interpretation and report writing in research methodology

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

• << Previous: Scholarly vs. Popular Publications
• Next: Qualitative Methods >>
• Last Updated: Jun 11, 2024 1:40 PM
• URL: https://libguides.usc.edu/writingguide

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

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 and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• 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 ).

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

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, other interesting articles, frequently asked questions about methodology.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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 ? How did you prevent bias from affecting your data?

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 generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and 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 July 4–8, 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.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

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 analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’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.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

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.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Next, you should indicate how you processed and analyzed 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 analyzing 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 : Categorizing 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 behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized 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 generalizable.

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.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

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 systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. & George, T. (2023, November 20). What Is a Research Methodology? | Steps & Tips. Scribbr. Retrieved June 11, 2024, from https://www.scribbr.com/dissertation/methodology/

Is this article helpful?

Shona McCombes

Other students also liked, what is a theoretical framework | guide to organizing, what is a research design | types, guide & examples, qualitative vs. quantitative research | differences, examples & methods, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Writing up a Research Report

• First Online: 04 January 2024

Cite this chapter

• Stefan Hunziker 3 &
• Michael Blankenagel 3  

466 Accesses

A research report is one big argument about how and why you came up with your conclusions. To make it a convincing argument, a typical guiding structure has developed. In the different chapters, there are distinct issues that need to be addressed to explain to the reader why your conclusions are valid. The governing principle for writing the report is full disclosure: to explain everything and ensure replicability by another researcher.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Barros, L. O. (2016). The only academic phrasebook you’ll ever need . Createspace Independent Publishing Platform.

Google Scholar  

Field, A. (2016). An adventure in statistics. The reality enigma . SAGE.

Field, A. (2020). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE.

Früh, M., Keimer, I., & Blankenagel, M. (2019). The impact of Balanced Scorecard excellence on shareholder returns. IFZ Working Paper No. 0003/2019. https://zenodo.org/record/2571603#.YMDUafkzZaQ . Accessed: 9 June 2021.

Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.

Yin, R. K. (2013). Case study research: Design and methods (5th ed.). SAGE.

Download references

Author information

Authors and affiliations.

Wirtschaft/IFZ, Campus Zug-Rotkreuz, Hochschule Luzern, Zug-Rotkreuz, Zug, Switzerland

Stefan Hunziker & Michael Blankenagel

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Stefan Hunziker .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Springer Fachmedien Wiesbaden GmbH, part of Springer Nature

About this chapter

Hunziker, S., Blankenagel, M. (2024). Writing up a Research Report. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-42739-9_4

Download citation

DOI : https://doi.org/10.1007/978-3-658-42739-9_4

Published : 04 January 2024

Publisher Name : Springer Gabler, Wiesbaden

Print ISBN : 978-3-658-42738-2

Online ISBN : 978-3-658-42739-9

eBook Packages : Business and Management Business and Management (R0)

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.

The research paper we write have:

• Precision and Clarity
• Zero Plagiarism
• High-level Encryption
• Authentic Sources

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.

You May Also Like

Discover Canadian doctoral dissertation format: structure, formatting, and word limits. Check your university guidelines.

What is a manuscript? A manuscript is a written or typed document, often the original draft of a book or article, before publication, undergoing editing and revisions.

Academic integrity: a commitment to honesty and ethical conduct in learning. Upholding originality and proper citation are its cornerstones.

Ready to place an order?

USEFUL LINKS

Learning resources, company details.

• How It Works

Automated page speed optimizations for fast site performance

Research Methodology WRITING A RESEARCH REPORT

• September 2021
• In book: Research Methodology in Social Sciences (A Short Manual) (pp.177)
• Publisher: New Delhi: Corvette Press

• Himachal Pradesh University

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Miriam Hill
• Derek Jansen
• Kerryn Warren
• Kretchmer Paul
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

• Privacy Policy

Home » Data Interpretation – Process, Methods and Questions

Data Interpretation – Process, Methods and Questions

Table of Contents

Data Interpretation

Definition :

Data interpretation refers to the process of making sense of data by analyzing and drawing conclusions from it. It involves examining data in order to identify patterns, relationships, and trends that can help explain the underlying phenomena being studied. Data interpretation can be used to make informed decisions and solve problems across a wide range of fields, including business, science, and social sciences.

Data Interpretation Process

Here are the steps involved in the data interpretation process:

• Define the research question : The first step in data interpretation is to clearly define the research question. This will help you to focus your analysis and ensure that you are interpreting the data in a way that is relevant to your research objectives.
• Collect the data: The next step is to collect the data. This can be done through a variety of methods such as surveys, interviews, observation, or secondary data sources.
• Clean and organize the data : Once the data has been collected, it is important to clean and organize it. This involves checking for errors, inconsistencies, and missing data. Data cleaning can be a time-consuming process, but it is essential to ensure that the data is accurate and reliable.
• Analyze the data: The next step is to analyze the data. This can involve using statistical software or other tools to calculate summary statistics, create graphs and charts, and identify patterns in the data.
• Interpret the results: Once the data has been analyzed, it is important to interpret the results. This involves looking for patterns, trends, and relationships in the data. It also involves drawing conclusions based on the results of the analysis.
• Communicate the findings : The final step is to communicate the findings. This can involve creating reports, presentations, or visualizations that summarize the key findings of the analysis. It is important to communicate the findings in a way that is clear and concise, and that is tailored to the audience’s needs.

Types of Data Interpretation

There are various types of data interpretation techniques used for analyzing and making sense of data. Here are some of the most common types:

Descriptive Interpretation

This type of interpretation involves summarizing and describing the key features of the data. This can involve calculating measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and creating visualizations such as histograms, box plots, and scatterplots.

Inferential Interpretation

This type of interpretation involves making inferences about a larger population based on a sample of the data. This can involve hypothesis testing, where you test a hypothesis about a population parameter using sample data, or confidence interval estimation, where you estimate a range of values for a population parameter based on sample data.

Predictive Interpretation

This type of interpretation involves using data to make predictions about future outcomes. This can involve building predictive models using statistical techniques such as regression analysis, time-series analysis, or machine learning algorithms.

Exploratory Interpretation

This type of interpretation involves exploring the data to identify patterns and relationships that were not previously known. This can involve data mining techniques such as clustering analysis, principal component analysis, or association rule mining.

Causal Interpretation

This type of interpretation involves identifying causal relationships between variables in the data. This can involve experimental designs, such as randomized controlled trials, or observational studies, such as regression analysis or propensity score matching.

Data Interpretation Methods

There are various methods for data interpretation that can be used to analyze and make sense of data. Here are some of the most common methods:

Statistical Analysis

This method involves using statistical techniques to analyze the data. Statistical analysis can involve descriptive statistics (such as measures of central tendency and dispersion), inferential statistics (such as hypothesis testing and confidence interval estimation), and predictive modeling (such as regression analysis and time-series analysis).

Data Visualization

This method involves using visual representations of the data to identify patterns and trends. Data visualization can involve creating charts, graphs, and other visualizations, such as heat maps or scatterplots.

Text Analysis

This method involves analyzing text data, such as survey responses or social media posts, to identify patterns and themes. Text analysis can involve techniques such as sentiment analysis, topic modeling, and natural language processing.

Machine Learning

This method involves using algorithms to identify patterns in the data and make predictions or classifications. Machine learning can involve techniques such as decision trees, neural networks, and random forests.

Qualitative Analysis

This method involves analyzing non-numeric data, such as interviews or focus group discussions, to identify themes and patterns. Qualitative analysis can involve techniques such as content analysis, grounded theory, and narrative analysis.

Geospatial Analysis

This method involves analyzing spatial data, such as maps or GPS coordinates, to identify patterns and relationships. Geospatial analysis can involve techniques such as spatial autocorrelation, hot spot analysis, and clustering.

Applications of Data Interpretation

Data interpretation has a wide range of applications across different fields, including business, healthcare, education, social sciences, and more. Here are some examples of how data interpretation is used in different applications:

• Business : Data interpretation is widely used in business to inform decision-making, identify market trends, and optimize operations. For example, businesses may analyze sales data to identify the most popular products or customer demographics, or use predictive modeling to forecast demand and adjust pricing accordingly.
• Healthcare : Data interpretation is critical in healthcare for identifying disease patterns, evaluating treatment effectiveness, and improving patient outcomes. For example, healthcare providers may use electronic health records to analyze patient data and identify risk factors for certain diseases or conditions.
• Education : Data interpretation is used in education to assess student performance, identify areas for improvement, and evaluate the effectiveness of instructional methods. For example, schools may analyze test scores to identify students who are struggling and provide targeted interventions to improve their performance.
• Social sciences : Data interpretation is used in social sciences to understand human behavior, attitudes, and perceptions. For example, researchers may analyze survey data to identify patterns in public opinion or use qualitative analysis to understand the experiences of marginalized communities.
• Sports : Data interpretation is increasingly used in sports to inform strategy and improve performance. For example, coaches may analyze performance data to identify areas for improvement or use predictive modeling to assess the likelihood of injuries or other risks.

When to use Data Interpretation

Data interpretation is used to make sense of complex data and to draw conclusions from it. It is particularly useful when working with large datasets or when trying to identify patterns or trends in the data. Data interpretation can be used in a variety of settings, including scientific research, business analysis, and public policy.

