires a lot of time, funds, and human resources; however, getting data in some contexts may be problematic due to privacy and security considerations that hinder people from engaging in data collection endeavours. This situation is often overcome by using anonymous surveys [ 2 ].

According to Bajpai [ 9 ], the tool used to gather data must be dependable and repetitive to be useful. In addition, researchers argue that this tool must fulfil stringent validity criteria, such as reliability and responsiveness, to be regarded as a robust measuring device. During the sample period, survey questionnaires were the primary means of collecting data. Participants in the study were asked to complete a survey in which the test variables and research topics were addressed [ 40 ]. As a result of the survey, a quantifiable framework for assessing senior leadership commitment to H&S in construction work was established to help with future research.

For this paper, Google Forms TM was selected as the survey tool because while it is offered for free, it comes with a user-friendly interface for both the respondent and the researcher. It also comes with a myriad of tools, such as graphs, and it can output the captured data to an M.S. Excel spreadsheet for further processing. This tool also offers better validity for collected data than paper surveys because it automatically prevents the respondent from making invalid selections. The researcher produced a simple, short set of guidelines to precede every survey section to ensure understanding of the context and answering requirements [ 41 ].

The request for survey participation was administered via email to n = 18 (ideally, two for each CIDB level) respondents for the pilot study and n = 180 (ideally, 20 to represent each CIDB level) respondents for the actual multi-level perspective study. The same target group of top leaders in the construction industry were targeted for participation. In the request, a link to the online survey was provided.

3.6. Ethical Considerations Regarding Data Collection

To begin with, it is necessary to discuss the study’s ethical implications [ 42 ]. According to Hay [ 42 ], the ethical concepts of justice, beneficence, non-malfeasance, and respect must be included in any investigation conducted. This ensures that protection measures for participants and the institution are addressed. For this paper, this is particularly amplified by the ethical requirements of the University’s policies. These ethical considerations were discussed thus:

Ethical intent to achieve autonomy —brief instructions were provided in the interview and survey questionnaire forms to ensure that the respondents were as autonomous as possible and that dependence on the interviewer was limited.
Ethical intent to achieve beneficence —beneficence is how the study will benefit. For this paper, this was demonstrated by the novelty of the mixed method presented and how this method led to the fulfilment of the research objectives.
Ethical intent to achieve non-maleficence —To ensure just and unbiased participation, demographical information about gender, race, political affiliation, religious beliefs, ethnicity, family orientation, marital status and health conditions of each respondent was not considered or collected. Additionally, ranges of experience rather than discreet numbers were used to provide uniformity among the respondents.
Ethical intent to achieve justice —The risks for participants were covered by a disclaimer and the voluntary participation of the participants, as well as their anonymity. All human rights defined by state laws to institutional laws were observed. The selection process applied for the respondents ensured that the participation of top organisational leaders was inclusive of all groups, without consideration of any form of segregation or target (blind process).

3.7. Validity of the Collected Data

For validity, an instrument must be able to accurately compute the value it is meant to ascertain [ 43 ]. The internal consistency of collected data is also critical for its validity [ 22 ]. Furthermore, the collected data must fulfil the minimum requirements defined by the sampling method in terms of quantity and form [ 44 ]; this is particularly useful in dealing with erroneous or missing data. In this study, validity was approached by adopting the analysis of variance, where at least 90% has been set as the cut-off point for valid responses in both the interview and the survey data collection phases. Similar to grounded theory, the thoroughness of the procedure adopted determines the validity of the findings [ 7 , 45 , 46 , 47 ].

The online data collection tool adopted for the survey questionnaires prevented the participants from improper selections and ensured that only valid options were selectable from the Likert scale survey questions. This ensured that all submitted forms contained upwards of 95% acceptable data. Furthermore, using a 5-point Likert scale for measurements ensured that the extremities of the data were catered for, whilst a middle ground was also provided for respondents that had a somewhat equal distribution between the extremities in certain questions.

The interview answers’ verbatim transcription, completeness, language, and relevance are also critical for validity criteria [ 41 ]. This means that the selection of recording media becomes critical at this stage. For this study, validity was achieved using tape recording and interview answer sheets, which the interviewer consistently completed in all nine interviews with the top leaders. Furthermore, using the probing techniques described in Table 3 ensured that the respondents provided complete and relevant responses to each question.

Intercoder framework method.

Source: Adopted from Gale et al. [ 50 ].

3.8. Reliability of the Collected QUAL Data

Academic assessment systems must provide reliable and accurate data to ensure repeated performance verification [ 48 ]. A study’s perceived reliability is bolstered by the precision with which its data were collected and coded (McHugh, 2012). This study adopted a coding process by Adu [ 49 ]; however, for reliability, it followed the seven-stage Framework Method by Gale et al. [ 50 ], as shown in Table 3 . It follows that the intercoder reliability method described and recommended by Freelon [ 51 ], Neuendorf [ 52 ], Mayring [ 53 ], Krippendorff [ 44 ] and Hayashi et al. [ 48 ], amongst others, was adopted in this study. Everitt and Skrondal [ 54 ] and Krippendorff [ 55 ] describe the inter-rater agreement as to the coordination level between multiple investigators, assessors, or empirical evaluations. This approach was selected to ensure that sources of errors in coded interview data were eliminated or minimised.

The sophistication of the coding procedure affects the likelihood of mistakes in the data-coding stage [ 56 ]. Non-exclusive coding methods are more subject to problems. Although Adu [ 49 ] has suggested that a single method for intercoder reliability would suffice, in this paper, several methods were used, following the suggestions from Freelon [ 51 ] for providing a strong estimate of reliability.

To achieve the intended multiplatform intercoder reliability, a web-based intercoder reliability calculation platform, ReCal™—developed by Freelon [ 51 ]—was selected and then applied for calculating the intercoder reliability. A multi-tool intercoder reliability approach applied Percentage Agreement, Scott’s Pi coefficient, Cohen’s Kappa coefficient and Krippendorff’s Alpha coefficient accordingly in this study.

(a) Percent agreement

Percent agreement is defined by Hayes and Krippendorff [ 41 ] as a framework for assessing reliability in which two raters select the proportion of elements with comparable attributes. Using this metric, two raters may be distinctive in the form of a percentage [ 57 ]. The following formula gives the Percentage Agreement:

where: PAo = Observed magnitude of agreement; A = Number of unanimities between the coders; and n = Total number of decisions between the coders.

In this reliability measure, the principle recommended by numerous scholars suggests that the ranges of 75% to 90% are permissible in terms of the proportion of arbitrary consensus [ 58 ]. This is the first measure of reliability applied to the QUAN data in this paper. However, this metric does not give a strong level of confidence for reliability precision since it is straightforward and excludes chance as a consideration [ 41 , 59 ]. Yet, its utility remains intact, and as a result, it is appropriate to utilise and include it in this assessment [ 52 ].

(b) Holsti’s Method

While Holsti’s Method is a variation of the Percentage Agreement, Wang [ 60 ] states that if both coders use the same coding units, the findings of this approach will be identical to that of the Percentage Agreement. It would also use the same formula as that of the Percentage Agreement; however, should the coders code different datasets, the following formula is applicable:

where: PAo = Observed magnitude of agreement; A = Number of unanimities between the coders; and N1/N2 = Total number of decisions for each respective coder.

This research was designed so that the same set of data was coded individually by the two coders; hence the deployment of Holsti’s Method was not evaluated and was predicated on Percentage Agreement, as indicated by Wang [ 60 ]. The Percentage Agreement that was utilised is therefore sufficient.

Krippendorff [ 44 ] presents Scott’s Pi as an enhancement of the fundamental Percentage Agreement that addresses the predicted consensus amongst the coders for objects that are not tied quantitatively to their descriptions. Percentage Agreement and Holsti’s Method lack the consensus of probability that this metric, which considers the weight of the collective viewpoints, gives [ 60 ]. Reliability rigour is seen as having a crucial role in chance [ 41 , 52 , 53 ]. Landis and Koch [ 61 ] used comparative intensities in the attained coefficient to show the gauge of acceptance in reliability while utilising Scott’s Pi. Even though the technique supplied by these authors is optional, it provides good guidance and a benchmark for assessing the robustness of intercoder efficiency when employing both Scott’s Pi and Cohen’s Kappa. Table 4 shows the approach by Landis and Koch [ 61 ] in the acceptance criteria of the achieved coefficients.

Intercoder reliability coefficient acceptability.

Source: Landis and Koch [ 61 ].

In this paper, Scott’s Pi is applied without considering the confidence interval; however, a confidence interval is supposed to demonstrate how high the achieved reliability can get.

(d) Cohen’s Kappa (κ)

Everitt and Skrondal [ 54 ] explain Cohen’s Kappa as a matrix eventuality tabular array that determines the percentage probability of data points, bringing consensus by taking likelihood into account. Interrater reliability testing relies heavily on this powerful statistical tool [ 57 ]. Like Scott’s Pi, Cohen’s Kappa has an unweighted formula (without a confidence interval). There are several ways to solve the issue of a rating between more than two raters, including Fleiss kappa; however, for this paper, Cohen’s Kappa will suffice [ 57 ]. There is an important distinction between the two: unlike Cohen’s Kappa, Fleiss kappa does not have enforced weighting [ 52 ]. Using confidence intervals, a statistician may begin to evaluate the utility of the obtained Kappa, according to McHugh [ 57 ]. To show rigour in kappa values, confidence intervals must be utilised instead of Percentage Agreement, which is an exact indication and not an estimate. Confidence Intervals (C.I.s) are described by Sim and Wright [ 62 ] and Mukherjee et al. [ 63 ] as the degree of trust, which entails that the CI has to be specified before the review of the results. For social research, a lower limit (CI LL ) and upper limit (CI UL ) CI of 95% is ordinarily used [ 63 ]. Confidence intervals use this formula:

where: CI = Confidence interval (coefficient); X = Sample mean; z = Confidence level value; s = Sample standard deviation; and n = Sample size.

