key research methodologies

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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199 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

• 3 minute read

Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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Published by Nicolas at March 21st, 2024 , Revised On March 12, 2024

The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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A Comprehensive Guide to Methodology in Research

Table of Contents

Research methodology plays a crucial role in any study or investigation. It provides the framework for collecting, analyzing, and interpreting data, ensuring that the research is reliable, valid, and credible. Understanding the importance of research methodology is essential for conducting rigorous and meaningful research.

In this article, we'll explore the various aspects of research methodology, from its types to best practices, ensuring you have the knowledge needed to conduct impactful research.

What is Research Methodology?

Research methodology refers to the system of procedures, techniques, and tools used to carry out a research study. It encompasses the overall approach, including the research design, data collection methods, data analysis techniques, and the interpretation of findings.

Research methodology plays a crucial role in the field of research, as it sets the foundation for any study. It provides researchers with a structured framework to ensure that their investigations are conducted in a systematic and organized manner. By following a well-defined methodology, researchers can ensure that their findings are reliable, valid, and meaningful.

When defining research methodology, one of the first steps is to identify the research problem. This involves clearly understanding the issue or topic that the study aims to address. By defining the research problem, researchers can narrow down their focus and determine the specific objectives they want to achieve through their study.

How to Define Research Methodology

Once the research problem is identified, researchers move on to defining the research questions. These questions serve as a guide for the study, helping researchers to gather relevant information and analyze it effectively. The research questions should be clear, concise, and aligned with the overall goals of the study.

After defining the research questions, researchers need to determine how data will be collected and analyzed. This involves selecting appropriate data collection methods, such as surveys, interviews, observations, or experiments. The choice of data collection methods depends on various factors, including the nature of the research problem, the target population, and the available resources.

Once the data is collected, researchers need to analyze it using appropriate data analysis techniques. This may involve statistical analysis, qualitative analysis, or a combination of both, depending on the nature of the data and the research questions. The analysis of data helps researchers to draw meaningful conclusions and make informed decisions based on their findings.

Role of Methodology in Research

Methodology plays a crucial role in research, as it ensures that the study is conducted in a systematic and organized manner. It provides a clear roadmap for researchers to follow, ensuring that the research objectives are met effectively. By following a well-defined methodology, researchers can minimize bias, errors, and inconsistencies in their study, thus enhancing the reliability and validity of their findings.

In addition to providing a structured approach, research methodology also helps in establishing the reliability and validity of the study. Reliability refers to the consistency and stability of the research findings, while validity refers to the accuracy and truthfulness of the findings. By using appropriate research methods and techniques, researchers can ensure that their study produces reliable and valid results, which can be used to make informed decisions and contribute to the existing body of knowledge.

Steps in Choosing the Right Research Methodology

Choosing the appropriate research methodology for your study is a critical step in ensuring the success of your research. Let's explore some steps to help you select the right research methodology:

Identifying the Research Problem

The first step in choosing the right research methodology is to clearly identify and define the research problem. Understanding the research problem will help you determine which methodology will best address your research questions and objectives.

Identifying the research problem involves a thorough examination of the existing literature in your field of study. This step allows you to gain a comprehensive understanding of the current state of knowledge and identify any gaps that your research can fill. By identifying the research problem, you can ensure that your study contributes to the existing body of knowledge and addresses a significant research gap.

Once you have identified the research problem, you need to consider the scope of your study. Are you focusing on a specific population, geographic area, or time frame? Understanding the scope of your research will help you determine the appropriate research methodology to use.

Reviewing Previous Research

Before finalizing the research methodology, it is essential to review previous research conducted in the field. This will allow you to identify gaps, determine the most effective methodologies used in similar studies, and build upon existing knowledge.

Reviewing previous research involves conducting a systematic review of relevant literature. This process includes searching for and analyzing published studies, articles, and reports that are related to your research topic. By reviewing previous research, you can gain insights into the strengths and limitations of different methodologies and make informed decisions about which approach to adopt.

During the review process, it is important to critically evaluate the quality and reliability of the existing research. Consider factors such as the sample size, research design, data collection methods, and statistical analysis techniques used in previous studies. This evaluation will help you determine the most appropriate research methodology for your own study.

Formulating Research Questions

Once the research problem is identified, formulate specific and relevant research questions. These questions will guide your methodology selection process by helping you determine what type of data you need to collect and how to analyze it.

Formulating research questions involves breaking down the research problem into smaller, more manageable components. These questions should be clear, concise, and measurable. They should also align with the objectives of your study and provide a framework for data collection and analysis.

When formulating research questions, consider the different types of data that can be collected, such as qualitative or quantitative data. Depending on the nature of your research questions, you may need to employ different data collection methods, such as interviews, surveys, observations, or experiments. By carefully formulating research questions, you can ensure that your chosen methodology will enable you to collect the necessary data to answer your research questions effectively.

Implementing the Research Methodology

After choosing the appropriate research methodology, it is time to implement it. This stage involves collecting data using various techniques and analyzing the gathered information. Let's explore two crucial aspects of implementing the research methodology:

Data Collection Techniques

Data collection techniques depend on the chosen research methodology. They can include surveys, interviews, observations, experiments, or document analysis. Selecting the most suitable data collection techniques will ensure accurate and relevant data for your study.

Data Analysis Methods

Data analysis is a critical part of the research process. It involves interpreting and making sense of the collected data to draw meaningful conclusions. Depending on the research methodology, data analysis methods can include statistical analysis, content analysis, thematic analysis, or grounded theory.

Ensuring the Validity and Reliability of Your Research

In order to ensure the validity and reliability of your research findings, it is important to address these two key aspects:

Understanding Validity in Research

Validity refers to the accuracy and soundness of a research study. It is crucial to ensure that the research methods used effectively measure what they intend to measure. Researchers can enhance validity by using proper sampling techniques, carefully designing research instruments, and ensuring accurate data collection.

Ensuring Reliability in Your Study

Reliability refers to the consistency and stability of the research results. It is important to ensure that the research methods and instruments used yield consistent and reproducible results. Researchers can enhance reliability by using standardized procedures, ensuring inter-rater reliability, and conducting pilot studies.

A comprehensive understanding of research methodology is essential for conducting high-quality research. By selecting the right research methodology, researchers can ensure that their studies are rigorous, reliable, and valid. It is crucial to follow the steps in choosing the appropriate methodology, implement the chosen methodology effectively, and address validity and reliability concerns throughout the research process. By doing so, researchers can contribute valuable insights and advances in their respective fields.

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Research Methodology Explained: A Beginner's Guide

Research methodology stands as the backbone of credible study, guiding the generation and analysis of data towards solving research queries. It encompasses not just the practical aspects of data collection but also the theoretical framework that shapes the study's direction, distinguishing methodology in research from mere methods.

This foundational process, characterized by its systematic, logical, empirical, and replicable nature, underscores the importance of research methodology in contributing to the vast expanse of knowledge across disciplines.

Beyond a mere overview, we will explore varied research methodology types such as applied, basic, and correlational research, offering insight into how each approach serves the objectives of research methodology. Through a methodological approach, readers will gain knowledge of the critical steps and decisions that shape a robust study, from selecting the right research methodology to interpreting findings.

Understanding Research Methodology

Research methodology is essential in scientific investigations, providing a structured approach to data collection, analysis, and interpretation. This systematic method ensures that research findings are reliable, valid, and generalizable, making it possible to draw credible conclusions that contribute to existing knowledge.

Key Elements of Research Methodology

• Research Design : This includes the overall strategy that outlines the procedures for collecting, analyzing, and interpreting data. The design is crucial as it helps align the research methods with the objectives of the study, ensuring that the results are effective in addressing the research questions.
• Data Collection Methods : Depending on the nature of the study, researchers may employ various techniques such as surveys, interviews, or observation. Each method is chosen based on its ability to gather the necessary data effectively.
• Data Analysis Techniques : After data collection, the next step is analyzing this data to derive meaningful insights. Techniques vary widely from statistical analysis in quantitative studies to content analysis in qualitative research.

Research Approaches and Their Applications

• Qualitative Methods : These are used to gather in-depth insights into people’s attitudes, behaviors, and experiences and often involve methods like interviews and focus groups.
• Quantitative Methods : In contrast, quantitative methods focus on numerical data and often employ statistical tests to validate hypotheses.
• Mixed Methods : Combining both qualitative and quantitative approaches, mixed methods provide a comprehensive analysis that strengthens the research findings by addressing the limitations of each method alone.

By employing a well-structured research methodology, scientists and scholars can ensure that their studies are robust, replicable, and impactful. This foundation not only supports the validity of the research findings but also enhances the overall credibility of the scientific inquiry.

Types of Research Methodology

Overview of methodological approaches.

The landscape of research methodology is dominated by three primary approaches: quantitative, qualitative, and mixed methods. Each approach offers unique insights and tools for investigation, catering to different research objectives.

• Objective : Focuses on quantifying data and generalizing results from a sample to a larger population.
• Methods : Employs structured techniques such as surveys and statistical analysis to produce numerical data.
• Applications : Ideal for testing hypotheses, establishing patterns, and making predictions.
• Objective : Aims to provide a detailed description and interpretation of research subjects.
• Methods : Utilizes interviews, focus groups, and observations to gather in-depth, non-numerical data.
• Applications : Best suited for exploring complex concepts and understanding underlying motivations or behaviors.
• Objective : Combines elements of both qualitative and quantitative research to cover more ground.
• Methods : Integrates numerical data analysis with detailed descriptions, enhancing the robustness of the findings.
• Applications : Useful for validating quantitative data with qualitative insights and explaining anomalies.

Data Collection and Analysis Techniques

Each methodological approach employs specific techniques for data collection and analysis, tailored to its unique requirements.

• Data Collection : Includes sampling, surveys, and structured observations.
• Data Analysis : Features statistical methods such as regression analysis, correlation, and descriptive statistics.
• Data Collection : Comprises one-on-one interviews, document reviews, and qualitative observations.
• Data Analysis : Involves methods like thematic analysis, discourse analysis, and narrative analysis.
• Data Collection : A combination of both quantitative and qualitative data collection methods.
• Data Analysis : Integrates quantitative statistical analysis with qualitative content analysis.

Sampling Designs

The choice of sampling design plays a critical role in the credibility and generalizability of the research.

• Types : Includes simple random, stratified, systematic, and cluster sampling.
• Feature : Each member of the population has a known chance of being selected.
• Types : Encompasses convenience, purposive, snowball, and quota sampling.
• Feature : Selection is based on the researcher’s judgment, often used when probability sampling is not feasible.

