Organizing Your Social Sciences Research Assignments

  • Annotated Bibliography
  • Analyzing a Scholarly Journal Article
  • Group Presentations
  • Dealing with Nervousness
  • Using Visual Aids
  • Grading Someone Else's Paper
  • Types of Structured Group Activities
  • Group Project Survival Skills
  • Leading a Class Discussion
  • Multiple Book Review Essay
  • Reviewing Collected Works
  • Writing a Case Analysis Paper
  • Writing a Case Study
  • About Informed Consent
  • Writing Field Notes
  • Writing a Policy Memo
  • Writing a Reflective Paper
  • Writing a Research Proposal
  • Generative AI and Writing
  • Acknowledgments

Definition and Introduction

Journal article analysis assignments require you to summarize and critically assess the quality of an empirical research study published in a scholarly [a.k.a., academic, peer-reviewed] journal. The article may be assigned by the professor, chosen from course readings listed in the syllabus, or you must locate an article on your own, usually with the requirement that you search using a reputable library database, such as, JSTOR or ProQuest . The article chosen is expected to relate to the overall discipline of the course, specific course content, or key concepts discussed in class. In some cases, the purpose of the assignment is to analyze an article that is part of the literature review for a future research project.

Analysis of an article can be assigned to students individually or as part of a small group project. The final product is usually in the form of a short paper [typically 1- 6 double-spaced pages] that addresses key questions the professor uses to guide your analysis or that assesses specific parts of a scholarly research study [e.g., the research problem, methodology, discussion, conclusions or findings]. The analysis paper may be shared on a digital course management platform and/or presented to the class for the purpose of promoting a wider discussion about the topic of the study. Although assigned in any level of undergraduate and graduate coursework in the social and behavioral sciences, professors frequently include this assignment in upper division courses to help students learn how to effectively identify, read, and analyze empirical research within their major.

Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students make the most of Scholarly Articles." Library Management 33 (2012): 525-535.

Benefits of Journal Article Analysis Assignments

Analyzing and synthesizing a scholarly journal article is intended to help students obtain the reading and critical thinking skills needed to develop and write their own research papers. This assignment also supports workplace skills where you could be asked to summarize a report or other type of document and report it, for example, during a staff meeting or for a presentation.

There are two broadly defined ways that analyzing a scholarly journal article supports student learning:

Improve Reading Skills

Conducting research requires an ability to review, evaluate, and synthesize prior research studies. Reading prior research requires an understanding of the academic writing style , the type of epistemological beliefs or practices underpinning the research design, and the specific vocabulary and technical terminology [i.e., jargon] used within a discipline. Reading scholarly articles is important because academic writing is unfamiliar to most students; they have had limited exposure to using peer-reviewed journal articles prior to entering college or students have yet to gain exposure to the specific academic writing style of their disciplinary major. Learning how to read scholarly articles also requires careful and deliberate concentration on how authors use specific language and phrasing to convey their research, the problem it addresses, its relationship to prior research, its significance, its limitations, and how authors connect methods of data gathering to the results so as to develop recommended solutions derived from the overall research process.

Improve Comprehension Skills

In addition to knowing how to read scholarly journals articles, students must learn how to effectively interpret what the scholar(s) are trying to convey. Academic writing can be dense, multi-layered, and non-linear in how information is presented. In addition, scholarly articles contain footnotes or endnotes, references to sources, multiple appendices, and, in some cases, non-textual elements [e.g., graphs, charts] that can break-up the reader’s experience with the narrative flow of the study. Analyzing articles helps students practice comprehending these elements of writing, critiquing the arguments being made, reflecting upon the significance of the research, and how it relates to building new knowledge and understanding or applying new approaches to practice. Comprehending scholarly writing also involves thinking critically about where you fit within the overall dialogue among scholars concerning the research problem, finding possible gaps in the research that require further analysis, or identifying where the author(s) has failed to examine fully any specific elements of the study.

In addition, journal article analysis assignments are used by professors to strengthen discipline-specific information literacy skills, either alone or in relation to other tasks, such as, giving a class presentation or participating in a group project. These benefits can include the ability to:

  • Effectively paraphrase text, which leads to a more thorough understanding of the overall study;
  • Identify and describe strengths and weaknesses of the study and their implications;
  • Relate the article to other course readings and in relation to particular research concepts or ideas discussed during class;
  • Think critically about the research and summarize complex ideas contained within;
  • Plan, organize, and write an effective inquiry-based paper that investigates a research study, evaluates evidence, expounds on the author’s main ideas, and presents an argument concerning the significance and impact of the research in a clear and concise manner;
  • Model the type of source summary and critique you should do for any college-level research paper; and,
  • Increase interest and engagement with the research problem of the study as well as with the discipline.

Kershaw, Trina C., Jennifer Fugate, and Aminda J. O'Hare. "Teaching Undergraduates to Understand Published Research through Structured Practice in Identifying Key Research Concepts." Scholarship of Teaching and Learning in Psychology . Advance online publication, 2020; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students make the most of Scholarly Articles." Library Management 33 (2012): 525-535; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946.

Structure and Organization

A journal article analysis paper should be written in paragraph format and include an instruction to the study, your analysis of the research, and a conclusion that provides an overall assessment of the author's work, along with an explanation of what you believe is the study's overall impact and significance. Unless the purpose of the assignment is to examine foundational studies published many years ago, you should select articles that have been published relatively recently [e.g., within the past few years].

Since the research has been completed, reference to the study in your paper should be written in the past tense, with your analysis stated in the present tense [e.g., “The author portrayed access to health care services in rural areas as primarily a problem of having reliable transportation. However, I believe the author is overgeneralizing this issue because...”].

Introduction Section

The first section of a journal analysis paper should describe the topic of the article and highlight the author’s main points. This includes describing the research problem and theoretical framework, the rationale for the research, the methods of data gathering and analysis, the key findings, and the author’s final conclusions and recommendations. The narrative should focus on the act of describing rather than analyzing. Think of the introduction as a more comprehensive and detailed descriptive abstract of the study.

Possible questions to help guide your writing of the introduction section may include:

  • Who are the authors and what credentials do they hold that contributes to the validity of the study?
  • What was the research problem being investigated?
  • What type of research design was used to investigate the research problem?
  • What theoretical idea(s) and/or research questions were used to address the problem?
  • What was the source of the data or information used as evidence for analysis?
  • What methods were applied to investigate this evidence?
  • What were the author's overall conclusions and key findings?

Critical Analysis Section

The second section of a journal analysis paper should describe the strengths and weaknesses of the study and analyze its significance and impact. This section is where you shift the narrative from describing to analyzing. Think critically about the research in relation to other course readings, what has been discussed in class, or based on your own life experiences. If you are struggling to identify any weaknesses, explain why you believe this to be true. However, no study is perfect, regardless of how laudable its design may be. Given this, think about the repercussions of the choices made by the author(s) and how you might have conducted the study differently. Examples can include contemplating the choice of what sources were included or excluded in support of examining the research problem, the choice of the method used to analyze the data, or the choice to highlight specific recommended courses of action and/or implications for practice over others. Another strategy is to place yourself within the research study itself by thinking reflectively about what may be missing if you had been a participant in the study or if the recommended courses of action specifically targeted you or your community.

Possible questions to help guide your writing of the analysis section may include:

Introduction

  • Did the author clearly state the problem being investigated?
  • What was your reaction to and perspective on the research problem?
  • Was the study’s objective clearly stated? Did the author clearly explain why the study was necessary?
  • How well did the introduction frame the scope of the study?
  • Did the introduction conclude with a clear purpose statement?

Literature Review

  • Did the literature review lay a foundation for understanding the significance of the research problem?
  • Did the literature review provide enough background information to understand the problem in relation to relevant contexts [e.g., historical, economic, social, cultural, etc.].
  • Did literature review effectively place the study within the domain of prior research? Is anything missing?
  • Was the literature review organized by conceptual categories or did the author simply list and describe sources?
  • Did the author accurately explain how the data or information were collected?
  • Was the data used sufficient in supporting the study of the research problem?
  • Was there another methodological approach that could have been more illuminating?
  • Give your overall evaluation of the methods used in this article. How much trust would you put in generating relevant findings?

Results and Discussion

  • Were the results clearly presented?
  • Did you feel that the results support the theoretical and interpretive claims of the author? Why?
  • What did the author(s) do especially well in describing or analyzing their results?
  • Was the author's evaluation of the findings clearly stated?
  • How well did the discussion of the results relate to what is already known about the research problem?
  • Was the discussion of the results free of repetition and redundancies?
  • What interpretations did the authors make that you think are in incomplete, unwarranted, or overstated?
  • Did the conclusion effectively capture the main points of study?
  • Did the conclusion address the research questions posed? Do they seem reasonable?
  • Were the author’s conclusions consistent with the evidence and arguments presented?
  • Has the author explained how the research added new knowledge or understanding?

Overall Writing Style

  • If the article included tables, figures, or other non-textual elements, did they contribute to understanding the study?
  • Were ideas developed and related in a logical sequence?
  • Were transitions between sections of the article smooth and easy to follow?

Overall Evaluation Section

The final section of a journal analysis paper should bring your thoughts together into a coherent assessment of the value of the research study . This section is where the narrative flow transitions from analyzing specific elements of the article to critically evaluating the overall study. Explain what you view as the significance of the research in relation to the overall course content and any relevant discussions that occurred during class. Think about how the article contributes to understanding the overall research problem, how it fits within existing literature on the topic, how it relates to the course, and what it means to you as a student researcher. In some cases, your professor will also ask you to describe your experiences writing the journal article analysis paper as part of a reflective learning exercise.

Possible questions to help guide your writing of the conclusion and evaluation section may include:

  • Was the structure of the article clear and well organized?
  • Was the topic of current or enduring interest to you?
  • What were the main weaknesses of the article? [this does not refer to limitations stated by the author, but what you believe are potential flaws]
  • Was any of the information in the article unclear or ambiguous?
  • What did you learn from the research? If nothing stood out to you, explain why.
  • Assess the originality of the research. Did you believe it contributed new understanding of the research problem?
  • Were you persuaded by the author’s arguments?
  • If the author made any final recommendations, will they be impactful if applied to practice?
  • In what ways could future research build off of this study?
  • What implications does the study have for daily life?
  • Was the use of non-textual elements, footnotes or endnotes, and/or appendices helpful in understanding the research?
  • What lingering questions do you have after analyzing the article?

NOTE: Avoid using quotes. One of the main purposes of writing an article analysis paper is to learn how to effectively paraphrase and use your own words to summarize a scholarly research study and to explain what the research means to you. Using and citing a direct quote from the article should only be done to help emphasize a key point or to underscore an important concept or idea.

Business: The Article Analysis . Fred Meijer Center for Writing, Grand Valley State University; Bachiochi, Peter et al. "Using Empirical Article Analysis to Assess Research Methods Courses." Teaching of Psychology 38 (2011): 5-9; Brosowsky, Nicholaus P. et al. “Teaching Undergraduate Students to Read Empirical Articles: An Evaluation and Revision of the QALMRI Method.” PsyArXi Preprints , 2020; Holster, Kristin. “Article Evaluation Assignment”. TRAILS: Teaching Resources and Innovations Library for Sociology . Washington DC: American Sociological Association, 2016; Kershaw, Trina C., Jennifer Fugate, and Aminda J. O'Hare. "Teaching Undergraduates to Understand Published Research through Structured Practice in Identifying Key Research Concepts." Scholarship of Teaching and Learning in Psychology . Advance online publication, 2020; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Reviewer's Guide . SAGE Reviewer Gateway, SAGE Journals; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946; Gyuris, Emma, and Laura Castell. "To Tell Them or Show Them? How to Improve Science Students’ Skills of Critical Reading." International Journal of Innovation in Science and Mathematics Education 21 (2013): 70-80; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students Make the Most of Scholarly Articles." Library Management 33 (2012): 525-535.

Writing Tip

Not All Scholarly Journal Articles Can Be Critically Analyzed

There are a variety of articles published in scholarly journals that do not fit within the guidelines of an article analysis assignment. This is because the work cannot be empirically examined or it does not generate new knowledge in a way which can be critically analyzed.

