explanatory research design thesis

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

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Explanatory Research – Types, Methods, Guide

Table of Contents

Explanatory research is a type of research that seeks to explain the causes and effects of specific phenomena or to understand the “why” behind relationships. It’s typically used to explore complex problems, uncover reasons behind trends, or understand underlying factors of behavior. This research type is especially valuable in fields such as social sciences, psychology, and marketing, where understanding causation is crucial. This guide explains the purpose, types, methods, and key steps in conducting explanatory research effectively.

Explanatory Research

Explanatory research, also called causal research, aims to identify the cause-and-effect relationships between variables. It is often used after exploratory research has provided some insight into a topic, enabling researchers to investigate why something occurs. Unlike descriptive research, which only describes phenomena, explanatory research seeks to answer questions like “Why does this happen?” and “What factors influence this outcome?”

For instance, a marketing team might use explanatory research to understand why a particular advertising campaign was more successful than another or to uncover the factors driving customer satisfaction.

Key Characteristics of Explanatory Research

• Cause-and-Effect Focus : Emphasizes understanding relationships between variables and identifying causation.
• Structured Approach : Often uses experiments or surveys to establish clear and measurable links between variables.
• Specific Hypotheses : Explanatory research is hypothesis-driven, aiming to test specific assumptions about relationships.
• Quantitative or Mixed Methods : Typically involves quantitative data, though qualitative data may supplement findings.

Types of Explanatory Research

• Experimental Research Experimental research is a controlled approach where researchers manipulate one variable to observe its impact on another. For example, a psychologist might change the lighting in a room to see its effect on mood. This approach allows researchers to establish causation with greater certainty by controlling for external variables.
• Correlational Research Correlational research examines the relationship between two or more variables to determine if they are associated. Although correlational research doesn’t establish causation, it highlights potential links that may justify further exploration. For example, researchers may examine the relationship between exercise frequency and mental well-being.
• Case Studies A case study is an in-depth examination of a specific case, individual, group, or situation, aiming to uncover causal relationships. Case studies are particularly useful for complex or unique phenomena that cannot be studied experimentally. For instance, a company might conduct a case study to understand the reasons behind a specific department’s high productivity.
• Longitudinal Studies Longitudinal research involves observing the same subjects over an extended period. This approach helps establish patterns over time and can reveal the effects of certain variables, such as the impact of a particular teaching method on student performance across multiple years.

Methods of Conducting Explanatory Research

• Surveys and Questionnaires Surveys allow researchers to gather data on specific behaviors, attitudes, or characteristics from a large sample. When structured to test hypotheses, surveys can help identify correlations or trends and support causal analysis.
• Experiments Experiments are the most rigorous method for establishing causation. Researchers manipulate an independent variable and observe its effects on a dependent variable, controlling other variables to isolate the effect.
• Observational Studies Observational studies allow researchers to observe subjects in their natural environment without interference. While less controlled than experiments, observation can still yield valuable insights into causal relationships.
• Interviews and Focus Groups While explanatory research is often quantitative, qualitative methods such as interviews and focus groups can offer deeper insights, especially in the early stages. Interviews with experts or focus groups can help identify potential variables for further quantitative analysis.
• Secondary Data Analysis Analyzing existing data, such as government reports, market research, or health records, can provide insights into causal relationships. Secondary data analysis is cost-effective and time-efficient, particularly for identifying trends across large datasets.

Steps for Conducting Explanatory Research

• Define the Research Question Start with a specific question focused on causation. For example, “What factors contribute to increased customer loyalty?” The question should guide the research process and determine the methods used.
• Develop a Hypothesis Formulate a hypothesis that predicts a relationship between variables. For example, “Regular follow-up emails improve customer retention rates.” The hypothesis should be clear, testable, and focused.
• Choose the Research Method Select a method suited to your research question. If causation is the focus, experiments or longitudinal studies may be appropriate. For identifying correlations, surveys or secondary data analysis could be effective.
• Collect Data Gather data according to your chosen method. For example, if conducting an experiment, manipulate the independent variable while controlling for extraneous factors. Collect data systematically to ensure consistency.
• Analyze the Data Use statistical tools to analyze data and test your hypothesis. Common analyses include regression analysis, ANOVA, or correlation coefficients, depending on the type of data and research goals.
• Interpret Results Interpret the results in the context of your hypothesis and research question. Consider any potential biases or limitations that could affect findings. For example, if a survey indicates a positive correlation between two factors, acknowledge that correlation does not imply causation.
• Report Findings Present the findings in a structured format, explaining how the research contributes to understanding the topic. Use tables, graphs, and charts to visualize relationships and provide a detailed explanation of the results.