This structured approach to understanding the types of research methodology not only clarifies the distinctions between them but also highlights their specific applications and techniques, providing a comprehensive framework for researchers to base their methodological choices.

Choosing the Right Research Methodology

Assessing research goals and context.

• Clarify Research Objectives : It's crucial to start by clearly understanding the research goals, objectives, and questions. This clarity will guide the choice of methodology, ensuring it aligns with what you aim to discover or prove.
• Evaluate the Setting and Participants : Consider the physical, social, or cultural context of the study along with the characteristics of the population involved. This assessment helps in choosing a methodology that is sensitive to contextual variables and participant demographics.

Methodological Considerations

• Review Previous Studies : Look at the methodologies employed in previous research within the same discipline or those that addressed similar objectives. This can provide insights into what methods might be most effective or what new approaches could offer fresh perspectives.
• Practical Constraints : Acknowledge any practical limitations such as experimental conditions, resource availability, and time constraints. These factors can significantly influence the feasibility of certain research methodologies over others.

Choosing Between Qualitative and Quantitative Approaches

• Quantitative Research : Opt for quantitative methods when the goal is to quantify data and generalize results from a sample to a larger population. This approach is suitable for establishing facts or testing hypotheses.
• Qualitative Research : Choose qualitative methods if the aim is to gain a deeper understanding of people’s experiences or perspectives. This approach is ideal for exploring complex issues in detail.
• Mixed Methods : Consider using mixed methods to leverage the strengths of both qualitative and quantitative approaches, especially when the research aims to provide a comprehensive analysis of the topic.

By carefully considering these factors, researchers can select the most appropriate methodology to address their specific research questions effectively and efficiently.

Key Components of Research Methodology

Research design and planning.

• Clarify Research Objectives : Begin by defining clear and measurable objectives, which guide all subsequent decisions in the research process.
• Select Research Type : Determine whether the study is exploratory, descriptive, explanatory, or experimental, as this shapes the research design.
• Choose Appropriate Methods : Based on the research type, select methods for data collection and analysis that best suit the study's needs.

Data Collection and Analysis

• Qualitative : Includes interviews, focus groups, and observations, which provide depth and context.
• Quantitative : Involves surveys and experiments that yield quantifiable data for statistical analysis.
• Probability Sampling : Ensures every member of the population has a known chance of selection.
• Nonprobability Sampling : Used when probability sampling isn't feasible; based on researcher’s judgment.

Ethical Considerations and Methodological Rigor

• Ethical Standards : Adhere to ethical guidelines such as informed consent, confidentiality, and minimizing harm.
• Validity and Reliability : Implement measures to ensure the research is both valid (accurately measures what it is supposed to measure) and reliable (yields consistent results).
• Pilot Testing : Conduct preliminary testing to refine data collection strategies and address potential issues.

By integrating these components, researchers can enhance the credibility and impact of their studies, ensuring that findings are both trustworthy and actionable.

Throughout this exploration of research methodology, we have journeyed from the foundational principles that delineate methodology from mere methods to the intricate distinctions between qualitative, quantitative, and mixed methods research.

This comprehensive guide underscores the pivotal role that a well-structured methodology plays in validating research findings, enhancing the credibility of scientific inquiries, and ultimately, contributing to the vast expanse of knowledge across various fields.

For those looking to dive deeper into the intricacies of research methods or seeking to refine their methodology choice, tools like TLDR This offer valuable resources for further exploration and understanding. By continually engaging with research methodologies and embracing their evolution, the scientific community can forge new paths of discovery, innovation, and impact.

1. How can one describe their research methodology effectively?

To effectively describe your research methodology, follow these steps:

• Begin by restating your thesis or research problem.
• Detail the approach you chose for the research.
• Mention any unique methodologies you employed.
• Describe the data collection process.
• Explain how the data was analyzed.

2. What are the main types of research methodologies?

The four primary research methodologies are:

• Qualitative research, which focuses on understanding concepts, thoughts, or experiences.
• Quantitative research, which involves the statistical, mathematical, or numerical analysis of data.
• Mixed methods research, which combines elements of both qualitative and quantitative research.

3. What does the term "research methodology" mean for beginners?

Research methodology refers to the section in a research paper that outlines the tools, techniques, and procedures used to gather and analyze data. This section is crucial as it helps readers assess the study's reliability and validity.

4. What are the seven fundamental research methods commonly used?

The seven basic research methods frequently utilized in studies are:

• Observation and Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis or Archival Study
• Mixed Methods, which is a combination of several of the aforementioned methods.

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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Research Methods (sociology-focused)

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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Research Methodologies

• What are research designs?

What are research methodologies?

Quantitative research methodologies, qualitative research methodologies, mixed method methodologies, selecting a methodology.

• What are research methods?
• Additional Sources

According to Dawson (2019),a research methodology is the primary principle that will guide your research.  It becomes the general approach in conducting research on your topic and determines what research method you will use. A research methodology is different from a research method because research methods are the tools you use to gather your data (Dawson, 2019).  You must consider several issues when it comes to selecting the most appropriate methodology for your topic.  Issues might include research limitations and ethical dilemmas that might impact the quality of your research.  Descriptions of each type of methodology are included below.

Quantitative research methodologies are meant to create numeric statistics by using survey research to gather data (Dawson, 2019).  This approach tends to reach a larger amount of people in a shorter amount of time.  According to Labaree (2020), there are three parts that make up a quantitative research methodology:

• Sample population
• How you will collect your data (this is the research method)
• How you will analyze your data

Once you decide on a methodology, you can consider the method to which you will apply your methodology.

Qualitative research methodologies examine the behaviors, opinions, and experiences of individuals through methods of examination (Dawson, 2019).  This type of approach typically requires less participants, but more time with each participant.  It gives research subjects the opportunity to provide their own opinion on a certain topic.

Examples of Qualitative Research Methodologies

• Action research:  This is when the researcher works with a group of people to improve something in a certain environment.  It is a common approach for research in organizational management, community development, education, and agriculture (Dawson, 2019).
• Ethnography:  The process of organizing and describing cultural behaviors (Dawson, 2019).  Researchers may immerse themselves into another culture to receive in "inside look" into the group they are studying.  It is often a time consuming process because the researcher will do this for a long period of time.  This can also be called "participant observation" (Dawson, 2019).
• Feminist research:  The goal of this methodology is to study topics that have been dominated by male test subjects.  It aims to study females and compare the results to previous studies that used male participants (Dawson, 2019).
• Grounded theory:  The process of developing a theory to describe a phenomenon strictly through the data results collected in a study.  It is different from other research methodologies where the researcher attempts to prove a hypothesis that they create before collecting data.  Popular research methods for this approach include focus groups and interviews (Dawson, 2019).

A mixed methodology allows you to implement the strengths of both qualitative and quantitative research methods.  In some cases, you may find that your research project would benefit from this.  This approach is beneficial because it allows each methodology to counteract the weaknesses of the other (Dawson, 2019).  You should consider this option carefully, as it can make your research complicated if not planned correctly.

What should you do to decide on a research methodology?  The most logical way to determine your methodology is to decide whether you plan on conducting qualitative or qualitative research.  You also have the option to implement a mixed methods approach.  Looking back on Dawson's (2019) five "W's" on the previous page , may help you with this process.  You should also look for key words that indicate a specific type of research methodology in your hypothesis or proposal.  Some words may lean more towards one methodology over another.

Quantitative Research Key Words

• How satisfied

Qualitative Research Key Words

• Experiences
• Thoughts/Think
• Relationship
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Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

• Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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.

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.

Cite this Scribbr article

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McCombes, S. (2022, October 10). What Is a Research Methodology? | Steps & Tips. Scribbr. Retrieved 29 April 2024, from https://www.scribbr.co.uk/thesis-dissertation/methodology/

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Research Methodologies

This course is part of Market Research Specialization

Taught in English

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Instructors: Athanasia Lampraki +1 more

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Beginner level

For market research analysts, social media strategists, marketing managers and market researchers to enhance their knowledge of key methodologies.

What you'll learn

Different research methodologies

Different sampling approaches

Primary and secondary data collection approaches

Skills you'll gain

• Collect primary and secondary data
• Use qualitative and quantitative research methodologies
• Adopt different sampling approaches
• Evaluate different data collection approaches

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There are 4 modules in this course

This course focuses on research methodologies. In this vein, the focus will be placed on qualitative and quantitative research methodologies, sampling approaches, and primary and secondary data collection. The course begins with a discussion on qualitative research approaches, looking at focus groups, personal interviews, ethnography, case studies and action research. We will also discuss quantitative research methods with a focus on experimental research design and survey methodology. There will be an exploration of the sampling design process and different sampling approaches, including probability and non-probability sampling as well as sample size and non-response issues. We will look at the nature and scope of primary and secondary data, and the importance of measurement. We will look at the role of the Internet in market research as well as non-comparative scaling techniques. The course ends with a discussion on different data collection approaches, with a focus on observation, content analysis, narrative research, phenomenology, and the collection of data using ethnography.

This week begins with a discussion on qualitative research approaches, looking at focus groups, personal interviews, ethnography, case study and action research. The week ends with a discussion of quantitative research methods with a focus on experimental research design and survey methodology.

What's included

4 videos 6 readings 5 quizzes 5 discussion prompts

4 videos • Total 15 minutes

• Qualitative Research: An Overview • 4 minutes • Preview module
• Focus groups • 3 minutes
• Ethnography • 4 minutes
• Experimental Study • 2 minutes

6 readings • Total 60 minutes

• Importance of Qualitative Research • 10 minutes
• Crafting a Qualitative Study • 10 minutes
• Personal in-depth Interviews • 10 minutes
• Case Study • 10 minutes
• Action Research • 10 minutes
• Survey Methodology • 10 minutes

5 quizzes • Total 150 minutes

• Refresh Your knowledge • 30 minutes
• Refresh Your Knowledge • 30 minutes
• Test Your Knowledge • 30 minutes

5 discussion prompts • Total 50 minutes

• Qualitative Research • 10 minutes
• Focus Groups and Interviews • 10 minutes
• Action Research Cycle • 10 minutes
• Survey Research • 10 minutes

The week begins with a discussion on the sampling design process and continues with different sampling approaches, including probability and non-probability sampling. The week ends with a discussion on sample size and non-response issues.