(e) Krippendorff’s Alpha (α)

By drawing or assigning probabilistic variables amongst ordinary, unstructured elements, Krippendorff [ 55 ] defines Krippendorff’s alpha ( α ) as an internal consistency coefficient that measures the consensus of raters or research instruments to demonstrate validity. In content analysis, Krippendorff’s alpha ( α ) is widely considered among the more precise and adaptable agreement metrics, which provides substantial dependability and rigour (Krippendorff, 2018). Compared to other specialised coefficients, Krippendorff’s alpha offers a more general technique. This allows for a wide range of measurements typically neglected by conventional assessments, such as contrasts between multiple raters, discarding missing data, adjusting to varied test ranges (nominal, ordinal, ratio and interval) and allowing for comparisons across an extensive range of measures [ 41 , 48 ]. The formular for Krippendorff’s Alpha (α) is given by:

where: α = Magnitude of agreement (coefficient);

PAo = Observed magnitude of disagreement for analysis values. PAo is given thus:

PA E = magnitude of disagreement anticipated through chance, given thus:

Similar to Cohen’s Kappa (κ), for Krippendorff’s alpha (α) values, a confidence interval (CI) of 95% was introduced in this paper. This choice of CI shows that Krippendorff’s alpha reliability indicator is both reliable and dependable [ 44 , 53 ]. For the acceptance of the test results, the number of values was 0 to 1, with 0 representing absolute conflict and 1 representing absolute consensus. Krippendorff [ 44 ] posits that it is typical to expect an alpha value of 0.800 as an acceptable baseline, while 0.667 can be regarded as the lower reasonable threshold (L.L.) for which preliminary assumptions are permissible.

3.9. Reliability of the Collected QUAN Data

The term “reliability” refers to the consistency of the results obtained from different calculations of the same thing [ 64 ]. Outcomes in correctly conducted functional test experiments are partially attained in research by following the scientific evidence strategy, rendering QUAN analysis dispersion and validity characterisation a factor that allows the report’s outcomes to give rigour to the research. Rigour refers to the extent to which researchers strive to enhance the consistency of their studies [ 65 ]. Heale and Twycross [ 65 ] identify three characteristics of reliability: homogeneity or internal consistency, steadiness and commonality. Of the Cronbach’s alpha, split-half, Guttman, Parallel and Strict parallel approaches, Cronbach’s alpha has been recognised by several researchers as the instrument of preference for basic, coefficient-based reliability assessments provided by IBM SPSS [ 58 , 65 , 66 ].

(a) Cronbach’s alpha

To determine how effectively a group of variables or items accurately captures a singular, simplistic, latent concept, Cronbach’s alpha (α) is used. Many experts propose an alpha coefficient of between 0.65 and 0.8 as a good range, whereas an alpha coefficient of less than 0.5 is considered poor—especially for ordinal measurements [ 66 ]. There is a decent level of confidence for coefficients of 0.7 and higher, and alpha values are often interpreted as follows: high = 0.90; medium = 0.70–0.90 and poor = 0.55–0.69 [ 65 ]. According to Louangrath [ 67 ], using Cronbach’s alpha to calibrate experiments is inaccurate. This is particularly amplified in non-parametric datasets, as shown in Table 5 . The idea is that the instrument’s dependability must not rely on reactions after design and testing. This paper considered alternative reliability methods to Cronbach’s alpha for QUAN datasets, as shown in Table 5 .

Type of data distribution.

Source: Adopted from Ezie [ 68 ].

(b) Determination of the QUAN data reliability tool

Louangrath [ 67 ] proposed a set of interconnected tests for determining reliability in non-parametric data, including raw reliability estimates, Monte Carlo simulation and N.K. Landscape optimisation simulation. It immediately follows that a test of normality for this study was conducted to establish, first, if the dataset was normally distributed; then, secondly, the application of the correct tool for reliability. Others mention generalisation, such as Razali and Wah [ 69 ] and Heale and Twycross [ 65 ]. This research used a technique described by Ezie [ 68 ] for non-parametric data analysis.

3.10. Interview Data Processing Approach

To extract relevant assumptions, QUAN analysis involves the statistical data analysis of many sample examples, while QUAL analysis relies on chosen semi-representative cases or descriptive representations in metanalyses [ 23 ]. The data analysis of the QUAL data was based on the inferential qualitative content analysis described by Mayring [ 53 ] and followed the coding procedure described by Adu [ 49 ] in this paper. Compiling interview transcripts is a normal first step in qualitative content research, according to Erlingsson and Brysiewicz [ 70 ] and Adu [ 49 ]. The qualitative content analysis aims to organise and summarise large amounts of material [ 2 ]. Extracting data from transcoded interviews to generate ideas or trends involves deep harvesting of data from apparent and semantic content to tacit inferences [ 7 ]. This research applied Atlas.ti ® technology to reduce and code data, then handle the resultant data using SPSS and M.S. Excel to display it in tables and figures for descriptive statistics. Figure 2 demonstrates the adoption process for the QUAL data coding.

An external file that holds a picture, illustration, etc.
Object name is ijerph-19-07276-g002.jpg

Data coding strategy (Adapted from Adu, [ 49 ]).

3.11. Survey Data Processing Approach

The QUAN data statistical analysis tool chosen was SPSS. The raw data was assessed for parametric or non-parametric fit before picking a particular tool for model fit and hypothesis testing [ 44 , 53 ]. The analysis approach generally followed Saunders et al. [ 47 ]. Following normality tests, which were comprised principally of the degree of Skewness and Multivariate Kurtosis as guiding descriptors, correlation and regression of the model variables were applied.

(a) Model fit criterion

A generalised, structured component analysis model, such as the non-parametric model, may be used to meet model fit requirements, according to Cho et al. [ 71 ]. While model fit refers to how well a model fits the data, rather than how well the model’s variables correlate, reliability relates to how well a model matches the data. Since each model fit must statistically fulfil specific criteria before being labelled a data fit, the criterion must be defined before data collection [ 71 ].

(b) Further analysis

After the model fit criterion is met, further statistical analysis deals with the model variables and targets how the model variables behave when correlated to each other. Hypothesis testing is the last step in the model analysis, and it is performed as a critical step to test if the defined and refined study hypothesis still holds or should be rejected.

In this paper, the use of the exploratory sequential approach mixed method is demonstrated in the three chosen stages (Stages 1, 2 and 3) and illustrated in Figure 1 and Table 1 —which were the first stage (QUAL), where non-probability, purposive sampling was used to secure semi-structured interviews which were used to establish theories based on the conceptual model and hypothesis, followed by the two QUAN stages (2 and 3), where the survey data was collected first to establish the validity of the survey tool by conducting a pilot survey with 10% of the target sample, and then secondly to gather perspectives on top leadership commitment to H&S by conducting a multi-level perspective survey on two top leadership levels.

4.1. The Overall Data Collected

The QUAL study comprised n = 9 interviews representative of respondents in top leadership in all nine CIDB grades. In both the QUAL and the QUAN study, there was sufficient representation in the upper echelon—spanning all nine CIDB grades—and overall, several years of experience, a generally good higher education and experience in public infrastructure projects were demonstrated. Table 6 shows the participation demographic results for both the QUAL and the QUAN portions of this study.

Demographic.

4.2. Validity of the QUAL Results

The fullness of the interview questions established the preliminary interview validity and whether the intended group was attained [ 44 ]. The subsequent validity originated from the quality of the information provided—in this example, the techniques with which the answers were given, their overall depth and relevancy, and the vocabulary utilised throughout the conversation. The second crucial feature of this validation step was capturing data from the discussion to accomplish accurate transcribing that would meet the relevance criteria for the obtained data. For this research, the QUAL data collecting procedure described in the preceding sections met the criterion for this level of validity. The successive phases of validity are discussed in the sections to come.

4.3. Coding of the Collected Data

From the QUAL study, 387 (43 × 9) responses were collected and transcribed verbatim, then coded into 86 codes (74 deductive and 12 inductive) and 23 anchor codes. From the QUAN study, 7826 (43 × 182) responses were collected and transcribed, and then 43 codes (ordinal, 5-point Likert scale) were developed for the data for analysis.

For the QUAL study, a CAQDAS platform—Atlas.ti ® —was used, where 414 quotations were identified from the 387 responses, resulting in a total of 86 codes developed via the content analysis of these responses. The process for categorising these codes involved reference to the questions, where a code-synthesis and categorisation process was applied consistently with the process described by Adu [ 49 ], as shown in Figure 2 . The QUAN data was numerical, and the coding was performed in the survey questionnaire itself, making further coding post-data collection unnecessary.

4.4. Results from the Reliability Tests

Intercoder reliability in the QUAL study was achieved from 86 valid cases, where 79 agreed between the codes; disagreements comprised seven cases. A total of 172 decisions were taken. Four methods were applied simultaneously; the results achieved are presented in Table 7 .

These generic results are above 0.73 on average, with the Percentage Agreement exceeding 90%, signifying that the results are all within the acceptability criteria set for each of the reliability methods defined under Section 3.8 of this paper. This provides confidence that the coding process adopted offers sufficient accuracy and relevance and that the data analysis method will render accurate results.

Similarly, reliability, validity and hypothesis testing for the QUAN study also employed a robust process, where the distribution normality test was applied, followed by correlation and regression. The distribution normality test results were achieved for validity and reliability: Kolmogorov–Smirnov Sig Index = 0.000 (non-parametric). This meant that the dataset was non-parametric. Therefore, Spearman Rank Correlation, Ordinal Logistic Regression and Multinomial generalised linear modelling were adopted and applied to the dataset for statistical analysis.

4.5. Results from the Statistical Analysis

For this study, a statistical analysis of the QUAL dataset was not conducted because it followed the content analysis method; nonetheless, the statistical analysis of the collected QUAN dataset was robust and yielded the following summarised results:

Spearman rank correlation results: Rho LC to CF = 0.421; ST = 0.101; LC = 1.000; CC = 0.239; NC = 0.317; CO = −0.184
Ordinal logistic regression: Pseudo R-square (Nagelkerke) index = 0.593; Deviance Sig = 1.000; Chi-square Sig = 0.000

The model fit data from both the correlation and regression tests demonstrated that the model fit the data well and that there was a positive correlation between the independent variable, the factor and all the covariates—except for the H&S culture outcomes variable, which was seen to be a residual variable from the outcomes of the top leadership H&S commitment.

4.6. Hypothesis Testing

Multinomial generalised linear modelling was applied for hypothesis testing on the QUAN dataset, focusing on the model construct. The following results were achieved: Wald Chi-square Sig LC-CF = 0.023; S.T. = 0.261; CC = 0.000; CO = 0.427. This signifies that the conditions for rejecting the null hypothesis associated with the top leadership style were not met, and the null hypothesis was therefore not rejected. All the other hypotheses were not rejected. Simply put, the practical methods provided by styles and models in developing the critical elements required in top leadership did not add value to organisational H&S outcomes.

4.7. Descriptive Statistics

The descriptive statistics results from both the QUAL and QUAN studies are summarised and themed as focus areas of theory as follows: H&S as a core organisational leadership function; top leadership type and style impact; top leadership H&S commitment; top leadership contextual H&S competence; the effect of national and industry contextual setting and the H&S culture outcomes. Since there are many of these tables, this paper does not intend to discuss such results; however, it aims to demonstrate the processes to be followed.

