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Explanatory Research – Types, Methods, Guide

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Explanatory Research

Definition :

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

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

Types of Explanatory Research

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

Experimental Research

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

Quasi-experimental Research

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

Correlational Research

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

Case study Research

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

Historical Research

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

Survey Research

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

Explanatory Research Methods

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

Experiments

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

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

Correlational studies

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

Case studies

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

Secondary Data Analysis

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

Data Analysis Methods

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

Correlation Analysis

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

Regression Analysis

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

Path Analysis

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

Structural Equation Modeling (SEM)

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

Factor Analysis

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

Content Analysis

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

Applications of Explanatory Research

The applications of explanatory research include:

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

Explanatory Research Question

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

Examples of explanatory research questions include:

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

Examples of Explanatory Research

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

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

When to use Explanatory Research

Here are some situations where explanatory research might be appropriate:

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

How to Conduct Explanatory Research

Here are the steps to conduct explanatory research:

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

Purpose of Explanatory Research

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

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

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

Advantages of Explanatory Research

Here are some advantages of explanatory research:

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

Limitations of Explanatory Research

Here are some limitations of explanatory research:

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

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Explanatory Research – Guide with Definition & Examples

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

Inhaltsverzeichnis

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

Explanatory Research – In a Nutshell

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

Definition: Explanatory Research

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

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

The usage of explanatory research

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

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

Explanatory research questions

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

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

Explanatory research: Data collection

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

The following are some of the most prevalent research methods:

• Literature reviews
• Interviews and focus groups
• Pilot studies

Explanatory research: Data analysis

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

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

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

The requirements for causal evidence are:

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

The 5 Steps of explanatory research with examples

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

Step 1 of explanatory research: Research question

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

Suppose you are interested in adult language retention rates.

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

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

Step 2 of explanatory research: Hypothesis

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

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

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

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

Step 3 of explanatory research: Methodology and data collection

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

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

You compare:

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

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

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

Step 4 of explanatory research: Analysis and results

After data collection, assess and report results.

After experimenting, you examine the data and observe that:

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

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

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

Step 5 of explanatory research: Interpretation and recommendation

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

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

After the intervention, this difference becomes large.

You decide to do more research and suggest some topics:

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

Explanatory vs. exploratory research

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

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

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

Advantages vs. disadvantages

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

What is explanatory research?

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

Is explanatory research quantitative or qualitative?

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

When should I use explanatory research?

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

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What is explanatory research?

Last updated

12 June 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

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

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

Analyze explanatory research

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• Explanatory research: definition

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

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

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

• Descriptive research vs. explanatory research

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

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

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

• Exploratory research vs. explanatory research

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

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

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

• When to use explanatory research

Explanatory research may be helpful in the following situations:

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

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

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

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

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

• Types of explanatory research

Here are the different types of explanatory research:

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

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

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

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

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

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

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

• How to conduct explanatory research

Take the following steps when conducting explanatory research:

Develop the research question

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

Formulate a hypothesis

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

Select the research type

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

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

Collect the data

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

Analyze the data

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

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

Communicate the results

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

• Examples of explanatory research

Here are some real-life examples of explanatory research:

Understanding what causes high crime rates in big cities

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

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

Factors that influence students’ academic performance

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

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

Examining what causes economic disparity in certain areas

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

• Advantages of explanatory research

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

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

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

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

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

• Disadvantages of explanatory research

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

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

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

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

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

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Explanatory Research: Definition, Types & Guide

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

What is Explanatory Research?

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

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

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

Characteristics of Explanatory Research 

Among the most critical characteristics of explanatory research are:

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

Types of Explanatory Research

The most popular methods of explanatory research:

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

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Importance of explanatory research

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

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

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

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

Explanatory Reseach Quick Guide

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

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

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

Advantages and Conclusions

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

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

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Causal Research (Explanatory research)

Causal research, also known as explanatory research is conducted in order to identify the extent and nature of cause-and-effect relationships. Causal research can be conducted in order to assess impacts of specific changes on existing norms, various processes etc.

Causal studies focus on an analysis of a situation or a specific problem to explain the patterns of relationships between variables. Experiments  are the most popular primary data collection methods in studies with causal research design.

The presence of cause cause-and-effect relationships can be confirmed only if specific causal evidence exists. Causal evidence has three important components:

1. Temporal sequence . The cause must occur before the effect. For example, it would not be appropriate to credit the increase in sales to rebranding efforts if the increase had started before the rebranding.