Advantages and Limitations of Explanatory Research

Advantages :

• Establishes Causal Links : Helps identify cause-and-effect relationships.
• Improves Understanding of Complex Issues : Provides insights into the factors influencing behavior or outcomes.
• Supports Decision-Making : Offers evidence to guide strategic decisions in business, healthcare, and other fields.

Limitations :

• Potential for Bias : Especially in observational studies or case studies where variables are less controlled.
• Resource-Intensive : Experiments and longitudinal studies require significant time, cost, and effort.
• Limited Generalizability : Case studies and experiments in controlled settings may not apply to larger populations.

Explanatory Research Example

Imagine a company investigating why a recent product failed to attract customers. Through a combination of customer surveys, market analysis, and focus groups, researchers identify a misalignment between the product’s features and customer preferences. They then test a hypothesis that adjusting certain features could increase satisfaction. This explanatory approach allows the company to understand the causes behind low engagement and make informed product changes.

Explanatory research is a powerful tool for discovering causal relationships and uncovering the “why” behind phenomena. By carefully defining a hypothesis, choosing an appropriate method, and interpreting results, researchers can make meaningful contributions to fields such as marketing, psychology, and social sciences. Although resource-intensive, explanatory research offers critical insights that can drive impactful decisions and improve understanding of complex issues.

• Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Sage Publications.
• Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students . Pearson Education.
• Zikmund, W. G., & Babin, B. J. (2019). Exploring Marketing Research . Cengage Learning.
• Punch, K. F. (2013). Introduction to Social Research: Quantitative and Qualitative Approaches . Sage Publications.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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5.5 Mixed Methods Study Designs

There are diverse reasons why researchers undertake mixed methods research. 10 When researchers commence their study with a quantitative phase before a qualitative phase, often the aim is to use the initial information gathered to identify the best participants who may be recruited in the follow-up qualitative phase or to explain the mechanism behind the quantitative results. 10 For research studies where the quantitative phase follows the qualitative phase, the researcher may develop either a survey instrument, an intervention, or a program informed by the qualitative findings/ evidence. The choice of a mixed methods design should be informed by theoretical and/ or conceptual frameworks that support the study aims/ objectives. 9

The following mixed methods designs listed below are not exhaustive but only highlight common designs used in health care. Convergent, sequential and embedded are the basic designs, while multiphase goes beyond these basic designs but has been included here for your knowledge. 25 It is important to note that there are more complex designs, and the research question drives them.

Convergent (parallel or concurrent) design : A mixed methods design in which quantitative and qualitative data are collected simultaneously but analyzed separately, and the results are merged or integrated (Figure 5.2). 25 This type of design allows for the collection of rich, detailed data and provides a comprehensive understanding of the research problem. 2 5 An example of a concurrent mixed method design is the study by Rosenkranz, Wang and Hu., 2015 which aimed to explore, identify and explain what motivates and demotivates medical students to do research. The study used a convergent parallel mixed methods study where quantitative data were collected via a survey and qualitative data via semi-structured interviews. Data were analysed separately, and the results were compared and merged. 26 

The benefit of the convergent mixed methods design used in the study by Rosenkranz et al., 26 is that it allowed for a more comprehensive and nuanced understanding of what motivates and demotivates medical students to do research by drawing on both types of data. The survey results showed that students who had experienced exposure to the uncertainties of clinical practice through clerkships and supported compulsory research activities, were more likely to view future research activities positively. The semi-structured interviews revealed that these activities were particularly important because they helped the students to see research as a social activity which has clinical relevance and builds confidence. Overall, the study design provided evidence for the motivating effects of Competence and Relatedness in relation to medical students doing research.  In this particular study, the researchers were able to not only identify the factors that motivate and demotivate medical students to do research but also gain an in-depth understanding of why those factors were important. The study design also increased the validity of the research as the limitations of the survey data were addressed by using qualitative data to provide a more in-depth understanding of the research question.

Sequential (exploratory or explanatory) designs: In this type of mixed methods design, the aim is to use the results of one method to develop or build another method. These designs may begin with a qualitative method followed by a quantitative approach (exploratory) or a quantitative investigation followed up with a qualitative enquiry (explanatory). 25

Exploratory sequential design : This technique involves the initial collection of qualitative data, and the findings are used to guide the design and development of quantitative data collection tools. 25 The quantitative and qualitative data results are then integrated to provide a more comprehensive understanding of the phenomenon (Figure 5.3). This method is useful when developing and testing a new instrument. An example is the study by Jafer et al., 2020 which investigated dental patients’ behaviour, thoughts, opinions and needs for oral cancer information, and dentists’ behaviour regarding the prevention and examination of oral cancer. 27 The qualitative methodology was utilised to discover the emerging patterns in the patient’s thoughts, opinions and expectations regarding oral cancer. Following the qualitative investigation, a descriptive quantitative observational study was conducted on a larger sample of dental patients to analyse and quantify oral cancer-related features. 27