5. Findings

The findings from the data collected from the QUAL study resulted in the refinement and revision of the initial hypothesis. This revised hypothesis was then tested using the model construct and data collected in the QUAN study, resulting in one of the hypotheses being rejected while the remaining were not dismissed. This signifies the importance of employing a robust tool consisting of a series of consistency tests to ensure that the presence of errors in research is minimised. The top leadership effect on the H&S function has been pivotal, hence the overwhelming number of valid responses and participation in a study that questions their interest in H&S and overall involvement in the field.

The other finding is demonstrated in the choice of robust tools and how they were applied differently in both the QUAL and the QUAN study. The normality test was significant in ensuring that the assumptions of simply applying Cronbach’s alpha to any dataset, as an example, were omitted. This is a particularly useful point of departure in dataset analysis, particularly in non-parametric datasets.

6. Discussion

6.1. convergence of the applied research tool.

Firstly, the choice of the exploratory sequential approach in mixed-method research that focused on the contextual top leadership interventions in construction H&S became very useful during the reliability stages of the QUAN data, where the test of normality results revealed that the dataset was non-parametric before the selection of the appropriate reliability tool. This reinforces the assertion by Cresswell [ 3 ] and Edmonds and Kennedy [ 1 ] of the benefits offered by this type of approach.

Secondly, the model complexity of the resultant model characteristics—because the contextual setting of top leadership is uncertain of the H&S culture’s consequences—required that the coding method adopted offer very good accuracy, and this has been demonstrated in the intercoder reliability of the QUAL data, which adopted a robust, multi-tool process that demonstrated very good outcomes. This reinforces the assertion by Cresswell [ 3 ], Bairagi and Munot [ 5 ] and Palm III [ 4 ], who emphasise accuracy in the validation process.

Thirdly, an all-rounded process, as described by Zikmund et al. [ 19 ], where multiple tools are applied to ensure valid results, was set up first by the QUAL study, which sought to refine the applicability of the theory on the conceptual model and hypothesis by interviewing the subject-matter experts (in this regard, top leaders)—a process which then limited the number of respondents to ensure saturation ( n = 9) was met, following Guest et al. [ 31 ] and Galvin [ 32 ]. This was then followed by the QUAN study, divided into two sections which were to establish the validity of the survey tool by conducting a pilot survey with 10% of the target sample and to gather perspectives on top leadership commitment to H&S by conducting a multi-level perspective survey of two top leadership levels.

6.2. The Impact of the Tool on Research

A practical approach that may be used for the external validity of this model is an analytical generalisation. Analytic generalisation is when case studies are applied to a theory. Then the outcomes of those case studies are acknowledged as a basic guideline for that concept, strengthening the hypothesis confirmability and its practical significance [ 72 ]. Typically, in verification procedures, assumptions are utilised for evaluating models and methods [ 73 ]. For validity to be carried out effectively in case studies, the collected data serves as evidence, and this evidence should be collected from at least five sections of the model—namely: the internal structure of the model; the variable connectedness to each other; the process of the responses; the content of the test and the implications of the test [ 74 ].

In his paper, the model structure is already described; thus, the case study would need to demonstrate the outcomes from the standpoint of the contractor and top leader(s) being evaluated. It is critical to establish a benchmark before modifying the participants; thus, the study outcomes have demonstrated the current status of the top leader in H&S functioning and their importance in H&S matters.

6.3. Future Study Focus

This paper established a mixed-method approach that can be applied to contextual top leadership interventions in construction H&S by adopting an exploratory sequential approach. The method itself was the paper’s focus. The in-depth details of certain aspects such as the statistical analysis, descriptive analysis, the data coding process, and the theory of top leadership in the construction H&S were not discussed but highlighted. The description of the tool is sufficient for its adoption by other researchers in the future. Future studies are encouraged, and scholars are highly invited to familiarise themselves with the methodological tool established in this study and utilise it in comparable studies and general practice to advance research and knowledge.

6.4. Contribution Made by This Study

A rigorous approach for designing an exploratory, sequential research method using both interviews and survey data was created in this work. The tool’s novelty was established in its point of departure from the norm in applying reliability tools prior to testing for normality and applying a rigorous process of multi-tool intercoder reliability, which also adopted a web-based tool to augment the spreadsheet calculations. Using generalised linear modelling in a study of this kind also signifies a point of departure from the norm.

The main highlights of this tool are that it is effectively managed to handle non-parametric, QUAN/QUAL data by offering a robust coding approach, data validation, model fit and reliability approaches that can be applied consistently in similar QUAN/QUAL data. The tool further offered validation capabilities for QUAN data and multi-variate hypothesis testing in an exploratory sequential method for dealing with data for H&S research of this kind; this study on the establishment of contextual top leadership interventions in construction H&S was made successful by applying this approach.

7. Conclusions

This paper provides guidelines for using the exploratory sequential approach in mixed-method research to effectively deal with contextual issues based on non-parametric modelling data in top leadership H&S interventions. The main focus was established by extensively providing elements that demonstrated rigour in a qualitative and quantitative study in a mixed-method environment and by distinctly placing a sequential study into the relevant context for this type of research.

The key contribution of this paper is the provision of a novel process marked by the intricacies of the reliability approaches adopted and the type of modelling analysis incorporated into the study. More specifically, this contribution can be summarised thus:

In the QUAL phase, the intercoder analysis was marked by a multi-tool approach augmented by a web-based platform. This demonstrated a robust method for approaching the reliability of such data in which the harmonious agreement of various tools provides a higher level of trust in the chosen approach.
In the QUAN analysis phase, the application of the test of distribution was appropriately placed to enable the selection of the reliability tool early in the analysis process, ensuring correctness in selecting the reliability test tool.
A significant point of departure from a multitude of methods in the analysis of QUAN data was the qualification of the use of Cronbach’s alpha on the dataset after the distribution test to ensure that its merits for testing such datasets were established and justified.
The QUAL data coding approach summarised in Figure 2 is novel and anchored on established approaches arising from extensive literature on coding.
The consistent application of different tools to the model, comprised of a non-parametric dataset, provided a significant advantage in applying such tools in datasets that are similar to this one in research. This further validated the propositions by Ezie [ 68 ] on the approaches to be adopted in such research.

The impact provided by the methodological tool presented in this paper is therefore established to be novel and offers a distinct advantage in the body of knowledge.

Acknowledgments

The authors wish to acknowledge the University of Johannesburg for the resources used to conduct this study (SPSS and AtlasTi).

Funding Statement

This research is funded and part of collaborative research at the Centre of Applied Research and Innovation in the Built Environment (CARINBE).

Author Contributions

S.G. developed the methodology concept, developed the coding scheme, collected and analysed the data and wrote the manuscript. I.M. provided supervision and conceptualised the direction and contribution of the manuscript to the body of knowledge. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Faculty of Engineering and Built Environment (FEBE) Ethics and Plagiarism Committee (FEPC) of the University of Johannesburg (protocol code UJ_FEBE_FEPC_00196 and 6 June 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The author(s) declare no potential conflict of interest concerning this article’s research, authorship, and/or publication.

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Research Article

The development and structural validity testing of the Person-centred Practice Inventory–Care (PCPI-C)

Contributed equally to this work with: Brendan George McCormack, Paul F. Slater, Fiona Gilmour, Denise Edgar, Stefan Gschwenter, Sonyia McFadden, Ciara Hughes, Val Wilson, Tanya McCance

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Medicine and Health, Susan Wakil School of Nursing and Midwifery/Sydney Nursing School, The University of Sydney, Camperdown Campus, New South Wales, Australia

Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Affiliation Institute of Nursing and Health Research, Ulster University, Belfast, Northern Ireland

Roles Data curation, Investigation, Methodology, Writing – review & editing

Affiliation Division of Nursing, Queen Margaret University, Edinburgh, Scotland

Roles Data curation, Formal analysis, Writing – review & editing

Affiliation Nursing and Midwifery Directorate, Illawarra Shoalhaven Local Health District, New South Wales, Australia

Roles Data curation, Methodology, Validation, Writing – review & editing

Affiliation Division of Nursing Science with Focus on Person-Centred Care Research, Karl Landsteiner University of Health Sciences, Krems, Austria

Roles Data curation, Investigation, Validation, Writing – review & editing

Affiliation Prince of Wales Hospital, South East Sydney Local Health District, New South Wales, Australia

Roles Conceptualization, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

Brendan George McCormack,
Paul F. Slater,
Fiona Gilmour,
Denise Edgar,
Stefan Gschwenter,
Sonyia McFadden,
Ciara Hughes,
Val Wilson,
Tanya McCance

Published: May 10, 2024
https://doi.org/10.1371/journal.pone.0303158
Reader Comments

Person-centred healthcare focuses on placing the beliefs and values of service users at the centre of decision-making and creating the context for practitioners to do this effectively. Measuring the outcomes arising from person-centred practices is complex and challenging and often adopts multiple perspectives and approaches. Few measurement frameworks are grounded in an explicit person-centred theoretical framework.

In the study reported in this paper, the aim was to develop a valid and reliable instrument to measure the experience of person-centred care by service users (patients)–The Person-centred Practice Inventory-Care (PCPI-C).

Based on the ‘person-centred processes’ construct of an established Person-centred Practice Framework (PCPF), a service user instrument was developed to complement existing instruments informed by the same theoretical framework–the PCPF. An exploratory sequential mixed methods design was used to construct and test the instrument, working with international partners and service users in Scotland, Northern Ireland, Australia and Austria. A three-phase approach was adopted to the development and testing of the PCPI-C: Phase 1 –Item Selection : following an iterative process a list of 20 items were agreed upon by the research team for use in phase 2 of the project; Phase 2 –Instrument Development and Refinement : Development of the PCPI-C was undertaken through two stages. Stage 1 involved three sequential rounds of data collection using focus groups in Scotland, Australia and Northern Ireland; Stage 2 involved distributing the instrument to members of a global community of practice for person-centred practice for review and feedback, as well as refinement and translation through one: one interviews in Austria. Phase 3 : Testing Structural Validity of the PCPI-C : A sample of 452 participants participated in this phase of the study. Service users participating in existing cancer research in the UK, Malta, Poland and Portugal, as well as care homes research in Austria completed the draft PCPI-C. Data were collected over a 14month period (January 2021-March 2022). Descriptive and measures of dispersion statistics were generated for all items to help inform subsequent analysis. Confirmatory factor analysis was conducted using maximum likelihood robust extraction testing of the 5-factor model of the PCPI-C.