4 videos 6 readings 6 quizzes 4 discussion prompts

4 videos • Total 14 minutes

• Sample or Census? • 4 minutes • Preview module
• Probability vs Non Probability Sampling • 3 minutes
• Sampling Techniques: Non Probability Sampling • 3 minutes
• Sample Size • 3 minutes
• The Sampling Design Process: Target Population, Sampling Frame, Sampling Techniques • 10 minutes
• The sampling Design Process: Sampling Process, Sample Validation, Non Response Issues in Sampling • 10 minutes
• Types of Probability Sampling: Single Random Sampling, Systematic Sampling, Stratified and Cluster • 10 minutes
• Quota Sampling • 10 minutes
• Snowball Sampling • 10 minutes
• Non Response Aspects in Sampling • 10 minutes

6 quizzes • Total 180 minutes

4 discussion prompts • total 40 minutes.

• Sampling Design Process • 10 minutes
• Probability Sampling • 10 minutes
• Non Probability Sampling • 10 minutes
• Non Response Aspects • 10 minutes

The week begins with a discussion of the nature and scope of secondary data and continues with a discussion of primary data and the importance of measurement. The week ends with the role of the Internet in market research and a discussion about non-comparative scaling techniques.

4 videos • Total 16 minutes

• Nature and Scope of Secondary Data • 4 minutes • Preview module
• Nature and Aims of Internal Secondary Data • 4 minutes
• Primary Data Collection With Emphasis on Experiments • 3 minutes
• Importance of Internet in Market Research: Online Surveys • 4 minutes
• Combining Various Types of Data • 10 minutes
• External Secondary Data: Syndicated Sources of Secondary Data • 10 minutes
• Research Drives Business Strategy • 10 minutes
• Importance of Proper Measurement • 10 minutes
• Comparative Scaling Techniques • 10 minutes
• Non-comparative Scaling Techniques • 10 minutes
• Secondary Data • 10 minutes
• Proper Measurement • 10 minutes
• Role of Internet in Market Research & Non Comparative Scaling Techniques • 10 minutes

The week starts a discussion on different data collection approaches with a focus on observation, case study and content analysis. The week continues with a focus on narrative research, phenomenology and the action research project. The week ends with a discussion of collecting data using ethnography.

4 videos 5 readings 5 quizzes 1 peer review 5 discussion prompts

4 videos • Total 20 minutes

• Collecting Data Through Observation • 5 minutes • Preview module
• Data Collection: Narrative Research • 4 minutes
• The Action Research Project • 6 minutes
• Collecting Data Through Ethnography • 3 minutes

5 readings • Total 50 minutes

• Data Collection: Case Study • 10 minutes
• Data Collection: Content Analysis • 10 minutes
• Data Collection: Phenomenology • 10 minutes
• Developing and Implementing the Action Research project I • 10 minutes
• Developing and Implementing the Action Research Project II • 10 minutes

1 peer review • Total 60 minutes

• Synthesise your knowledge • 60 minutes
• Collecting Data: Observation • 10 minutes
• Phenomenology • 10 minutes
• The Action Research Project • 10 minutes
• Reflecting on Different Data Collection Approaches • 10 minutes
• Reflection of the Knowledge Gained • 10 minutes

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The course is very informative and guides you well through research methodologies as a beginner.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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Methodology – The 5 Key Components for Your Research

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The methodology section of a research paper lets your readers analyze your approach to the study. It states the topic of your study and the tools used to conduct the research.

The methodology guides the researcher in effectively gathering, analyzing, and interpreting data, ultimately ensuring the credibility and reliability of the study’s results.

Inhaltsverzeichnis

• 1 Methodology – In a Nutshell
• 2 Definition: Methodology
• 3 1. Inductive vs. Deductive research
• 4 2. Setting and participants in methodology
• 5 3. Data collection in methodology
• 6 4. Data analysis in methodology
• 7 5. Ethical considerations in methodology
• 8 Overview of methodology articles

Methodology – In a Nutshell

• Research methodology shows that a research project is valid and reliable.
• Researchers follow a systematic approach in collection and analysis of data.
• Data analysis is subject to the type of data and the techniques applicable in its collection and analysis.
• Meaningful insights can be drawn from qualitative and quantitative sources.

Definition: Methodology

Methodology in research refers to the scientific framework adopted in the research process . Data for research in methodology is collected through surveys, interviews, group discussions, and tracking online trends.

1. Inductive vs. Deductive research

• Inductive reasoning in methodology is a blank-page approach, where researchers create hypotheses from observed patterns.
• Deductive reasoning in methodology is concerned with creating hypotheses around existing theories.
• A mixed approach combines inductive and deductive research in different parts of the study where either is best suited.

Examples of each approach

2. setting and participants in methodology.

Setting in research methodology can be a physical, social, or cultural context where a study is based. Participants are the target population involved directly in a research study.

3. Data collection in methodology

Data collection is a systematic process of gathering, classifying, and labeling essential variables in a study. Researchers use data to formulate valid hypotheses in research methodology.

Qualitative vs. Quantitative data collection

Qualitative data collection includes interviews, group discussions, and observations. It involves close-ended questionnaires, polls, and surveys to collect numerical data.

Below are the strengths and weaknesses of each method:

Primary vs. Secondary data collection

Primary data collection is the process of collecting raw data through surveys, experiments, and interviews. Field surveys are considered a reliable source of primary data. They allow researchers to control sample size and quality.

Secondary data refers to primary data used by researchers in a new study. Sources of secondary data include websites, books, newspapers, and journals.

Descriptive vs. Experimental data collection

Descriptive data collection highlights what the subject matter is rather than why it exists. For example, a descriptive study would focus on what many millennials do in their free time rather than why they do it.

Experimental data collection is a more hands-on method of research. It involves altering certain variables to see how they affect others. For example, changes in prices often change demand for the same product.

Overview of data collection methods

Research methods guide the process of data collection and analysis. The following are some standard research methods and their relevance:

4. Data analysis in methodology

Data analysis is the logical process of applying statistical techniques to describe, sort, and summarize data. It condenses raw data inputs into usable insights in research methodology.

Qualitative data analysis

This is reviewing and analyzing text data to identify and explain themes and underlying correlations. It is applied to non-numerical case studies of social, economic, and political phenomena, among other areas.

Quantitative data analysis

Quantitative analysis is concerned with analyzing numbers. Numerical data is collected, and mathematical models are used to derive relationships between the data sets.

Overview of data analysis methods

Data analysis can be distilled into various forms and applications:

5. Ethical considerations in methodology

This component of research methodology outlines and explains ethical concerns noted and observed during research. Ethical considerations in research show how matters of integrity and ethical behavior were upheld during the course of study. It is important to ensure research does not infringe on the rights and freedoms of the population.

Overview of methodology articles

• Between-subjects design
• Cluster sampling
• Conceptual framework
• Confounding variables
• Construct validity
• Control group
• Control variables
• Controlled experiment
• Correlation vs causation
• Correlational research
• Cross-sectional study
• Data cleansing
• Data collection
• Discourse analysis
• Double-blind study
• Ethical considerations
• Ethnography
• Experimental design
• Explanatory research
• Explanatory vs response variables
• Exploratory research
• Extraneous variables
• Face validity
• Focus group
• Independent vs dependent variables
• Inductive and deductive reasoning
• Internal vs external validity
• Likert scale
• Literature review
• Mediator vs moderator
• Mixed methods research
• Multistage sampling
• Naturalistic observation
• Observational study
• Peer review
• Population vs sample
• Qualitative vs quantitative research
• Quasi experimental design
• Questionnaire
• Random assignment
• Random vs systematic errors
• Reliability vs validity
• Sampling bias
• Semi-structured interview
• Simple random sampling
• Stratified sampling
• Structured interview
• Systematic sampling
• Textual analysis
• Thematic analysis
• Transcribing interviews
• Triangulation in research
• Types of interviews
• Types of research
• Types of variables in research
• Unstructured interview
• Within-subjects design

What is an example of methodology?

Interviews. Besides interviews, other research methods are focused on discussions, surveys, and polls. They serve as data collection practices.

What are secondary sources of data?

Secondary sources of information are pre-recorded data sources. They are used in quantitative and qualitative research. Examples of secondary sources are journals, newspapers, and videotapes.

What is descriptive research?

Descriptive research identifies and lists all the observable features of a research topic. It is used by researchers in methodology to create the scope of a research topic.

What is the importance of research methodology?

Research methodology is an integral part of research papers. It articulately shows the method used to collect and analyze data. Researchers develop a methodology to make their research usable and verifiable.

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The American Trends Panel survey methodology

The American Trends Panel (ATP), created by Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have internet access at home are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by Ipsos.

Data in this report is drawn from ATP Wave 142, conducted from Feb. 7 to 11, 2024, and includes an oversample of non-Hispanic Asian adults, non-Hispanic Black men, and Hispanic men in order to provide more precise estimates of the opinions and experiences of these smaller demographic subgroups. These oversampled groups are weighted back to reflect their correct proportions in the population. A total of 10,133 panelists responded out of 11,117 who were sampled, for a response rate of 91%. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 3%. The break-off rate among panelists who logged on to the survey and completed at least one item is less than 1%. The margin of sampling error for the full sample of 10,133 respondents is plus or minus 1.5 percentage points.

Panel recruitment

The ATP was created in 2014, with the first cohort of panelists invited to join the panel at the end of a large, national, landline and cellphone random-digit-dial survey that was conducted in both English and Spanish. Two additional recruitments were conducted using the same method in 2015 and 2017, respectively. Across these three surveys, a total of 19,718 adults were invited to join the ATP, of whom 9,942 (50%) agreed to participate.

In August 2018, the ATP switched from telephone to address-based sampling (ABS) recruitment. A study cover letter and a pre-incentive are mailed to a stratified, random sample of households selected from the U.S. Postal Service’s Delivery Sequence File. This Postal Service file has been estimated to cover as much as 98% of the population, although some studies suggest that the coverage could be in the low 90% range. 1 Within each sampled household, the adult with the next birthday is asked to participate. Other details of the ABS recruitment protocol have changed over time but are available upon request. 2

We have recruited a national sample of U.S. adults to the ATP approximately once per year since 2014. In some years, the recruitment has included additional efforts (known as an “oversample”) to boost sample size with underrepresented groups. For example, Hispanic adults, Black adults and Asian adults were oversampled in 2019, 2022 and 2023, respectively.

Across the six address-based recruitments, a total of 23,862 adults were invited to join the ATP, of whom 20,917 agreed to join the panel and completed an initial profile survey. Of the 30,859 individuals who have ever joined the ATP, 11,920 remained active panelists and continued to receive survey invitations at the time this survey was conducted.

The American Trends Panel never uses breakout routers or chains that direct respondents to additional surveys.