In scientific research, data interpretation is often used to draw conclusions from experiments or studies. Researchers use statistical analysis and data visualization techniques to interpret their data and to identify patterns or relationships between variables. This can help them to understand the underlying mechanisms of their research and to develop new hypotheses.

In business analysis, data interpretation is used to analyze market trends and consumer behavior. Companies can use data interpretation to identify patterns in customer buying habits, to understand market trends, and to develop marketing strategies that target specific customer segments.

In public policy, data interpretation is used to inform decision-making and to evaluate the effectiveness of policies and programs. Governments and other organizations use data interpretation to track the impact of policies and programs over time, to identify areas where improvements are needed, and to develop evidence-based policy recommendations.

In general, data interpretation is useful whenever large amounts of data need to be analyzed and understood in order to make informed decisions.

Data Interpretation Examples

Here are some real-time examples of data interpretation:

• Social media analytics : Social media platforms generate vast amounts of data every second, and businesses can use this data to analyze customer behavior, track sentiment, and identify trends. Data interpretation in social media analytics involves analyzing data in real-time to identify patterns and trends that can help businesses make informed decisions about marketing strategies and customer engagement.
• Healthcare analytics: Healthcare organizations use data interpretation to analyze patient data, track outcomes, and identify areas where improvements are needed. Real-time data interpretation can help healthcare providers make quick decisions about patient care, such as identifying patients who are at risk of developing complications or adverse events.
• Financial analysis: Real-time data interpretation is essential for financial analysis, where traders and analysts need to make quick decisions based on changing market conditions. Financial analysts use data interpretation to track market trends, identify opportunities for investment, and develop trading strategies.
• Environmental monitoring : Real-time data interpretation is important for environmental monitoring, where data is collected from various sources such as satellites, sensors, and weather stations. Data interpretation helps to identify patterns and trends that can help predict natural disasters, track changes in the environment, and inform decision-making about environmental policies.
• Traffic management: Real-time data interpretation is used for traffic management, where traffic sensors collect data on traffic flow, congestion, and accidents. Data interpretation helps to identify areas where traffic congestion is high, and helps traffic management authorities make decisions about road maintenance, traffic signal timing, and other strategies to improve traffic flow.

Data Interpretation Questions

Data Interpretation Questions samples:

• Medical : What is the correlation between a patient’s age and their risk of developing a certain disease?
• Environmental Science: What is the trend in the concentration of a certain pollutant in a particular body of water over the past 10 years?
• Finance : What is the correlation between a company’s stock price and its quarterly revenue?
• Education : What is the trend in graduation rates for a particular high school over the past 5 years?
• Marketing : What is the correlation between a company’s advertising budget and its sales revenue?
• Sports : What is the trend in the number of home runs hit by a particular baseball player over the past 3 seasons?
• Social Science: What is the correlation between a person’s level of education and their income level?

In order to answer these questions, you would need to analyze and interpret the data using statistical methods, graphs, and other visualization tools.

Purpose of Data Interpretation

The purpose of data interpretation is to make sense of complex data by analyzing and drawing insights from it. The process of data interpretation involves identifying patterns and trends, making comparisons, and drawing conclusions based on the data. The ultimate goal of data interpretation is to use the insights gained from the analysis to inform decision-making.

Data interpretation is important because it allows individuals and organizations to:

• Understand complex data : Data interpretation helps individuals and organizations to make sense of complex data sets that would otherwise be difficult to understand.
• Identify patterns and trends : Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships.
• Make informed decisions: Data interpretation provides individuals and organizations with the information they need to make informed decisions based on the insights gained from the data analysis.
• Evaluate performance : Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made.
• Communicate findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.

Characteristics of Data Interpretation

Here are some characteristics of data interpretation:

• Contextual : Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.
• Iterative : Data interpretation is an iterative process, meaning that it often involves multiple rounds of analysis and refinement as more data becomes available or as new insights are gained from the analysis.
• Subjective : Data interpretation is often subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. It is important to acknowledge and address these biases when interpreting data.
• Analytical : Data interpretation involves the use of analytical tools and techniques to analyze and draw insights from data. These may include statistical analysis, data visualization, and other data analysis methods.
• Evidence-based : Data interpretation is evidence-based, meaning that it is based on the data and the insights gained from the analysis. It is important to ensure that the data used in the analysis is accurate, relevant, and reliable.
• Actionable : Data interpretation is actionable, meaning that it provides insights that can be used to inform decision-making and to drive action. The ultimate goal of data interpretation is to use the insights gained from the analysis to improve performance or to achieve specific goals.

Advantages of Data Interpretation

Data interpretation has several advantages, including:

• Improved decision-making: Data interpretation provides insights that can be used to inform decision-making. By analyzing data and drawing insights from it, individuals and organizations can make informed decisions based on evidence rather than intuition.
• Identification of patterns and trends: Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships. This information can be used to improve performance or to achieve specific goals.
• Evaluation of performance: Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made. By analyzing data, organizations can identify strengths and weaknesses and make changes to improve their performance.
• Communication of findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.
• Better resource allocation: Data interpretation can help organizations allocate resources more efficiently by identifying areas where resources are needed most. By analyzing data, organizations can identify areas where resources are being underutilized or where additional resources are needed to improve performance.
• Improved competitiveness : Data interpretation can give organizations a competitive advantage by providing insights that help to improve performance, reduce costs, or identify new opportunities for growth.

Limitations of Data Interpretation

Data interpretation has some limitations, including:

• Limited by the quality of data: The quality of data used in data interpretation can greatly impact the accuracy of the insights gained from the analysis. Poor quality data can lead to incorrect conclusions and decisions.
• Subjectivity: Data interpretation can be subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. This can lead to different interpretations of the same data.
• Limited by analytical tools: The analytical tools and techniques used in data interpretation can also limit the accuracy of the insights gained from the analysis. Different analytical tools may yield different results, and some tools may not be suitable for certain types of data.
• Time-consuming: Data interpretation can be a time-consuming process, particularly for large and complex data sets. This can make it difficult to quickly make decisions based on the insights gained from the analysis.
• Incomplete data: Data interpretation can be limited by incomplete data sets, which may not provide a complete picture of the situation being analyzed. Incomplete data can lead to incorrect conclusions and decisions.
• Limited by context: Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.

Difference between Data Interpretation and Data Analysis

Data interpretation and data analysis are two different but closely related processes in data-driven decision-making.

Data analysis refers to the process of examining and examining data using statistical and computational methods to derive insights and conclusions from it. It involves cleaning, transforming, and modeling the data to uncover patterns, relationships, and trends that can help in understanding the underlying phenomena.

Data interpretation, on the other hand, refers to the process of making sense of the findings from the data analysis by contextualizing them within the larger problem domain. It involves identifying the key takeaways from the data analysis, assessing their relevance and significance to the problem at hand, and communicating the insights in a clear and actionable manner.

In short, data analysis is about uncovering insights from the data, while data interpretation is about making sense of those insights and translating them into actionable recommendations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Appendices – Writing Guide, Types and Examples

Dissertation – Format, Example and Template

What is a Hypothesis – Types, Examples and...

APA Table of Contents – Format and Example

Research Recommendations – Examples and Writing...

Institutional Review Board – Application Sample...

• My presentations

Auth with social network:

Download presentation

We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!

Presentation is loading. Please wait.

Interpretation and Report Writing. Interpretation & Report Writing After collecting and analyzing the data, the researcher has to accomplish the task.

Published by Edgar Lawson Modified over 8 years ago

Similar presentations

Presentation on theme: "Interpretation and Report Writing. Interpretation & Report Writing After collecting and analyzing the data, the researcher has to accomplish the task."— Presentation transcript:

Conducting Research Investigating Your Topic Copyright 2012, Lisa McNeilley.

Critical Reading Strategies: Overview of Research Process

How should the HDR Thesis Hang Together?

Summary  recapitulates the entire content of the paper.  pertinent features are described briefly. highlight the significant results explains how.

HOW TO WRITE AN ACADEMIC PAPER

By G. Kiran Kumar Research Scholar, DLISc, University of Mysore, Mysore-06 1.

WRITING RESEARCH PAPERS Puvaneswary Murugaiah. INTRODUCTION TO WRITING PAPERS Conducting research is academic activity Research must be original work.

Dissertation Writing.

The Research Problem PE 357. Selecting the problem Can be for research or a literature review -To break the problem down more … -needs to be of interest.

Technical Writing II Acknowledgement: –This lecture notes are based on many on-line documents. –I would like to thank these authors who make the documents.

Research Proposal and Dissertation Daing Nasir Ibrahim.

Formal Lab Reports –Some personal comments –Structure First and foremost Know what it is to plagiarize Don’t do it !!! Disclaimer: This is not a substitute.

Guidance for Your Research Project

Report Writing Three phases of report writing Exploratory phase (MAPS)

Writing Reports Ian McCrum Material from

Y.L SOMASHEKARA Research Scholar DOS In Library & Information Science MGM Mysore.

RESEARCH REPORT PREPARATION AND PRESENTATION

Dr. Alireza Isfandyari-Moghaddam Department of Library and Information Studies, Islamic Azad University, Hamedan Branch

About project

© 2024 SlidePlayer.com Inc. All rights reserved.

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

APA Sample Paper

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

Note:  This page reflects the latest version of the APA Publication Manual (i.e., APA 7), which released in October 2019. The equivalent resource for the older APA 6 style  can be found here .