The testing of the PCPI-C resulted in a final 18 item instrument. The results demonstrate that the PCPI-C is a psychometrically sound instrument, supporting a five-factor model that examines the service user’s perspective of what constitutes person-centred care.

Conclusion and implications

This new instrument is generic in nature and so can be used to evaluate how person-centredness is perceived by service users in different healthcare contexts and at different levels of an organisation. Thus, it brings a service user perspective to an organisation-wide evaluation framework.

Citation: McCormack BG, Slater PF, Gilmour F, Edgar D, Gschwenter S, McFadden S, et al. (2024) The development and structural validity testing of the Person-centred Practice Inventory–Care (PCPI-C). PLoS ONE 19(5): e0303158. https://doi.org/10.1371/journal.pone.0303158

Editor: Nabeel Al-Yateem, University of Sharjah, UNITED ARAB EMIRATES

Received: January 26, 2023; Accepted: April 20, 2024; Published: May 10, 2024

Copyright: © 2024 McCormack et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because of ethical reason. Data are available from the Ulster University Institutional Data Access / Ethics Committee (contact via email on [email protected] ) for researchers who meet the criteria for access to confidential data

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Person-centred healthcare focuses on placing the beliefs and values of service users at the centre of decision-making and creating the context for practitioners to do this effectively. Person-centred healthcare goes beyond other models of shared decision-making as it requires practitioners to work with service users (patients) as actively engaged partners in care [ 1 ]. It is widely agreed that person-centred practice has a positive influence on the care experiences of all people associated with healthcare, service users and staff alike. International evidence shows that person-centred practice has the capacity to have a positive effect on the health and social care experiences of service users and staff [ 1 – 4 ]. Person-centred practice is a complex health care process and exists in the presence of respectful relationships, attitudes and behaviours [ 5 ]. Fundamentally, person-centred healthcare can be seen as a move away from neo-liberal models towards the humanising of healthcare delivery, with a focus on the development of individualised approaches to care and interventions, rather than seeing people as ‘products’ that need to be moved through the system in an efficient and cost-effective way [ 6 ].

Person-centred healthcare is underpinned by philosophical and theoretical constructs that frame all aspects of healthcare delivery, from the macro-perspective of policy and organisational practices to the micro-perspective of person-to-person interaction and experience of healthcare (whether as professional or service user) and so is promoted as a core attribute of the healthcare workforce [ 1 , 7 ]. However, Dewing and McCormack [ 8 ] highlighted the problems of the diverse application of concepts, theories and models all under the label of person-centredness, leading to a perception of person-centred healthcare being poorly defined, non-specific and overly generalised. Whilst person-centredness has become a well-used term globally, it is often used interchangeably with other terms such as ’woman-centredness’ [ 9 ], ’child-centredness’ [ 10 ], ’family-centredness’ [ 11 ], ’client-centredness’ [ 12 ] and ’patient-centredness’ [ 13 ]. In their review of person-centred care, Harding et al [ 14 ] identified three fundamental ‘stances’ that encompass person-centred care— Person-centred care as an overarching grouping of concepts : includes care based on shared-decision making, care planning, integrated care, patient information and self-management support; Person-centred care emphasising personhood : people being immersed in their own context and a person as a discrete human being; Person-centred care as partnership : care imbued with mutuality, trust, collaboration for care, and a therapeutic relationship.

Harding et al. adopt the narrow focus of ’care’ in their review, and others contend that for person-centred care to be operationalised there is a need to understand it from an inclusive whole-systems perspective [ 15 ] and as a philosophy to be applied to all persons. This inclusive approach has enabled the principles of person-centredness to be integrated at different levels of healthcare organisations and thus enable its embeddedness in health systems [ 16 – 19 ]. This inclusive approach is significant as person-centred care is impossible to sustain if person-centred cultures do not exist in healthcare organisations [ 20 , 21 ].

McCance and McCormack [ 5 ] developed the Person-centred Practice Framework (PCPF) to highlight the factors that affect the delivery of person-centred practices. McCormack and McCance published the original person-centred nursing framework in 2006. The Framework has evolved over two decades of research and development activity into a transdisciplinary framework and has made a significant contribution to the landscape of person-centredness globally. Not only does it enable the articulation of the dynamic nature of person-centredness, recognising complexity at different levels in healthcare systems, but it offers a common language and a shared understanding of person-centred practice. The Person-centred Practice Framework is underpinned by the following definition of person-centredness:

[A]n approach to practice established through the formation and fostering of healthful relationships between all care providers , service users and others significant to them in their lives . It is underpinned by values of respect for persons , individual right to self-determination , mutual respect and understanding . It is enabled by cultures of empowerment that foster continuous approaches to practice development [ 16 ].

The Person-centred Practice Framework ( Fig 1 ) comprises five domains: the macro context reflects the factors that are strategic and political in nature that influence the development of person-centred cultures; prerequisites focus on the attributes of staff; the practice environment focuses on the context in which healthcare is experienced; the person-centred processes focus on ways of engaging that are necessary to create connections between persons; and the outcome , which is the result of effective person-centred practice. The relationships between the five domains of the Person-centred Practice Framework are represented pictorially, that being, to reach the centre of the framework, strategic and policy frames of reference need to be attended to, then the attributes of staff must be considered as a prerequisite to managing the practice environment and to engaging effectively through the person-centred processes. This ordering ultimately leads to the achievement of the outcome–the central component of the framework. It is also important to recognise that there are relationships and there is overlap between the constructs within each domain.

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https://doi.org/10.1371/journal.pone.0303158.g001

In 2015, Slater et al. [ 22 ] developed an instrument for staff to use to measure person centred practice- the Person-centred Practice Inventory- Staff (PCPI-S). The PCPI-S is a 59-item, self-report measure of health professionals’ perceptions of their person-centred practice. The items in the PCPI-S relate to seventeen constructs across three domains of the PCPF (prerequisites, practice environment and person-centred processes). The PCPI-S has been widely used, translated into multiple languages and has undergone extensive psychometric testing [ 23 – 28 ].

No instrument exists to measure service users’ perspectives of person-centred care that is based on an established person-centred theoretical framework or that is designed to compare with service providers perceptions of it. In an attempt to address this gap in the evidence base, this study set out to develop such a valid and reliable instrument. The PCPI-C focuses on the person-centred processes domain, with the intention of measuring service users’ experiences of person-centred care. The person-centred processes are the components of care that directly affect service users’ experiences. The person-centred processes enable person-centred care outcomes to be achieved and include working with the person’s beliefs and values, sharing decision-making, engaging authentically, being sympathetically present and working holistically. Based on the ‘person-centred processes’ construct of the PCPF and relevant items from the PCPI-S, a version for service users was developed.

This paper describes the processes used to develop and test the instrument–The Person-centred Practice Inventory-Care (PCPI-C). The PCPI-C has the potential to enable healthcare services to understand service users’ experience of care and how they align with those of healthcare providers.

Materials and methods

The aim of this research was to develop and test the face validity of a service users’ version of the person-centred practice inventory–The Person-centred Practice Inventory-Care.

The development and testing of the instrument was guided by the instrument development principles of Boateng et al [ 29 ] ( Fig 2 ) and reported in line with the COSMIN guidelines for instrument testing [ 30 , 31 ]. An exploratory sequential mixed methods design was used to construct and test the instrument [ 29 , 30 ] working with international partners and service users. A three-phase approach was adopted to the development and testing of the PCPI-C. As phases 1 and 2 intentionally informed phase 3 (the testing phase), these two phases are included here in our description of methods.

https://doi.org/10.1371/journal.pone.0303158.g002

Ethical approval

Ethics approval was sought and gained for each phase of the study and across each of the participating sites. For phase 2 of the study, a generic research protocol was developed and adapted for use by the Scottish, Australian and Northern Irish teams to apply for local ethical approval. In Scotland, ethics approval was gained from Queen Margaret University Edinburgh Divisional Research Ethics Committee; in Australia, ethics approval was gained from The University of Wollongong and in Northern Ireland ethics approval was gained from the Research Governance Filter Committee, Nursing and Health Research, Ulster University. For phase 3 of the study, secondary analysis of an existing data set was undertaken. For the original study from which this data was derived (see phase 3 for details), ethical approval was granted by the UK Office of Research Ethics Committee Northern Ireland (ORECNI Ref: FCNUR-21-019) and Ulster University Research Ethics Committee. Additional local approvals were obtained for each partner site as required. In addition, a data sharing agreement was generated to facilitate sharing of study data between European Union (EU) sites and the United Kingdom (UK).

Phase 1 –Item selection

An initial item pool for the PCPI-C was identified by <author initials to be added after peer-review> by selecting items from the ‘person-centred processes’ sub-scale of the PCPI-S ( Table 1 ). Sixteen items were extracted, and the wording of the statements was adjusted to reflect a service-user perspective. Additional items were identified (n = 4) to fully represent the construct from a service-user perspective. A final list of 20 items was agreed upon and this 20-item questionnaire was used in Phase 2 of the instrument development.

https://doi.org/10.1371/journal.pone.0303158.t001

Phase 2 –Instrument development and refinement

Testing the validity of PCPI-C was undertaken through three sequential rounds of data collection using focus groups in Scotland, Australia and Northern Ireland. The purpose of these focus groups was to work with service users to share and compare understandings and views of their experiences of healthcare and to consider these experiences in the context of the initial set of PCPI-C items generated in phase 1 of the study. These countries were selected as the lead researchers had established relationships with healthcare partners who were willing to host the research. The inclusion of multiple countries provided different perspectives from service users who used different health services. In Scotland, a convenience sample of service users (n = 11) attending a palliative care day centre of a local hospice was selected. In Australia a cancer support group for people living with a cancer diagnosis (n = 9) was selected and in Northern Ireland, people with lived experience who were attending a community group hosted by a Cancer Charity (n = 9) were selected. All service users were current users of healthcare and so the challenge of memory recall was avoided. The type of conditions/health problems of participants was not the primary concern. Instead, we targeted persons who had recent experiences of the health system. The three centres selected were known to the researchers in those geographical areas and relationships were already established, which helped with gaining access to potential participants. Whilst the research team had potential access to other centres in each country, it was evident at focus group 3 that no significant new issues were being identified from the participants and thus we agreed to not do further rounds of refinement.