If you are required to locate a research study on your own, avoid selecting these types of journal articles:

  • Theoretical essays which discuss concepts, assumptions, and propositions, but report no empirical research;
  • Statistical or methodological papers that may analyze data, but the bulk of the work is devoted to refining a new measurement, statistical technique, or modeling procedure;
  • Articles that review, analyze, critique, and synthesize prior research, but do not report any original research;
  • Brief essays devoted to research methods and findings;
  • Articles written by scholars in popular magazines or industry trade journals;
  • Pre-print articles that have been posted online, but may undergo further editing and revision by the journal's editorial staff before final publication; and
  • Academic commentary that discusses research trends or emerging concepts and ideas, but does not contain citations to sources.

Journal Analysis Assignment - Myers . Writing@CSU, Colorado State University; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36.

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Primary research involves collecting data about a given subject directly from the real world. This section includes information on what primary research is, how to get started, ethics involved with primary research and different types of research you can do. It includes details about interviews, surveys, observations, and analysis.

Analysis is a type of primary research that involves finding and interpreting patterns in data, classifying those patterns, and generalizing the results. It is useful when looking at actions, events, or occurrences in different texts, media, or publications. Analysis can usually be done without considering most of the ethical issues discussed in the overview, as you are not working with people but rather publicly accessible documents. Analysis can be done on new documents or performed on raw data that you yourself have collected.

Here are several examples of analysis:

  • Recording commercials on three major television networks and analyzing race and gender within the commercials to discover some conclusion.
  • Analyzing the historical trends in public laws by looking at the records at a local courthouse.
  • Analyzing topics of discussion in chat rooms for patterns based on gender and age.

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  • Finding and collecting documents.
  • Specifying criteria or patterns that you are looking for.
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Research Method

Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

  • Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
  • Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
  • Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
  • Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
  • Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
  • Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
  • Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
  • Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
  • Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

  • Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
  • Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
  • Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

  • How often do you use social media per day?
  • Less than 30 minutes
  • 30 minutes to 1 hour
  • 1 to 2 hours
  • 2 to 4 hours
  • More than 4 hours
  • Which social media platforms do you use?
  • Others (Please specify)
  • How often do you experience the following on social media?
  • Social comparison (comparing yourself to others)
  • Cyberbullying
  • Fear of Missing Out (FOMO)
  • Have you ever experienced any of the following mental health problems in the past month?
  • Do you think social media use has a positive or negative impact on your mental health?
  • Very positive
  • Somewhat positive
  • Somewhat negative
  • Very negative
  • In your opinion, which factors contribute to the negative impact of social media on mental health?
  • Social comparison
  • In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
  • Education on healthy social media use
  • Counseling for mental health problems caused by social media
  • Social media detox programs
  • Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

  • Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
  • Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
  • Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
  • Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
  • Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

  • For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
  • For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
  • To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
  • To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

  • To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
  • To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
  • To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
  • To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

  • Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
  • Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
  • Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
  • Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
  • Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
  • Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

  • Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
  • Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
  • Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
  • Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
  • Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
  • Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

  • Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
  • Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
  • Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
  • Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
  • Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
  • Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

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Analysis in Research Papers

To analyze means to break a topic or concept down into its parts in order to inspect and understand it, and to restructure those parts in a way that makes sense to you. In an analytical research paper, you do research to become an expert on a topic so that you can restructure and present the parts of the topic from your own perspective.

For example, you could analyze the role of the mother in the ancient Egyptian family. You could break down that topic into its parts--the mother's duties in the family, social status, and expected role in the larger society--and research those parts in order to present your general perspective and conclusion about the mother's role.

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Analyzing the Differences: Research Paper vs. Analysis Paper

This article seeks to analyze the differences between two types of writing – research papers and analysis papers. While both require a similar level of thought, each type requires a different approach when it comes to researching and presenting information. Through an examination of the respective characteristics that distinguish these forms of writing from one another, we can gain valuable insight into what factors make for effective written communication in either format. Furthermore, by considering how best to utilize these features within our own work, we can enhance its overall quality and effectiveness in conveying our ideas and messages accurately.

I. Introduction: Exploring the Distinction between a Research Paper and an Analysis Paper

Ii. understanding the purpose of a research paper, iii. defining elements of analysing in an analysis paper, iv. identifying common formats for writing each type of paper.

  • V. Assessing Sources Appropriate to Use for each Kind of Assignment
  • VI. Examining Strategies Used by Writers When Composing either Type of Document

VII. Conclusion: Analyzing the Key Differences Between A Research and An Analysis Paper

Understanding the Variance in Research and Analysis Papers

It is essential to understand how research papers and analysis papers differ, as many of their features can be easily confused. They are both academic documents used for assessment or scholarly communication, but they present information differently. The most notable distinction between them lies in the presentation of evidence: while a research paper relies on facts gathered from an extensive background search, an analysis paper takes this data further by exploring deeper implications that provide greater insight into the topic at hand.

The first step when writing either type of document is proper organization; structure is key to getting your point across accurately and effectively. When constructing a research paper you must maintain objectivity with clear explanations supported by accurate sources; conversely, an analysis involves interpretation rather than straightforward facts – so strong reasoning skills should take precedence here as well. In addition to providing reliable arguments based upon sound logic throughout your composition, there are other areas where these two forms vary substantially including content length and depth of discussion required around each issue addressed within them respectively.

  • Research Paper:
  • >May be longer (5-10 pages)

Research Papers vs Analysis Papers

At first glance, the terms research paper and analysis paper may appear interchangeable. However, these two types of writing projects have distinct purposes that must be understood before starting any project. A research paper involves a deep dive into a particular subject to uncover new facts or data while an analysis paper uses those facts and data in order to form an argument.

When conducting research for a research paper, it is important to source information from reliable sources such as academic journals and books written by professionals on the topic at hand. With this knowledge, authors are then able to generate their own original ideas regarding the researched material which can further inform their findings in additional ways than what was originally found through researching existing literature on said topics. This newfound understanding can provide insight into different interpretations of similar material which adds depth and understanding beyond simply recounting someone else’s work; it provides readers with various perspectives based off objective fact-finding methods rather than personal opinion or bias towards one side over another.

In contrast, when writing an analysis essay all of this prior contextual information serves only as evidence that informs your conclusion – not necessarily as primary content within your argument itself; meaning instead you should focus on organizing these pieces of evidence provided alongside relevant examples/data (elements like logos & ethos) with well structured statements designed around persuasively conveying your perspective(s). Additionally depending upon who you’re attempting to reach via said piece you should also seek out counterarguments along with rebuttals so that any audience reading feels both informed and engaged throughout each part of its composition without feeling bias coming through too strongly either way at times too – resulting in effective arguments more akin most closely resembling judicial decisions rather than complex philosophical musings about life!

Exploring the Different Types of Analysis Papers

When writing an analysis paper, it’s important to understand that there are two primary types: research papers and analytical papers. Research papers present information about a specific topic through investigation, while analytical papers focus more on exploring and breaking down a concept or idea into its components in order to explain how they work together. Each type serves different purposes depending upon the scope of the assignment; however, both share some common elements.

The defining elements for analyzing in an analysis paper include gathering relevant data related to the topic at hand, evaluating this data objectively with logical reasoning processes such as deductive thinking methods, researching evidence-based sources for further clarification and validation of points being made within the paper itself. Further understanding can be gained by constructing strong arguments based on supportive evidence that has been collected from reliable source material. Ultimately any conclusions should be drawn from these objective evaluations and supported with thorough research so as not to bias opinion when forming argumentative claims throughout one’s essay.

When writing papers, the formatting and content of each document may vary based on its purpose. To ensure your paper is correctly formatted, it’s important to consider which type you are creating. Here are two popular formats for different types of documents:

  • Research Papers:
  • Analysis Papers:

In conclusion, there are a number of key differences between research and analysis papers. Research focuses on investigating existing knowledge from primary and secondary sources while analysis centers around interpretation of the collected information to generate new ideas or draw specific conclusions. A research paper involves extensive literature review which helps build an understanding for further investigation into a topic, whereas an analysis paper requires one to delve deeper into data in order to dissect patterns that may exist within it.

When creating either type of document, researchers should be sure they approach the task with the right mindset: when researching ask “what has been said”; when analyzing ask “how does this change what we know?” To truly understand both concepts fully is paramount for successful outcomes – whether it is uncovering trends through statistical methods or writing compelling essays based on evidence found from credible sources.

The analysis of the differences between research papers and analysis papers has been explored in great detail, providing useful insights for readers. From outlining the characteristics of each type to highlighting the appropriate purpose for each paper, this article has provided a comprehensive look at how these two types of writing differ from one another. Furthermore, it is important that students recognize when an assignment calls for a research paper or an analysis paper so they can successfully meet their academic requirements. Ultimately, with all this information now available to them regarding analyzing the differences between research papers and analytical papers, students should be well-equipped to tackle any task ahead of them!

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How to Write an Analysis

Last Updated: January 31, 2023 Fact Checked

This article was co-authored by Christopher Taylor, PhD and by wikiHow staff writer, Megaera Lorenz, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. There are 14 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 283,631 times.

An analysis is a piece of writing that looks at some aspect of a document in detail. To write a good analysis, you’ll need to ask yourself questions that focus on how and why the document works the way it does. You can start the process by gathering information about the subject of your analysis and defining the questions your analysis will answer. Once you’ve outlined your main arguments, look for specific evidence to support them. You can then work on putting your analysis together into a coherent piece of writing.

Gathering Information and Building Your Argument

Step 1 Review your assignment carefully.

  • If your analysis is supposed to answer a specific question or focus on a particular aspect of the document you are analyzing.
  • If there are any length or formatting requirements for the analysis.
  • The citation style your instructor wants you to use.
  • On what criteria your instructor will evaluate your analysis (e.g., organization, originality, good use of references and quotations, or correct spelling and grammar).

Step 2 Gather basic information about the subject of your analysis.

  • The title of the document (if it has one).
  • The name of the creator of the document. For example, depending on the type of document you’re working with, this could be the author, artist, director, performer, or photographer.
  • The form and medium of the document (e.g., “Painting, oil on canvas”).
  • When and where the document was created.
  • The historical and cultural context of the work.

Step 3 Do a close reading of the document and take notes.

  • Who you believe the intended audience is for the advertisement.
  • What rhetorical choices the author made to persuade the audience of their main point.
  • What product is being advertised.
  • How the poster uses images to make the product look appealing.
  • Whether there is any text in the poster, and, if so, how it works together with the images to reinforce the message of the ad.
  • What the purpose of the ad is or what its main point is.

Step 4 Determine which question(s) you would like to answer with your analysis.

  • For example, if you’re analyzing an advertisement poster, you might focus on the question: “How does this poster use colors to symbolize the problem that the product is intended to fix? Does it also use color to represent the beneficial results of using the product?”

Step 5 Make a list of your main arguments.

  • For example, you might write, “This poster uses the color red to symbolize the pain of a headache. The blue elements in the design represent the relief brought by the product.”
  • You could develop the argument further by saying, “The colors used in the text reinforce the use of colors in the graphic elements of the poster, helping the viewer make a direct connection between the words and images.”

Step 6 Gather evidence and examples to support your arguments.

  • For example, if you’re arguing that the advertisement poster uses red to represent pain, you might point out that the figure of the headache sufferer is red, while everyone around them is blue. Another piece of evidence might be the use of red lettering for the words “HEADACHE” and “PAIN” in the text of the poster.
  • You could also draw on outside evidence to support your claims. For example, you might point out that in the country where the advertisement was produced, the color red is often symbolically associated with warnings or danger.

Tip: If you’re analyzing a text, make sure to properly cite any quotations that you use to support your arguments. Put any direct quotations in quotation marks (“”) and be sure to give location information, such as the page number where the quote appears. Additionally, follow the citation requirements for the style guide assigned by your instructor or one that's commonly used for the subject matter you're writing about.

Organizing and Drafting Your Analysis

Step 1 Write a brief...

  • For example, “The poster ‘Say! What a relief,’ created in 1932 by designer Dorothy Plotzky, uses contrasting colors to symbolize the pain of a headache and the relief brought by Miss Burnham’s Pep-Em-Up Pills. The red elements denote pain, while blue ones indicate soothing relief.”