Media Files: APA Sample Student Paper  ,  APA Sample Professional Paper

This resource is enhanced by Acrobat PDF files. Download the free Acrobat Reader

Note: The APA Publication Manual, 7 th Edition specifies different formatting conventions for student  and  professional  papers (i.e., papers written for credit in a course and papers intended for scholarly publication). These differences mostly extend to the title page and running head. Crucially, citation practices do not differ between the two styles of paper.

However, for your convenience, we have provided two versions of our APA 7 sample paper below: one in  student style and one in  professional  style.

Note: For accessibility purposes, we have used "Track Changes" to make comments along the margins of these samples. Those authored by [AF] denote explanations of formatting and [AWC] denote directions for writing and citing in APA 7. 

APA 7 Student Paper:

Apa 7 professional paper:.

Positivism Research Philosophy

It has to be acknowledged that it is difficult to explain positivism research philosophy in a precise and succinct manner. This is because there are vast differences between settings in which positivism is used by researchers. The number variations in explaining positivism may be equal to the number of authors who addressed the area of  research philosophy . Nevertheless, in its essence, positivism is based on the idea that science is the only way to learn about the truth. The text below explains positivism research philosophy with the focus on business studies in particular.

Positivism:  Introduction  

As a philosophy, positivism adheres to the view that only “factual” knowledge gained through observation  (the senses), including measurement, is trustworthy. In positivism studies the role of the researcher is limited to  data collection  and  interpretation   in an objective way. In other words, the researcher is an objective analyst and she distances herself from personal values in conducting the study. In these types of studies research findings are usually observable and quantifiable.

Positivism depends on quantifiable observations that lead to statistical analyses. It has been a dominant form of research in business and management disciplines for decades. It has been noted that “as a philosophy, positivism is in accordance with the empiricist view that knowledge stems from human experience. It has an atomistic, ontological view of the world as comprising discrete, observable elements and events that interact in an observable, determined and regular manner” [1] .

Moreover, in positivism studies the researcher is independent form the study and there are no provisions for human interests within the study. Crowther and Lancaster (2008) [2]  argue that as a general rule, positivist studies usually adopt  deductive approach , whereas  inductive research approach  is usually associated with a  phenomenology philosophy . Moreover, positivism relates to the viewpoint that researcher needs to concentrate on facts, whereas phenomenology concentrates on the meaning and has provision for human interest.

Researchers warn that “if you assume a positivist approach to your study, then it is your belief that you are independent of your research and your research can be purely objective. Independent means that you maintain minimal interaction with your research participants when carrying out your research.” [3]  In other words, studies with positivist paradigm are based purely on facts and consider the world to be external and objective.

The five main principles of positivism research philosophy can be summarized as the following:

• There are no differences in the logic of inquiry across sciences.
• The research should aim to explain and predict.
• Research should be empirically observable via human senses. Inductive reasoning should be used to develop statements (hypotheses) to be tested during the research process.
• Science is not the same as the common sense. The common sense should not be allowed to bias the research findings.
• Science must be value-free and it should be judged only by logic.

The following are a few examples for studies that adhere to positivism research philosophy:

• A study into the impact of COVID-19 pandemic on the equity of fashion brands in North America.
• An analysis of effects of foreign direct investment in information technology industry on GDP growth in
• A study of relationship between diffusion of innovation of mobile applications and saturation of applications in

The following Table 1 illustrates ontology, epistemology, axiology and typical research methods associated with positivism research philosophy:

Table 1 Ontology, epistemology, axiology and typical research methods associated with positivism research philosophy

Science as an Underlying Ground for Positivism  

Positivism often involves the use of existing theory to develop hypotheses to be tested during the research process. Positivist researchers tend to use highly structured research methodology in order to allow the replication of the same study in the future. Science can be specified as a cornerstone in positivism research philosophy.  Specifically, positivism relies on the following aspects of science.

1. Science is deterministic . Scientific approach is based on the assumption that X causes Y under certain circumstances. The role of researcher when following the scientific approach is to discover specific nature of cause and effect relationships.

2. Science is mechanistic . Mechanical nature of scientific approach can be explained in a way that researchers develop hypotheses to be proved or disproved via application of specific research methods. This leads to the fact that

3. Science uses method . Chosen methods are applied mechanically in order to operationalize theory or hypothesis. Application of methodology involves selection of sample, measurements, analysis and reaching conclusions about hypotheses.

4. Science deals with empiricism . In other words, science only deals with what can be seen or measured. From this perspective, science can be assessed as objective.

Differences between Positivism and Interpretivism

The key features of positivism and social constructionism philosophical approaches are presented in the following Table 2 by Ramanathan (2008) [4] .

Table 2 Differences between positivism and social constructionism

Alternatively, the differences between positivist and  phenomenology  paradigms are best illustrated by Easterby-Smith et al. (2008) [5]  in the following manner:

Table 3 Positivist and phenomenology paradigms

Shortcomings of Positivism

Positivism as an epistemology is associated with the following set of disadvantages:

Firstly, positivism relies on experience as a valid source of knowledge. However, a wide range of basic and important concepts such as cause, time and space are not based on experience. There might be many additional factors that have impacted research findings and positivism research philosophy fails to acknowledge the effect of these factors.

Secondly, positivism assumes that all types of processes can be perceived as a certain variation of actions of individuals or relationships between individuals.

Thirdly, adoption of positivism in business studies and other studies can be criticized for reliance on status quo. In other words, research findings in positivism studies are only descriptive, thus they lack insight into in-depth issues.

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

John Dudovskiy

[1] Collins, H. (2010) “Creative Research: The Theory and Practice of Research for the Creative Industries” AVA Publications, p.38

[2] Crowther, D. & Lancaster, G. (2008) “Research Methods: A Concise Introduction to Research in Management and Business Consultancy” Butterworth-Heinemann

[3] Wilson, J. (2010) “Essentials of Business Research: A Guide to Doing Your Research Project” SAGE Publications

[4] Ramanathan, R. (2008) “The Role of Organisational Change Management in Offshore Outsourcing of Information Technology Services” Universal Publishers

[5] Easterby-Smith, M, Thorpe, R. & Jackson, P. (2008) “Management Research” 3rd ed,SAGE Publications Ltd., London

The independent source for health policy research, polling, and news.

A New Use for Wegovy Opens the Door to Medicare Coverage for Millions of People with Obesity

Juliette Cubanski , Tricia Neuman , Nolan Sroczynski , and Anthony Damico Published: Apr 24, 2024

The FDA recently approved a new use for Wegovy (semaglutide), the blockbuster anti-obesity drug, to reduce the risk of heart attacks and stroke in people with cardiovascular disease who are overweight or obese. Wegovy belongs to a class of medications called GLP-1 (glucagon-like peptide-1) agonists that were initially approved to treat type 2 diabetes but are also highly effective anti-obesity drugs. The new FDA-approved indication for Wegovy paves the way for Medicare coverage of this drug and broader coverage by other insurers. Medicare is currently prohibited by law from covering Wegovy and other medications when used specifically for obesity. However, semaglutide is covered by Medicare as a treatment for diabetes, branded as Ozempic.

What does the FDA’s decision mean for Medicare coverage of Wegovy?

The FDA’s decision opens the door to Medicare coverage of Wegovy, which was first approved by the FDA as an anti-obesity medication. Soon after the FDA’s approval of the new use for Wegovy, the Centers for Medicare & Medicaid Services (CMS) issued a memo indicating that Medicare Part D plans can add Wegovy to their formularies now that it has a medically-accepted indication that is not specifically excluded from Medicare coverage . Because Wegovy is a self-administered injectable drug, coverage will be provided under Part D , Medicare’s outpatient drug benefit offered by private stand-alone drug plans and Medicare Advantage plans, not Part B, which covers physician-administered drugs.

How many Medicare beneficiaries could be eligible for coverage of Wegovy for its new use?

Of these 3.6 million beneficiaries, 1.9 million also had diabetes (other than Type 1) and may already have been eligible for Medicare coverage of GLP-1s as diabetes treatments prior to the FDA’s approval of the new use of Wegovy.

Not all people who are eligible based on the new indication are likely to take Wegovy, however. Some might be dissuaded by the potential side effects and adverse reactions . Out-of-pocket costs could also be a barrier. Based on the list price of $1,300 per month (not including rebates or other discounts negotiated by pharmacy benefit managers), Wegovy could be covered as a specialty tier drug, where Part D plans are allowed to charge coinsurance of 25% to 33%. Because coinsurance amounts are pegged to the list price, Medicare beneficiaries required to pay coinsurance could face monthly costs of $325 to $430 before they reach the new cap on annual out-of-pocket drug spending established by the Inflation Reduction Act – around $3,300 in 2024, based on brand drugs only, and $2,000 in 2025. But even paying $2,000 out of pocket would still be beyond the reach of many people with Medicare who live on modest incomes . Ultimately, how much beneficiaries pay out of pocket will depend on Part D plan coverage and formulary tier placement of Wegovy.

Further, some people may have difficulty accessing Wegovy if Part D plans apply prior authorization and step therapy tools to manage costs and ensure appropriate use. These factors could have a dampening effect on use by Medicare beneficiaries, even among the target population.

When will Medicare Part D plans begin covering Wegovy?