Sample design

The overall target population for this survey was noninstitutionalized persons ages 18 and older living in the U.S., including Alaska and Hawaii. It featured a stratified random sample from the ATP in which Hispanic men, non-Hispanic Black men and non-Hispanic Asian adults were selected with certainty. The remaining panelists were sampled at rates designed to ensure that the share of respondents in each stratum is proportional to its share of the U.S. adult population to the greatest extent possible. Respondent weights are adjusted to account for differential probabilities of selection as described in the Weighting section below.

Questionnaire development and testing

The questionnaire was developed by Pew Research Center in consultation with Ipsos. The web program was rigorously tested on both PC and mobile devices by the Ipsos project management team and Pew Research Center researchers. The Ipsos project management team also populated test data that was analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey.

All respondents were offered a post-paid incentive for their participation. Respondents could choose to receive the post-paid incentive in the form of a check or a gift code to Amazon.com or could choose to decline the incentive. Incentive amounts ranged from $5 to $15 depending on whether the respondent belongs to a part of the population that is harder or easier to reach. Differential incentive amounts were designed to increase panel survey participation among groups that traditionally have low survey response propensities.

Data collection protocol

The data collection field period for this survey was Feb. 7 to 11, 2024. Postcard notifications were mailed to a subset of ATP panelists with a known residential address on Feb. 7. 3

Invitations were sent out in two separate launches: soft launch and full launch. Sixty panelists were included in the soft launch, which began with an initial invitation sent on the morning of Feb. 7. The ATP panelists chosen for the initial soft launch were known responders who had completed previous ATP surveys within one day of receiving their invitation. All remaining English- and Spanish-speaking sampled panelists were included in the full launch and were sent an invitation on the afternoon of Feb. 7.

All panelists with an email address received an email invitation and up to two email reminders if they did not respond to the survey. All ATP panelists who consented to SMS messages received an SMS invitation and up to two SMS reminders.

Data quality checks

To ensure high-quality data, the Center’s researchers performed data quality checks to identify any respondents showing clear patterns of satisficing. This includes checking for whether respondents left questions blank at very high rates or always selected the first or last answer presented. As a result of this checking, two ATP respondents were removed from the survey dataset prior to weighting and analysis.

The ATP data is weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. First, each panelist begins with a base weight that reflects their probability of selection for their initial recruitment survey. These weights are then rescaled and adjusted to account for changes in the design of ATP recruitment surveys from year to year. Finally, the weights are calibrated to align with the population benchmarks in the accompanying table to correct for nonresponse to recruitment surveys and panel attrition. If only a subsample of panelists was invited to participate in the wave, this weight is adjusted to account for any differential probabilities of selection.

Among the panelists who completed the survey, this weight is then calibrated again to align with the population benchmarks identified in the accompanying table and trimmed at the 2nd and 98th percentiles to reduce the loss in precision stemming from variance in the weights. This trimming is performed separately among non-Hispanic Black, non-Hispanic Asian, Hispanic and all other respondents. Sampling errors and tests of statistical significance take into account the effect of weighting.

The following table shows the unweighted sample sizes and the error attributable to sampling that would be expected at the 95% level of confidence for different groups in the survey.

Sample sizes and sampling errors for other subgroups are available upon request. In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.

Dispositions and response rates

© Pew Research Center 2024

• AAPOR Task Force on Address-based Sampling. 2016. “AAPOR Report: Address-based Sampling.” ↩
• Email [email protected] . ↩
• Postcard notifications are sent to 1) panelists who have been provided with a tablet to take ATP surveys, 2) panelists who were recruited within the last two years, and 3) panelists recruited prior to the last two years who opt to continue receiving postcard notifications. ↩

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• Open access
• Published: 01 May 2024

Health system lessons from the global fund-supported procurement and supply chain investments in Zimbabwe: a mixed methods study

• Abaleng Lesego 1 ,
• Lawrence P. O. Were 1 , 2 ,
• Tsion Tsegaye 1 ,
• Rafiu Idris 3 ,
• Linden Morrison 3 ,
• Tatjana Peterson 3 ,
• Sheza Elhussein 3 ,
• Esther Antonio 4 ,
• Godfrey Magwindiri 4 ,
• Ivan Dumba 5 ,
• Cleyland Mtambirwa 5 ,
• Newman Madzikwa 5 ,
• Raiva Simbi 5 ,
• Misheck Ndlovu 6 &
• Tom Achoki 1  

BMC Health Services Research volume  24 , Article number:  557 ( 2024 ) Cite this article

Metrics details

The Global Fund partnered with the Zimbabwean government to provide end-to-end support to strengthen the procurement and supply chain within the health system. This was accomplished through a series of strategic investments that included infrastructure and fleet improvement, training of personnel, modern equipment acquisition and warehouse optimisation. This assessment sought to determine the effects of the project on the health system.

This study employed a mixed methods design combining quantitative and qualitative research methods. The quantitative part entailed a descriptive analysis of procurement and supply chain data from the Zimbabwe healthcare system covering 2018 – 2021. The qualitative part comprised key informant interviews using a structured interview guide. Informants included health system stakeholders privy to the Global Fund-supported initiatives in Zimbabwe. The data collected through the interviews were transcribed in full and subjected to thematic content analysis.

Approximately 90% of public health facilities were covered by the procurement and distribution system. Timeliness of order fulfillment (within 90 days) at the facility level improved from an average of 42% to over 90% within the 4-year implementation period. Stockout rates for HIV drugs and test kits declined by 14% and 49% respectively. Population coverage for HIV treatment for both adults and children remained consistently high despite the increasing prevalence of people living with HIV. The value of expired commodities was reduced by 93% over the 4-year period.

Majority of the system stakeholders interviewed agreed that support from Global Fund was instrumental in improving the country's procurement and supply chain capacity. Key areas include improved infrastructure and equipment, data and information systems, health workforce and financing. Many of the participants also cited the Global Fund-supported warehouse optimization as critical to improving inventory management practices.

It is imperative for governments and donors keen to strengthen health systems to pay close attention to the procurement and distribution of medicines and health commodities. There is need to collaborate through joint planning and implementation to optimize the available resources. Organizational autonomy and sharing of best practices in management while strengthening accountability systems are fundamentally important in the efforts to build institutional capacity.

Peer Review reports

The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund), together with its Zimbabwean national and international stakeholders have continued to invest in health system strengthening to improve public health in the country. These investments have been guided by systemwide strategic assessments to understand the fundamental challenges facing the Zimbabwean health system [ 1 , 2 , 3 , 4 ].

Given the strategic importance of access to essential medicines, vaccines, and other health technologies as a strategic pillar of any health system [ 4 , 5 ], the Global Fund supported the Ministry of Health and Child Care (MOHCC) through the United Nations Development Program (UNDP) to undertake a comprehensive assessment of the national procurement and supply chain management (PSCM) system in 2013 [ 6 ]. This assessment aimed to develop a strategic vision and costed action plan for improvement. This covered both upstream and downstream aspects of PSCM. The strategic purpose was to help the MOHCC launch a coordinated approach to invest in PSCM improvements and enhance coordination and cooperation in managing all health commodities across the health system [ 3 , 6 , 7 ].

Subsequently, the Global Fund supported several initiatives aimed at the realization of the improvements proposed by the comprehensive assessment undertaken by UNDP [ 6 ]. These initiatives primarily encompassed end-to-end supply chain assistance in critical areas, including demand quantification and forecasting, warehousing capacity development, fleet improvement, distribution systems, and waste management systems. Other support aspects focused on warehouse optimization, data and information management systems, and personnel training [ 7 , 8 ]. In general, warehouse optimization is the process of improving the efficiency and effectiveness of warehouse operations. It involved refining workflows, leveraging technology, enhancing spatial utilization, and ensuring precise inventory management [ 6 , 7 ].

More specifically, the Global Fund provided funds for the construction of warehouses for the National Pharmaceutical Company of Zimbabwe (NATPHARM) to facilitate the smooth handling of health commodities. Additionally, this support extended to water supplies (in terms of sinking boreholes) for various warehouses, water tanks, and booster pumps for the other branches. Global Fund also supported NATPHARM in constructing two incinerators in the two main cities, Harare, and Bulawayo to handle pharmaceutical waste effectively [ 9 , 10 ].

Through Global Fund support, NATPHARM also received modern warehouse equipment such as forklifts, pallet jacks and rolling ladders, and data and information management system support for better visibility and effective handling of health commodities. The support further extended to optimising the Harare branch warehouse with modern receiving and transit capabilities to serve other feeder locations and the procurement of modern delivery trucks to facilitate the distribution of commodities. Global Fund support also extended to personnel training and retention for the effective functioning of NATPHARM and the broader procurement and supply system [ 3 , 6 , 9 ].

Focusing on quality assurance and safety of medicines and other health commodities, Global Fund supported the Medicines Control Authority of Zimbabwe (MCAZ) to develop capacity in quality testing of all commodities procured through grants. MCAZ was also supported with the installation of solar panels that allowed for an uninterrupted power supply to facilitate smooth operations at the organization. The Global Fund support was also critical in the upgrading of the biology and chemistry laboratories to attain WHO prequalification standards [ 8 , 9 , 11 ]. Similarly, there was direct support from the Global Fund to facilitate pharmacovigilance activities, such as adverse drug reactions reporting using electronic systems. All these measures were meant to ensure that the medicines and health commodities consumed in the Zimbabwean health system were safe and quality-assured [ 4 , 5 , 9 , 11 ].

The overarching project by the Global Fund to support the Zimbabwean government was designed and implemented in response to the nationally identified gaps and opportunities that were established through various research and consultative efforts [ 3 , 4 , 6 , 8 ]. The project was kickstarted in the first quarter of 2019 and continued through 2021, with various project components being implemented in a phased approach to achieve the national targets [ 3 , 4 , 7 , 10 , 11 ]. To ensure effective coordination, accountability, and avoidance of duplicative efforts, the project was designed and implemented in close coordination with other partners represented in the National Health Development Partners Coordination Forum (HDPCF), Health Sector Technical Working Group (HSTWG), and the Global Fund Country Coordination Mechanism (CCM), among others [ 3 , 4 , 6 , 8 , 10 ]. There was regular reporting to the respective coordination mechanisms to track progress as well as troubleshoot any implementation issues as they arose [ 6 , 9 ].

Overall, the gaps identified through the various assessments commissioned by the Global Fund included a lack of effective coordination, poor inventory and order management, human resource constraints, and warehousing and storage inefficiencies [ 12 , 13 ]. Therefore, the focus of the Global Fund support was to retool the Zimbabwean PSCM system to be efficient, cost-effective and responsive to the population's health needs, particularly in the face of global epidemics and pandemics such as HIV/AIDS and COVID-19 [ 14 , 15 , 16 , 17 , 18 ].