A Focus Group guide was developed ( Fig 3 ). Participants were invited to draw on their experiences as a user of the service; particularly remembering what they saw, the way they felt and what they imagined was happening [ 32 ]. The participants were invited to independently complete the PCPI-C and the purpose of the exercise was reiterated i.e. to think about how each question of the PCPI-C reflected their own experiences and their answers to the questions. Following completion of the questionnaire, participants were asked to comment on each question in the PCPI-C (20 questions), with a specific focus on their understanding of the question, what they thought about when they read the question, and any suggestions to improve readability. The focus group was concluded with a discussion on the overall usability of the PCPI-C. Each focus group was audiotaped and the audio recordings were transcribed in full. The facilitators of the focus group then listened to the audio recordings, alongside the transcripts, and identified the common issues that arose from the discussions and noted against each of the questions in the draft PCPI-C. Revisions were made to the questions in accordance with the comments and recommendations of the participants. At the end of the analysis phase of each focus group, a table of comments and recommendations mapped to the questions in the instrument was compiled and sent to the whole research team for review and consideration. The comments and recommendations were reviewed by the research team and amendments made to the draft PCPI-C. The amended draft was then used in the next focus group until a final version was agreed. Focus group 1 was held in Scotland, focus group 2 in Australia and focus group 3 in Northern Ireland. Table 2 presents a summary of the feedback from the final focus group.

https://doi.org/10.1371/journal.pone.0303158.g003

https://doi.org/10.1371/journal.pone.0303158.t002

A final stage of development involved distributing the agreed version of the PCPI-C to members of ‘The International Community of Practice for Person-centred Practice’ (PcP-ICoP) for review and feedback. The PcP-ICoP is an international community of higher education, health and care organisations and individuals who are committed to advancing knowledge in the field of person-centredness. No significant changes to the distributed version were suggested by the PcP-ICoP members, but several members requested permission to translate the instrument into their national language. PcP-ICoP members at the University of Vienna, who were leading on a large research project with nursing homes in the region of Lower Austria, agreed to undertake a parallel translation project as a priority, so they could use the PCPI-C in their research project. The instrument was culturally and linguistically adapted to the nursing home setting in an iterative process by the Austrian research team in collaboration with the international research team. Data were collected through face-to-face interviews by trained research staff. Residents of five nursing homes for older persons in Lower Austria were included. All residents who did not have a cognitive impairment or were physically unable to complete the questionnaire (because of ill-health) (n = 235) were included. 71% of these residents (N = 167) managed to complete the questionnaire. Whilst in Austria, formal ethical approval for non-intervention studies is not required, the team sought informed consent from participants. Particular attention was paid throughout the interviews to assure ongoing consent of residents by carefully guided conversations.

Phase 3: Testing structural validity of the PCPI-C

The aim of this phase was to test the structural validity of the PCPI-C using confirmatory factor analysis with an international sample of service users. The PCPI-C comprises 20 items measured on a 5-point scale ranging from ‘strongly disagree’ to ‘strongly agree. The 20 items represent the 5 constructs comprising the final model to be tested, which is outlined in Table 3 .

https://doi.org/10.1371/journal.pone.0303158.t003

A sample of 452 participants was selected for this phase of the study. The sample selected comprised two groups. Group 1 (n = 285) were service users with cancer (breast, urological and other) receiving radiotherapy in four Cancer Treatment Centres in four European Countries–UK, Malta, Poland and Portugal. These service users were participants in a wider SAFE EUROPE ( www.safeeurope.eu ) project exploring the education and professional migration of therapeutic radiographers in the European Union. In the UK a study information poster with a link to the PCPI-C via Qualtrics © survey was disseminated via UK cancer charity social media websites. Service user information and consent were embedded in the online survey and presented to the participant following the study link. At the non-UK sites, hard copy English versions of the surveys were available in clinical departments where a convenience sampling approach was used, inviting everyone in their final few days of radiotherapy to participate. The ‘DeepL Translator’ software (DeepL GmbH, Cologne, Germany) was used to make the necessary terminology adaptions for both the questionnaire and the participant information sheet across the various countries. Fluent speakers based in the participating sites and who were members of the SAFE EUROPE project team confirmed the accuracy of this process by checking the accuracy of the translated version against the original English version. Participants were provided with study information and had at least 24 hours to decide if they wished to participate. Willing participants were then invited to provide written informed consent by the local study researcher. The study researcher provided the hard copy survey to the service user but did not engage with or assist them during completion. Service users were informed they could take the survey home for completion if they wished. Completed surveys were returned to a drop box in the department or returned by post (data collected May 2021-March 2022). Group 2 were residents in nursing homes in Lower Austria (n = 125). No participating residents had a cognitive impairment and were physically able to complete the questionnaire. Data were collected through face-to-face interviews by trained research staff (data collected January 2021-March 2021).

Statistical analysis

Descriptive and measures of dispersion statistics were generated for all items to help inform subsequent analysis. Measures of appropriateness to conduct factor analysis were conducted using The Kaiser-Meyer-Olkin Measures of Sampling Adequacy and Bartletts Test of Sphericity. Inter-item correlations were generated to examine for collinearity prior to full analysis. Confirmatory factor analysis was conducted using maximum likelihood robust extraction testing of the 5-factor model.

Acceptable fit statistics were set at Root Mean Square Estimations of Approximation (RMSEA) of 0.05 or below; 90% RMSEA higher bracket below 0.08; and Confirmation Fit Indices (CFI) of 0.95 or higher and SRMR below 0.05 [ 33 – 35 ]. Internal consistency was measured using Cronbach alpha scores for factors in the accepted factor model.

The model was re-specified using the modification indices provided in the statistical output until acceptable and a statistically significant relationship was identified. All re-specifications of the model were guided by principles of (1) meaningfulness (a clear theoretical rationale); (2) transitivity (if A is correlated to B, and B correlated to C, then A should correlate with C); and (3) generality (if there is a reason for correlating the errors between one pair of errors, then all pairs for which that reason applies should also be correlated) [ 36 ].

Acceptance modification criteria of:

The items to first order factors were initially fitted.
Correlated error variance permitted as all items were measuring the same unidimensional construct.
Only statistically significant relationship retained to help produce as parsimonious a model as possible.
Factor loadings above 0.40 to provide a strong emergent factor structure.

Factor loading scores were based on Comrey and Lee’s [ 37 ] guidelines (>.71 = excellent, >.63 = very good, >.55 = good, >.45 = fair and >.32 = poor) and acceptable factor loading given the sample size (n = 452) were set at >0.3 [ 33 , 38 ].

Results and discussion

Demographic details.

The sample of 452 participants represented an international sample of respondents drawn from across five countries: UK (14.6% n = 66), Portugal (47.8%. n = 216), Austria (27.7%, n = 125), Malta (6.6, n = 30) and Poland (3.3%, n = 15). Table 4 outline the demographic characteristics of the sample. The final sample of 452 participants provides an acceptable ratio 33 of 22:1 respondent to items.

https://doi.org/10.1371/journal.pone.0303158.t004

The means scores indicate that respondents scored the items neutrally. The measures of skewness and kurtosis were acceptable and satisfied the conditions of normality of distribution for further psychometric testing. Examination of the Kaiser Meyer Olkin (0.947) and the Bartlett test for sphericity (4431.68, df = 190, p = 0.00) indicated acceptability of performing factor analysis on the items. Cronbach alpha scores for each of the constructs confirm the acceptability and unidimensionality of each construct.

Examination of the correlation matrix between items shows a range of between 0.144 and 0.740, indicating a broadness in the areas of care the questionnaire items address, as well as no issues of collinearity. The original measurement model was examined using maximum likelihood extraction and the original model had mixed fit statistics. All factor loadings (except for items 11 and 13) were above the threshold of 0.4 ( Table 3 ). Six further modifications were introduced into the original model based on highest scored modification indices until the fit statistics were deemed acceptable ( Table 5 for model fit statistics and Fig 4 for items correlated errors). Two item correlated error modifications were within factors and 4 between factors. The accepted model factor structure is displayed in Fig 4 .

https://doi.org/10.1371/journal.pone.0303158.g004

https://doi.org/10.1371/journal.pone.0303158.t005

Measuring person-centred care is a complex and challenging endeavour [ 39 ]. In a review of existing measures of person-centred care, DeSilva [ 39 ] identified that whilst there are many tools available to measure person-centred care, there was no agreement about which tools were most worthwhile. The complexity of measurement is further reinforced by the multiplicity of terms used that imply a person-centred approach being adopted without explicitly setting out the meaning of the term. Further, person-centred care is multifaceted and comprises a multitude of methods that are held together by a common philosophy of care and organisational goals that focus on service users having the best possible (personalised) experience of care. As DeSilva suggested, “it is a priority to understand what ‘person-centred’ means . Until we know what we want to achieve , it is difficult to know the most appropriate way to measure it . (p 3)” . However, it remains the case that many of the methods adopted are poorly specified and not embedded in clear conceptual or theoretical frameworks [ 40 , 41 ]. A clear advantage of the study reported here is that the PCPI-C is embedded in a theoretical framework of person-centredness (the PCPF) that clearly defines what we mean by person-centred practice. The PCPI-C is explicitly informed by the ‘person-centred processes’ domain of the PCPF, which has an explicit focus on the care processes used by healthcare workers in providing healthcare to service-users.

In the development of the PCPI-C, initial items were selected from the Person-centred Practice Inventory-Staff (PCPI-S) and these items are directly connected with the person-centred processes domain of the PCPF. The PCPI-S has been translated, validated and adopted internationally [ 23 – 28 ] and so provides a robust theoretically informed starting point for the development of the PCPI-C. This starting point contributed to the initial acceptability of the instrument to participants in the focus groups. Like DeSilva, [ 39 ] McCormack et al [ 42 ] and McCormack [ 41 ] have argued that measuring person-centred care as an isolated activity from the evaluation of the impact of contextual factors on the care experienced, is a limited exercise. As McCormack [ 41 ] suggests “ Evaluating person-centred care as a specific intervention or group of interventions , without understanding the impact of these cultural and contextual factors , does little to inform the quality of a service . ” (p1) Using the PCPI-C alongside other instruments such as the PCPI-S helps to generate contrasting perspectives from healthcare providers and healthcare service users, informed by clear definitions of terms that can be integrated in quality improvement and practice development programmes. The development of the PCPI-C was conducted in line with good practice guidelines in instrument development [ 29 ] and underpinned by an internationally recognised person-centred practice theoretical framework, the PCPF [ 5 ]. The PCPI-C provides a psychometrically robust tool to measure service users’ perspectives of person-centred care as an integrated and multi-faceted approach to evaluating person-centredness more generally in healthcare organisations.