Some Part D plans have already announced that they will begin covering Wegovy this year, although it is not yet clear how widespread coverage will be in 2024. While Medicare drug plans can add new drugs to their formularies during the year to reflect new approvals and expanded indications, plans are not required to cover every new drug that comes to market. Part D plans are required to cover at least two drugs in each category or class and all or substantially all drugs in six protected classes . However, facing a relatively high price and potentially large patient population for Wegovy, many Part D plans might be reluctant to expand coverage now, since they can’t adjust their premiums mid-year to account for higher costs associated with use of this drug. So, broader coverage in 2025 could be more likely.

How might expanded coverage of Wegovy affect Medicare spending?

The impact on Medicare spending associated with expanded coverage of Wegovy will depend in part on how many Part D plans add coverage for it and the extent to which plans apply restrictions on use like prior authorization; how many people who qualify to take the drug use it; and negotiated prices paid by plans. For example, if plans receive a 50% rebate on the list price of $1,300 per month (or $15,600 per year), that could mean annual net costs per person around $7,800. If 10% of the target population (an estimated 360,000 people) uses Wegovy for a full year, that would amount to additional net Medicare Part D spending of $2.8 billion for one year for this one drug alone.

It’s possible that Medicare could select semaglutide for drug price negotiation as early as 2025, based on the earliest FDA approval of Ozempic in late 2017 . For small-molecule drugs like semaglutide, at least seven years must have passed from its FDA approval date to be eligible for selection, and for drugs with multiple FDA approvals, CMS will use the earliest approval date to make this determination. If semaglutide is selected for negotiation next year, a negotiated price would be available beginning in 2027. This could help to lower Medicare and out-of-pocket spending on semaglutide products, including Wegovy as well as Ozempic and Rybelsus, the oral formulation approved for type 2 diabetes. As of 2022, gross Medicare spending on Ozempic alone placed it sixth among the 10 top-selling drugs in Medicare Part D, with annual gross spending of $4.6 billion, based on KFF analysis . This estimate does not include rebates, which Medicare’s actuaries estimated to be  31.5% overall in 2022  but could be as high as  69%  for Ozempic, according to one estimate.

What does this mean for Medicare coverage of anti-obesity drugs?

For now, use of GLP-1s specifically for obesity continues to be excluded from Medicare coverage by law. But the FDA’s decision signals a turning point for broader Medicare coverage of GLP-1s since Wegovy can now be used to reduce the risk of heart attack and stroke by people with cardiovascular disease and obesity or overweight, and not only as an anti-obesity drug. And more pathways to Medicare coverage could open up if these drugs gain FDA approval for other uses . For example, Eli Lilly has just reported clinical trial results showing the benefits of its GLP-1, Zepbound (tirzepatide), in reducing the occurrence of sleep apnea events among people with obesity or overweight. Lilly reportedly plans to seek FDA approval for this use and if approved, the drug would be the first pharmaceutical treatment on the market for sleep apnea.

If more Medicare beneficiaries with obesity or overweight gain access to GLP-1s based on other approved uses for these medications, that could reduce the cost of proposed legislation to lift the statutory prohibition on Medicare coverage of anti-obesity drugs. This is because the Congressional Budget Office (CBO), Congress’s official scorekeeper for proposed legislation, would incorporate the cost of coverage for these other uses into its baseline estimates for Medicare spending, which means that the incremental cost of changing the law to allow Medicare coverage for anti-obesity drugs would be lower than it would be without FDA’s approval of these drugs for other uses. Ultimately how widely Medicare Part D coverage of GLP-1s expands could have far-reaching effects on people with obesity and on Medicare spending.

• Medicare Part D
• Chronic Diseases
• Heart Disease
• Medicare Advantage

news release

• An Estimated 1 in 4 Medicare Beneficiaries With Obesity or Overweight Could Be Eligible for Medicare Coverage of Wegovy, an Anti-Obesity Drug, to Reduce Heart Risk

Also of Interest

• An Overview of the Medicare Part D Prescription Drug Benefit
• FAQs about the Inflation Reduction Act’s Medicare Drug Price Negotiation Program
• What Could New Anti-Obesity Drugs Mean for Medicare?
• Medicare Spending on Ozempic and Other GLP-1s Is Skyrocketing

The Impact of Technology on the Workplace: 2024 Report

The impact of technology on the workplace over the last year has been nothing if not substantial. From the integration of generative AI platforms like ChatGPT to the increase in data breaches across the industry, keeping up with shifting trends is a full-time job at this point in history.

Fortunately, you’ve got Tech.co to help you out. In our inaugural annual report on this subject, we’ve embarked on an in-depth journey to quantify and explain a wide range of workplace trends, noting the influence of technology as a primary driver.

We surveyed over 1000 US business leaders to ensure an accurate depiction of the workplace heading in to 2024, and help you to strategize for the year ahead.

Below, we’ll introduce our 2024 workplace report and give you a preview of its key findings. Make sure to download the full report if you want the learn more about how the workplace is changing in the face of evolving technology.

Impact of Tech on the Workplace Report 2024: Key Findings

Our Impact of Tech on the Workplace report found a wide range of statistics that point to how the world is adapting to new technology. Here are some of the key findings we identified, which are further outlined below:

• Using more collaboration tools and AI results in higher productivity
• 59% of people who use AI have greater job satisfaction
• ChatGPT is the most popular AI tool used amongst businesses
• Digital natives and businesses that use AI are more open to the idea of a 4-day working week
• The majority of companies found it challenging to hire new staff – but remote working organizations find it easier
• Remote working organizations report higher levels of productivity
• Phishing attacks were the most common cause of a data breach

1. Using more collaboration tools and AI results in higher productivity

The use of online tools and digital resources is certainly not new to the business world. In 2023, collaboration tools and generative AI platforms took that usage to another level, adding a robust set of functionalities to the average business’ operations .

Did they actually have an impact? According to our research, j ust over half of businesses (56%) report high productivity levels , so it appears that there is a positive effect associated with this kind of technology.

More specifically, the use of AI platforms and features has seriously improved productivity for businesses of all sizes. Our research found that 72% of respondents who use AI extensively report high organizational productivity, compared to 55% of respondents who use AI to a limited extent .

2. 59% of people who use AI have greater job satisfaction

It’s no secret that AI entered the workforce in a big way in 2023. As soon as the technology became advanced enough to handle certain operations, businesses started integrating it into their systems in hopes of improving productivity. It’s a trend guaranteed to continue in 2024 and beyond.

How did employees who were encouraged to use the technology feel about AI’s rapid rise in the workplace? While many headlines you read claim that workers dread AI and fear it’s only there to steal their jobs, our research actually found that 59% of people who use AI have great job satisfaction , quelling such concerns.

Given this, businesses should feel more comfortable rolling out this technology in 2024, as many are still lagging behind on the full adoption of the technology. In fact, we found only 1 in 25 companies have fully integrated AI throughout their organization .

3. ChatGPT is the most popular AI tool used among businesses

In November 2022, ChatGPT launched. The value of this groundbreaking technology was apparent almost immediately, and businesses were scrambling for ways to use its generative functionality to improve their businesses as much as possible.

Since then, a myriad of ChatGPT alternatives from big tech firms like Google and Microsoft have rolled in 2023. From Bard and Copilot to Claude and Jasper, these alternatives have their merits, but ChatGPT still reigns supreme.

In fact, our research found that 65% of businesses say they use ChatGPT , well ahead of the second place AI chatbot Google Bard, which boasts only 49% usage. Other alternatives included Bing AI Chat (20%), Claude AI (10%), and Jasper Chat (9%), with 8% of respondents using a lesser known “Other” platform.

4. The majority of companies found it challenging to hire new staff, but remote working organizations find it easier

The Great Resignation was the big story last year, with scores of employees leaving their positions after the pandemic gave them a taste of the flexibility while working from home. As a result, our research found that companies are still having a tough time when it comes to recruiting.

However, not all companies are having a hard time attracting new employees. Specifically, organizations offering remote job roles are recruiting with much greater ease compared to fully in-office and even hybrid working businesses.

All that to say, if an in-office policy is that important to you, employee retention should be an equally high priority for your team.

5. Digital natives and businesses that use AI are more open to the idea of a 4-day working week

Now that remote and hybrid work have become the new normal for many businesses, the newest employee perk to pique our interest is the 4-day workweek. Study after study has shown that the shortened week for the same pay has a notably positive impact on productivity, employee wellbeing, turnover, and absenteeism.

Many business owners and decision makers are coming around on it too, but the acceptance definitely depends on age. Our research found that  65% of senior leadership aged 35-44 (Millennials and Gen X) would consider implementing a 4-day working week or have already implemented it, while only 45% of senior leadership aged 55-64 (Baby Boomers) felt the same.

Beyond age, business owners of AI-powered companies are fully embracing the new work policy. In fact, a staggering 93% of senior leadership of organizations where AI plays a central role in operations are either considering a 4-day working week or have already implemented it.

There are many companies offering a 4-day workweek and some US states with 4-day week policies for employees, so if you’re tired of working on Friday, there are some serious opportunities for you out in the world.

6. Remote working organizations report higher levels of productivity

Since the pandemic, remote work has indeed become a standard for many businesses. In fact, our research found that almost all businesses have the tools to facilitate remote working , from video conferencing software to project tracking services.

The remote work had some unintended benefits including boosts to employee mental health and productivity. Our research found that 64% of remote businesses report high productivity levels compared to 54% of in-office businesses . Suffice to say, remote work is good for your bottom line.

However, despite all the studies that show remote work to be beneficial for employers and employees alike, business owners have started demanding their employees return to the office. Our research found that, in 2023, over half of companies (52%) expect their employees in the office 5-days per week.