Tip: Your instructor might have specific directions about which information to include in your thesis statement (e.g., the title, author, and date of the document you are analyzing). If you’re not sure how to format your thesis statement or topic sentence, don’t hesitate to ask.

Step 2 Create an outline...

  • a. Background
  • ii. Analysis/Explanation
  • iii. Example
  • iv. Analysis/Explanation
  • III. Conclusion

Step 3 Draft an introductory paragraph.

  • For example, “In the late 1920s, Kansas City schoolteacher Ethel Burnham developed a patent headache medication that quickly achieved commercial success throughout the American Midwest. The popularity of the medicine was largely due to a series of simple but eye-catching advertising posters that were created over the next decade. The poster ‘Say! What a relief,’ created in 1932 by designer Dorothy Plotzky, uses contrasting colors to symbolize the pain of a headache and the relief brought by Miss Burnham’s Pep-Em-Up Pills.”

Step 4 Use the body of the essay to present your main arguments.

  • Make sure to include clear transitions between each argument and each paragraph. Use transitional words and phrases, such as “Furthermore,” “Additionally,” “For example,” “Likewise,” or “In contrast . . .”
  • The best way to organize your arguments will vary based on the individual topic and the specific points you are trying to make. For example, in your analysis of the poster, you might start with arguments about the red visual elements and then move on to a discussion about how the red text fits in.

Step 5 Compose a conclusion...

  • For example, you might end your essay with a few sentences about how other advertisements at the time might have been influenced by Dorothy Plotzky’s use of colors.

Step 6 Avoid presenting your personal opinions on the document.

  • For example, in your discussion of the advertisement, avoid stating that you think the art is “beautiful” or that the advertisement is “boring.” Instead, focus on what the poster was supposed to accomplish and how the designer attempted to achieve those goals.

Polishing Your Analysis

Step 1 Check that the organization of your analysis makes sense.

  • For example, if your essay currently skips around between discussions of the red and blue elements of the poster, consider reorganizing it so that you discuss all the red elements first, then focus on the blue ones.

Step 2 Look for areas where you might clarify your writing or add details.

  • For example, you might look for places where you could provide additional examples to support one of your major arguments.

Step 3 Cut out any irrelevant passages.

  • For example, if you included a paragraph about Dorothy Plotzky’s previous work as a children’s book illustrator, you may want to cut it if it doesn’t somehow relate to her use of color in advertising.
  • Cutting material out of your analysis may be difficult, especially if you put a lot of thought into each sentence or found the additional material really interesting. Your analysis will be stronger if you keep it concise and to the point, however.

Step 4 Proofread your writing and fix any errors.

  • You may find it helpful to have someone else go over your essay and look for any mistakes you might have missed.

Tip: When you’re reading silently, it’s easy to miss typos and other small errors because your brain corrects them automatically. Reading your work out loud can make problems easier to spot.

Sample Analysis Outline and Conclusion

what is a research analysis paper

Expert Q&A

Christopher Taylor, PhD

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  • ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-make-sure-i-understand-an-assignment-.html
  • ↑ https://www.bucks.edu/media/bcccmedialibrary/pdf/HOWTOWRITEALITERARYANALYSISESSAY_10.15.07_001.pdf
  • ↑ https://owl.purdue.edu/owl/general_writing/visual_rhetoric/analyzing_visual_documents/elements_of_analysis.html
  • ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-can-i-create-stronger-analysis-.html
  • ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-decide-what-i-should-argue-.html
  • ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-effectively-integrate-textual-evidence-.html
  • ↑ https://writingcenter.uagc.edu/writing-a-thesis
  • ↑ https://owl.purdue.edu/owl/general_writing/visual_rhetoric/analyzing_visual_documents/organizing_your_analysis.html
  • ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-write-an-intro--conclusion----body-paragraph.html
  • ↑ http://utminers.utep.edu/omwilliamson/engl0310/Textanalysis.htm
  • ↑ https://owl.purdue.edu/owl/graduate_writing/graduate_writing_topics/graduate_writing_organization_structure_new.html
  • ↑ https://owl.purdue.edu/owl/general_writing/mechanics/sentence_clarity.html
  • ↑ https://writingcenter.unc.edu/tips-and-tools/conciseness-handout/
  • ↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/

About This Article

Christopher Taylor, PhD

If you need to write an analysis, first look closely at your assignment to make sure you understand the requirements. Then, gather background information about the document you’ll be analyzing and do a close read so that you’re thoroughly familiar with the subject matter. If it’s not already specified in your assignment, come up with one or more specific question’s you’d like your analysis to answer, then outline your main arguments. Finally, gather evidence and examples to support your arguments. Read on to learn how to organize, draft, and polish your analysis! Did this summary help you? Yes No

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Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

  • 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

  • 📝 Analysis Example
  • 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

This image shows the main sections of a research article.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

  • Why was the research carried out?
  • What gaps does it try to fill, or what controversies to settle?
  • How does the study contribute to its field?
  • Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

  • Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
  • Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
  • Is the study biased in any way?
  • What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

  • What are the major findings of the study?
  • Did the author present the results clearly and unambiguously?
  • Are the findings statistically significant ?
  • Does the author provide sufficient information on the validity and reliability of the results?
  • Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

  • What findings did the authors use to support their conclusions?
  • How do the researchers’ conclusions compare to other studies’ findings?
  • How does this study contribute to its field?
  • What future research directions do the authors suggest?
  • What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

This image shows how to analyze a research article.

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

  • The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
  • The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
  • Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

  • The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
  • Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
  • The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

  • Analyze whether the study’s purpose is significant and relevant.
  • Examine whether the chosen methodology allows for answering the research questions.
  • Evaluate how the authors presented the results.
  • Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

This image shows how to write a research analysis.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

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Data Analysis in Research: Types & Methods

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Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

  • Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
  • Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
  • Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

  • Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
  • Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
  • Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
  • Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

  • Fraud: To ensure an actual human being records each response to the survey or the questionnaire
  • Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
  • Procedure: To ensure ethical standards were maintained while collecting the data sample
  • Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

  • Count, Percent, Frequency
  • It is used to denote home often a particular event occurs.
  • Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

  • Mean, Median, Mode
  • The method is widely used to demonstrate distribution by various points.
  • Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

  • Range, Variance, Standard deviation
  • Here the field equals high/low points.
  • Variance standard deviation = difference between the observed score and mean
  • It is used to identify the spread of scores by stating intervals.
  • Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

  • Percentile ranks, Quartile ranks
  • It relies on standardized scores helping researchers to identify the relationship between different scores.
  • It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

  • Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
  • Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

  • Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
  • Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
  • Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
  • Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
  • Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

LEARN ABOUT: Best Data Collection Tools

  • The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
  • Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
  • The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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  • Korean J Anesthesiol
  • v.71(2); 2018 Apr

Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

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Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

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Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

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Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

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Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

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Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

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Object name is kjae-2018-71-2-103f6.jpg

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

Research Analysis Paper: How to Analyze a Research Article [2024]

Do you need to write a research analysis paper but have no idea how to do that? Then you’re in the right place.

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While completing this type of assignment, your key aim is to critically analyze a research article. An article from a serious scientific journal would be a good choice. You can analyze and interpret either quantitative or qualitative research.

Below, you’ll find a how-to guide on research analysis paper writing prepared by our experts. It contains outlining and formatting tips, topics, and examples of research articles analysis.

  • Scan the Paper
  • Examine the Content
  • Check the Format
  • Critique & Evaluate
  • ✅ Key Questions

🔗 References

🔎 how to analyze a research article.

This analysis will be beneficial for you since it develops your critical thinking and research skills. So, let us present the main steps that should be undertaken to read and evaluate the paper correctly.

Now, let’s figure out what an analysis paper should include. There are several essential elements the reader should identify:

  • logical reasons for conducting the study;
  • the description of the methodology applied in the research;
  • concise and clear report of the findings;
  • a logical conclusion based on the results.

You can use free paper samples for college students before you work with your own writing to get a feel of how the analyzing process goes.

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Step 1: Scan the Paper

First, briefly look through the found paper and evaluate whether it’s appropriate for your research. Scanning helps you to start the content analysis and get the general idea of the study.

To scan the paper effectively, follow these simple steps:

  • Get familiar with the title, abstract , and introduction . Carefully read these parts and make sure you got the author’s point.
  • Read the headings of each section and sub-section. But don’t spend time to get familiar with the content.
  • Look through the conclusions. Check the overall one and the last sentence of each section.
  • Scan the references. Have you read any of these sources before? Highlight them and decide whether they are appropriate for your research or not.

Have you completed these steps of your research paper’s critical analysis? Now, you should be able to answer these questions:

  • What kind of a paper is it (qualitative research, quantitative research, a case study, etc.)?
  • What is the research paper topic? How is it connected to your subject of study?
  • Do you feel like the findings and the conclusions are valid?
  • How can the source contribute to your study?
  • Is the paper clear and well-written?

After completing this step, you should have a clear image of the text’s general idea. Also, here you can decide whether the given paper is worth further examination.

Step 2: Examine the Content

The next step leads to a deeper understanding of the topic. Here, again, you can try the following course of action to take the maximum benefit from the evaluation of the source.

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  • Find the author’s thesis. A thesis statement is usually the last sentence of the introduction (or several sentences). It is an essential part of the paper since it reflects the author’s main point. Make sure you determined the thesis statement and understood it.
  • Consider the author’s arguments. How does the author support his position? What are the key arguments they present in their research paper? Are they logical? Evaluate whether the points are clear and concise enough for any reader to get. Do they support the author’s thesis?
  • Check the evidence. Try to find all the proof provided by the writer. A successful research paper should have valid evidence for every argument. These can be statistics, diagrams, facts taken from documentaries or books, experiments hold by researchers, etc.
  • Determine the limits of the study. An author is supposed to set limits to avoid making their research too broad. Find out what are the variables the writer relied on while determining the exact field of study. Keep them in mind when you decide whether the paper accomplished its goals within limits.
  • Establish the author’s perspective. What position does the author take? What methods are applied to prove the correctness of the writer’s point? Does it match with your opinion? Why/ why not?

Sometimes, even after the second step of evaluation, the writer’s perspective is not evident. What to do in this case? There are three scenarios:

  • Stop investigating the paper and hope that you will not need it for your research.
  • Read some background information on the given topic. Then, reread the paper. This might help you to comprehend the general idea.
  • Don’t give up and move on to the next step of the evaluation.

Step 3: Check the Format and Presentation

At this stage, analyze the research paper format and the general presentation of the arguments and facts. Start with the evaluation of the sentence levels. In the research paper, there should be a hierarchy of sentences. To trace the research paper structure, take a look at the tips:

  • First-level sentences. They include only general statements and present the ideas that will be explored further in the paper.
  • Middle-level sentences. These sentences summarize, give a narrower idea, and present specific arguments.
  • Deep-level sentences. They contain specific facts and evidence that correspond to the arguments stated in middle-level sentences.

Your research paper analysis should also include format evaluation. This task might be challenging unless you have the formatting style manual open in front of your eyes.

Figure out what citation style the author applied and check whether all the requirements are met. Here is a mini checklist you have to follow:

  • in-text citations
  • reference list
  • font style and size, spacing
  • abstract (if needed)
  • appendix (if needed)

Step 4: Critique & Evaluate

This step requires attention to every detail in the paper. Identify each of the author’s assumptions and question them. Do you agree with the author’s evidence? How would you support the arguments? What are your opinions regarding the author’s ideas?

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For starters:

Try to re-implement the entire paper from your perspective and see how your version differs from the initial work. This trick will help you to determine the strong and weak sides of the work.

Then, move on to criticism. An effective way to evaluate a research paper consists of asking the right questions and assessing the crucial aspects, like:

  • The author’s objective and whether it was reached. Did you get the author’s main idea? Did the writer reach their aim and explain the arguments in great detail? Remember that even if the reader is not majoring in the study field, they should understand the objective. Is there something that remained unclear for you? In your opinion, what is the cause of your inability to comprehend the material?
  • The role in the broader context. Make sure the author’s arguments and evidence sound adequately in the larger context. Do the writer’s ideas contradict social norms. If so, why? Also, check the sources the author uses for their research. Make sure they are reliable and not outdated.
  • Grammar and organization. A professional research paper should not contain any mistakes. Make sure the text is flawless regarding grammar and structure. The ideas have to follow the logical flow; the tone should be academic; the paper should include transitions, summaries should be on point (which is easier to achieve with the help of a paper summarizer ) and so on.
  • What the reader learns. The primary aim of an author is to deliver useful information to the reader. Did you, as a reader, find some new insights? Were they relevant and valuable? Consider whether you’ve read something similar before and how the data fit within limits set by the author.