With the advancement of Patient Reported Outcome Measures (PROMS) [ 43 , 44 ], Patient Reported Experience Measures (PREMS) [ 45 ] and the World Health Organization (WHO) [ 15 ] emphasis on the development of people-centred and integrated health systems, greater emphasis has been placed on developing measures to determine the person-centredness of care experienced by service users. Several instruments have been developed to measure the effectiveness of person-centred care in specific services, such as mental health [ 45 ], primary care [ 46 , 47 ], aged care [ 48 , 49 ] and community care [ 50 ]. However only one other instrument adopts a generic approach to evaluating services users’ experiences of person-centred care [ 51 ]. The work of Fridberg et al (The Generic Person-centred Care Questionnaire (GPCCQ)) is located in the Gothenburg Centre for Person-centred Care (GPCC) concept of person-centredness—patient narrative, partnership and documentation. Whilst there are clear connections between the GPCCQ and the PCPI-C, a strength of the PCPI-C is that it is set in a broader system of evaluation that views person-centredness as a whole system issue, with all parts of the system needing to be consistent in concepts used, definitions of terms and approaches to evaluation. Whilst the PCPI-S evaluates how person-centredness is perceived at different levels of the organisation, using the same theoretical framework and the same definition of terms, the PCPI-C brings a service user perspective to an organisation-wide evaluation framework.

A clear strength of this study lies in the methods engaged in phase 2. Capturing service user experiences of healthcare has become an important part of the evaluation of effectiveness. Service user experience evaluation methodologies adopt a variety of methods that aim to capture key transferrable themes across patient populations, supported by granular detail of individual specific experience [ 43 ]. This kind of service evaluation depends on systematically capturing a variety of experiences across different service-user groups. In the research reported here, service users from a variety of services including palliative care and cancer services from three countries, engaged in the focus group discussions and were freely able to discuss their experiences of care and consider them in the context of the questionnaire items. The use of focus groups in three different countries enabled different cultural perspectives to be considered in the way participants engaged with discussions and considered the relevance of items and their wording. The sequential approach enabled three rounds of refinement of the items and this enabled the most relevant wording to be achieved. The range of comments and depth of feedback prevented ‘knee-jerk’ changes being made based on one-off comments, but instead, it was possible to compare and contrast the comments and feedback and achieve a more considered outcome. The cultural relevance of the instrument was reinforced through the translation of the instrument to the German language in Austria, as few changes were made to the original wording in the translation process. This approach combined the capturing of individual lived experience with the systematic generation of key themes that can assist with the systematic evaluation of healthcare services. Further, adopting this approach provides a degree of confidence to users of the PCPI-C that it represents real service-user experiences.

The factorial validity of the instrument was supported by the findings of the study. The modified models fit indices suggest a good model fit for the sample [ 31 , 34 , 35 ]. The Confirmation Fit Indices (CFI) fall short of the threshold of >0.95. However, this is above 0.93 which is considered an acceptable level of fit [ 52 ]. Examination of the alpha scores confirm the reliability (internal consistency) of each construct [ 53 ]. All factor loadings were at a statistically significant level and above the acceptable criteria of 0.3 recommended for the sample size [ 38 ]. All but 2 of the loadings (v11 –‘ Staff don’t assume they know what is best for me’ and v13 – ‘My family are included in decisions about my care only when I want them to be’ ) were above the loadings considered as good to excellent [ 37 ]. At the level of construct, previous research by McCance et al [ 54 ] showed that all five constructs of the person-centred processes domain of the Person-centred Practice Framework carried equal significance in shaping how person-centred practice is delivered, and this is borne out by the approval of a 5-factor model in this study. However, it is also probable that there is a degree of overlap between items across the constructs, reflected in the 2 items with lower loadings. Other items in the PCPI-C address perspectives on shared decision-making and family engagement and thus it was concluded that based on the theoretical model and statistical analysis, these 2 items could be removed without compromising the comprehensiveness of the scale, resulting in a final 18-item version of the PCPI-C (available on request).

Whilst a systematic approach to the development of the PCPI-C was adopted, and we engaged with service users in several care settings in different countries, further research is required in the psychometric testing of the instrument across differing conditions, settings and with culturally diverse samples. Whilst the sample does provide an acceptable respondent to item ratio, and the sample contains international respondents, the model structure is not examined across international settings. Likewise, further research is required across service users with differing conditions and clinical settings. Whilst this is a limitation of this study reported here, the psychometric testing of an instrument is a continuous process and further testing of the PCPI-C is welcomed.

Conclusions

This paper has presented the systematic approach adopted to develop and test a theoretically informed instrument for measuring service users’ perspectives of person-centred care. The instrument is one of the first that is generic and theory-informed, enabling it to be applied as part of a comprehensive and integrated framework of evaluation at different levels of healthcare organisations. Whilst the instrument has good statistical properties, ongoing testing is recommended.

Acknowledgments

The authors of this paper acknowledge the significant contributions of all the service users who participated in this study.

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Open access
Published: 10 May 2024

An exploratory study on spatiotemporal clustering of suicide in Korean adolescents

Won-Seok Choi 1 na1 ,
Beop-Rae Roh 2 na1 ,
Duk-In Jon 3 ,
Vin Ryu 3 ,
Yunhye Oh 3 &
Hyun Ju Hong 3 , 4

Child and Adolescent Psychiatry and Mental Health volume 18 , Article number: 54 ( 2024 ) Cite this article

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Adolescent suicides are more likely to form clusters than those of other age groups. However, the definition of a cluster in the space–time dimension has not been established, neither are the factors contributing to it well known. Therefore, this study aimed to identify space–time clusters in adolescent suicides in Korea and to examine the differences between clustered and non-clustered cases using novel statistical methods.

From 2016 to 2020, the dates and locations, including specific addresses from which the latitude and longitude of all student suicides (aged 9–18 years) in Korea were obtained through student suicide reports. Sociodemographic characteristics of the adolescents who died by suicide were collected, and the individual characteristics of each student who died by suicide were reported by teachers using the Strengths and Difficulties Questionnaire (SDQ). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis was used to assess the clustering of suicides.

We identified 23 clusters through the data analysis of 652 adolescent suicides using DBSCAN. By comparing the size of each cluster, we identified 63 (9.7%) spatiotemporally clustered suicides among adolescents, and the temporal range of these clusters was 7–59 days. The suicide cluster group had a lower economic status than the non-clustered group. There were no significant differences in other characteristics between the two groups.

This study has defined the space–time cluster of suicides using a novel statistical method. Our findings suggest that when an adolescent suicide occurs, close monitoring and intervention for approximately 2 months are needed to prevent subsequent suicides. Future research using DBSCAN needs to involve a larger sample of adolescents from various countries to further corroborate these findings.

Introduction

Suicide is a global social issue in adolescents aged 15–19 years old, for whom it was the fourth leading cause of death globally in 2019 [ 1 ]. This makes it a great social burden. Post the advent of the COVID-19 pandemic in 2020, there has been a global increase in suicide attempts and suicidal ideation among youth, making it an even more critical issue today [ 2 ]. Suicide in adolescents is heterogeneous and distinguished from the suicide of adults by complicated factors, including family, school, and individual components [ 3 ].

Suicide incidents do not always occur randomly; sometimes, they occur in clusters. This phenomenon has been described as ‘contagion’ or ‘clustering of suicide.’ Although the two words are often used interchangeably, “contagion” was considered as a mechanism of “clustering of suicide” and more recently, “social transmission” is regarded as a narrower and more explicit mechanism for clustering [ 4 , 5 , 6 ]. Two main types of suicide clusters are argued in the previous study—mass clusters, which is a media-related phenomenon that suicide rates increase in a wide population in a time period, and space–time clusters, where suicides occur in unusually concentrated within a specific locality of time and space [ 6 , 7 ]. Clinically, space–time clustered suicide may refer to suicides influenced by the suicide of someone around them, such as a friend. Previous studies have shown that suicides of 15–24 years of age are more likely to cluster than other age groups [ 8 ] and account for 1–6% of suicides among youth [ 8 , 9 , 10 , 11 ]. Temporal and spatial definitions are useful in terms of suicide prevention. If a youth suicide occurs, more close monitoring of follow-up suicides, management of risk factors, and crisis intervention during the period and legion corresponding to the cluster may contribute to suicide prevention.

Since the clustering of suicide began to be discussed in the clinical field approximately 40 years ago [ 12 , 13 ], several statistical techniques for detecting and defining of space–time clusters of suicide has been used to detect and define space–time clusters of suicide [ 6 , 8 , 10 , 11 , 14 , 15 , 16 , 17 , 18 ]. However, there is currently no specific definition or gold standard for detecting suicide clusters [ 5 , 17 , 18 ].

The Knox procedure, used in earlier studies, considers all possible pairs of suicide cases and the temporal and spatial distances between them. This method established clustering by demonstrating a positive relationship between the temporal and spatial distances of a pair. The Knox method requires the specification of critical values of time and space to define closeness, and previous studies have set the county level spatially and 7, 14, 30 and 60 days temporally [ 8 , 14 , 15 ].

Scan statistics represents a more advanced method than the Knox procedure. It investigates clustering within a variable time window across varying geographical areas and compares the expected number of cases and actual number of cases inside and outside the scanning window [ 10 , 19 , 20 , 21 ]. The results of this type of analysis are a set of cylinders, where the base represents the area of the potential cluster, and the height represents the time period of the cluster. Previous studies analyzed the presence of clustered by setting a specific window of various ranges and a temporal window from 7 days to 2 years [ 10 , 11 , 17 , 22 , 23 , 24 , 25 ]. However, previous studies using scan statistics have some limitations, primarily in their focus on detecting clusters with a circular shape [ 26 ] and its focus on larger spatial regions, such as those represented in county-level data [ 11 , 17 , 22 , 23 , 27 , 28 , 29 ].

In terms of the analytic method, previous studies have defined spatiotemporal parameters in advance and somewhat arbitrarily based on the researchers’ judgment, resulting in the clusters of suicides showing spatiotemporal closeness being confirmed. For example, the temporal parameters were set to 7, 14, 30, and 60 days [ 8 , 14 , 15 ], but suicide clusters could occur outside this window. Therefore, identifying the more sensitive periods for suicide clusters is an important research objective.

Several previous studies targeting the entire population, including adolescents and young adults, have compared the characteristics of clustered and non-clustered suicides and reported that clustering was more common among young men than women [ 23 , 30 ], those living in rural areas, [ 23 , 25 , 30 , 31 ], and those experiencing economic deprivation [ 31 ]. However, when narrowing the target population to include only adolescents, one study found no definite differences in clinical characteristics between the suicide cluster and non-cluster groups [ 5 ], while several studies reported that the suicide cluster group had a lower economic level and included more adolescent boys than the non-cluster groups [ 5 , 6 , 18 ].