The difference between remote and hybrid work policies is notable here as well, with 38% of employees at hybrid working organizations going to the office more than they are required , based on company policy. This means that these strict return-to-office policies might not even be necessary in some situations, as your team will still commute if needed.

7. Phishing attacks were the most common cause of a data breach

Not all advancements in technology have been good for the workplace. As a result of evolving tech, bad actors have been able to ramp up their hacking activity, leading to an online security crisis that is costing businesses millions of dollars .

So, what kind of nefarious behavior should you be on the lookout for? Our research found that 23% of data breaches were caused by phishing attacks , according to senior leadership employees that we spoke to. Computer virus (22%) was also quite common, followed by employee error (12%), advanced persistent threats (9%), and unsecure Wi-Fi (8%).

Simply put, protecting your business online must be a top priority in the new year, particularly if your business works with any sensitive information.

Research Methodology

To inform how technology is impacting the workplace in 2024, Tech.co surveyed a large sample of senior leadership professionals from businesses based in the United States. Senior leadership professionals had job titles ranging from manager to director.

We surveyed companies with 10 or more employees to ensure that our data captured the experiences and perspectives of individuals holding key leadership roles within established organizations.

To ensure an impartial and unbiased sample, we also gathered data through a survey with participants selected via a third-party panel provider. Data collection was obtained in October and finalized in November of 2023.

Finally, to guarantee an accurate reflection of US businesses, a total of 1047 responses were obtained at a confidence level of 99.9%.

About Tech.co

If you’ve stumbled across our 2024 workplace report and are looking for answers on the brains behind the booklet, here’s a little more information on who we are.

Tech.co was established in 2006 as a networking platform for companies working out of the Chicago area, and has transformed into a fully-fledged media company with readers around the world.

We aim to translate our passion for technology into insightful news and analysis, helpful buyers’ guides and practical resources, so SMBs across the US and beyond can grow their revenues, work smarter, and secure their success – now and in the future.

Each year, Tech.co carries out thousands of hours of independent product testing and market analyses to support over 5 million professionals annually in their pursuit to learn more about technology and make the right purchasing decisions.

We also work directly with dozens of Fortune 500 clients, such as Salesforce, monday.com, HubSpot and Zoom, to help advise on their strategies and enable them to reach brand new audiences.

To stay informed on the latest developments and find the right technology for your workplace, you can sign up to our newsletter , or learn more about Tech.co here .

Get the latest tech news, straight to your inbox

Stay informed on the top business tech stories with Tech.co's weekly highlights reel.

By signing up to receive our newsletter, you agree to our Privacy Policy . You can unsubscribe at any time.

Stay informed on the top business tech stories with Tech.co's weekly highlights reel.

We're sorry this article didn't help you today – we welcome feedback, so if there's any way you feel we could improve our content, please email us at [email protected]

• Artificial Intelligence
• Business Trends

Written by:

Adobe Issues AI Training Promise After User Outrage

Software giant responds to backlash, telling users that...

ChatGPT Coming to Host of Apple Apps with Latest OS Updates

WWDC 2024 keynote speech reveals ChatGPT integration for...

Fully Remote Jobs at Google You Can Apply for in June 2024

Summer is a great time to turn over a new leaf, so check...

ChatGPT Shirks Election Questions After Inaccurate Answers

The popular AI chatbot made incorrect statements about the...

Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Volume 30, Number 7—July 2024

Highly Pathogenic Avian Influenza A(H5N1) Clade 2.3.4.4b Virus Infection in Domestic Dairy Cattle and Cats, United States, 2024

Suggested citation for this article

We report highly pathogenic avian influenza A(H5N1) virus in dairy cattle and cats in Kansas and Texas, United States, which reflects the continued spread of clade 2.3.4.4b viruses that entered the country in late 2021. Infected cattle experienced nonspecific illness, reduced feed intake and rumination, and an abrupt drop in milk production, but fatal systemic influenza infection developed in domestic cats fed raw (unpasteurized) colostrum and milk from affected cows. Cow-to-cow transmission appears to have occurred because infections were observed in cattle on Michigan, Idaho, and Ohio farms where avian influenza virus–infected cows were transported. Although the US Food and Drug Administration has indicated the commercial milk supply remains safe, the detection of influenza virus in unpasteurized bovine milk is a concern because of potential cross-species transmission. Continued surveillance of highly pathogenic avian influenza viruses in domestic production animals is needed to prevent cross-species and mammal-to-mammal transmission.

Highly pathogenic avian influenza (HPAI) viruses pose a threat to wild birds and poultry globally, and HPAI H5N1 viruses are of even greater concern because of their frequent spillover into mammals. In late 2021, the Eurasian strain of H5N1 (clade 2.3.4.4b) was detected in North America ( 1 , 2 ) and initiated an outbreak that continued into 2024. Spillover detections and deaths from this clade have been reported in both terrestrial and marine mammals in the United States ( 3 , 4 ). The detection of HPAI H5N1 clade 2.3.4.4b virus in severe cases of human disease in Ecuador ( 5 ) and Chile ( 6 ) raises further concerns regarding the pandemic potential of specific HPAI viruses.

In February 2024, veterinarians were alerted to a syndrome occurring in lactating dairy cattle in the panhandle region of northern Texas. Nonspecific illness accompanied by reduced feed intake and rumination and an abrupt drop in milk production developed in affected animals. The milk from most affected cows had a thickened, creamy yellow appearance similar to colostrum. On affected farms, incidence appeared to peak 4–6 days after the first animals were affected and then tapered off within 10–14 days; afterward, most animals were slowly returned to regular milking. Clinical signs were commonly reported in multiparous cows during middle to late lactation; ≈10%–15% illness and minimal death of cattle were observed on affected farms. Initial submissions of blood, urine, feces, milk, and nasal swab samples and postmortem tissues to regional diagnostic laboratories did not reveal a consistent, specific cause for reduced milk production. Milk cultures were often negative, and serum chemistry testing showed mildly increased aspartate aminotransferase, gamma-glutamyl transferase, creatinine kinase, and bilirubin values, whereas complete blood counts showed variable anemia and leukocytopenia.

In early March 2024, similar clinical cases were reported in dairy cattle in southwestern Kansas and northeastern New Mexico; deaths of wild birds and domestic cats were also observed within affected sites in the Texas panhandle. In > 1 dairy farms in Texas, deaths occurred in domestic cats fed raw colostrum and milk from sick cows that were in the hospital parlor. Antemortem clinical signs in affected cats were depressed mental state, stiff body movements, ataxia, blindness, circling, and copious oculonasal discharge. Neurologic exams of affected cats revealed the absence of menace reflexes and pupillary light responses with a weak blink response.

On March 21, 2024, milk, serum, and fresh and fixed tissue samples from cattle located in affected dairies in Texas and 2 deceased cats from an affected Texas dairy farm were received at the Iowa State University Veterinary Diagnostic Laboratory (ISUVDL; Ames, IA, USA). The next day, similar sets of samples were received from cattle located in affected dairies in Kansas. Milk and tissue samples from cattle and tissue samples from the cats tested positive for influenza A virus (IAV) by screening PCR, which was confirmed and characterized as HPAI H5N1 virus by the US Department of Agriculture National Veterinary Services Laboratory. Detection led to an initial press release by the US Department of Agriculture Animal and Plant Health Inspection Service on March 25, 2024, confirming HPAI virus in dairy cattle ( 7 ). We report the characterizations performed at the ISUVDL for HPAI H5N1 viruses infecting cattle and cats in Kansas and Texas.

Materials and Methods

Milk samples (cases 2–5) and fresh and formalin-fixed tissues (cases 1, 3–5) from dairy cattle were received at the ISUVDL from Texas on March 21 and from Kansas on March 22, 2024. The cattle exhibited nonspecific illness and reduced lactation, as described previously. The tissue samples for diagnostic testing came from 3 cows that were euthanized and 3 that died naturally; all postmortem examinations were performed on the premises of affected farms.

The bodies of 2 adult domestic shorthaired cats from a north Texas dairy farm were received at the ISUVDL for a complete postmortem examination on March 21, 2024. The cats were found dead with no apparent signs of injury and were from a resident population of ≈24 domestic cats that had been fed milk from sick cows. Clinical disease in cows on that farm was first noted on March 16; the cats became sick on March 17, and several cats died in a cluster during March 19–20. In total, >50% of the cats at that dairy became ill and died. We collected cerebrum, cerebellum, eye, lung, heart, spleen, liver, lymph node, and kidney tissue samples from the cats and placed them in 10% neutral-buffered formalin for histopathology.

At ISUVDL, we trimmed, embedded in paraffin, and processed formalin-fixed tissues from affected cattle and cats for hematoxylin/eosin staining and histologic evaluation. For immunohistochemistry (IHC), we prepared 4-µm–thick sections from paraffin-embedded tissues, placed them on Superfrost Plus slides (VWR, https://www.vwr.com ), and dried them for 20 minutes at 60°C. We used a Ventana Discovery Ultra IHC/ISH research platform (Roche, https://www.roche.com ) for deparaffinization until and including counterstaining. We obtained all products except the primary antibody from Roche. Automated deparaffination was followed by enzymatic digestion with protease 1 for 8 minutes at 37°C and endogenous peroxidase blocking. We obtained the primary influenza A virus antibody from the hybridoma cell line H16-L10–4R5 (ATCC, https://www.atcc.org ) and diluted at 1:100 in Discovery PSS diluent; we incubated sections with antibody for 32 minutes at room temperature. Next, we incubated the sections with a hapten-labeled conjugate, Discovery anti-mouse HQ, for 16 minutes at 37°C followed by a 16-minute incubation with the horse radish peroxidase conjugate, Discovery anti-HQ HRP. We used a ChromoMap DAB kit for antigen visualization, followed by counterstaining with hematoxylin and then bluing. Positive controls were sections of IAV-positive swine lung. Negative controls were sections of brain, lung, and eyes from cats not infected with IAV.