✅ Research Analysis Paper: Key Questions

As you can see, the task requires a lot of time and effort. That is why we’ve prepared a list of questions you should ask while analyzing a research paper. Use them as a ground for critical reading and evaluation.

Research Article Analysis Topics

  • Research article analysis: Using Evidence-Based Practice to Prevent Ventilator-Associated Pneumonia .
  • Critical analysis of Seligman’s research article on post-traumatic stress disorder.
  • Analyze the article on the role of interprofessional communication in healthcare.
  • Examine the articles on the controversy of stem cell research.
  • Write a critical analysis of a research article on abortion .
  • Discuss a research article on nursing and proactive care program.
  • Analyze a quantitative research article on the efficiency of methods used in nursing education .
  • Critical analysis of the research article on the role of environmental biology.
  • Analysis of the articles about primary quantitative and qualitative research .
  • Evaluate Goeders and Guerin’s research on the connection between stress and drug use.
  • Study Angela F. Clark’s research article on the efficacy of a nursing education program.
  • Analyze the research article by Park, Nisch, and Baptiste examining the connection between immigrants’ mental health and the length of stay in the United States.
  • Discuss the scholarly articles researching the connection between obesity and depression.
  • Analysis of nursing research article on level of education .
  • Write a critical analysis of the scholarly article The Effect of Nurse Staffing on Patient Safety Outcomes .
  • Examine a recent research article on spinal cord injuries.
  • Analyze Ronald F. Wright’s research article examining the specifics of jury selection.
  • Study the article by McConnell et al. on the impact of domestic animals on human well-being.
  • Critical evaluation and analysis of the article on ethics and informed consent in research.
  • Analysis of a research article on preventing hospital falls .
  • Write an analysis of the research article studying the challenges of implementing research findings into practice in nursing.
  • Examine the article on the thrombosis process by Bruce Furie and Barbara C. Furie.
  • Analyze Mendenhall and Doherty’s research on a new diabetes management approach.
  • Qualitative research article critique.
  • Critical analysis of a research article on the effectiveness of drug round tabards .
  • Discuss quantitative research about the barriers to electronic commerce implementation.
  • Study the article Health Information Source Use by Jessica Gall Myrick and Michael Hendryx.
  • Analyze a research article by Lengyel et al. That studies the amount of sugar in school breakfast .
  • Write a critical analysis of the research studying the quality of pain management .
  • Examine the research article The Mental Health of Indigenous Peoples in Canada by Sarah E Nelson and Kathi Wilson.
  • Analysis of the article Development of a Proactive Care Program .
  • Study the article on nursing REST: Break Through to Resilience by Rajamohan et al.
  • Critically analyze the research article Quality Management in Healthcare: The Pivotal Desideratum .
  • Examine and interpret the academic article In Defense of the Randomized Controlled Trial by Rosen et al.
  • Write an analysis of a research article Cardiovascular Changes Resulting from Sexual Activity by Bispo, De Lima Lopes, and De Barros.
  • Study the topicality and consistency of Dillner’s article Obstetrician Suspended After Research Inquiry .
  • Critical analysis of research article on nosocomial pneumonia .
  • Discuss the methods used by Johanna Brenner in her research on intersections and class relations.
  • Analyze the research article by Ansari et al. examining the connection between type 2 diabetes and environmental factors.
  • Analysis of research article Nurses’ Perceptions of Research Utilization in a Corporate Health Care System .
  • Examine the importance of the research Effectiveness of Hand Hygiene Interventions in Reducing Illness Absence .
  • Analyze and interpret the article on the toolkit for postgraduate research supervisors by E. Blass & S. Bertone.
  • Discuss the utility and credibility of K. Than’s article A Brief History of Twin Studies .
  • Write a critical analysis of the article researching the current US gun policy and its effect on the rates of gun violence cases.
  • Analysis of articles on evidence-based prevention of surgical site infections.
  • Examine the research article Nurses’ Knowledge about Palliative Care by Etafa et al.
  • Analyze the research conducted by Sandelowski et al. on the stigmatization of HIV-positive women .
  • Discuss the theoretical framework and methodology of a research article on psychological studies .
  • Analysis of a research article about sports and creatine .
  • Study the presentation of research findings in the scholarly article Leadership Characteristics and Digital Transformation .

Congrats! Now you know how to write a research paper analysis. You are welcome to check out our writing tips available on the website and save a ton of time on your academic papers. Share the link with your peers who may need our advice as well.

  • An Introduction to Critical Analysis of Publications in Experimental Biomedical Science, the Research Paper in Basic Medical Sciences: K. Rangachari, modified by D.J. Crankshaw, McMaster University Honours Biology & Pharmacology Program
  • Critical Analysis Template: Keiran Rankin and Sara Wolfe, the Writing Centre, Thompson Rivers University
  • How to Read a Paper: S. Keshav, David R. Cheriton, School of Computer Science, the University of Waterloo
  • How to Read a Research Paper: School of Engineering and Applied Sciences, Harvard University
  • Reading Research Effectively, Organizing Your Social Sciences Research Paper: Research Guides at the University of Southern California
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Research Paper

29 December 2023

last updated

A research paper is a product of seeking information, analysis, human thinking, and time. Basically, when scholars want to get answers to questions, they start to search for information to expand, use, approve, or deny findings. In simple words, research papers are results of processes by considering writing works and following specific requirements. Besides, scientists research and expand many theories, developing social or technological aspects of human science. However, in order to write relevant papers, they need to know a definition of the research, structure, characteristics, and types.

Definition of What Is a Research Paper and Its Meaning

A research paper is a common assignment. It comes to a situation when students, scholars, and scientists need to answer specific questions by using sources. Basically, a research paper is one of the types of papers where scholars analyze questions or topics , look for secondary sources , and write papers on defined themes. For example, if an assignment is to write a research paper on some causes of global warming or any other topic, a person must write a research proposal on it, analyzing important points and credible sources . Although essays focus on personal knowledge, writing a research paper means analyzing sources by following academic standards. Moreover, scientists must meet the structure of research papers. Therefore, writers need to analyze their research paper topics , start to research, cover key aspects, process credible articles, and organize final studies properly.

The Structure of a Research Work

The structure of research papers depends on assignment requirements. In fact, when students get their assignments and instructions, they need to analyze specific research questions or topics, find reliable sources , and write final works. Basically, the structure of research papers consists of the abstract , outline , introduction , literature review , methodology, results , discussion, recommendations, limitations, conclusion , acknowledgments , and references. However, students may not include some of these sections because of assigned instructions that they have and specific types of research papers. For instance, if instructions of papers do not suppose to conduct real experiments, the methodology section can be skipped because of the data’s absence. In turn, the structure of the final work consists of:

research paper

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🔸 The First Part of a Research Study

Abstract or an executive summary means the first section of a research paper that provides the study’s purpose, research questions or suggestions, main findings with conclusions. Moreover, this paragraph of about 150 words should be written when the whole work is finished already. Hence, abstract sections should describe key aspects of studies, including discussions about the relevance of findings.

Outline serves as a clear map of the structure of a research study.

Introduction provides the main information on problem statements, the indication of methodology, important findings, and principal conclusion. Basically, this section of a research paper covers rationales behind the work or background research, explanation of the importance, defending its relevance, a brief description of experimental designs, defined research questions, hypotheses, or key aspects.

🔸 Literature Review and Research or Experiment

Literature Review is needed for the analysis of past studies or scholarly articles to be familiar with research questions or topics. Hence, this section summarizes and synthesizes arguments and ideas from scholarly sources without adding new contributions. In turn, this part is organized around arguments or ideas, not sources.

Methodology or Materials and Methods covers explanations of research designs. Basically, techniques for gathering information and other aspects related to experiments must be described in a research paper. For instance, students and scholars document all specialized materials and general procedures. In this case, individuals may use some or all of the methods in further studies or judge the scientific merit of the work. Moreover, scientists should explain how they are going to conduct their experiments.

Results mean the gained information or data after the research or experiment. Basically, scholars should present and illustrate their findings. Moreover, this section may include tables or figures.

🔸 Analysis of Findings

Discussion is a section of a research paper where scientists review the information in the introduction part, evaluate gained results, or compare it with past studies. In particular, students and scholars interpret gained data or findings in appropriate depth. For example, if results differ from expectations at the beginning, scientists should explain why that may have happened. However, if results agree with rationales, scientists should describe theories that the evidence is supported.

Recommendations take its roots from a discussion section where scholars propose potential solutions or new ideas based on obtained results in a research paper. In this case, if scientists have any recommendations on how to improve this research so that other scholars can use evidence in further studies, they must write what they think in this section.

Limitations mean a consideration of research weaknesses and results to get new directions. For instance, if researchers found any limitations of studies that could affect experiments, scholars must not use such knowledge because of the same mistakes. Moreover, scientists should avoid contradicting results, and, even more, they must write it in this section.

🔸 The Final Part of a Conducted Research

Conclusion includes final claims of a research paper based on findings. Basically, this section covers final thoughts and the summary of the whole work. Moreover, this section may be used instead of limitations and recommendations that would be too small by themselves. In this case, scientists do not need to use headings for recommendations and limitations. Also, check out conclusion examples .

Acknowledgments or Appendix may take different forms, from paragraphs to charts. In this section, scholars include additional information on a research paper.

References mean a section where students, scholars, or scientists provide all used sources by following the format and academic rules.

Research Characteristics

Any type of work must meet some standards. By considering a research paper, this work must be written accordingly. In this case, the main characteristics of research papers are the length, style, format, and sources. Firstly, the length of research work defines the number of needed sources to analyze. Then, the style must be formal and covers impersonal and inclusive language. In turn, the format means academic standards of how to organize final works, including its structure and norms. Finally, sources and their number define works as research papers because of the volume of analyzed information. Hence, these characteristics must be considered while writing research papers.

Types of Research Papers

In general, the length of assignments can be different because of instructions. For example, there are two main types of research papers, such as typical and serious works. Firstly, a typical research paper may include definitive, argumentative, interpretive, and other works. In this case, typical papers are from 2 to 10 pages, where students analyze research questions or specific topics. Then, a serious research study is the expanded version of typical works. In turn, the length of such a paper is more than 10 pages. Basically, such works cover a serious analysis with many sources. Therefore, typical and serious works are two types of research papers.

Typical Research Papers

Basically, typical research works depend on assignments, the number of sources, and the paper’s length. So, a typical research paper is usually a long essay with the analyzed evidence. For example, students in high school and colleges get such assignments to learn how to research and analyze topics. In this case, they do not need to conduct serious experiments with the analysis and calculation of data. Moreover, students must use the Internet or libraries in searching for credible secondary sources to find potential answers to specific questions. As a result, students gather information on topics and learn how to take defined sides, present unique positions, or explain new directions. Hence, typical research papers require an analysis of primary and secondary sources without serious experiments or data.

Serious Research Studies

Although long papers require a lot of time for finding and analyzing credible sources, real experiments are an integral part of research work. Firstly, scholars at universities need to analyze the information from past studies to expand or disapprove of researched topics. Then, if scholars want to prove specific positions or ideas, they must get real evidence. In this case, experiments can be surveys, calculations, or other types of data that scholars do personally. Moreover, a dissertation is a typical serious research paper that young scientists write based on the research analysis of topics, data from conducted experiments, and conclusions at the end of work. Thus, serious research papers are studies that take a lot of time, analysis of sources with gained data, and interpretation of results.