In an attempt to resolve these issues, healthcare systems around the world are working on streamlining their supply chains through various health system strengthening measures [ 19 , 20 , 21 , 22 ]. Therefore, the objective of this analysis was to assess the overall effects of the Global Fund-supported investments in the Zimbabwean PSCM system and document the lessons learned to inform future programming efforts to strengthen healthcare systems.

The assessment covered a period of 2018 -2021 and employed both qualitative and quantitative research methods. Figure 1 . illustrates the convergent mixed methods study design that was applied. In this study design, both the qualitative and quantitative data collection and analyses are implemented simultaneously, and the insights merged to provide a fuller picture [ 23 ].

Convergent mixed methods design

The quantitative part of the study entailed collecting and analysing administrative data covering priority indicators that are routinely reported on the Zimbabwe PSCM. Meanwhile, the qualitative part comprised of key informant interviews (KII) focusing on stakeholders within the healthcare system to give perspective to the observed data trends. Insight from the two parts of the analysis were merged and subjected to comparative assessment and interpretation to ensure that a consistent picture emerged [ 23 , 24 ]. More details on the methods are provided in later sections.

Analytical framework

The overall analytical approach espoused in this assessment was anchored on the logical relationships of the building blocks of the health system as described by the World Health Organization (WHO) health system framework [ 5 ]. Figure 2 shows the analytical framework, which illustrates the results chain cascading from the Global Fund-supported initiatives to the expected improvements in intermediate and long-term outcomes related to PSCM, including the availability of medicines, reduced wastage, and overall improvements in population-level coverage [ 4 , 5 ].

Analytic framework

Overall, the framework graphically displays the results Global Fund intended to achieve through its support to the Zimbabwean PSCM. The "theory of change" that underlies the Global Fund’s strategy is revealed through the arrows in the diagram that identify “causal” linkages through which various intermediate results interact to make progress toward the overall goal of improving health system performance [ 3 , 5 ].

Quantitative research

The quantitative research entailed a detailed descriptive analysis of the operational data that was routinely reported across the Zimbabwe PSCM system. Table 1 . shows some of the key performance indicators (KPI) that were considered in our analysis.

Data collection, management and analysis

The data used in this analysis were obtained from the routinely reported operational data that included the NATPHARM-operated warehouses and healthcare facilities in the country. The data were extracted from the various data management systems operated by the different institutions, cleaned, and collated into a comprehensive dataset in the form of a spreadsheet covering the period of the assessment. The database was examined for completeness and accuracy by cross-referencing the corresponding progress reports for specific periods. Trends of priority indicators were compared over time, as they related to Global Fund support to the PSCM space.

Qualitative research

The qualitative assessment entailed KIIs with health system stakeholders who were knowledgeable and intimately involved in the Global Fund-supported initiatives and its intended beneficiaries. This included provincial and district management teams, hospital and clinic personnel, and other stakeholders in the Zimbabwe health system. Informed consent was obtained from each study participant involved in the study. The data collection protocol ensured that all study participants fully understood the objectives of the study and consented verbally to provide the required information.

As previously stated, the literature review helped map and identify critical organisations involved in the PSCM space, and more specifically, those involved in the procurement and health system strengthening activities supported by the Global Fund. A full list of those organisations is provided on Table  2 .

Sampling techniques

Convenience purposive sampling was used to select key informants and in-depth interviews [ 23 ]. Our sample was supplemented using snowball sampling methods (also called chain sampling). The initial respondents referred other potential respondents until no new information was forthcoming or achieved saturation. Efforts were made to be all-inclusive, involving various stakeholder groups and organisations intimately linked to the operations of the Zimbabwe PSCM landscape.

This comprised of KIIs using a structured interview guide that covered various thematic areas relevant to the assessment to obtain a comprehensive perspective of the impact of the Global Fund-supported initiatives in the country. In its development, testing and validation, the key informant guide was pretested and adapted to ensure suitability for the task. In view of the restrictions imposed to prevent the spread of COVID-19 infections at the time of the study, some KIIs were conducted online using multimedia channels such as Zoom, Skype, and telephonically.

Three research assistants supported the two project leaders in conducting the KIIs. After each interview, all notes taken by the research assistant were checked by the two project leaders to ensure completeness and readability to minimise recording errors. In addition, a tape recorder was used for interviews to assist with reference post data collection. All the recordings were stored in a pin-protected cloud storage which was only accessible by the two project evaluation leaders. Qualitative data obtained from the KIIs were transcribed in full and then manually analysed applying thematic content analysis. Where there was a divergence of opinion, an agreement was established through discussion with three members of the project evaluation team. In thematic analysis, data from interview transcripts were grouped into similar concepts. This approach was appropriate for semi-structured expert interviews as it is used to code text with a predefined coding system that can then be refined and completed with new themes emerging [ 23 , 24 ]. Our initial coding system was defined during the desk review stage and continuously updated in the successive phases of data collection employing a deductive approach of qualitative research. The emerging themes were not preconceived (desk review) but emerged from the data during the coding process, while the global themes were the highest-order themes that emerged from the data and were broad enough to capture the essence of the entire dataset [ 23 ]. The codes are presented in a tabular format in the results section below.

This section presents both the quantitative and qualitative research results from the study. The quantitative results comprise of trends of the priority operational PSCM indicators for the relevant period. The qualitative results present the perspectives of the key health stakeholders involved in the Zimbabwe healthcare system.

Quantitative results

Table 3 shows that the total warehouse capacity across the Zimbabwean health system increased by 37.8% between 2018 and 2021.

Of the 1500 public health facilities in Zimbabwe coverage by the PSCM system was consistently high between the years 2018 and 2021, averaging 94%, and increasing by 13.6% over the same period. However, order fulfillment rate within 90 days, for 1410 reporting health facilities was consistently below 50% from 2018 to 2020, despite the reported high coverage for the health facilities by the PSCM in the country. Notably, this indicator showed remarkable improvement to 91% in 2021, from an average of 42% from the previous three years. More specifically, the order fill rate for Tenofovir 300mg/ lamivudine 300mg/efavirenz 600mg (TLE 600mg) improved despite the significant drop observed in 2020. However, when comparing 2018 and 2021, the order fill rate for this specific HIV drug increased by around 36.5%, while the stockout rates for the same drug at the central stores declined by about 14.5% over the same period.

Table 3 further shows a 44% drop of order fill rates for the Determine HIV Test Kit between 2018 and 2020 for the 1410 reporting health facilities, only to recover in the year 2021, where order fill rates improved to 83%. At the same time, the stockout rates for the Determine HIV Test Kit at the central stores declined by 49% between 2018 and 2021.

Figure  3 shows the estimated average population coverage for HIV treatment for adults and children, from 2018 to 2020, at 92% and 71%, respectively. The figure shows that there was limited variation in the population level coverage over the years, despite the estimated increase in the number of people living with HIV over the same period. The national target for this indicator is 95%.

Adult and paediatric HIV treatment population coverage

Figure  4 shows the proportion of the value of the expired stock in the 7 warehouses, over three years, between 2019 and 2021, which demonstrates a declining trend over time. The highest expiry was in quarter 4 2019 at 1.9%, compared with the lowest in quarter 3 2021 at 0.1%. This represents a 93% reduction in value of expired stock.

Percentage value of expired stock

Figure  5 shows the combined stock-taking variance valued in United States Dollar terms across 7 warehouses over a three-year period. The stock variance shows a declining trend over the three-year period to negligible values at the end of 2021.

Stock-taking variance

Figure  6 shows the temporal trend of the number of days that it took NATPHARM to resolve the stock variances across the different warehouses in the country. Overall, there is a decline from the average of 8 days from the December 2018 stocktake (with Harare warehouse as an outliner at 25 days), to an average of 1 day in the December 2021 stocktake, where all warehouses converge.

Duration to resolve stock variance

Table 4 shows the funding levels in USD$ to support the diagnostic capacity for Covid-19, comprising of the polymerase chain reaction test (PCR) and rapid diagnostic tests (RDT). The table further shows the PSCM related costs, the total test done, and positive cases identified over the two-year period. The total funding between 2020 and 2021 increased by 290%, with testing levels increasing by 490% over the same period. The average Covid-19 positivity rate in 2020 was 6.4% while the positivity rate for 2021, was 3.1%, indicating a greater than 50% drop.

Qualitative results

Most of the participants interviewed acknowledged that the Global Fund support to NATPHARM and the broader Zimbabwean health system had been central in improving the overall performance of the health system through improved availability of essential medicines and other health commodities. This was largely achieved by ramping up the various components of the PSCM value chain and related operations, leading to efficiency, effectiveness and reliability.

Table 5 shows the codes, emerging and global themes from the thematic content analysis. The emerging themes revolved around the lack of infrastructure and equipment curtailing warehouse operations before the Global Fund support. Data gaps and poor product visibility were also emerging themes, as were the effects of the old fleet on the overall supply and distribution system. Similarly, issues of infrastructure, capacity, and personnel training gaps emerged as crucial themes hindering quality assurance within the PSCM. Global themes also largely focused on infrastructural inadequacy leading to underperformance. Improvements leading to better handling of commodities; data and information systems, enhancing visibility and supporting accuracy in forecasts; improvements in the distribution systems enabled by newer fleets also featured as global themes. Similarly, better trained and motivated personnel, able to perform critical functions; capacity to ensure the quality and safety of medicines and other health commodities; and the need for effective multistakeholder partnerships to improve effectiveness and sustainability of health systems, were key themes.

NATPHARM operations

According to the NATPHARM management, warehouse improvement and optimisation exercise resulted in better visibility and improved efficiency in the operations related to the commodity handling across the entire value chain. More specifically, the processes related to stock taking improved markedly over time according to the reports presented by various organizations that had been commissioned to undertake the stock audits.

“ …. warehouse optimisation supported implementing an inventory management system which conformed with bin location and variant codes, according to different donors. The result was improved, faster and more accurate stock takes, a sharp reduction of variances and more streamlined order processing ” Participant, NATPHARM.

Further, it was reported by various participants that order processing and deliveries had improved to be timely and on schedule as a result of the improved visibility and efficiency harnessed across the PSCM. Similarly, there was consensus that receiving processes and documentation had significantly improved through the support offered by Global Fund particularly towards warehouse optimization. The improvements in the data management systems and related trainings were also cited as contributory to the overall trend that was observed.