2. Concomitant variation . The variation must be systematic between the two variables. For example, if a company doesn’t change its employee training and development practices, then changes in customer satisfaction cannot be caused by employee training and development.

3. Nonspurious association . Any covarioaton between a cause and an effect must be true and not simply due to other variable. In other words, there should be no a ‘third’ factor that relates to both, cause, as well as, effect.

The table below compares the main characteristics of causal research to exploratory and descriptive research designs: [1]

Main characteristics of research designs

 Examples of Causal Research (Explanatory Research)

The following are examples of research objectives for causal research design:

• To assess the impacts of foreign direct investment on the levels of economic growth in Taiwan
• To analyse the effects of re-branding initiatives on the levels of customer loyalty
• To identify the nature of impact of work process re-engineering on the levels of employee motivation

Advantages of Causal Research (Explanatory Research)

• Causal studies may play an instrumental role in terms of identifying reasons behind a wide range of processes, as well as, assessing the impacts of changes on existing norms, processes etc.
• Causal studies usually offer the advantages of replication if necessity arises
• This type of studies are associated with greater levels of internal validity due to systematic selection of subjects

Disadvantages of Causal Research (Explanatory Research)

• Coincidences in events may be perceived as cause-and-effect relationships. For example, Punxatawney Phil was able to forecast the duration of winter for five consecutive years, nevertheless, it is just a rodent without intellect and forecasting powers, i.e. it was a coincidence.
• It can be difficult to reach appropriate conclusions on the basis of causal research findings. This is due to the impact of a wide range of factors and variables in social environment. In other words, while casualty can be inferred, it cannot be proved with a high level of certainty.
• It certain cases, while correlation between two variables can be effectively established; identifying which variable is a cause and which one is the impact can be a difficult task to accomplish.

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: Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning

Introducing Research Designs

• First Online: 10 November 2021

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• Stefan Hunziker 3 &
• Michael Blankenagel 3  

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We define research design as a combination of decisions within a research process. These decisions enable us to make a specific type of argument by answering the research question. It is the implementation plan for the research study that allows reaching the desired (type of) conclusion. Different research designs make it possible to draw different conclusions. These conclusions produce various kinds of intellectual contributions. As all kinds of intellectual contributions are necessary to increase the body of knowledge, no research design is inherently better than another, only more appropriate to answer a specific question.

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Hunziker, S., Blankenagel, M. (2021). Introducing Research Designs. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_1

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• Explanatory Research | Definition, Guide, & Examples

Explanatory Research | Definition, Guide & Examples

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

Explanatory research is a research method that explores why something occurs when limited information is available. It can help you increase your understanding of a given topic, ascertain how or why a particular phenomenon is occurring, and predict future occurrences.

Explanatory research can also be explained as a ’cause and effect’ model, investigating patterns and trends in existing data that haven’t been previously investigated. For this reason, it is often considered a type of causal research .

Table of contents

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

Explanatory research is used to investigate how or why a phenomenon takes place. Therefore, this type of research is often one of the first stages in the research process, serving as a jumping-off point for future research. While there is often data available about your topic, it’s possible the particular causal relationship you are interested in has not been robustly studied.

Explanatory research helps you analyse these patterns, formulating hypotheses that can guide future endeavors. If you are seeking a more complete understanding of a relationship between variables, explanatory research is a great place to start. However, keep in mind that it will likely not yield conclusive results.

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Explanatory research answers ‘why’ and ‘what’ questions, leading to an improved understanding of a previously unresolved problem or providing clarity for related future research initiatives.

Here are a few examples:

• Why do undergraduate students obtain higher average grades in the first semester than in the second semester?
• How does marital status affect labour market participation?
• Why do multilingual individuals show more risky behaviour during business negotiations than monolingual individuals?
• How does a child’s ability to delay immediate gratification predict success later in life?
• Why are teenagers more likely to litter in a highly littered area than in a clean area?

After choosing your research question, there is a variety of options for research and data collection methods to choose from.

A few of the most common research methods include:

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

The method you choose depends on several factors, including your timeline, your budget, and the structure of your question.

If there is already a body of research on your topic, a literature review is a great place to start. If you are interested in opinions and behaviour, consider an interview or focus group format. If you have more time or funding available, an experiment or pilot study may be a good fit for you.

In order to ensure you are conducting your explanatory research correctly, be sure your analysis is definitively causal in nature, and not just correlated.

Always remember the phrase ‘correlation doesn’t imply causation’. Correlated variables are merely associated with one another: when one variable changes, so does the other. However, this isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in the independent variable bring about changes in the dependent variable. In other words, there is a direct cause-and-effect relationship between variables.