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  • Knowledge Base

The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

  • Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
  • Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
  • Null hypothesis: Parental income and GPA have no relationship with each other in college students.
  • Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

  • In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
  • In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
  • In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

  • In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
  • In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
  • In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
  • Experimental
  • Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

  • Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
  • Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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Population vs sample

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

  • Probability sampling: every member of the population has a chance of being selected for the study through random selection.
  • Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

  • your sample is representative of the population you’re generalizing your findings to.
  • your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

  • Will you have resources to advertise your study widely, including outside of your university setting?
  • Will you have the means to recruit a diverse sample that represents a broad population?
  • Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

  • Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
  • Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
  • Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
  • Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

  • Organizing data from each variable in frequency distribution tables .
  • Displaying data from a key variable in a bar chart to view the distribution of responses.
  • Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

  • Mode : the most popular response or value in the data set.
  • Median : the value in the exact middle of the data set when ordered from low to high.
  • Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

  • Range : the highest value minus the lowest value of the data set.
  • Interquartile range : the range of the middle half of the data set.
  • Standard deviation : the average distance between each value in your data set and the mean.
  • Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

  • Estimation: calculating population parameters based on sample statistics.
  • Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

  • A point estimate : a value that represents your best guess of the exact parameter.
  • An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

  • A test statistic tells you how much your data differs from the null hypothesis of the test.
  • A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

  • Comparison tests assess group differences in outcomes.
  • Regression tests assess cause-and-effect relationships between variables.
  • Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

  • A simple linear regression includes one predictor variable and one outcome variable.
  • A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

  • A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
  • A z test is for exactly 1 or 2 groups when the sample is large.
  • An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

  • If you have only one sample that you want to compare to a population mean, use a one-sample test .
  • If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
  • If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
  • If you expect a difference between groups in a specific direction, use a one-tailed test .
  • If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

  • a t value (test statistic) of 3.00
  • a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

  • a t value of 3.08
  • a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval

Methodology

  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hostile attribution bias
  • Affect heuristic

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  • 12 February 2024

China conducts first nationwide review of retractions and research misconduct

  • Smriti Mallapaty

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Technicians wearing full PPE work in a lab

The reputation of Chinese science has been "adversely affected" by the number of retractions in recent years, according to a government notice. Credit: Qilai Shen/Bloomberg/Getty

Chinese universities are days away from the deadline to complete a nationwide audit of retracted research papers and probe of research misconduct. By 15 February, universities must submit to the government a comprehensive list of all academic articles retracted from English- and Chinese-language journals in the past three years. They need to clarify why the papers were retracted and investigate cases involving misconduct, according to a 20 November notice from the Ministry of Education’s Department of Science, Technology and Informatization.

The government launched the nationwide self-review in response to Hindawi, a London-based subsidiary of the publisher Wiley, retracting a large number of papers by Chinese authors. These retractions, along with those from other publishers, “have adversely affected our country’s academic reputation and academic environment”, the notice states.

A Nature analysis shows that last year, Hindawi issued more than 9,600 retractions, of which the vast majority — about 8,200 — had a co-author in China. Nearly 14,000 retraction notices, of which some three-quarters involved a Chinese co-author, were issued by all publishers in 2023.

This is “the first time we’ve seen such a national operation on retraction investigations”, says Xiaotian Chen, a library and information scientist at Bradley University in Peoria, Illinois, who has studied retractions and research misconduct in China. Previous investigations have largely been carried out on a case-by-case basis — but this time, all institutions have to conduct their investigations simultaneously, says Chen.

Tight deadline

The ministry’s notice set off a chain of alerts, cascading to individual university departments. Bulletins posted on university websites required researchers to submit their retractions by a range of dates, mostly in January — leaving time for universities to collate and present the data.

Although the alerts included lists of retractions that the ministry or the universities were aware of, they also called for unlisted retractions to be added.

what is a research analysis paper

More than 10,000 research papers were retracted in 2023 — a new record

According to Nature ’s analysis, which includes only English-language journals, more than 17,000 retraction notices for papers published by Chinese co-authors have been issued since 1 January 2021, which is the start of the period of review specified in the notice. The analysis, an update of one conducted in December , used the Retraction Watch database, augmented with retraction notices collated from the Dimensions database, and involved assistance from Guillaume Cabanac, a computer scientist at the University of Toulouse in France. It is unclear whether the official lists contain the same number of retracted papers.

Regardless, the timing to submit the information will be tight, says Shu Fei, a bibliometrics scientist at Hangzhou Dianzi University in China. The ministry gave universities less than three months to complete their self-review — and this was cut shorter by the academic winter break, which typically starts in mid-January and concludes after the Chinese New Year, which fell this year on 10 February.

“The timing is not good,” he says. Shu expects that universities are most likely to submit only a preliminary report of their researchers’ retracted papers included on the official lists.

But Wang Fei, who studies research-integrity policy at Dalian University of Technology in China, says that because the ministry has set a deadline, universities will work hard to submit their findings on time.

Researchers with retracted papers will have to explain whether the retraction was owing to misconduct, such as image manipulation, or an honest mistake, such as authors identifying errors in their own work, says Chen: “In other words, they may have to defend themselves.” Universities then must investigate and penalize misconduct. If a researcher fails to declare their retracted paper and it is later uncovered, they will be punished, according to the ministry notice. The cost of not reporting is high, says Chen. “This is a very serious measure.”

It is not known what form punishment might take, but in 2021, China’s National Health Commission posted the results of its investigations into a batch of retracted papers. Punishments included salary cuts, withdrawal of bonuses, demotions and timed suspensions from applying for research grants and rewards.

The notice states explicitly that the first corresponding author of a paper is responsible for submitting the response. This requirement will largely address the problem of researchers shirking responsibility for collaborative work, says Li Tang, a science- and innovation-policy researcher at Fudan University in Shanghai, China. The notice also emphasizes due process, says Tang. Researchers alleged to have committed misconduct have a right to appeal during the investigation.

The notice is a good approach for addressing misconduct, says Wang. Previous efforts by the Chinese government have stopped at issuing new research-integrity guidelines that were poorly implemented, she says. And when government bodies did launch self-investigations of published literature, they were narrower in scope and lacked clear objectives. This time, the target is clear — retractions — and the scope is broad, involving the entire university research community, she says.

“Cultivating research integrity takes time, but China is on the right track,” says Tang.

It is not clear what the ministry will do with the flurry of submissions. Wang says that, because the retraction notices are already freely available, publicizing the collated lists and underlying reasons for retraction could be useful. She hopes that a similar review will be conducted every year “to put more pressure” on authors and universities to monitor research integrity.

What happens next will reveal how seriously the ministry regards research misconduct, says Shu. He suggests that, if the ministry does not take further action after the Chinese New Year, the notice could be an attempt to respond to the reputational damage caused by the mass retractions last year.

The ministry did not respond to Nature ’s questions about the misconduct investigation.

Chen says that, regardless of what the ministry does with the information, the reporting process itself will help to curb misconduct because it is “embarrassing to the people in the report”.

But it might primarily affect researchers publishing in English-language journals. Retraction notices in Chinese-language journals are rare.

Nature 626 , 700-701 (2024)

doi: https://doi.org/10.1038/d41586-024-00397-x

Data analysis by Richard Van Noorden.

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This paper is in the following e-collection/theme issue:

Published on 22.2.2024 in Vol 26 (2024)

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Authors of this article:

Author Orcid Image

Original Paper

  • Dongxiao Gu 1 , PhD   ; 
  • Qin Wang 1 , MD   ; 
  • Yidong Chai 1 , PhD   ; 
  • Xuejie Yang 1 , PhD   ; 
  • Wang Zhao 1 , PhD   ; 
  • Min Li 1 , MD   ; 
  • Oleg Zolotarev 2 , PhD   ; 
  • Zhengfei Xu 1 , MD   ; 
  • Gongrang Zhang 1 , PhD  

1 School of Management, Hefei University of Technology, Hefei, China

2 Russian New University, Moscow, Russian Federation

Corresponding Author:

Dongxiao Gu, PhD

School of Management, Hefei University of Technology

193 Tunxi Road

Hefei, 230009

Phone: 86 13866167367

Email: [email protected]

Background: Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information.

Objective: This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR?

Methods: This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR.

Results: Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body’s immune system and lead to the development of allergies.

Conclusions: Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

Introduction

Over the past few decades, the prevalence of chronic diseases has increased significantly, becoming a global public health concern. The World Health Organization has listed allergic diseases as one of the disease types that require priority research and prevention in the 21st century [ 1 ]. As a common chronic disease, allergic rhinitis (AR) is a multifactorial disease that is induced by environmental conditions or certain genes [ 2 ]. AR not only has a significant impact on individuals’ sleep, social life, and work attendance but also triggers comorbidities such as conjunctivitis, atopic dermatitis, and asthma [ 3 ]. Large-scale flow survey data showed that AR currently affects several people in China alone [ 4 ] and with an estimated prevalence between 15% and 20% worldwide [ 5 ]. The direct and indirect costs associated with the management of AR are also a significant burden on society. For instance, the total cost of AR in Sweden, with a population of 9.5 million, was estimated at €1.3 (US $1.41) billion annually [ 6 ]. These unexpectedly high costs could be related to the high prevalence of disease, in combination with the previously often underestimated indirect costs that arise from reduced work efficiency and absenteeism and the potential costs associated with treating AR comorbidities [ 6 ].

Currently, there is no cure for AR, and individuals need to avoid the disease risk factors such as exposure to allergens and inhalation irritants [ 7 ] during the long self-management process. Therefore, identifying AR risk factors can provide a reference for patients to help reduce the condition in their daily lives [ 8 ].

A plethora of studies have been proposed to identify AR risk factors. These studies recruited participants with symptoms of AR and control participants without AR symptoms from a specific age group or a particular geographical area. These studies collected demographic information, lifestyle habits, family history, comorbidities, and residential areas through questionnaires. Subsequently, they used correlation methods to explore the relationship between these data and AR, aiming to identify the risk factors for AR within the specified age group or geographical area [ 9 ]. However, these studies have 2 limitations. First, these studies specifically target certain age groups or geographical areas, and questionnaires can only gather data on specific pieces of information. Owing to the constraints of questionnaire surveys, it is challenging to identify potential risk factors that may be present in individuals’ daily lives. As a result, the risk factors identified through survey-based studies have a limited scope and are incomplete. As such, they provide limited insights for a broader patient population. Second, the survey-based approach demands a commitment to long-term investigation and a substantial effort to collect representative responses [ 10 ]. In contrast, collecting information from social media platforms can cover large geographical areas at a comparatively low cost [ 10 ]. Social media platforms allow users to share experiences and opinions on various topics [ 11 , 12 ], including personal health issues [ 13 ]. Over time, highly unstructured and implicit knowledge has been generated in communities where users frequently participate [ 14 , 15 ], which can provide daily health records that are difficult to obtain from traditional questionnaire surveys. Therefore, social media can become a potential source of information for identifying risk factors for diseases such as AR [ 16 ].

Text-mining techniques are an effective tool for using voluminous social media data [ 17 ]. Some studies have combined social media data analysis to obtain knowledge about disease risk factors [ 18 , 19 ]. However, the abovementioned studies on disease risk factors used only shallow text features such as the number of social media text items and word cooccurrences, which are not conducive to identifying disease risk factors in the context of colloquial and diverse user expressions [ 20 ]. In this study, we designed a text-processing framework to automatically identify risk factors from social media data [ 21 ]. We used social media comments to construct a natural language processing–based AR risk factor identification method, aiming to tackle the problems of omission and low accuracy in traditional disease-related information identification methods that rely solely on shallow text features such as word frequency.

To be more specific, we developed an AR risk factor identification method that integrates pretrained word embeddings with text convolutional neural networks (CNNs). The Word2vec algorithm has proven to be superior in text vector representation [ 20 ]. This is a prediction-based approach that predicts the neighboring words that are most likely to appear within a window size around a center word in a corpus, resulting in high-dimensional vector representations that capture semantic aggregation. As social media users may mention related topics, such as symptoms and treatments, when describing risk factors in their comments, we used a local context window to achieve better semantic aggregation of AR risk factors, a method that has been demonstrated to be effective for such aggregation. In addition, using the Skip-gram model to train word pairs enables the incorporation of word thematic information, thus improving attention to risk factor phrases. The convolutional network can convolve the text in the word vector dimension and extract critical information through the max-pooling layer operation. In addition, this study used a clustering method with review mechanisms to concentrate on a large amount of text that contains risk factors within the observable range, thereby ensuring the usefulness of the content obtained through text mining.