A new analytical method using machine learning [ 32 , 33 ] that does not preset spatiotemporal parameters with a narrower unit of spatiotemporal data of adolescents can increase the understanding of the space–time clusters in adolescent suicide, which is not well known.

In Korea, adolescent suicide is a serious social problem and is the leading cause of death among young people aged 10–19 years [ 34 ]. In particular, during the COVID-19 pandemic, the suicide rate among adolescents increased at a faster rate than that of older adults [ 35 ]. The suicide rate among adolescents was higher after the pandemic than before [ 35 ], reaching 9.5 per 100,000 in 2021 for adolescents aged 15–17 years, compared to 5.8 per 100,000 in 2017 and 7.5 per 100,000 in 2018 [ 36 ].

This study analyzed an entire dataset of students who died by suicide from 2016 to 2020 that was collected through the Korean Ministry of Education and included the date of death and the specific address from which latitude and longitude coordinates can be extracted. We hypothesized that there would be space–time clusters of suicides among Korean adolescents, and that if clustered and non-clustered suicides were distinguishable, there would be differences in their characteristics. This study will contribute to suicide prevention efforts by identifying the critical period in which subsequent suicides are most likely.

This study used data from student suicide reports collected by the Korean Ministry of Education from January 1, 2016, to December 31, 2020. In Korea, when a student dies by suicide, the school is required to report the relevant information to the Ministry of Education in the student suicide report, which includes teachers’ observations, parental reports regarding the circumstances of death, and official education records collected by the school. Furthermore, these reports were collated as part of the national student suicide prevention policy during the abovementioned period. The evaluation items and answer format were determined through intensive discussion within the research team and feedback from teachers during the report’s development process. Additionally, specific examples of items and answers were provided in the form to simplify it and enable the teachers to understand and respond better. During the coding process, unclear answers were deciphered through discussion within the research team and confirmed by contacting the teacher directly [ 37 , 38 ]. These data represent the total number of students who died by suicide in Korea during the study period. Details of the student suicide reports have been described previously [ 38 ]. The number of students who died by suicide during the study period was 654, and all cases were included in the analyses except for two students whose death dates could not be determined. Considering that Korea has compulsory education up to middle school and the dropout rate of high school in 2021 is 1.5% [ 39 ], these cases may closely represent the general characteristics of suicides among children and adolescents in Korea.

The variables used in this study were the address of the school, sex, date of death, school type, family structure, economic status, suicide method, usual concerns revealed at school, presence of a psychiatric disorder, history of suicide attempt, and history of self-injury. The teacher-rated Strengths and Difficulties Questionnaire (SDQ) [ 40 ] was used to evaluate students' emotional and behavioral status. The teacher-rated SDQ consists of Prosocial Behavior (Cronbach’s α = 0.873), Hyperactivity/Inattention (Cronbach’s α = 0.793), Peer Relationship Problems (Cronbach’s α = 0.770), Emotional Symptoms (Cronbach’s α = 0.681), Conduct Problems (Cronbach’s α = 0.638) subscales and a Total Difficulties score (Cronbach’s α = 0.837). The SDQ has been included in the database since 2018. This study was approved by the Institutional Review Board of Hallym University Sacred Heart Hospital (2021-05-015).

The school addresses of students who died by suicide were converted to latitude and longitude coordinates to examine the proximity of both the space and time of suicidal events, with the time of occurrence set on the day of the event. For cases with incomplete information regarding the date of death, information on the time of discovery was used. As approximately 70% of cases of adolescent suicide in Korea die by jumping from a height, the interval between the time of a suicide attempt and the time of death was expected to be short.

Clustering analysis using density-based spatial clustering of applications with noise (DBSCAN) [ 33 , 41 ] was used to examine the spatiotemporal patterns of suicidal events and define the space–time clusters of suicides. Density-based clustering refers to unsupervised learning methods that identify distinctive groups or clusters in the data based on the idea that a cluster in a data space is a contiguous region of high point density, separated from other clusters by contiguous regions of low point density. The data points in the separating regions of low point density are typically considered noise/outliers [ 33 , 41 ]. In particular, this method is useful when there is an outlier in the spatial information that is included in a cluster and distorted [ 42 ].

The two main conditions to be considered in DBSCAN for the derivation of clusters are the minimum number of cases to be included in the cluster and the cluster radius. In this study, the minimum number of suicide clusters was set at three. The radius of the cluster was selected by examining the change in the distance of the k-nearest neighborhood (kNN). The k value was set to three to simulate the kNN point change, which was equal to the minimum number of clustering cases. R version 4.2.2 was used for analysis and the cluster analysis was performed using the R language DBSCAN package (Hahsler et al.). The proximity among cases within the cluster is represented by the mean distance (mdis), where a lower numerical value indicates closer clustering of cases.

The final step was to compare the characteristics of clustered and non-clustered suicides. It is unreasonable to regard all the clusters derived using DBSCAN as suicide clusters. When a specific metropolitan area has a high population density, such as Seoul, suicide cases can be clustered based on regional density. Therefore, we selected a group with a high probability of suicide clusters based on a comparison of the size of the derived cluster (i.e., the number of suicide deaths) and the radius of the clusters. For group comparisons, data were examined using cross-tabulation and t -tests and finally included binary logistic regression analysis. In the logistic regression analysis, both the size of the region (i.e., metropolitan areas and others) and the year of suicidewere included as independent variables.

Spatiotemporal distribution of suicide

Figure 1 presents the spatiotemporal distribution of the suicide cases. Figure 1 a shows the distribution of suicide case events on the map of South Korea, and the year of the event is also marked in a different color. Many cases were distributed around large cities with dense populations. However, even in areas with relatively sparse populations, suicide cases occur at a certain level. Figure 1 b shows the results of standardizing the latitude, longitude, and time to place the case in 3-dimensional space and demonstrates that the distribution of suicide events does not occur randomly but rather clusters in a specific space–time area.

Spatiotemporal distribution of suicide cases- a The spatial distribution of suicide deaths marked on the map of Korea. b The spatiotemporal distribution of suicide cases. Lat latitude, log longitude

Figure 2 a presents the results of the analyses that examined the change in the distance of the 3-nearest neighborhood to determine the criterion of the radius of the cluster prior to DBSCAN. In the figure, the knee appears around the distance of 60. Figure 2 b presents the clustering results when the radius was set to 60 and the minimum number of cases belonging to a cluster was set to three. Each cluster is presented as a polygon. Outliers that did not belong to any cluster were marked as separate dots. As shown in the figure, the size of the cluster and the number of included cases varied. The largest cluster at the top of the figure reflects spatially concentrated suicide cases in densely populated areas in the Seoul metropolitan area. However, these suicides demonstrated a wide temporal distribution spanning approximately 4 years.

Suicide clusters converted to 2-dimensional image. 3-NN distance 3 nearest-neighborhood distance; PC principal component

This led to a substantial number of cases forming the cluster ( n = 395). Therefore, these cases cannot be regarded as meaningful spatiotemporal clusters of suicide in this study.

Table 1 presents the characteristics of the clusters derived using DBSCAN. Along with the closeness of the cluster (mdis) and number of cases in each cluster’s data distribution, the table also shows the proportion of males, high school students, middle school students, and mean age. Next, the first occurrence date, last occurrence date, longitude, and latitude of the schools attended by the students who died by suicide are presented. The latitude and longitude of the clusters were determined using the average latitude and longitude of the schools within the clusters. Significant clusters are listed in order of the smallest mdis size. Finally, they are listed based on the size of the clusters. For example, in the case of Cluster 1, which is the cluster with the most substantial spatiotemporal proximity, five cases of suicide centered on a specific area occurred within approximately 3 weeks. All patients in this cluster were high school students, and all but one were adolescent boys.

Characteristics of defined spatiotemporal clusters for student suicide in Korea, 2016–2020

We identified 23 clusters through data analysis of 652 cases using DBSCAN. The largest cluster (class ID = 23) comprised of 395 patients. The period of the events covered approximately 5 years. As mentioned above, this cluster could result from demographic concentration, especially in the context of urban South Korea with high population density, rather than from space–time suicide clusters. Therefore, defining a significant suicide cluster that shows a remarkably high spatiotemporal adjacency.

Comparing closeness of clusters & defining meaningful spatiotemporal clusters

Figure 3 presents the results of comparing the cluster closeness (mdis) and the number of cases in the cluster data. The ranking on the horizontal axis is the result of sorting by area. The upper part of Fig. 3 presents all the clusters, and the lower part shows the figure, excluding the largest cluster. As shown in the figure, the area and number of cases rapidly increased after the 15th cluster. Based on this finding, the meaningful spatiotemporal cluster of suicide was defined as up to the 15th cluster (class ID = 12) based on the rank number. We identified 63 (9.7%) spatiotemporally clustered suicides among adolescents, with a temporal range between 7 and 59 days. In the case of spatial range, each cluster was analyzed in a polygonal form, making it difficult to precisely ascertain the average spatial area. Nonetheless, cases classified into significant clusters were predominantly within the same administrative regions. When considering the top three clusters with the highest spatiotemporal clustering (Ranks 1–3 in Table 1 ), the closest distance between the two suicide cases was approximately 6 km, and the greatest distance observed was approximately 32 km.

Size comparison of each cluster identified by DBCSAN-The 15th cluster is marked with a red dashed line in sequential order of distance

Difference of characteristics between clustered and non-clustered suicides

Table 2 shows the comparison of the characteristics of a group that showed high spatiotemporal clustering in suicide with those of a group that did not. Chi-square analysis revealed that the characteristic that was statistically different between the two groups was economic status (χ 2 = 9.79, df = 2, p < 0.05). The clustered suicide group was relatively low. Although no difference was observed at the stochastic significance level, participants showing clustered groupness were relatively more likely to experience peer problems. In the group without significant spatiotemporally clustered groupness, 15.1% (n = 89) reported peer problems, and in the group with clustering, 23.0% (n = 15) reported problems with peer relationships. The reported rate of psychiatric disorders was 29.7% (n = 19) in the clustering group and 40.8% (n = 231) in the other groups.

Table 3 presents the results of the group comparisons using the SDQ. The results of the t- tests indicated that there were no statistically significant differences between the two groups for SDQ total and subscale scores.

In Table 4 , the binary logistic regression analysis results are presented, with the highly clustered group being the outcome variable and the non-clustered cases being the reference group. The demographic and clinical characteristics that were found to significantly differ based on group included economic status (e.g., poverty) and the presence of a psychiatric disorder ( p < 0.05). As the economic level decreases (indicative of poverty), there is an increased tendency for spatiotemporal clustering. However, the less likely the cases included reported psychiatric disorders, the more likely they were to be in a highly clustered group. Groups that reported peer problems had a higher likelihood of being highly clustered, even though the statistical significance of this result was low ( p < 0.10).