We diluted milk samples 1:3 vol/vol in phosphate buffered saline, pH 7.4 (Gibco/Thermo Fisher Scientific, https://www.thermofisher.com ) by mixing 1 unit volume of milk and 3 unit volumes of phosphate buffered saline. We prepared 10% homogenates of mammary glands, brains, lungs, spleens, and lymph nodes in Earle’s balanced salt solution (Sigma-Aldrich, https://www.sigmaaldrich.com ). Processing was not necessary for ocular fluid, rumen content, or serum samples. After processing, we extracted samples according to a National Animal Health Laboratory Network (NAHLN) protocol that had 2 NAHLN-approved deviations for ISUVDL consisting of the MagMax Viral RNA Isolation Kit for 100 µL sample volumes and a Kingfisher Flex instrument (both Thermo Fisher Scientific).

We performed real-time reverse transcription PCR (rRT-PCR) by using an NAHLN-approved assay with 1 deviation, which was the VetMAX-Gold SIV Detection kit (Thermo Fisher Scientific), to screen for the presence of IAV RNA. We tested samples along with the VetMAX XENO Internal Positive Control to monitor the possible presence of PCR inhibitors. Each rRT-PCR 96-well plate had 2 positive amplification controls, 2 negative amplification controls, 1 positive extraction control, and 1 negative extraction control. We ran the rRT-PCR on an ABI 7500 Fast thermocycler and analyzed data with Design and Analysis Software 2.7.0 (both Thermo Fisher Scientific). We considered samples with cycle threshold (Ct) values <40.0 to be positive for virus.

After the screening rRT-PCR, we analyzed IAV RNA–positive samples for the H5 subtype and H5 clade 2.3.4.4b by using the same RNA extraction and NAHLN-approved rRT-PCR protocols as described previously, according to standard operating procedures. We performed PCR on the ABI 7500 Fast thermocycler by using appropriate controls to detect H5-specific IAV. We considered samples with Ct values <40.0 to be positive for the IAV H5 subtype.

We conducted genomic sequencing of 2 milk samples from infected dairy cattle from Texas and 2 tissue samples (lung and brain) from cats that died at a different Texas dairy. We subjected the whole-genome sequencing data to bioinformatics analysis to assemble the 8 different IAV segment sequences according to previously described methods ( 8 ). We used the hemagglutinin (HA) and neuraminidase (NA) sequences for phylogenetic analysis. We obtained reference sequences for the HA and NA segments of IAV H5 clade 2.3.4.4 from publicly available databases, including GISAID ( https://www.gisaid.org ) and GenBank. We aligned the sequences by using MAFFT version 7.520 software ( https://mafft.cbrc.jp/alignment/server/index.html ) to create multiple sequence alignments for subsequent phylogenetic analysis. We used IQTree2 ( https://github.com/iqtree/iqtree2 ) to construct the phylogenetic tree from the aligned sequences. The software was configured to automatically identify the optimal substitution model by using the ModelFinder Plus option, ensuring the selection of the most suitable model for the dataset and, thereby, improving the accuracy of the reconstructed tree. We visualized the resulting phylogenetic tree by using iTOL ( https://itol.embl.de ), a web-based platform for interactive tree exploration and annotation.

Gross Lesions in Cows and Cats

All cows were in good body condition with adequate rumen fill and no external indications of disease. Postmortem examinations of the affected dairy cows revealed firm mammary glands typical of mastitis; however, mammary gland lesions were not consistent. Two cows that were acutely ill before postmortem examination had grossly normal milk and no abnormal mammary gland lesions. The gastrointestinal tract of some cows had small abomasal ulcers and shallow linear erosions of the intestines, but those observations were also not consistent in all animals. The colon contents were brown and sticky, suggesting moderate dehydration. The feces contained feed particles that appeared to have undergone minimal ruminal fermentation. The rumen contents had normal color and appearance but appeared to have undergone minimal fermentation.

The 2 adult cats (1 intact male, 1 intact female) received at the ISUVDL were in adequate body and postmortem condition. External examination was unremarkable. Mild hemorrhages were observed in the subcutaneous tissues over the dorsal skull, and multifocal meningeal hemorrhages were observed in the cerebrums of both cats. The gastrointestinal tracts were empty, and no other gross lesions were observed.

Microscopic Lesions in Cows and Cats

Figure 1 . Mammary gland lesions in cattle in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. A, B) Mammary gland...

The chief microscopic lesion observed in affected cows was moderate acute multifocal neutrophilic mastitis ( Figure 1 ); however, mammary glands were not received from every cow. Three cows had mild neutrophilic or lymphocytic hepatitis. Because they were adult cattle, other observed microscopic lesions (e.g., mild lymphoplasmacytic interstitial nephritis and mild to moderate lymphocytic abomasitis) were presumed to be nonspecific, age-related changes. We did not observe major lesions in the other evaluated tissues. We performed IHC for IAV antigen on all evaluated tissues; the only tissues with positive immunoreactivity were mastitic mammary glands from 2 cows that showed nuclear and cytoplasmic labeling of alveolar epithelial cells and cells within lumina ( Figure 1 ) and multifocal germinal centers within a lymph node from 1 cow ( Table 1 ).

Figure 2 . Lesions in cat tissues in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Tissue sections were stained with...

Both cats had microscopic lesions consistent with severe systemic virus infection, including severe subacute multifocal necrotizing and lymphocytic meningoencephalitis with vasculitis and neuronal necrosis, moderate subacute multifocal necrotizing and lymphocytic interstitial pneumonia, moderate to severe subacute multifocal necrotizing and lymphohistiocytic myocarditis, and moderate subacute multifocal lymphoplasmacytic chorioretinitis with ganglion cell necrosis and attenuation of the internal plexiform and nuclear layers ( Table 2 ; Figure 2 ). We performed IHC for IAV antigen on multiple tissues (brain, eye, lung, heart, spleen, liver, and kidney). We detected positive IAV immunoreactivity in brain (intracytoplasmic, intranuclear, and axonal immunolabeling of neurons), lung, and heart, and multifocal and segmental immunoreactivity within all layers of the retina ( Figure 2 ).

PCR Data from Cows and Cats

We tested various samples from 8 clinically affected mature dairy cows by IAV screening and H5 subtype-specific PCR ( Table 3 ). Milk and mammary gland homogenates consistently showed low Ct values: 12.3–16.9 by IAV screening PCR, 17.6–23.1 by H5 subtype PCR, and 14.7–20.0 by H5 2.3.4.4 clade PCR (case 1, cow 1; case 2, cows 1 and 2; case 3, cow 1; and case 4, cow 1). We forwarded the samples to the National Veterinary Services Laboratory, which confirmed the virus was an HPAI H5N1 virus strain.

When available, we also tested tissue homogenates (e.g., lung, spleen, and lymph nodes), ocular fluid, and rumen contents from 6 cows by IAV and H5 subtype-specific PCR ( Table 3 ). However, the PCR findings were not consistent. For example, the tissue homogenates and ocular fluid tested positive in some but not all cows. In case 5, cow 1, the milk sample tested negative by IAV screening PCR, but the spleen homogenate tested positive by IAV screening, H5 subtype, and H5 2.3.4.4 PCR. For 2 cows (case 3, cow 1; and case 4, cow 1) that had both milk and rumen contents available, both samples tested positive for IAV. Nevertheless, all IAV-positive nonmammary gland tissue homogenates, ocular fluid, and rumen contents had markedly elevated Ct values in contrast to the low Ct values for milk and mammary gland homogenate samples.

We tested brain and lung samples from the 2 cats (case 6, cats 1 and 2) by IAV screening and H5 subtype-specific PCR ( Table 3 ). Both sample types were positive by IAV screening PCR; Ct values were 9.9–13.5 for brain and 17.4–24.4 for lung samples, indicating high amounts of virus nucleic acid in those samples. The H5 subtype and H5 2.3.4.4 PCR results were also positive for the brain and lung samples; Ct values were consistent with the IAV screening PCR ( Table 3 ).

Phylogenetic Analyses

We assembled the sequences of all 8 segments of the HPAI viruses from both cow milk and cat tissue samples. We used the hemagglutinin (HA) and neuraminidase (NA) sequences specifically for phylogenetic analysis to delineate the clade of the HA gene and subtype of the NA gene.

Figure 3 . Phylogenetic analysis of hemagglutinin gene sequences in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Colors indicate different...

For HA gene analysis, both HA sequences derived from cow milk samples exhibited a high degree of similarity, sharing 99.88% nucleotide identity, whereas the 2 HA sequences from cat tissue samples showed complete identity at 100%. The HA sequences from the milk samples had 99.94% nucleotide identities with HA sequences from the cat tissues, resulting in a distinct subcluster comprising all 4 HA sequences, which clustered together with other H5N1 viruses belonging to clade 2.3.4.4b ( Figure 3 ). The HA sequences were deposited in GenBank (accession nos. PP599465 [case 2, cow 1], PP599473 [case 2, cow 2], PP692142 [case 6, cat 1], and PP692195 [case 6, cat 2]).