The benefit of using an exploratory sequential design in the study by Jafer et al., 27 is that it allowed for an in-depth exploration of the dental patients’ thoughts, opinions, and needs for oral cancer information, and dentists’ behaviour regarding the prevention and examination of oral cancer. By using qualitative methods to explore the emerging patterns in the patients’ views and needs, the researchers were able to identify key themes and issues that would have been missed in a purely quantitative study. The subsequent quantitative study, which involved a larger sample of dental patients, allowed the researchers to test and confirm their findings from the qualitative study in a more representative sample. By combining both qualitative and quantitative methods, the researchers were able to gain a more comprehensive understanding of the research problem and provide more nuanced and insightful recommendations for improving oral cancer prevention and examination practices in dental settings.

Explanatory sequential design: this method is characterised by the collection and analysis of quantitative data, followed by the collection and analysis of qualitative data. 25 The goal is to use the qualitative findings to explain and interpret the quantitative results (Figure 5.4). This method is popular in health research. 25 An example of explanatory sequential design is the study by Albert et al., 2022 which explored the views of General Practitioners (GPs) and Exercise Physiologists (EPs) as key stakeholders for optimizing patient care and efficiency of physical activity referral schemes (PARS). 28 

The authors used quantitative methods to investigate these health professionals’ knowledge, beliefs, and attitudes towards PARS in the first phase of the study. This initial phase provided an overall understanding of the topic, indicated that the participants valued PARS and the findings guided the development of the interview guide and participant selection for the second (qualitative) phase. In the second phase, the authors used semi-structured interviews to gather in-depth information on participants’ perceptions about care coordination through PARS. The qualitative data allowed for a more nuanced understanding of the research question and helped the researchers to identify the key factors that influence the success of PARS. This design helped the authors to develop a robust and accurate understanding of a complex phenomenon and provided insights that can inform the development of interventions and policies to improve patient care and the efficiency of PARS.

Embedded design : This design is also known as nested design. 25 It involves embedding one research design into another to generate new insights (Figure 5.5). Embedded designs may be convergent or sequential. 25 As an illustration, this technique could embed qualitative research within a broader quantitative study. 25 The quantitative study is used to offer a larger understanding of the research problem, whereas the qualitative study provides a more in-depth understanding of specific parts of the research topic. 25 An example is the study by Yue et al., 2022 which aimed to investigate nurses’ perceptions and experiences with the transition to a new nursing information system (Care Direct) 2 years after its first introduction. The study used an embedded design in which qualitative data and quantitative data were collected concurrently with the qualitative data given priority. 29

The embedded mixed methods design allowed the authors to explore both the prevalence of certain attitudes or behaviors and to gain insight into why these attitudes and behaviors were present. The use of qualitative data as a priority in the study allowed the researchers to explore the complexity and richness of the nurses’ experiences with the new system. This approach is particularly useful when trying to understand the factors that contribute to or impede successful implementation of new technologies. Additionally, the qualitative data was used to develop a theoretical framework that informed the development of the quantitative survey instrument. This strategy ensured that the quantitative data collected was grounded in the context of the nurses’ experiences with the new system, thereby enhancing the quality and relevance of the research findings.

Multiphase design: In this approach, multiple projects with a common goal are conducted. 25 This method requires multiple designs to be conducted over time with linkages in place to ensure that each phase builds on the previous one. 25 A project could start with a qualitative design and proceed to a quantitative design, then return to a qualitative design, and so on (Figure 5.6). The design may contain convergent or sequential elements. 25 For example, Lee et al., 2018 , conducted a study that sought to evaluate an intervention program – The Prevention and Wellness Trust Fund (PWTF). 30 The program was designed to address hypertension, paediatric asthma, falls among older adults, and tobacco use in Massachusetts. The aim was to improve health outcomes through prevention and disease management strategies and reduce healthcare costs. 30 A multi-phase, explanatory sequential mixed methods design (qualitative to quantitative to qualitative) was used to gain a more comprehensive understanding of the implementation of the Prevention and Wellness Trust Fund interventions. 30

The multi-phase, explanatory sequential mixed methods design used in this study enabled the researchers to provide a more holistic, comprehensive, and actionable evaluation of the PWTF intervention program. The findings from the study can help program developers and policymakers to identify the most effective strategies for addressing the target health issues and design programs that are sustainable and cost-effective.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.