This study identified space–time clusters of cases of adolescent suicide using DBSCAN based on Korean student suicide data from 2016 to 2020. As a result, 9.7% ( n = 63) corresponded to the space–time suicide cluster, and each cluster consisted of 3–9 suicide events and suicides temporally occurring between 7 and 59 days and corresponded to the distances between suicide cases within the top three most concentrated clusters, ranging from 6 to 32 km spatially. The suicide cluster group had low economic status and fewer psychiatric disorders compared to the non-clustered group. To the best of our knowledge, this is the first study to use latitude and longitude for spatial analysis and exact suicide dates for temporal analysis in the clustering of adolescent suicides, and it uses narrower spatiotemporal units of analysis than previous studies using DBSCAN without pre-setting spatiotemporal parameters.

Clustered suicides of adolescents in Korea during 2016–2020

In this study, 9.7% of adolescent suicides were classified into spatiotemporal suicide clusters, which was a higher percentage than previously reported. This increase could be attributed to differences in the analytic methods. The current findings suggest that interrelated suicides may be more frequent than expected in adolescents [ 5 ]. However, this study statistically identified spatiotemporal suicide clusters but did not confirm that suicides within clusters were actually related to suicides. Suicides that occurred within similar time periods in similar locations could have been classified into this space–time suicide cluster, even if there was no real connection. Future research should include a detailed case study of the suicide cases in these clusters.

The mechanisms leading to suicide clusters include social transmission, particularly person-to-person transmission and the media [ 5 , 6 , 18 ]. In addition, clustered suicide occurs through perceptions that suicidal behavior is widespread and assortative, leading to susceptible young people being likely to socialize with at-risk peers, and the social cohesion of the peer group contributes to the spread of ideas and attitudes [ 5 , 6 ]. The effect of suicide clusters on schools is usually profound, and the early identification of suicide clusters and initiation of appropriate interventions is critical for preventing subsequent suicides. This study suggests that once an adolescent died by suicide, close monitoring and intervention may be needed to prevent subsequent suicides for about 2 months.

Characteristics of clustered adolescent suicides in Korea: Comparison with previous studies

Several features of the clustered adolescent suicides in this study were similar to the socioeconomic characteristics of previously identified clustered suicides. Previous studies have identified deprivation [ 31 , 43 ], poverty [ 22 , 23 , 43 , 44 , 45 ], and geological isolation [ 25 , 30 , 46 ] as significant risk factors for clustered suicide. In this study, economic status was lower among the clustered suicides than the non-clustered suicides in both the chi-square test and logistic regression analysis, which mirrors the results of previous studies.

In previous studies, young men were more frequently included in the clustered suicide groups than were young women [ 24 , 47 , 48 ]. However, this finding has not been replicated in other population-based studies targeting young adults and adolescents [ 5 , 11 , 25 ]. Similarly, there was no difference in the gender ratio between clustered and non-clustered suicides in our study, the first to report the gender characteristics of clustered suicides in Korean adolescents. This could be due to differences in the analytical method (DBSCAN) used to identify suicide clusters between this study and previous studies. Furthermore, 654 suicides were included in the analysis, which is fewer than in previous studies; this could have potentially influenced the results. Hence, future studies that target a larger number of suicides over an extended period are needed.

Regression analysis revealed that the clustered suicide groups had fewer psychiatric disorders than the non-clustered suicide group. This differs from previous findings and suggests that psychiatric history is a risk factor for clustered suicides [ 6 ]. However, it should be noted that the assessment of psychiatric disorders among students who died by suicide was based on parental reports after suicide rather than the direct application of standardized diagnostic tools, thus potentially failing to adequately capture the frequency of psychiatric disorders. Even if the students had clinically diagnosed psychiatric disorders, they may not have visited hospitals because of negative perceptions associated with mental health or that parents did not accurately report due to concerns about potential disadvantages the students might face at school. Additionally, no statistically significant differences were observed regarding the presence of psychiatric disorders between the two groups in the chi-square test. Given the limited number of participants, further research is necessary to address these findings.

Another distinctive characteristic of clustered group was their low economic status, which is consistent with previous studies [ 18 , 22 , 23 , 31 , 43 , 44 , 45 ]. However, earlier studies have not clarified the relationship between socioeconomic status and suicide clustering. In some studies [ 22 ], low economic status has been suggested as a proxy for factors associated with the clustering of suicides, such as limited access to mental health treatment. Since limited information was collected from each participant, our study could not clearly explain the underlying mechanism. Considering the multidimensional risk factors of adolescent suicide [ 49 ], and the general social stigma against psychiatric disorders in South Korea [ 50 ], having a low economic status might also decrease help-seeking behavior for the early detection of mental health problems of clustered suicide adolescents in Korea.

Although differences in peer problems were a non-significant trend ( p < 0.10) between the groups, the clustered suicide group reported more peer problems than the non-clustered group. When examining each case of clustered suicide, it is apparent that the students included in the clustered suicide did not exhibit considerable vulnerability to suicide on a personal level. Considering the other characteristics mentioned above, this finding may be because they grew up in economically disadvantaged households with vulnerable support systems, delayed their development of introspection and help-seeking behaviors, and lacked resilience, leading to their immersion in peer relationships.

In summary, by using DBSCAN to analyze clustered adolescent suicides in Korea, we found a higher rate (9.7%) than that reported in previous studies. Moreover, the temporal range for the clustered suicides identified was within 2 months. These suicides were characterized by lower economic status, which is consistent with previous studies [ 22 , 23 , 43 , 44 , 45 ]. Our study differs from previous studies in that we used a methodology that did not use a specific window, providing a basis for identifying the critical time and regions for subsequent adolescent suicide prevention.

Limitations

This study has several limitations. First, suicide cases among adolescents used in our study only included those reported by schools; thus, out-of-school adolescents were excluded. Second, our study exclusively focused on Korean students who died by suicide over 5 years, resulting in a limited sample size. This is because our study was a secondary analysis of data collected during a limited period, 2016–2020, as part of a suicide prevention policy in Korea. Correspondingly, given the exclusive focus on Korean adolescents, the distinct attributes of suicide may be influenced by national and cultural contexts, impeding the generalization of this study’s outcomes to diverse international settings. Third, we defined clustering as involving a minimum of three suicides; thus, cases in which two consecutive suicides occurred in a spatiotemporal context similar to clustered suicides were not included. Fourth, we did not account for factors that could link adolescents who died by suicide, even when not in geographically similar spaces, such as the Internet or social network services. Therefore, clustered suicides among adolescents might not have been adequately identified. Finally, the geographic data employed in this study were derived from school addresses rather than the residential addresses of adolescents who died by suicide, consequently failing to accurately reflect the specific locations of suicide incidents. However, Korean students are assigned to schools through a system known as the school district [ 51 ], wherein the proximity of a student’s residence serves as the paramount criterion for school assignment. Therefore, the addresses of the schools utilized in our research can indirectly represent the actual places of residence, and this window is much narrower than the previous studies that used county-level data. Additionally, in the Korean context, the living environment and peer groups of adolescents are often organized on a school-based scale, thereby highlighting the significance of the findings of this study.

In this study, the clustering of suicides was analyzed using a novel analytical method (DBSCAN) that differs from previous studies. As a result, a higher prevalence of clustered suicides (9.3%) among the total population of adolescent suicides was observed compared to previous research. Also, this study suggests that once an adolescent suicide occurs, close monitoring and intervention is needed for approximately 2 months to prevent subsequent suicides. Notably, this clustering was pronounced among those with low social-economic status. Future research using DBSCAN needs to involve a larger sample of adolescents from various countries. Clarifying the underlying mechanisms behind clustered suicides among adolescents could help enhance efforts to prevent adolescent suicide.

Availability of data and materials

The data from the Korean student suicide reports used in our study will not be publicly available. Interested parties can obtain the data by contacting the corresponding author (HJH) through a reasonable request.

Abbreviations

Strengths and difficulties questionnaire

Clustering analysis using density-based spatial clustering of applications with noise

K-Nearest neighborhood

Mean distance

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Acknowledgements

We thank the Korean Ministry of Education, regional offices of education, and all schools that reported the student suicide reports as well as the Suicide and School Mental Health Institute for managing the database.

This study was supported by the Jisan Cultural Psychiatry Research Fund (2021) from the Korean Foundation of Neuropsychiatric Research.

Author information

Won-Seok Choi and Beop-Rae Roh have authors contributed equally to this work.

Authors and Affiliations

Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Won-Seok Choi

Department of Social Welfare, Pukyong National University, Busan, Republic of Korea

Beop-Rae Roh

Department of Psychiatry, Hallym University Sacred Heart Hospital, Hallym University, 22, Gwanpyeong-ro 170Beon-gil, Dongan-gu, Anyang, Gyeonggi-do, Republic of Korea

Duk-In Jon, Vin Ryu, Yunhye Oh & Hyun Ju Hong

Hallym University Suicide and School Mental Health Institute, Anyang, Republic of Korea

Hyun Ju Hong

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Contributions

Authors HJH, WSC, BR, DIJ, VR, and YO designed the study and made conception of this work. WSC and BR drafted and prepared the manuscript. BR contributed to data analysis and interpretation. DIJ, VR and YO revised the manuscript. HJH supervised the entire work, including the data analysis.

Corresponding author

Correspondence to Hyun Ju Hong .

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Ethical approval and consent to participate.

This study was approved by the Institutional Review Board of Hallym University Sacred Heart Hospital (2021-05-015).

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The authors declare that they have no competing interests.

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Choi, WS., Roh, BR., Jon, DI. et al. An exploratory study on spatiotemporal clustering of suicide in Korean adolescents. Child Adolesc Psychiatry Ment Health 18 , 54 (2024). https://doi.org/10.1186/s13034-024-00745-9

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Exploratory Research – Types, Methods and Examples

Exploratory Research

Exploratory Research Methods

Literature Review

Pilot Study

Focus Groups

Expert Opinion

Observational Research

Open-ended Surveys

Data Analysis Methods

Content Analysis

Thematic Analysis

Cluster Analysis

Network Analysis

Grounded Theory

Applications of Exploratory Research

Examples of Exploratory Research

How to Conduct Exploratory Research

When to use Exploratory Research

Purpose of Exploratory Research

Characteristics of Exploratory Research

Advantages of Exploratory Research

Limitations of Exploratory Research

About the author

Muhammad Hassan

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Exploratory Research: Definition, Types, Examples

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