Causal evidence must meet three criteria:

• Temporal : What you define as the ’cause’ must precede what you define as the ‘effect’.
• Variation : Intervention must be systematic between your independent variable and dependent variable.
• Non-spurious : Be careful that there are no mitigating factors or hidden third variables that confound your results.

Correlation doesn’t imply causation, but causation always implies correlation. In order to get conclusive causal results, you’ll need to conduct a full experimental design .

Your explanatory research design depends on the research method you choose to collect your data . In most cases, you’ll use an experiment to investigate potential causal relationships. We’ll walk you through the steps using an example.

Step 1: Develop the research question

The first step in conducting explanatory research is getting familiar with the topic you’re interested in, so that you can develop a research question .

Let’s say you’re interested in language retention rates in adults.

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

Step 2: Formulate a hypothesis

The next step is to address your expectations. In some cases, there is literature available on your subject or on a closely related topic that you can use as a foundation for your hypothesis . In other cases, the topic isn’t well studied, and you’ll have to develop your hypothesis based on your instincts or on existing literature on more distant topics.

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

Step 3: Design your methodology and collect your data

Next, decide what data collection and data analysis methods you will use and write them up. After carefully designing your research, you can begin to collect your data.

• Adults who were adopted from Colombia between 0 and 6 months of age
• Adults who were adopted from Colombia between 6 and 12 months of age
• Adults who were adopted from Colombia between 12 and 18 months of age
• Monolingual adults who have not been exposed to a different language

During the study, you test their Spanish language proficiency twice in a research design that has three stages:

• Pretest : You conduct several language proficiency tests to establish any differences between groups pre-intervention.
• Intervention : You provide all groups with 8 hours of Spanish class.
• Posttest : You again conduct several language proficiency tests to establish any differences between groups post-intervention.

You made sure to control for any confounding variables , such as age, gender, and proficiency in other languages.

Step 4: Analyse your data and report results

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

• The pre-exposed adults showed higher language proficiency in Spanish than those who had not been pre-exposed. The difference is even greater for the posttest.
• The adults who were adopted between 12 and 18 months of age had a higher Spanish language proficiency level than those who were adopted between 0 and 6 months or 6 and 12 months of age, but there was no difference found between the latter two groups.

To determine whether these differences are significant, you conduct a mixed ANOVA. The ANOVA shows that all differences are not significant for the pretest, but they are significant for the posttest.

Step 5: Interpret your results and provide suggestions for future research

As you interpret the results, try to come up with explanations for the results that you did not expect. In most cases, you want to provide suggestions for future research.

However, this difference is only significant after the intervention (the Spanish class).

You decide it’s worth it to further research the matter, and propose a few additional research ideas:

• Replicate the study with a larger sample
• Replicate the study for other maternal languages (e.g., Korean, Lingala, Arabic)
• Replicate the study for other language aspects, such as nativeness of the accent

It can be easy to confuse explanatory research with exploratory research. If you’re in doubt about the relationship between exploratory and explanatory research, just remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research questions often begin with ‘what’. They are designed to guide future research and do not usually have conclusive results. Exploratory research is often utilised as a first step in your research process, to help you focus your research question and fine-tune your hypotheses.

Explanatory research questions often start with ‘why’ or ‘how’. They help you study why and how a previously studied phenomenon takes place.

Like any other research design , exploratory research has its trade-offs: while it provides a unique set of benefits, it also has significant downsides:

• It gives more meaning to previous research. It helps fill in the gaps in existing analyses and provides information on the reasons behind phenomena.
• It is very flexible and often replicable, since the internal validity tends to be high when done correctly.
• As you can often use secondary research, explanatory research is often very cost- and time-effective, allowing you to utilise pre-existing resources to guide your research before committing to heavier analyses.

Disadvantages

• While explanatory research does help you solidify your theories and hypotheses, it usually lacks conclusive results.
• Results can be biased or inadmissible to a larger body of work and are not generally externally valid . You will likely have to conduct more robust (often quantitative ) research later to bolster any possible findings gleaned from explanatory research.
• Coincidences can be mistaken for causal relationships , and it can sometimes be challenging to ascertain which is the causal variable and which is the effect.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

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

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

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

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

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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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.

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.

• (c)   Scott’s Pi (π)

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.

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.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• DOI: 10.20525/ijrbs.v10i5.1262
• Corpus ID: 237949307

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• Mohammad Abu Sayed Toyon
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