Our main contributions were as follows:

  • First, this study proposed a framework (TopicS-ClusterREV) based on natural language processing for identifying the risk factors of AR. We used pretrained word embeddings and text convolutional networks to process social media text. Our model can identify more risk factors from social media comments with high accuracy and recall. To the best of our knowledge, this is the first study to use natural language processing techniques to identify risk factors for AR in social media comments.
  • Second, this study proposes a topic-enhanced word-embedding model. TopicS enhances the thematic information of words by adding a task that predicts the theme to which the center word belongs. This generates high-dimensional word vector representations with semantic aggregation and theme enhancement. We trained 2 types of word vectors using both the Skip-gram and TopicS models and separately input them into each risk factor classifier. The results showed that TopicS outperformed the baseline on the text classification task, demonstrating the effectiveness of our topic-enhanced word-embedding model.
  • Finally, we introduced automatic and manual review mechanisms to improve the single-pass algorithm, which allowed us to effectively identify and focus on a large amount of text that contains risk factors within the observable range. We ultimately identified 28 categories of risk factors including the common risk factors that lead to most individuals developing symptoms and previously overlooked risk factors that were not within the scope of previous research.

Identification of AR Risk Factors Through Surveys

AR has become a major global issue with a substantial increase in its prevalence in recent years. In Europe, the prevalence of AR among Danish adults progressively increased from 19% to 32% over the past 3 decades [ 22 ]. Understanding the risk factors, such as genetic, environmental, and lifestyle factors, helps in the management of AR, thus motivating many studies to focus on identifying potential risk factors. These studies are summarized in Table 1 . From Table 1 , we observed that the previous studies were based on survey methods, including cross-sectional surveys, cohort studies, and case-control studies.

a We searched for the literature related to AR risk factors and presented 9 papers from the past decade to showcase the methods and the identified risk factors.

These studies typically recruited participants with symptoms of AR and control participants without AR symptoms from a specific age group or a particular geographical area, collected demographic information through questionnaires, and then conducted correlation analysis, such as logistic regression, to explore the relationship between those metadata and AR [ 32 ]. For instance, Gao et al [ 9 ] conducted a cross-sectional survey to investigate the prevalence and risk factors of adult self-reported AR in the plain lands and hilly areas of Shenmu City in China and analyzed the differences between regions. The content of the web-based questionnaire included demographic factors, smoking status, the comorbidities of other allergic disorders, family history of allergies, and place of residence. The unconditional logistic regression analysis was used to screen for factors influencing AR. Finally, they found that the prevalence of AR existed in regional differences. Genetic and environmental factors were the important risk factors associated with AR. However, these studies have 2 limitations. First, these studies specifically targeted certain age groups or geographical areas, and questionnaires can only gather data on specific pieces of information. Owing to the constraints of questionnaire surveys, it is challenging to identify potential risk factors that may be present in individuals’ daily lives. As a result, the risk factors identified through survey-based studies have limited scope and are incomplete and they may provide limited insights for a broader patient population. Second, the survey-based approach demands a commitment to long-term investigation and a massive effort to collect representative responses [ 10 ].

Identification of Disease Risk Factors From Social Media Through Text Mining

Social media sites provide a convenient way for users to continuously update their day-to-day activities, which allows large groups of people to create and share information, opinions, and experiences about health conditions through web-based discussion [ 11 ]. Hence, social media can be considered a new data source to assess population health. As shown in Table 2 , some studies have combined text-mining techniques to classify and summarize voluminous social media data to obtain knowledge about chronic disease risk factors. Zhang and Ram [ 33 ] extracted behavioral features from Twitter posts of asthma users using keywords from an existing knowledge base. Griffis et al [ 34 ] collected 25,000 tweets containing and not containing diabetes, identified 5000 common words, used logistic regression to determine which common words were high-frequency expressions of diabetes, and finally grouped these high-frequency words using latent Dirichlet allocation to obtain the risk factors for diabetes. Schäfer et al [ 35 ] used syntactic analysis to identify portions of risk factors occurring before or after causal terms, grouped these portions using latent Dirichlet allocation, and obtained the risk factors for gastric discomfort. Pradeepa et al [ 19 ] performed clustering on stroke-related tweets using the Probability Neural Network, used the Apriori algorithm to identify frequent word sets related to risk, and thus identified risk factors for stroke [ 19 ]. In addition to the aforementioned approaches that use shallow text features such as keywords, frequent word sets, high-frequency words, and syntactic features for disease risk factor identification, other studies [ 36 - 38 ] trained risk factor classifiers using machine learning methods such as Naive Bayes, Maximum Entropy Model, and Naive Bayes Classifier–Term Frequency Inverse Document Frequency. These classifiers predict the presence of risk factors in text based on discrete vector representations such as bag-of-words and n-gram.

a We searched for studies related to identifying disease risk factors based on social media data. We found 7 papers from the past decade, highlighting the social media platforms, data, methods, features, diseases, and risk factors involved in research.

b LDA: latent Dirichlet allocation.

c MLP: multilayer perceptron.

The current methods for identifying disease risk factors on social media fall into 2 categories: shallow text feature methods and discrete word vector representations. Shallow text feature techniques often fail to capture important risk factors resulting in low accuracy, whereas discrete word vector approaches struggle to keep up with the dynamic vocabulary of social media text, missing new words, and trending expressions, thus inadequately representing the information conveyed.

Word Embedding and Text Classification Based on Deep Learning

Natural language processing technology promotes text analysis based on social media comments [ 39 ]; this technology can learn the deeper semantic features of the comment text and the features that are consistent with the current context, according to different training corpus, to input a better text vector representation for downstream classification tasks. Some researchers have used large-scale pretrained language models [ 40 ], global matrix decomposition [ 41 ], and local context windows [ 42 ] for text vector representation. Local context windows are more suitable for semantically aggregating AR risk factors [ 43 ]. Skip-gram and Continuous Bag-of-Words Model (CBOW) are prediction-based methods that learn the semantic representation of a center word by predicting the most likely neighboring words within a window size in a corpus. When users narrate risk factors in their comments, they may also mention symptoms, treatments, and other topics. These global contexts may dilute the key features of the risk factors expression. CBOW averages the context words to predict the target word and tends to predict high-frequency words in the corpus. In contrast, Skip-gram gives each word a chance to be a center word, making it better at predicting rare words compared with CBOW [ 44 ]. Therefore, in situations where social media users express a wide variety of ideas, the Skip-gram model can yield satisfactory outcomes. Moreover, the Skip-gram approach uses word pair training, which facilitates the incorporation of topic information into words [ 45 ], resulting in the generation of high-dimensional word vectors that feature semantic aggregation and topic enhancement. Therefore, we selected Skip-gram as the word-embedding model for our study.

Text classification has evolved to deep learning models, mainly including CNN-based models [ 46 ], recurrent neural network (RNN)–based models [ 47 ], and transformer models [ 48 ]. For the CNN algorithm, convolutional networks can convolve text on the word vector dimensions and extract key information through pooling layer operations. Consequently, this algorithm is capable of using essential data for classification tasks. Therefore, we used TextCNN for classifier training and evaluated the performance of RNN and transformer models on this task.

The framework used in this study consisted of 3 parts as shown in Figure 1 . The first part was data collection and processing, aimed at obtaining a clean data set. The second part was risk factor identification, which included the proposed TopicS method and training of a risk factor classifier. The implementation steps were as follows: (1) semiautomatically constructing a risk factor topic dictionary, (2) generating high-dimensional word vectors enhanced by TopicS-generated topics, and (3) vectorizing annotated text and training a risk factor classifier. The third part is text clustering and keyword extraction, which uses the ClusterREV method to cluster the identified risk factors and extract keywords from every category.

what is a research analysis paper

Zhihu is a Chinese social media platform where people discuss topics in an web-based forum format. In May 2022, the Zhihu subcommunity allergic rhinitis had 1.04 million discussions. The posts on this social media platform allow other users to comment [ 49 ], and people can explain their situations to provide support or seek help effectively. Therefore, these comments provide a rich source of data for investigating the risk factors reported by different users [ 50 ]. In this study, we trained domain-specific word representations based on experimental data. A relatively domain-specific input corpus [ 51 ] is better at extracting meaningful semantic relations than a generic pretrained language model [ 52 ]. We crawled all the data from May 2012 to May 2022 under the topic allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments, including the post ID, comment ID, and post and comment content.

In this study, we preprocessed the data through regularization, stop word removal, and word separation. First, we removed special symbols, such as URLs and emoticons, in the comments through regularization and stop word removal to reduce the interference of noise with the text analysis task. Then, we compiled a dictionary of 169 specialized terms, including types of AR, medications, and comorbidities, to reduce the probability of incorrect word segmentation. After word separation, we obtained a lexicon of 68,863 words and ranked the words according to the number of occurrences. We found that the top 10,000 words accounted for 94.83% of the total words, suggesting that many words recurred and a relatively simple word vector could effectively train the model [ 53 ]. This further confirms the efficacy of our decision to use Skip-gram as the foundational model.

We observed ultrashort comment noise in the comments (eg, “Thank you!”). It is important to note that these ultrashort comments do not include any personal medical information. The ultrashort comments were filtered, resulting in 33,039 valid comments. This operation can effectively minimize the impact of noise on downstream text classification tasks. Table S1 in Multimedia Appendix 1 presents the examples of valid comments.

The data must be labeled before supervised learning and then trained end to end. If a comment directly mentions an allergen or indicates a condition that leads to the appearance or worsening of symptoms, the comment will be labeled as 1, indicating the presence of risk factors, as shown in Figure 2 .

what is a research analysis paper

We randomly chose 2030 comments from the 33,039 comments, and 3 researchers labeled each comment as containing or not containing risk factors. To ensure high interannotator consistency, all 3 researchers annotated all 2030 comments. In cases with uncertainty in labeling, the 3 researchers discussed and arrived at a final label. After annotating and eliminating comments with religiously controversial content, 2000 labeled comments remained, consisting of 996 comments containing risk factors and 1004 comments not containing risk factors. The data set was divided into a 90% training set and a 10% test set. The 90% training set was further divided into 10 subsets, with 9 subsets used for training and the remaining subset used for validation, performing 10-fold cross-validation.

Topic Dictionary Construction

We used a combination of manual labeling and similarity calculation to identify keywords related to risk factors. Subsequently, we constructed a table of topic words using a semiautomated approach. The process of constructing the dictionary is depicted in Textbox 1 and is as follows: (1) label 400 randomly selected comments as described in the Annotation section, thereby obtaining 198 comments with risk factors; (2) extract risk factor phrases from annotated comments; (3) obtain risk factors topic word list; (4) remove duplicate word list, and the words in the current topic are used as seed words, word_set ; (5) use Skip-gram to find the top similar words to expand the topic words; (6) repeat steps 3 through 5 to expand the topic word; and (7) finally, obtain the topic words for the risk factor. A large weight was assigned to the risk factor theme words. Table S2 in Multimedia Appendix 1 shows examples of the risk factor topic dictionary.

Input: annotated comments

Output: topic dictionary

1. d i = Select Annotated data;

2. p i = Extract from d i

for w in p i :

list_i.append(w)

4. word_set=set(list)

5. for w in set: word_i.update(Skip-gram.mostsimilar(topn=n))

6. Loop step3, step4, step5

Ethical Considerations

As the use of text data from social media involves user privacy, this study adopted the following steps for deidentification: (1) We removed user account information and retained only anonymous comment information. (2) We used regular expressions to match and delete URLs and email addresses in the comments. (3) During the annotation process, annotators received only text that did not involve personal information. To evaluate the quality of deidentification, we randomly selected 500 text items for manual inspection and did not find any instances containing personal identity information. Our data are sourced from public discussions on Zhihu, a social media platform that can be accessed without registration. We followed strict ethical research protocols similar to the guidelines by Eysenbach and Till [ 54 ]. In addition, to protect the anonymity of participants, we have implemented measures including the removal of user information and avoiding verbatim quotations to prevent identification through search engines, protecting the privacy and security of personal data. It should be mentioned that our study was focused on the post level; we do not anticipate any negative ethical impact from our analysis.