Figure 4 . Phylogenetic analysis of neuraminidase gene sequences in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Colors indicate different...

For NA gene analysis, the 2 NA sequences obtained from cow milk samples showed 99.93% nucleotide identity. Moreover, the NA sequences derived from the milk samples exhibited complete nucleotide identities (100%) with those from the cat tissues. The 4 NA sequences were grouped within the N1 subtype of HPAI viruses ( Figure 4 ). The NA sequences were deposited in GenBank (accession nos. PP599467 [case 2, cow 1], PP599475 [case 2, cow 2], PP692144 [case 6, cat 1], and PP692197 [case 6, cat 2]).

This case series differs from most previous reports of IAV infection in bovids, which indicated cattle were inapparently infected or resistant to infection ( 9 ). We describe an H5N1 strain of IAV in dairy cattle that resulted in apparent systemic illness, reduced milk production, and abundant virus shedding in milk. The magnitude of this finding is further emphasized by the high death rate (≈50%) of cats on farm premises that were fed raw colostrum and milk from affected cows; clinical disease and lesions developed that were consistent with previous reports of H5N1 infection in cats presumably derived from consuming infected wild birds ( 10 – 12 ). Although exposure to and consumption of dead wild birds cannot be completely ruled out for the cats described in this report, the known consumption of unpasteurized milk and colostrum from infected cows and the high amount of virus nucleic acid within the milk make milk and colostrum consumption a likely route of exposure. Therefore, our findings suggest cross-species mammal-to-mammal transmission of HPAI H5N1 virus and raise new concerns regarding the potential for virus spread within mammal populations. Horizontal transmission of HPAI H5N1 virus has been previously demonstrated in experimentally infected cats ( 13 ) and ferrets ( 14 ) and is suspected to account for large dieoffs observed during natural outbreaks in mink ( 15 ) and sea lions ( 16 ). Future experimental studies of HPAI H5N1 virus in dairy cattle should seek to confirm cross-species transmission to cats and potentially other mammals.

Clinical IAV infection in cattle has been infrequently reported in the published literature. The first report occurred in Japan in 1949, where a short course of disease with pyrexia, anorexia, nasal discharge, pneumonia, and decreased lactation developed in cattle ( 17 ). In 1997, a similar condition occurred in dairy cows in southwest England leading to a sporadic drop in milk production ( 18 ), and IAV seroconversion was later associated with reduced milk yield and respiratory disease ( 19 – 21 ). Rising antibody titers against human-origin influenza A viruses (H1N1 and H3N2) were later again reported in dairy cattle in England, which led to an acute fall in milk production during October 2005–March 2006 ( 22 ). Limited reports of IAV isolation from cattle exist; most reports occurred during the 1960s and 1970s in Hungary and in the former Soviet Union, where H3N2 was recovered from cattle experiencing respiratory disease ( 9 , 23 ). Direct detection of IAV in milk and the potential transmission from cattle to cats through feeding of unpasteurized milk has not been previously reported.

An IAV-associated drop in milk production in dairy cattle appears to have occurred during > 4 distinct periods and within 3 widely separated geographic areas: 1949 in Japan ( 17 ), 1997–1998 and 2005–2006 in Europe ( 19 , 21 ), and 2024 in the United States (this report). The sporadic occurrence of clinical disease in dairy cattle worldwide might be the result of changes in subclinical infection rates and the presence or absence of sufficient baseline IAV antibodies in cattle to prevent infection. Milk IgG, lactoferrin, and conglutinin have also been suggested as host factors that might reduce susceptibility of bovids to IAV infection ( 9 ). Contemporary estimates of the seroprevalence of IAV antibodies in US cattle are not well described in the published literature. One retrospective serologic survey in the United States in the late 1990s showed 27% of serum samples had positive antibody titers and 31% had low-positive titers for IAV H1 subtype-specific antigen in cattle with no evidence of clinical infections ( 24 ). Antibody titers for H5 subtype-specific antigen have not been reported in US cattle.

The susceptibility of domestic cats to HPAI H5N1 is well-documented globally ( 10 – 12 , 25 – 28 ), and infection often results in neurologic signs in affected felids and other terrestrial mammals ( 4 ). Most cases in cats result from consuming infected wild birds or contaminated poultry products ( 12 , 27 ). The incubation period in cats is short; clinical disease is often observed 2–3 days after infection ( 28 ). Brain tissue has been suggested as the best diagnostic sample to confirm HPAI virus infection in cats ( 10 ), and our results support that finding. One unique finding in the cats from this report is the presence of blindness and microscopic lesions of chorioretinitis. Those results suggest that further investigation into potential ocular manifestations of HPAI H5N1 virus infection in cats might be warranted.

The genomic sequencing and subsequent analysis of clinical samples from both bovine and feline sources provided considerable insights. The HA and NA sequences derived from both bovine milk and cat tissue samples from different Texas farms had a notable degree of similarity. Those findings strongly suggest a shared origin for the viruses detected in the dairy cattle and cat tissues. Further research, case series investigations, and surveillance data are needed to better understand and inform measures to curtail the clinical effects, shedding, and spread of HPAI viruses among mammals. Although pasteurization of commercial milk mitigates risks for transmission to humans, a 2019 US consumer study showed that 4.4% of adults consumed raw milk > 1 time during the previous year ( 29 ), indicating a need for public awareness of the potential presence of HPAI H5N1 viruses in raw milk.

Ingestion of feed contaminated with feces from wild birds infected with HPAI virus is presumed to be the most likely initial source of infection in the dairy farms. Although the exact source of the virus is unknown, migratory birds (Anseriformes and Charadriiformes) are likely sources because the Texas panhandle region lies in the Central Flyway, and those birds are the main natural reservoir for avian influenza viruses ( 30 ). HPAI H5N1 viruses are well adapted to domestic ducks and geese, and ducks appear to be a major reservoir ( 31 ); however, terns have also emerged as an important source of virus spread ( 32 ). The mode of transmission among infected cattle is also unknown; however, horizontal transmission has been suggested because disease developed in resident cattle herds in Michigan, Idaho, and Ohio farms that received infected cattle from the affected regions, and those cattle tested positive for HPAI H5N1 ( 33 ). Experimental studies are needed to decipher the transmission routes and pathogenesis (e.g., replication sites and movement) of the virus within infected cattle.

In conclusion, we showed that dairy cattle are susceptible to infection with HPAI H5N1 virus and can shed virus in milk and, therefore, might potentially transmit infection to other mammals via unpasteurized milk. A reduction in milk production and vague systemic illness were the most commonly reported clinical signs in affected cows, but neurologic signs and death rapidly developed in affected domestic cats. HPAI virus infection should be considered in dairy cattle when an unexpected and unexplained abrupt drop in feed intake and milk production occurs and for cats when rapid onset of neurologic signs and blindness develop. The recurring nature of global HPAI H5N1 virus outbreaks and detection of spillover events in a broad host range is concerning and suggests increasing virus adaptation in mammals. Surveillance of HPAI viruses in domestic production animals, including cattle, is needed to elucidate influenza virus evolution and ecology and prevent cross-species transmission.

Dr. Burrough is a professor and diagnostic pathologist at the Iowa State University College of Veterinary Medicine and Veterinary Diagnostic Laboratory. His research focuses on infectious diseases of livestock with an emphasis on swine.

Acknowledgment

We thank the faculty and staff at the ISUVDL who contributed to the processing and analysis of clinical samples in this investigation, the veterinarians involved with clinical assessments at affected dairies and various conference calls in the days before diagnostic submissions that ultimately led to the detection of HPAI virus in the cattle, and the US Department of Agriculture National Veterinary Services Laboratory and NAHLN for their roles and assistance in providing their expertise, confirmatory diagnostic support, and communications surrounding the HPAI virus cases impacting lactating dairy cattle.