“ Reporting quality has greatly improved and is now timely, accurate and complete. This helps in accurate forecasting of demand, which in turn avoids unnecessary wastage and expiries” Participant MOHCC.

There was consensus from the majority of participants interviewed that the fleet improvements had improved the availability of essential commodities vital for the effective management of high burden diseases; HIV/AIDS, Malaria and Tuberculosis in Zimbabwe. According to participants from a local health facility, this was evidenced by low stockout rates for the key commodities needed to manage these three conditions effectively. The new fleet was reported to facilitate deliveries from various warehouses to the recipient health facilities on a regular basis. This level of distributional access coupled with better demand forecasting as a result of improved data use, was noted as critical in the improved availability of medicines and health commodities at the health facility levels.

Further, respondents in the leadership of NATPHARM revealed that the Global Fund support had benefited the overall financial position of the organisation by tapping into efficiencies harnessed through the various measures that have been implemented. Some of the support measures that resulted in efficiency improvements include, the warehouse optimization, pharmaceutical waste management and fleet improvements, which ultimately reduced operational costs.

For example, it was noted that running a newer fleet of vehicles led to lower maintenance and fuelling costs than previously was the case, when deliveries were done using older vehicles. Similarly, it was noted that pharmaceutical waste resulting from expired medicines and other health commodities was expensive to store and dispose, particularly when engaging third party organizations. However, this additional cost was reportedly in the decline, as a result of the investment in the incinerators for waste management.

“ The provision of incinerators for waste management has resulted in huge savings in terms of the cost of waste destruction. It has also resulted in significant compliance with environmental health regulations.”, Participant, NATPHARM.

MCAZ operations

Majority of the participants agreed that the Global Fund support to MCAZ strengthened its overall capacity to handle the requisite safety and quality assurance needs to effectively support the procurement functions for medicines and other health commodities within the country and regionally. The installation of solar panels to provide uninterrupted electricity power supply for the operations of the organization was cited as a huge advantage allowing for improved performance, in a country where power supply is unreliable. Similarly, other participants cited, the support for MCAZ laboratories to obtain the WHO prequalification status, as a major step towards effectiveness and sustainability for the organization; citing the fact that MCAZ is offering quality assurance services regionally at a fee.

“ We [MCAZ] now have the capacity to conduct the safety and quality assurance tests needed to support the procurement of commodities in the country and the region. We [MCAZ] even recently won the tender to support the regional procurement activities ”, Participant, MCAZ.

Based on the results framework provided in Fig.  3 , there is clear evidence that the Global Fund-supported initiatives resulted in positive improvements in the overall performance of the Zimbabwean PSCM system. However, it is important to recognise some of the assessment’s limitations in interpreting these findings. First, the results reported are for a limited observation period and a limited set of indicators, which are largely confined to the national level analysis, missing out on granular subnational and commodity-specific analysis that could be more informative. Secondly, this study was not conceptualised before the onset of the intervention reported here (i.e., Global Fund-supported initiatives), and therefore, no specific steps were taken to develop an appropriate prospective research design and data collection strategy to support a more rigorous assessment. Therefore, the study relied on secondary PSCM data that were sparse and covered a limited period. Third, the study could be subject to confounding relationships with other concurrent interventions being implemented by other health system stakeholders that have direct or indirect effects on the PSCM system, complicating impact attribution to specific interventions. Forth, the analysis focused only on a narrow subset of medicines and commodities related to HIV/AIDS and COVID-19. However, despite these limitations, every effort has been made to use the most up-to-date and complete information available, including validation using official reports and collaborative reported data with key informant interviews.

The estimated population coverage for HIV treatment for both adults and children remained consistently high despite the increasing prevalence in the country. It was estimated that adults living with HIV increased by 10% from a baseline of 2018, to reach 1.3M in 2020, while children living with HIV increased by 24%, from a baseline of 2018, to reach 75 000 in 2020 [ 3 , 4 , 9 ]. As a key last mile population outcome, it can be rightly assumed that high HIV treatment coverage in the Zimbabwean system emanated from strengthened inventory management functionality and improved delivery of orders supported by a modern fleet of vehicles, which allowed for meeting the supply target of four quarterly rounds [ 3 , 8 ]. Population coverage is an important performance measure for a health system. It unites two important concepts; need and utilisation of an intervention to improve health [ 25 ]. In our case, the intervention is HIV treatment and the population in need is those living with HIV needing treatment; and the proportion with access and able to use the treatment they need, represents population coverage. This is a fundamentally important consideration as various health systems, including low- and middle-income countries, are making universal health coverage (UHC) efforts. There is no question, that improved access to essential medicines and other health technologies is a fundamental cornerstone towards UHC [ 1 , 5 , 26 ].

Other intermediate indicators that are critical for progress towards improved availability of medicines and other health commodities and hence UHC, also showed significant improvements that could be attributed to Global Fund-supported initiatives. For example, reduced wastage and decreasing value of expired health commodities reported, point towards improving efficiency across the value chain. As noted earlier, efficiency is one of the fundamental expectations of an effective health system outlined in the WHO health system framework [ 3 , 5 ]. The diminishing value of expiries could be ascribed to various factors, including the improved workflow processes and data accuracy at NATPHARM. This improvement which is associated with better visibility of commodities across the value chain could be attributed to investments made by Global Fund such as the enterprise resource planning platform, coupled with concomitant training and supervision.

Through Global Fund’s assistance to NATPHARM, automation of tasks such as stock management, ordering, and other operational activities was central and contributory to driving the observed improvements in the handling of commodities; reduction of wastage and expiries and improving availability. Similarly, better inventory management and warehouse optimization activities such as decongestion resulted in quicker, timely, more accurate, and well-documented stock takes, improving overall commodity management.

Variances between stock on hand and physical counts were used to determine whether facilities are conducting period checks on their stocks and therefore calculating monthly consumption of commodities accurately. As such the variance across commodities should be zero. Low variance indicates that the stocks at hand are generally similar and do not vary widely from the physical stock counts, while high variance indicates that the respective values have greater variability and are more widely dispersed from one another. There is clear evidence pointing towards the reduction in stock variances when comparing stock on hand and physical counts across the different warehouses in the country over time. This trend can be attributed to better visibility of commodities at the warehouses and training of personnel which was supported by the Global Fund [ 2 , 9 ]. Similarly, the number of days it took the NATPHARM personnel to resolve stock variances showed a dramatic reduction, from an average of 8 days to 1 day in a span of 3 years. This observed trend could also further support the claim that overall, the Global Fund supported initiatives produced the desired results.

With the advent of Covid-19, the effects of the Global Fund support on the PSCM became evident considering the robust response the country was able to mount particularly in terms of diagnostics [ 9 ]. The country was able to rapidly roll out COVID-19 testing, reaching many people between 2020 and 2021. Similarly, the Covid-19 positivity rates declined from 6.4% to about 3.1% over the same period. High positivity rates may indicate that the health system is only testing the sickest patients who seek medical attention and is not casting a wide enough net to know how much of the virus is spreading within its communities. A low rate of positivity on the other hand, can be seen as a sign that a health system has sufficient testing capacity for the size of the Covid-19 outbreak and is testing enough of its population to make informed decisions about reopening the economy. The WHO guidance is that countries which have conducted extensive testing for COVID-19, should remain at 5% or lower positivity rate for at least 14 days.

Safe pharmaceutical waste management and disposal is a primary consideration of any effective health system in completing the PSCM loop [ 27 ]. The Global Fund supported the investment in MOHCC operated incinerators. These investments could largely be associated with reduction in the cost of storage, handling and disposal of the expired stock, particularly when considering that certain space was rented from third parties which often charged a premium. Safe pharmaceutical waste disposal also became more priority with the increased supplies that resulted from the efforts to tackle the Covid-19 pandemic.

Despite signs of progress, there was temporary faltering of indicators- namely, order fill and stockout rates; associated with key commodities for effective management of HIV in the year 2020, warranting an explanation. The drop in Tenofovir/Lamivudine/Efavirenz (TLE 600mg) in 2020 could be linked to several factors. In the year 2019, the Zimbabwe MOHCC adopted new treatment regimens containing Dolutegravir. This means, newly HIV positive clients were started on Dolutegravir regimen as standard of care rather than the previous first line treatment which then surged Tenofovir/Lamivudine/Dolutegravir 50mg order fill rate, while having the opposite effect on the old regimen. Lastly, the effects of COVID-19 pandemic cannot be underestimated as the global supply chain systems were logged with delays which caused disruptions and inefficiencies in health systems in many countries [ 28 ]. In the same period, Determine HIV Test Kit rebounded from stocking out in central stores because of strengthened warehouse optimization activities, including better inventory management, purposeful stock taking, and approval processes contributed to the lowering of stockout rates.

The Global Fund-supported initiatives were also instrumental in building capacity by training key personnel for the effective implementation of activities related to the procurement and supply chain management function [ 8 , 29 , 30 ]. Better quantification and forecasting capabilities (due to data availability through e-LMIS and personnel training), improved warehousing capacity to hold a wide portfolio of products, and direct delivery to facilities through a modern fleet could have contributed to the high population coverage reported [ 26 , 30 , 31 ]. According to the WHO health system framework, effective leadership is required to coordinate all the functions of the health system in order to achieve the desired outcomes [ 5 ]. Therefore, it is sensible to conclude that, the reported health system improvements could not have happened without effective leadership and well-trained staff tasked with coordination and management across the PSCM value chain. It can be further inferred that the training and capacity development measures offered to the NATPHARM personnel were consequential in supporting the broader health system to meet its overall objectives, including improving PSCM performance [ 2 , 7 , 29 ].

Similarly, adequate infrastructure, equipment, data, and information management systems are crucial ingredients for a well-functioning health system, according to the WHO health system framework [ 4 , 5 , 9 ]. The Global Fund-supported initiatives were central in supporting these aspects of the health system through improved warehousing capacity, of modern equipment, installation of solar panels, fleet improvement and deployment of an electronic-logistic management information system (e-LMIS). The cumulative benefits of these investments include optimised procurement and distribution of commodities leading to a reduction in stockout rates and timely order refills to meet the population health needs [ 9 ].

Based on these findings, it would be reasonable to conclude that the Global Fund-supported initiatives in Zimbabwe contributed positively to strengthening the health system, particularly through the improved performance of the various indicators linked to the PSCM system at national and regional warehouses, as well as health facilities. Considering the prevailing health needs in the country, the implementation of this project and the manner of investments provide a basis and playbook for further support to make progress. This is particularly true considering the various competing priorities in the Zimbabwean healthcare system amidst resource constraints [ 3 , 6 , 8 ]. This was largely underpinned on the overarching focus on UHC and the critical role that an effective PSCM plays towards that very objective [ 3 , 12 , 13 , 14 ].