Topic-Enhanced Word Embedding

TopicS performed 2 tasks during training, as shown in Figure 3 . The first task was to predict the neighboring words within the window of the central word. The second task was to predict the topic of the central word; the topic dictionary used for this purpose is described in the Topic Dictionary Construction section.

what is a research analysis paper

The specific formula calculations for the loss function design, parameter updates, and error backpropagation of TopicS are explained subsequently.

First, we defined the loss function. For each word in the corpus, we used it as the central word for a sliding operation with a window size of c ; let S be the training sequence ( w 1 ,w 2 ,...,w T ), whereas w i denotes the i th word in the sequence. The subscript T represents the total number of unique words in the corpus. In addition to predicting the contextual word of the central word, we must also predict the topic score of the central word. Therefore, the loss function comprised 2 parts: L cont and L topic , and the overall loss was denoted by L s . Our training objective was to minimize the loss function:

what is a research analysis paper

Finally, we can update the word representation.

Text Classification

In this study, we chose TextCNN as the classification model. In the risk factor identification task, some key semantic information is more important, and TextCNN can efficiently use the key information for classification with minimal cost consumption. We represented the manually annotated text as a vector matrix using high-dimensional word vector representations trained by the TopicS model, which aggregates local contextual and topic information and uses it as input for the TextCNN model. Then, the TextCNN algorithm leverages convolutional kernels of different sizes to extract multiple n-gram text features and uses convolutional operations in a fixed window to combine word representations to capture local information. Our input word vector combined the topic information of words, and the most important features in the convolution operation can be extracted using the maximum pooling operation as shown in Figure 4 .

what is a research analysis paper

Clustering With a Review Mechanism

The clustering task is to group similar risk factors. In this study, a large amount of text containing risk factors was clustered into a manually observable number of categories, making it easier to comprehend their content. This study enhances the single-pass algorithm and integrates it with a manual review to cluster the risk factors identified in the text classification, ensuring the validity of the clustering results. The main concept of single-pass clustering [ 55 ] is to match informational text items based on their similarity values without the need to determine the number of clusters in advance. This makes it suitable for clustering tasks with an unknown number of clusters. However, traditional single-pass clustering uses only one-loop traversal, which may result in previously entered text items completing the traversal earlier. This can cause their similarity to the previous topics to be slightly lower than the threshold and lead to them being recreated as new categories, ultimately affecting the clustering effect.

As shown in Figure 5 , we improved the single-pass algorithm by retraversing the categories that were clustered separately after all the text items had been traversed to handle any missed text. After the automated clustering was completed, we conducted a manual review to ensure the reliability of the clustering.

what is a research analysis paper

Moreover, this study uses a keyword cloud visualization of category content to quickly understand the themes and characteristics of each cluster and compare the differences between different clusters. TextRank [ 56 ] was selected to extract category keywords, which considers only the voting scores of words in a single document; common words that frequently appear in a single document easily obtain high scores [ 57 ]. We treated each category as a single document for keyword extraction. As risk factors appear more frequently in categories, TextRank can effectively extract risk factors and surrounding words, preserving category content information as much as possible and reflecting the true content of the risk factors.

In this section, we present the performance of the classifier and the findings based on the categorization of all the comments in the clean data set using the classifier. Our approach involved visualizing the clustering results of the risk factors to comprehend the primary elements of these factors. We also explored the pathogenic mechanisms associated with these risk factors.

Classifier Performance

We used standard text-mining evaluation metrics such as accuracy, precision, recall, and F 1 -score to evaluate the performance. Precision assesses how many risk factors the model identifies correctly, and recall measures how many risk factors the model can identify on its test set. As we aimed to identify as many AR risk factors as possible to provide comprehensive references for individuals, recall was more important than precision in our study.

We set 7-word embedding dimensions ranging from 100 to 400. Table 3 displays the classification results of the TextCNN classification model with the 7 dimensions of Skip-gram and TopicS word vectors. In addition, TextRNN and transformer models were evaluated with the 7-word embedding dimensions of TopicS or Skip-gram, as shown in Tables S3 and S4 in Multimedia Appendix 1 ; the classification models performed better when the word-embedding dimension was 100 or 150, as shown in Table 4 , which includes the results with best-performing dimensions. This study conducted word representation learning on a domain-specific input corpus, where low dimensionality was found to be sufficient to represent the features of the corpus [ 58 ]. Moreover, TopicS not only improved precision but also significantly increased recall for all 3 models, as shown in Table 4 .

a TopicS represents the topic-enhanced word-embedding model proposed in this paper.

b Italicization represents that the metrics of TopicS are better than Skip-gram for each metric.

a Embed_size represents the word-embedding size.

b Italicization represents that the metrics of TopicS are better than Skip-gram for each model.

Table 4 shows that TextCNN has the highest accuracy and recall rate among the 3 classification models. The highest accuracy achieved by our classification model was 0.9594, which used a 150-dimension word-embedding representation obtained from TopicS. In other words, TextCNN can detect more risk factors and minimize the loss of risk factors resulting from classification errors. The CNN model can extract key information similar to n-grams in sentences. The combination of TopicS and TextCNN can enhance topic information and achieve an aggregation effect. Our implementation process was the simplest and consumed the least resources. Our model examined 30,372 comments and identified 5221 comments containing risk factors.

Risk Factor Clustering Results

We clustered the text items obtained from the text classification into 28 categories and extracted keywords from each category to better understand the content. Table 5 shows the top 5 categories and their corresponding keywords. The complete list can be found in Table S5 in Multimedia Appendix 1 . We used category 1 as an example to explain the category formation process and demonstrate the validity of the qualitative results. As shown in Table 4 , we labeled category 1 as Season based on the analysis of keyword weights and relative comments. The comments related to this category focused on seasonally induced AR, with factors such as changes in the weather during seasonal transitions and colder temperatures during winter, which can exacerbate symptoms. We also counted the number of text items in each category and found that seasonal, regional, mites, and weather changes were common risk factors for most patients. In addition, patients’ unhealthy lifestyle habits were also important risk factors widely present in research investigations. Furthermore, most patients reported experiencing symptoms at specific times (eg, “morning”), but researchers have paid little attention to the timing of symptom occurrence (which we refer to as time points).

The Possible Pathway of Several Risk Factors Triggers AR

We referred to the relevant literature on the risk factors associated with AR to confirm whether the extracted risk factors were consistent with the general medical consensus. Our findings are novel compared with those in the literature [ 59 ]. Previous survey-based studies have explored only the correlation between risk factors and AR, whereas our experimental data provide insight into the potential pathogenesis of reported risk factors. The following section provides a theoretical discussion of potential pathways for several risk factors that trigger AR:

  • Season : (1) seasonal risk factors are manifested in pollen allergens. Tree allergens such as elm and cypress pollen are prevalent in early spring, followed by ash, pine, and birch pollen in late spring. In summer, grasses, artemisia, and flowering plants grow vigorously owing to increased rainfall, leading to increased pollen spread from these plants. In autumn, weeds account for the largest proportion of pollen allergens. (2) Different climatic conditions in different seasons contribute to the development of allergies. For example, in early spring, frequent cold and high-pressure air activity in East Asia causes intense atmospheric circulation, resulting in alternating hot and cold temperatures that impair the immune regulatory function of the human body, leading to increased allergy attacks. In autumn, changeable weather, large temperature differences, and sunlight and UV radiation can stimulate allergic reactions in people with weak lungs or those who are prone to AR. In addition, seasonal changes and increasing temperature differences between day and night can disrupt the human immune system.
  • Poor habits : major keywords for this topic were “smoking,” “staying up late,” and “resistance.” (1) Habits such as staying up late, lack of exercise, smoking, and alcohol abuse can weaken immunity and resistance. Gangl et al [ 60 ] found that smoking can reduce the integrity and barrier function of respiratory epithelial cells, thereby making smokers more susceptible to allergens. (2) An irregular diet can damage the spleen and stomach, which is also a key factor in the development of AR. (3) The frequent use of air conditioning in summer can cause nasal mucosa irritation owing to temperature fluctuations. Long-term exposure to adverse stimuli can cause dryness of the nasal cavity and weaken the resistance of the mucosal epithelium, which may lead to AR.
  • Allergens : we grouped clusters that included mites, plants, food, animals, and mold as allergens. (1) The findings of this study suggest that dust mites are the primary allergen, and exposure to a certain concentration of indoor dust mites can lead to AR. The ideal humidity level for dust mite growth is between 75% and 80%, and dust mites tend to thrive during spring and autumn and in warm and humid environments. Studies have shown that a large number of dust mites may be attached to uncleaned air conditioning filters, confirming that air conditioning is an important route of transmission for household dust mites [ 61 ]. (2) Allergenic pollen species are closely related to regions and seasons, and some regions now provide pollen concentration and allergy index broadcasts based on meteorological conditions, which is highly convenient for individuals experiencing allergy. (3) Food allergens such as milk, eggs, wheat, soybeans, and peanuts can also trigger AR. (4) Apart from dust mites, other perennial indoor allergens include animal dander, cockroach excrement, and molds.
  • Outdoor environment : this topic had “dust,” “air quality,” “trust,” and “allergen” as high scoring words. (1) Various substances present in the outdoor environment can trigger AR. Industrialization has increased the content of aromatic hydrocarbon particles, ethanol, and formaldehyde in diesel exhaust, which can damage the mucous membrane and serve as a strong stimulus for AR attacks. (2) Air pollution can affect the distribution of allergens such as mold and pollen. In hazy weather, allergens tend to stay in the air longer, increasing the chance and duration of contact with the human body and leading to AR. (3) High winds can raise dust, pollen, mites, bacteria, and other allergenic factors, increasing their concentration in the air and making it easier to trigger AR.
  • Time points : patients with AR are more likely to experience symptoms during 2 specific time points, morning and evening. Schenkel et al [ 62 ] assessed the severity of 4 nasal symptoms (sneezing, blockage, nasal runny nose, and nasal itch) at different times of the day, revealing that morning and evening symptoms were the most severe. This may be because of the circadian rhythm, pollen concentration, or personal behavior exacerbating the symptoms. In the evening, when the wind subsides, pollen settles closer to the ground and can be inhaled more easily. In addition, although humans rest at night in a horizontal position, nasal ventilation may be more difficult, leading to more severe symptoms. In the morning, low temperatures can cause congestion and swelling of the nasal mucosa because of the temperature difference between the environment and the body. This cluster had words such as “evening,” “get up early,” and “nose” as highly rated words.

This theoretical discussion regarding the potential pathway of risk factors that trigger AR can guide the development of detailed AR intervention measures. For example, patients with AR can pay attention to pollen concentration and temperature changes and adjust their outings and clothing accordingly based on the characteristics of the season; they can set the air conditioner to turn on or off based on their waking time to reduce the inhalation of cold air when waking up. Furthermore, they can adjust their sleeping position to reduce the frequency of nighttime symptoms.

Principal Findings

This study aimed to identify the risk factors for AR based on social media comments. To do so, a data set of comments related to AR was collected, processed, and analyzed. The data set covered a consecutive period from May 2012 to May 2022. Overall, this analysis provided new insights into three main questions: (1) How many comments contained AR risk factor information? (2) How many categories can these risk factors be summarized into? (3) How do these risk factors trigger AR?

In assessing the identification of AR risk factors, we found that TopicS enhanced both precision and recall. TextCNN outperformed other models, achieving an accuracy of 0.9594 with a 150-dimension TopicS embedding. Analyzing 30,372 comments, our model pinpointed 5221 comments with risk factors. Categorizing the text items led to 28 distinct categories, with seasonal factors, regional variations, mites, weather changes, and unhealthy lifestyle habits emerging as common risks.

Furthermore, our research into AR risk factors revealed how risk factors trigger AR and uncovered the frequently reported, but underresearched, risk factors by affected individuals. Seasonal changes, especially during spring and autumn, increase exposure to pollen allergens, with varying climatic conditions affecting the development of allergies. Poor habits, such as smoking, irregular sleep, and frequent use of air conditioning, compromise immunity and heighten AR susceptibility. Dust mites, influenced by humidity, stand out as a primary allergen, with food items and indoor factors, such as animal dander, also triggering AR. Industrial pollutants and outdoor environmental factors amplify AR risk. Notably, AR symptoms intensify during mornings and evenings, which is likely influenced by circadian rhythms and environmental factors.