• Caliendo  V , Lewis  NS , Pohlmann  A , Baillie  SR , Banyard  AC , Beer  M , et al. Transatlantic spread of highly pathogenic avian influenza H5N1 by wild birds from Europe to North America in 2021. Sci Rep . 2022 ; 12 : 11729 . DOI PubMed Google Scholar
• Bevins  SN , Shriner  SA , Cumbee  JC Jr , Dilione  KE , Douglass  KE , Ellis  JW , et al. Intercontinental movement of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4 virus to the United States, 2021. Emerg Infect Dis . 2022 ; 28 : 1006 – 11 . DOI PubMed Google Scholar
• Puryear  W , Sawatzki  K , Hill  N , Foss  A , Stone  JJ , Doughty  L , et al. Highly pathogenic avian influenza A(H5N1) virus outbreak in New England seals, United States. Emerg Infect Dis . 2023 ; 29 : 786 – 91 . DOI PubMed Google Scholar
• Elsmo  EJ , Wünschmann  A , Beckmen  KB , Broughton-Neiswanger  LE , Buckles  EL , Ellis  J , et al. Highly pathogenic avian influenza A(H5N1) virus clade 2.3.4.4b infections in wild terrestrial mammals, United States, 2022. Emerg Infect Dis . 2023 ; 29 : 2451 – 60 . DOI PubMed Google Scholar
• Bruno  A , Alfaro-Núñez  A , de Mora  D , Armas  R , Olmedo  M , Garcés  J , et al. First case of human infection with highly pathogenic H5 avian influenza A virus in South America: a new zoonotic pandemic threat for 2023? J Travel Med. 2023 ;30:taad032.
• Pulit-Penaloza  JA , Brock  N , Belser  JA , Sun  X , Pappas  C , Kieran  TJ , et al. Highly pathogenic avian influenza A(H5N1) virus of clade 2.3.4.4b isolated from a human case in Chile causes fatal disease and transmits between co-housed ferrets. Emerg Microbes Infect . 2024 ; 2332667 . DOI PubMed Google Scholar
• United States Department of Agriculture Animal and Plant Health Inspection Service . Federal and state veterinary, public health agencies share update on HPAI detection in Kansas, Texas dairy herds. 2024 [ cited 2024 Mar 29 ]. https://www.aphis.usda.gov/news/agency-announcements/federal-state-veterinary-public-health-agencies-share-update-hpai
• Sharma  A , Zeller  MA , Souza  CK , Anderson  TK , Vincent  AL , Harmon  K , et al. Characterization of a 2016–2017 human seasonal H3 influenza A virus spillover now endemic to U.S. swine. MSphere . 2022 ; 7 : e0080921 . DOI PubMed Google Scholar
• Sreenivasan  CC , Thomas  M , Kaushik  RS , Wang  D , Li  F . Influenza A in bovine species: a narrative literature review. Viruses . 2019 ; 11 : 561 . DOI PubMed Google Scholar
• Sillman  SJ , Drozd  M , Loy  D , Harris  SP . Naturally occurring highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b infection in three domestic cats in North America during 2023. J Comp Pathol . 2023 ; 205 : 17 – 23 . DOI PubMed Google Scholar
• Klopfleisch  R , Wolf  PU , Uhl  W , Gerst  S , Harder  T , Starick  E , et al. Distribution of lesions and antigen of highly pathogenic avian influenza virus A/Swan/Germany/R65/06 (H5N1) in domestic cats after presumptive infection by wild birds. Vet Pathol . 2007 ; 44 : 261 – 8 . DOI PubMed Google Scholar
• Keawcharoen  J , Oraveerakul  K , Kuiken  T , Fouchier  RAM , Amonsin  A , Payungporn  S , et al. Avian influenza H5N1 in tigers and leopards. Emerg Infect Dis . 2004 ; 10 : 2189 – 91 . DOI PubMed Google Scholar
• Kuiken  T , Rimmelzwaan  G , van Riel  D , van Amerongen  G , Baars  M , Fouchier  R , et al. Avian H5N1 influenza in cats. Science . 2004 ; 306 : 241 . DOI PubMed Google Scholar
• Herfst  S , Schrauwen  EJA , Linster  M , Chutinimitkul  S , de Wit  E , Munster  VJ , et al. Airborne transmission of influenza A/H5N1 virus between ferrets. Science . 2012 ; 336 : 1534 – 41 . DOI PubMed Google Scholar
• Agüero  M , Monne  I , Sánchez  A , Zecchin  B , Fusaro  A , Ruano  MJ , et al. Highly pathogenic avian influenza A(H5N1) virus infection in farmed minks, Spain, October 2022. Euro Surveill . 2023 ; 28 : 2300001 . DOI PubMed Google Scholar
• Leguia  M , Garcia-Glaessner  A , Muñoz-Saavedra  B , Juarez  D , Barrera  P , Calvo-Mac  C , et al. Highly pathogenic avian influenza A (H5N1) in marine mammals and seabirds in Peru. Nat Commun . 2023 ; 14 : 5489 . DOI PubMed Google Scholar
• Saito  K . An outbreak of cattle influenza in Japan in the fall of 1949. J Am Vet Med Assoc . 1951 ; 118 : 316 – 9 . PubMed Google Scholar
• Gunning  RF , Pritchard  GC . Unexplained sporadic milk drop in dairy cows. Vet Rec . 1997 ; 140 : 488 . PubMed Google Scholar
• Brown  IH , Crawshaw  TR , Harris  PA , Alexander  DJ . Detection of antibodies to influenza A virus in cattle in association with respiratory disease and reduced milk yield. Vet Rec . 1998 ; 143 : 637 – 8 . PubMed Google Scholar
• Crawshaw  TR , Brown  I . Bovine influenza. Vet Rec . 1998 ; 143 : 372 . PubMed Google Scholar
• Gunning  RF , Brown  IH , Crawshaw  TR . Evidence of influenza A virus infection in dairy cows with sporadic milk drop syndrome. Vet Rec . 1999 ; 145 : 556 – 7 . DOI PubMed Google Scholar
• Crawshaw  TR , Brown  IH , Essen  SC , Young  SCL . Significant rising antibody titres to influenza A are associated with an acute reduction in milk yield in cattle. Vet J . 2008 ; 178 : 98 – 102 . DOI PubMed Google Scholar
• Lopez  JW , Woods  GT . Influenza virus in ruminants: a review. Res Commun Chem Pathol Pharmacol . 1984 ; 45 : 445 – 62 . PubMed Google Scholar
• Jones-Lang  K , Ernst-Larson  M , Lee  B , Goyal  SM , Bey  R . Prevalence of influenza A virus (H1N1) antibodies in bovine sera. New Microbiol . 1998 ; 21 : 153 – 60 . PubMed Google Scholar
• Briand  FX , Souchaud  F , Pierre  I , Beven  V , Hirchaud  E , Hérault  F , et al. Highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus in domestic cat, France, 2022. Emerg Infect Dis . 2023 ; 29 : 1696 – 8 . DOI PubMed Google Scholar
• Frymus  T , Belák  S , Egberink  H , Hofmann-Lehmann  R , Marsilio  F , Addie  DD , et al. Influenza virus infections in cats. Viruses . 2021 ; 13 : 1435 . DOI PubMed Google Scholar
• Songserm  T , Amonsin  A , Jam-on  R , Sae-Heng  N , Meemak  N , Pariyothorn  N , et al. Avian influenza H5N1 in naturally infected domestic cat. Emerg Infect Dis . 2006 ; 12 : 681 – 3 . DOI PubMed Google Scholar
• Thiry  E , Zicola  A , Addie  D , Egberink  H , Hartmann  K , Lutz  H , et al. Highly pathogenic avian influenza H5N1 virus in cats and other carnivores. Vet Microbiol . 2007 ; 122 : 25 – 31 . DOI PubMed Google Scholar
• Lando  AM , Bazaco  MC , Parker  CC , Ferguson  M . Characteristics of U.S. consumers reporting past year intake of raw (unpasteurized) milk: results from the 2016 Food Safety Survey and 2019 Food Safety and Nutrition Survey. J Food Prot . 2022 ; 85 : 1036 – 43 . DOI PubMed Google Scholar
• Fourment  M , Darling  AE , Holmes  EC . The impact of migratory flyways on the spread of avian influenza virus in North America. BMC Evol Biol . 2017 ; 17 : 118 . DOI PubMed Google Scholar
• Guan  Y , Smith  GJD . The emergence and diversification of panzootic H5N1 influenza viruses. Virus Res . 2013 ; 178 : 35 – 43 . DOI PubMed Google Scholar
• de Araújo  AC , Silva  LMN , Cho  AY , Repenning  M , Amgarten  D , de Moraes  AP , et al. Incursion of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus, Brazil, 2023. Emerg Infect Dis . 2024 ; 30 : 619 – 21 . DOI PubMed Google Scholar
• American Veterinary Medical Association . States with HPAI-infected dairy cows grows to six. USDA provides guidance for veterinarians, producers on protecting cattle from the virus. 2024 [ cited 2024 Apr 10 ]. https://www.avma.org/news/states-hpai-infected-dairy-cows-grows-six
• Figure 1 . Mammary gland lesions in cattle in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. A, B) Mammary...
• Figure 2 . Lesions in cat tissues in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Tissue sections were stained...
• Figure 3 . Phylogenetic analysis of hemagglutinin gene sequences in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Colors indicate...
• Figure 4 . Phylogenetic analysis of neuraminidase gene sequences in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Colors indicate...
• Table 1 . Microscopic lesions observed in cattle in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024
• Table 2 . Microscopic lesions observed in cats in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024
• Table 3 . PCR results from various specimens in study of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024

Suggested citation for this article : Burrough ER, Magstadt DR, Petersen B, Timmermans SJ, Gauger PC, Zhang J, et al. Highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Emerg Infect Dis. 2024 Jul [ date cited ]. https://doi.org/10.3201/eid3007.240508

DOI: 10.3201/eid3007.240508

Original Publication Date: April 29, 2024

Table of Contents – Volume 30, Number 7—July 2024

Please use the form below to submit correspondence to the authors or contact them at the following address:

Eric R. Burrough, Iowa State University Veterinary Diagnostic Laboratory, 1937 Christensen Dr, Ames, IA 50011, USA

Comment submitted successfully, thank you for your feedback.

There was an unexpected error. Message not sent.

Exit Notification / Disclaimer Policy

• The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
• Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
• You will be subject to the destination website's privacy policy when you follow the link.
• CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.

Metric Details

Article views: 43164.

Data is collected weekly and does not include downloads and attachments. View data is from .

What is the Altmetric Attention Score?

The Altmetric Attention Score for a research output provides an indicator of the amount of attention that it has received. The score is derived from an automated algorithm, and represents a weighted count of the amount of attention Altmetric picked up for a research output.