The Global Fund-supported project in Zimbabwe worked through the existing national coordination mechanisms where various key stakeholders, including MOHCC and NATPHARM, were involved in all key strategic planning and implementation decisions, ensuring country leadership and ownership. It was clear from the outset that this approach required sound partnership, transparency, and accountability among all the involved stakeholders, to deliberate and find common ground, guided by the overarching objective to make progress towards UHC.

The question of securing the gains and ensuring sustainability is fundamental for donor supported health programs in low- and middle- income countries. To make progress, it is imperative for health system stakeholders, including governments and donor organizations that are keen to sustainably strengthen health systems to pay close attention to critical areas like the procurement and distribution of health commodities. It is critical to collaborate with key stakeholders through joint planning and implementation to optimize the available resources. Organizational autonomy coupled with strong data driven accountability systems and the sharing of best management practices are fundamentally important in this discourse.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request and once written permission is obtained from NATPHARM.

Abbreviations

Country Coordination Mechanism

Health Development Partners Coordination Forum

Health Sector Technical Working Group

Key Informant Interview

Medicines Control Authority of Zimbabwe

Ministry of Health and Child Care

National Pharmaceutical Company of Zimbabwe

Polymerase Chain Reaction

Procurement and Supply Chain Management

Rapid Diagnostic Tests

Tenofovir Lamivudine Efavirenz

Universal Health Coverage

United Nations Development Program

World Health Organization

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Acknowledgements

The authors would like to thank the participants from the different organizations in Zimbabwe that provided feedback during the study. They are also grateful to the management of the various organizations that allowed their staff to participate and provided premises and other resources that were used during the interviews. Gratitude to Sarah Gurrib who proofread the manuscript and offered useful comments.

The study was funded by the Global Fund to Fight AIDs, Tuberculosis and Malaria. The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the article. All authors had full access to study data and had final responsibility for the decision to submit for publication.

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Tom Achoki (TA), Rafiu Idris (RI), Lawrence Were (LW) and Abaleng Lesego (AL) conceptualized and designed the study. AL, TA, and Godfrey Magwindiri (GM) collected and analyzed data. TA and AL drafted the manuscript. Tsion Tsegaye (TT), Linden Morrison (LM), Tatjana Peterson (TP), Sheza Elhussein (SE), Esther Antonio(EA), Ivan Dumba (ID), Cleyland Mtambirwa (CM), Newman Madzikwa(NM), Raiva Simbi (RS), Misheck Ndlovu (MN) and LW did the critical revisions of the manuscript. All authors read and approved the final manuscript.

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Ethical approval including the consent procedure for participants was obtained from the Ethics Committee of the MOHCC Department of Research and NATPHARM, Harare Zimbabwe. The conduct and methods of this study adhered to the tenets outlined in the Declaration of Helsinki. Informed consent was obtained from each study participant involved in the study. The data collection protocol ensured that all study participants fully understood the objectives of the study and consented in writing to provide the required information. Before the interview commenced, the participants also consented verbally and confirmed that they had understood the objectives of the study and that they could opt out of the interview at any time without prejudice. No sensitive or personally identifying information was collected regarding the study participants.

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RI, LM, TP and SE declare that they are fulltime employees at the Global Fund to Fight AIDs, Tuberculosis and Malaria. EA and GM declare that they are fulltime employees at PricewaterhouseCoopers. ID, CM, NM and RS are fulltime employees at National Pharmaceutical Company of Zimbabwe. The rest of the authors declare that they have no competing interests.

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Lesego, A., Were, L.P.O., Tsegaye, T. et al. Health system lessons from the global fund-supported procurement and supply chain investments in Zimbabwe: a mixed methods study. BMC Health Serv Res 24 , 557 (2024). https://doi.org/10.1186/s12913-024-11028-6

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National Patterns of R&D Resources: 2019–20 Data Update

General notes.

National Patterns of R&D Resources provides current data on the levels and key trends of the performance and funding of research and experimental development in the United States. These detailed statistical tables supplement analytical InfoBriefs available at https://www.nsf.gov/statistics/natlpatterns/ , from the National Center for Science and Engineering Statistics (NCSES).

The National Patterns statistics draw primarily from NCSES’s national surveys of the R&D expenditures and funding of the organizations that perform the bulk of U.S. R&D—including businesses, federal and nonfederal government, higher education, and nonprofit organizations. Additional details on levels and trends are provided by type of R&D performed (i.e., basic research, applied research, and experimental development). The National Patterns data are reported in both current and inflation-adjusted dollars, with comparisons to the historical record for U.S. R&D (back to 1953) and to the corresponding pace of overall U.S. economic growth.

The data for 2020 are estimates based mainly on early findings from the 2020 sectoral R&D expenditure surveys and evident recent trends. Data for 2019 were included in the previous edition (2018–19) of this report series but are now revised to reflect new, nearly final input from the sectoral surveys. The numbers for 1953–2018 reflect largely final survey data and, in general, exhibit minor revisions (if any).

The statistical tables are arranged to exhibit the U.S. R&D data from two differing perspectives. The first perspective ( tables 2–5 ) is by type of R&D performer, with subsequent breakouts by the source of funds. The second perspective ( tables 6–9 ) is by source of funds, with subsequent breakouts by type of performer. The data in both groups of tables sum to the same overall U.S. R&D performance totals. Table 1 provides data mainly on the U.S. R&D-to-gross domestic product (GDP) ratio and its components. Table 10 presents state-level breakdowns of the U.S. R&D totals, by performing sector and source of funds, for 2019.

For trend comparisons, use only the historical data from the most recent publication, which incorporates the latest revisions and corrections. Do not use data published earlier.

Data Tables

Technical notes, overview of 2019–20 data update.

National Patterns of R&D Resources provides current data on the levels and key trends of the performance and funding of research and experimental development in the United States, with comparisons to the historical record (back to 1953). Detail is also provided for the U.S. total and by performer according to the types of R&D conducted (i.e., basic research, applied research, and experimental development).

Key Information on Publication Series

Report frequency . Reports in this series are typically published annually.

Current reference period . Data from 1953 to 2020 are provided for almost all variables. The data for 2020 are estimates and not previously reported in this series. The data for 2019 are revised from the previous edition (2018–19) of this series. The data for 1953 – 2018 reflect final survey data but may still include some further (typically minor) revisions.

Survey Data Sources for National Patterns

The National Patterns statistics draw primarily from the National Center for Science and Engineering Statistics’ (NCSES’s) annual national surveys of the R&D expenditures and funding of the organizations that perform the bulk of U.S. R&D. These organizations include the business sector, federal and nonfederal government, higher education, and nonprofit organizations. Each of these sectors are summarized—including links to other sections of the NCSES website with specifics on survey coverage, variables collected, and survey or sampling implementation—in the sections below.

Business Sector

For 2019, annual data on the R&D performed in the domestic United States by the business sector come from NCSES’s Business Enterprise Research and Development Survey (BERD), covering companies with 10 or more employees, and the Annual Business Survey (ABS), covering companies with 9 or fewer employees. BERD and ABS are conducted for NCSES by the Census Bureau in accordance with an agreement between the two agencies. Both are sample surveys designed to be nationally representative of all for-profit companies, publicly or privately held, in manufacturing and nonmanufacturing industries ( https://www.nsf.gov/statistics/srvyberd/ , https://www.nsf.gov/statistics/srvyabs/ ).

For 2017 and 2018, the source of the data for companies with 10 or more employees was the Business R&D Survey (BRDS), a predecessor to the BERD Survey, with similar survey characteristics and coverage. ABS was again the source of data on companies with 9 or few employees in both these years.

From 2008 to 2016, the primary source data on business R&D was the NCSES Business R&D and Innovation Survey (BRDIS), a predecessor of both BERD and BRDS. In a salient difference, BRDIS was designed to be nationally representative of all for-profit companies with 5 or employees. In 2016, a companion survey, the Business R&D and Innovation Survey–Microbusiness (BRDI-M), provided statistics on the R&D activities that year of businesses with 1–4 employees ( https://www.nsf.gov/statistics/srvymicrobus/ ). Prior to the 2016 data year, statistics on the R&D of these “micro” businesses ($4–$5 billion annually) were neither available nor reported in the business R&D totals reported by National Patterns .

As noted, BERD, BRDS, and BRDIS are related surveys. With regard to R&D, the topical coverage has been similar: R&D performance (in the United States and worldwide); total and R&D employment (in the United States and worldwide); sources of R&D funding; type of R&D activities (basic research, applied research, development); type of R&D costs; R&D capital expenditures; R&D application and technology focus areas; geographic location (within the United States and in foreign countries); sales (in the United States and worldwide); and patenting, licensing, other technology transfer activities. The ABS and BRDI-M surveys provided R&D performance and funding data compatible with those for the larger companies.

For 2007 and earlier years, the data come from the NCSES Survey of Industrial Research and Development (SIRD), the predecessor to BRDIS ( https://www.nsf.gov/statistics/srvyindustry/sird.cfm ). SIRD was also a sample survey, conducted annually (1953–2007), that provided national estimates of the R&D performed within the United States by industrial firms, whether U.S. or foreign owned. The SIRD target population consisted (same as BRDIS) of all for-profit companies with 5 or more employees, manufacturing and nonmanufacturing, that performed R&D in the United States. Data on the R&D of companies with fewer than 5 employees were not available throughout this period of time.

(Note: Data on business intramural R&D capital, as reported by BRDIS and its follow-on surveys, are not included as a component in the National Patterns total of business intramural R&D.)

Federal Government

Federal intramural R&D . Data on the intramural R&D performed by the federal government come from NCSES’s Survey of Federal Funds for Research and Development (Federal Funds Survey), which is completed annually by all federal agencies conducting R&D programs (approximately 28 federal departments or independent agencies; https://www.nsf.gov/statistics/srvyfedfunds/ ). The Federal Funds Survey data provide intramural R&D totals as outlays and obligations on a federal fiscal year basis. The obligations data are further detailed by federal agency, performer, type of R&D, geographical area, and field of science or engineering (for research, not for development). (Note: data on federal intramural R&D plant reported by the Federal Funds Survey are not included as a component in the National Patterns total of federal intramural R&D.)

Federally funded R&D centers (FFRDCs) . The nation’s FFRDCs are a second venue of federal R&D performance. (Currently, there are 42 FFRDCs, although the number can change from year to year; NCSES maintains a Master Government List of the population of FFRDCs, which it regularly updates: https://www.nsf.gov/statistics/ffrdclist/ .) FFRDCs are R&D-performing organizations administered by an industrial firm, a university, a nonprofit institution, or a consortium but that have funding exclusively or substantially from the federal government. An FFRDC is operated to provide R&D capability to serve an agency’s mission objectives or, in some cases, to provide major facilities at universities for research and training purposes. Since FY 2001, NCSES’s FFRDC Research and Development Survey (known previously as the Survey of Research and Development Expenditures at FFRDCs) has provided annual data (by federal fiscal year) on the R&D expenditures (with additional detail) of all the nation’s FFRDCs ( https://www.nsf.gov/statistics/srvyffrdc/ ). Prior to FY 2001, R&D expenditure data for the FFRDCs were collected as part of the major performer surveys: the Survey of R&D Expenditures at Universities and Colleges (for university-administered FFRDCs), the Survey of Industrial R&D (for industry-administered FFRDCs), and the Federal Funds Survey (for nonprofit-administered FFRDCs).

Nonfederal Government

The category of nonfederal government R&D performance included in National Patterns is the intramural R&D of state governments (i.e., the R&D performance of state agency and department employees and the services performed by others in support of internal R&D projects). Data on this state intramural R&D come from NCSES’s Survey of State Government Research and Development ( https://www.nsf.gov/statistics/srvystaterd/ ). This state survey is a comparatively recent NCSES initiative, with the first data year in FY 2006.

In addition, the BERD, FFRDC, and Higher Education Research and Development (HERD) surveys (see above and below) provide detail on R&D funding by nonfederal governments other than just state governments.

Higher Education Institutions

For academic FY 2010 and onward, data on the R&D performed in higher education come from NCSES’s HERD Survey. HERD is an annual census of universities and colleges that grant the bachelor’s degree or higher and expend at least $150,000 in separately accounted for R&D over the fiscal year of these academic institutions ( https://www.nsf.gov/statistics/srvyherd/ ). The survey collects information on R&D expenditures by field of research, source of funds, type of R&D, and head counts of R&D personnel.

The HERD Survey replaced the earlier Survey of Research and Development Expenditures at Universities and Colleges ( https://www.nsf.gov/statistics/srvyrdexpenditures/ ). For data years FY 2009 and earlier, the Universities and Colleges Survey provides the data on academic R&D.

Nonprofit Organizations

This sector refers to R&D performed in the United States by nonprofit organizations other than government or academia. Data on the R&D performed by such nonprofit organizations that are federally funded comes from the aforementioned NCSES Federal Funds Survey (reported annually, on a federal fiscal year basis). Data for the R&D performed by nonprofit organizations with funding from within the nonprofit sector and from business sources (both reported on a calendar year basis) are estimated, based on parameters from NCSES’s past comprehensive R&D surveys of the sector. NCSES’s most recent data on nonprofit organization R&D comes from its FY 2016 Nonprofit Research Activities Survey ( https://www.nsf.gov/statistics/2021/ncses21202 ). Before that were the Survey of Research and Development Funding and Performance by Nonprofit Organizations in 1973 and 1996–97 ( https://www.nsf.gov/statistics/srvyrdnonprofit/ ). Data for years prior to 1973 come from earlier National Patterns reports (which reflect, in part, surveys in 1957 and 1960).

Statistics on the U.S. Economy

Some of the trend analyses in National Patterns draw on National Income and Product Accounts data (e.g., U.S. gross domestic product [GDP], state domestic product) assembled by the U.S. Bureau of Economic Activity (BEA). For details on this U.S. economic data, see http://www.bea.gov/data/gdp .

In keeping with international conventions, U.S. R&D expenditures in current dollars are adjusted for inflation based on BEA’s implicit GDP price deflator.

Methodology

The National Patterns statistical picture of the U.S. R&D system arises from integration of the primary data from the NCSES sectoral R&D surveys. Adjustments to the primary data are needed in some cases to enable consistent addition. Furthermore, preliminary or otherwise estimated values may be used (and later revised) where final data from one or more of the surveys are not yet available but can reasonably be calculated.

Key features of the methodology include the following.

• Data from the NCSES business R&D surveys (BERD, BRDS, BRDIS, ABS, BRDI-M, SIRD) have been reported on a calendar year basis and are incorporated directly in the National Patterns totals. Data from the Federal Funds Survey and the FFRDC Research and Development Survey are reported on a federal fiscal year basis (1 October–30 September the next year) and are adjusted to the calendar year for National Patterns integration. The data from the HERD Survey are reported on an academic fiscal year basis (typically, 1 July–30 June the next year) and converted to calendar year. Data from the Survey of State Government R&D are reported according to each state’s fiscal year and are also converted to calendar year (presuming that 1 July–30 June the next year is typical).
• NCSES annual surveys of business R&D (particularly BRDIS and SIRD) had long covered the population of private sector companies with 5 or more employees. Inclusion of the BRDI-M data in the National Patterns data on business R&D totals in 2016 expanded the covered population to include companies with 1–4 employees, thereby adding $4.8 billion of R&D to the total reported by BRDIS. BRDS data in 2017 and 2018 covered the population of companies with 10 or more employees. Inclusion of the ABS data for 2017 and 2018 added coverage of companies with 1–9 employees to the National Patterns reported business R&D totals in these years ($5.7 billion and $4.5 billion, respectively) and a similar amount in 2019 ($5.2 billion).
• The method for estimating the type of R&D (i.e., basic research, applied research, experimental development) in the business sector was revised for 1998 and later years. This change resulted in a net decrease in the proportion of business R&D classified as basic research. Accordingly, the data for 1998 and later years are not directly comparable with data for 1997 and earlier years. The transition to BRDIS (data series starting in 2008) does not appear to have introduced further discontinuities in the type-of-R&D estimates. For universities and colleges, the type-of-R&D estimation method was revised in 1998 and again in 2010. Hence, the latest data for higher education are also not directly comparable to those reported in earlier years.
• The data for higher education in 2003 and later years include both science and engineering (S&E) and non-S&E fields. Prior to 2003, only S&E fields are included. Non-S&E R&D was $1.4 billion in FY 2003 and $4.9 billion in FY 2019. Similarly, for the business sector, social science R&D was not included until the 2008 data year, when it was $0.8 billion of the $290.7 billion business R&D total.
• The data for higher education R&D have included, since 1998, a net-out adjustment for R&D funds reported by academic institutions as passed through to other academic subrecipients. This adjustment was recently expanded (back to 1998) to also include pass-throughs to all noneducational recipients (i.e., businesses, nonprofit organizations, and others). The effect of this expanded adjustment is that the annual totals of higher education R&D performance are about $0.4 billion lower in 1998, the first year for this adjustment, to about $2.5 billion lower annually in 2012–15 than previously have been reported in National Patterns. In addition, for the 2010–20 survey years, this more comprehensive pass-through funding adjustment has been further improved by tabulating the adjustments on an institution-by-institution basis, which allows for a more precise estimate of the original nonfederal sources of pass-through funds and better estimation of the type of R&D (basic, applied, or experimental development) being passed through to other R&D performers.
• Data on federally funded R&D discussed in this report were derived from surveys of organizations that perform R&D, such as companies, universities, and FFRDCs. In the past, these amounts differed substantially from the R&D that federal agencies had reported funding. For example, in 2009, federal agencies reported obligating $141 billion for R&D funding to all R&D performers (including $53 billion to the business sector), compared with an estimated $127 billion in federal funding reported by all performers of R&D that year ($40 billion by businesses). More recently, with improvements in the methods for the survey of federal funding and adoption of revised R&D definitions, this effect has become less apparent.
• From 2016 forward, the data on federal intramural R&D explicitly exclude expenditures for pre-production development. (Pre-production development is nonexperimental work on a product or system before it goes into full production, e.g., activities and programs that are categorized as operational system development in the Department of Defense’s research, development, test and evaluation Budget Activity structure.) This change aligns the federal intramural data with a recent change introduced in the definition of R&D by the U.S. Office of Management and Budget. In FYs 2016–18, pre-production development for federal intramural R&D totaled between $5 billion and $7 billion each year.

Additional details on methodology and technical issues pertaining to specific variables are provided in the notes on each table. For further information about compiling the National Patterns statistics, contact the Project Officer.

Definitions

R&D performers and funders . The U.S. R&D system consists of the R&D activities of differing performers and sources of funding for these activities. The main categories of R&D performers tracked by NCSES are businesses; federal agencies; FFRDCs (administered by businesses, universities, or nonprofit organizations); nonfederal government agencies (specifically those of the 50 states and the District of Columbia); higher education; and nonprofit organizations. For R&D funding, the main categories are businesses; the federal government; nonfederal government (state, regional, local); higher education; and nonprofit organizations. Organizations that perform R&D often receive significant levels of outside funding; R&D funders may also be significant performers.

T ype of R&D . As defined by the Organisation for Economic Co-operation and Development’s Frascati Manual (in seven editions since the early 1960s), R&D spans three main types of activities:

Basic research : Experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view.

Applied research : Original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective.

Experimental development : Systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or improving existing products or processes. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Page 45. Paris: OECD Publishing. Available at http://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm ." data-bs-content="Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Page 45. Paris: OECD Publishing. Available at http://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm ." data-endnote-uuid="b96f9b90-6216-49a3-8a85-53659238e034">​ Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Page 45. Paris: OECD Publishing. Available at http://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm .

1 Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Page 45. Paris: OECD Publishing. Available at http://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm .

Suggested Citation and Acknowledgments

National Center for Science and Engineering Statistics (NCSES). 2022. National Patterns of R&D Resources: 2019–20 Data Update . NSF 22-320. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf22320 .

Mark Boroush of the National Center for Science and Engineering Statistics (NCSES) developed and oversaw preparation of this report under the guidance of John Jankowski, NCSES Program Director, and the leadership of Emilda B. Rivers, NCSES Director; Vipin Arora, NCSES Deputy Director; and John Finamore, NCSES Chief Statistician. Jock Black (NCSES) reviewed the report. Publication processing support was provided by Catherine Corlies, Tanya Gore, and Joe Newman (NCSES).

Report Author

Mark Boroush Senior Analyst Research and Development Statistics Program, NCSES Tel: (703) 292-8726 E-mail: [email protected]

National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences National Science Foundation 2415 Eisenhower Avenue, Suite W14200 Alexandria, VA 22314 Tel: (703) 292-8780 FIRS: (800) 877-8339 TDD: (800) 281-8749 E-mail: [email protected]

Source Data & Analysis

InfoBrief (NSF 22-330)

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