Limitations and Future Work

This study has some limitations. Our study was based on the self-reported nature of social media data, and the lack of more detailed information from the study participants was a concern. Our statistics showed that seasonal factors, regional variations, mites, weather changes, and unhealthy lifestyle habits emerge as common risk factors, which is consistent with the findings of other studies based on surveys. Although social media may lack in-depth patient information, it provides an effective method of collecting breadth of data. Social media data can be gathered 24 hours a day and are an extremely efficient way to rapidly update new knowledge into the risk factor knowledge base. In the future, our framework can be expanded in 2 ways. First, the framework can track the development trends and changes in AR risk factors by leveraging real-time internet data sets. Second, the framework can be generalized and extended to detect patterns, trends, and risk factors for other chronic diseases such as type 2 diabetes.

Conclusions

In this model improvement study, we proposed a topic-enhanced word-embedding model to improve the accuracy and recall of the text classification, namely to uncover less common or other types of risk factors based on social media data that have not been previously reported. The risk factors identified in this study can be a helpful reference for people with AR to reduce the development of the disease in their daily lives. This study establishes a knowledge base of potential risk factors for individuals who may not be aware of the factors that could trigger their symptoms. Patients can compare their lifestyle habits and medical history to identify their risk factors, which could help reduce the frequency of episodes and prevent the decline in their quality of life caused by blindly avoiding potential triggers. Our findings demonstrate the practicality and feasibility of using social media data for investigating disease knowledge. These findings may provide guidance for the development of management plans and interventions for AR.

Acknowledgments

The data set collection and analysis of this research were partially supported by the National Natural Science Foundation of China (grants 72131006, 72071063, and 72271082); Anhui Provincial Key Research and Development Plan Project (grant 2022i01020003); and the Fundamental Research Funds for the Central Universities (grant JS2023ZSPY0063).

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author upon reasonable request.

Authors' Contributions

DG conceptualized and investigated the study. QW drafted the methodology, performed the software analysis, and prepared the original draft. YC reviewed and edited the draft. XY completed the investigation. WZ drafted the methodology and supervised the study. ML supervised the study. ZX conceptualized the study. GZ and ZO supervised the study.

Conflicts of Interest

None declared.

Examples of social media text, topic dictionary examples, word-embedding dimension parameters with TextRNN, word-embedding dimension parameters with transformer, and social media category distribution and visualization.

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Abbreviations

Edited by A Mavragani; submitted 19.04.23; peer-reviewed by X Liu, Y Cao; comments to author 12.10.23; revised version received 30.10.23; accepted 03.01.24; published 22.02.24.

©Dongxiao Gu, Qin Wang, Yidong Chai, Xuejie Yang, Wang Zhao, Min Li, Oleg Zolotarev, Zhengfei Xu, Gongrang Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.02.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Income Research Paper Series Market Basket Measure research paper: An analysis of the equivalization method

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Introduction

Equivalence scales, appropriateness of the square root equivalence scale, external researchers’ evaluations of the square root scale, statistics canada’s evaluation of the square root scale, appendix c additional detail on statistics canada’s evaluation of the square root scale, equivalencies for other characteristics.

Text begins

On August 21, 2018, the Government of Canada released Opportunity for All: Canada’s First Poverty Reduction Strategy , which outlined long-term commitments to guide current and future government actions and investments to reduce poverty. The Poverty Reduction Act legislates key commitments made in the strategy and mandates that Statistics Canada review the content of Canada’s official measure of poverty, the Market Basket Measure (MBM), on a regular basis.

During consultations for the MBM ’s second comprehensive review, as well as during the analysis leading to the creation of the 2018-base MBM , several MBM research items were identified as requiring further study (e.g., MBM thresholds for remote regions, updating the other necessities component and a poverty index). Note These research topics and their related methodological underpinnings form the basis for the MBM ’s forward-looking research agenda and are being explored in detail through MBM research papers. The MBM research papers will be published in preparation for the third comprehensive review of the MBM , launched in June 2023. Note

This discussion paper begins by providing the reasons for using equivalization methods. Following this, the square root scale is described, and the motivations for its use are discussed. Finally, a series of new tests are conducted to evaluate the efficiency of the square root scale, and these results are discussed in some detail as they reveal many insights.

This paper also provides an opportunity for the public and stakeholders to share feedback and comments on measuring poverty by different family characteristics in Canada.

The MBM establishes poverty thresholds based on the cost of a basket of food, clothing, shelter, transportation and other necessities for a family of four that reflects a modest, basic standard of living. A family with a disposable income below the appropriate MBM threshold for the size of the family and the region of residence is considered to be living in poverty. Note

A basic needs measure, like the MBM , requires a reference family to cost the standards used to define the contents of the basket. The MBM uses a four-person family size that consists of one male and one female adult aged 25 to 49 with two children (a female child aged 9 and a male child aged 13). Note To arrive at thresholds for different family sizes, the MBM methodology uses a square root equivalence scale. Statistics Canada has been asked by the MBM user community to evaluate whether the square root equivalence method accurately adjusts the costs calculated of the four-person reference family to other family sizes.

This paper begins by providing an overview of the methodology behind the equivalence scale. It then explores other options for costing baskets for different family sizes before considering the reasons for using the equivalence scale. Finally, the paper concludes with a summary analysis that assesses the effectiveness of the square root equivalence method in adjusting the MBM basket for different family sizes.

It is common in discussions of family (or household) income to need to compare the incomes of families of different sizes or compositions and ask how well off they are relative to each other, or relative to another standard such as a poverty threshold. To make this comparison, families are often compared with one another in terms of their “equivalent incomes.” An equivalent income is the income a family with one set of characteristics needs to be as well off as a family with a different set of characteristics. Note

Debates around the poverty line in Canada often centre on the appropriateness of the methods used to make families with different characteristics comparable in terms of how well off they are. Note This is a value-laden judgment, but the usual practice is to compare expenditure shares on necessities such as food and deem that families are equally well off when they have the same share of income or total expenditures going to necessities. Note

With the MBM , Statistics Canada calculates the levels of disposable income needed for families living in each of the 66 different MBM regions of Canada to maintain a modest, basic standard of living. Note The MBM is built on the direct costing of a basket of goods and services deemed necessary to attain a basic, modest standard of living for a family of four. This family is called the “reference family.” Having a detailed description of an MBM reference family allows Statistics Canada to make a clear assessment of the needs of this family.

Equivalent incomes for other family types are then derived using a formula that yields the level of disposable income they each would need to meet the same modest, basic standard of living. The key advantage of using the reference family approach is that it is not feasible to calculate poverty thresholds for every family type. The assumption underlying the equivalization method is that two different families are equally well off when they have disposable income equal to their respective poverty lines. Note

The underlying equivalization theory can be described in the following way. Defining P i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGPbaabeaaaaa@37E6@  as the poverty line for family i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyAaaaa@36E4@  and P r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGYbaabeaaaaa@37EF@  as the poverty line for the reference family, P i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGPbaabeaaaaa@37E6@  is related to P r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGYbaabeaaaaa@37EF@  by the following equation:

P i = P r E                       ( 1 ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGPbaabeaakiabg2da9maaliaabaGaamiuamaaBaaaleaa caWGYbaabeaaaOqaaiaadweaaaaeaaaaaaaaa8qacaGGGcGaaiiOai aacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGa aiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckaca GGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaa cckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaai iOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGG GcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacc kacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiO aiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOa8aadaqadaqaa8 qacaaIXaaapaGaayjkaiaawMcaaaaa@8889@

where E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@  is an equivalence factor. For example, if it were the case that family i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyAaaaa@36E4@  needed half the income of the reference family to be considered not in poverty, then E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@  in the above equation would equal 2.

E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@  can be defined across any family dimension. In the MBM context, E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@  is defined according to family size in the following relationship:

E = S r S i                       ( 2 ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraiabg2 da9maaliaabaWaaOaaaeaacaWGtbWaaSbaaSqaaiaadkhaaeqaaaqa baaakeaadaGcaaqaaiaadofadaWgaaWcbaGaamyAaaqabaaabeaaaa GcqaaaaaaaaaWdbiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiO aiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGc GaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaaccka caGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOai aacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGa aiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckaca GGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaa cckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckapaWaaeWaae aapeGaaGOmaaWdaiaawIcacaGLPaaaaaa@8420@

where S i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uamaaBa aaleaacaWGPbaabeaaaaa@37E8@  is the size of family i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyAaaaa@36E4@  and S r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uamaaBa aaleaacaWGYbaabeaaaaa@37F2@  is the size of the reference family. This is an application of the square root equivalization scale, which is recommended for use internationally to create equivalized income for different family sizes. Note

Putting equations (1) and (2) together would yield

P i = P r × ( S i S r )                       ( 3 ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGPbaabeaakiabg2da9iaadcfadaWgaaWcbaGaamOCaaqa baGccqGHxdaTdaqadaqaamaaliaabaWaaOaaaeaacaWGtbWaaSbaaS qaaiaadMgaaeqaaaqabaaakeaadaGcaaqaaiaadofadaWgaaWcbaGa amOCaaqabaaabeaaaaaakiaawIcacaGLPaaaqaaaaaaaaaWdbiaacc kacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiO aiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGc GaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaaccka caGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOai aacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGa aiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckaca GGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaa cckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckapaWaaeWaae aapeGaaG4maaWdaiaawIcacaGLPaaaaaa@8E5E@

and because the reference family size for the MBM ( S r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uamaaBa aaleaacaWGYbaabeaaaaa@37F2@ ) is 4, equation (3) would simplify to

P i = P r × ( S i 2 )                       ( 4 ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaacaWGPbaabeaakiabg2da9iaadcfadaWgaaWcbaGaamOCaaqa baGccqGHxdaTdaqadaqaamaaliaabaWaaOaaaeaacaWGtbWaaSbaaS qaaiaadMgaaeqaaaqabaaakeaacaaIYaaaaaGaayjkaiaawMcaaaba aaaaaaaapeGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaai iOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGG GcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacc kacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiO aiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGc GaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaaccka caGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOai aacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGa aiiOaiaacckacaGGGcGaaiiOaiaacckapaWaaeWaaeaapeGaaGinaa WdaiaawIcacaGLPaaaaaa@9197@

In Table 1, the MBM disposable income amounts needed for the reference family of four to be considered above the poverty line are presented for the 2018 reference year and for selected MBM regions in Canada. The table also shows the disposable income amounts needed for other family sizes to be considered not living in poverty. These disposable income amounts are determined by creating equivalent incomes for the different family sizes—incomes at which they would be as well off as the reference family and escape poverty—using equation (4). Using this approach, E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ is equal to 2 if the family size equals one, 1.414 if the family size is two, 1.155 if the family size is three, 1 (unchanged) if the family size is four, 0.894 if the family size is five, etc.

The rest of this discussion paper concerns additional information and evaluations of the equivalization method used in the MBM .

Similar to the way the square root equivalence scales are used to adjust the poverty thresholds for different family sizes, another method could be used to adjust for differences in costs caused by geography. By costing out regionally defined baskets for 66 different MBM regions, the methodology implicitly establishes income levels at which families from these regions are considered equally well off. This practice could be replaced by using an equivalence formula for geographical adjustments, thereby reducing the data requirements and the number of calculations needed to derive the thresholds, and consequently, making the MBM more transparent to users. For an example of what this process could look like, see Appendix B.

One question that commonly arises is whether the square root scale is the most appropriate or whether some other set of equivalization factors would more accurately derive thresholds for other family sizes.

An in-depth statistical analysis of equivalence scales is described in Equivalence scales, well-being, inequality, and poverty: sensitivity estimates across ten countries using the Luxembourg income study (LIS) database . In this paper, the authors argue that most equivalization scales in use are well approximated by the formula:

W = D S e                       ( 5 ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4vaiabg2 da9maaliaabaGaamiraaqaaiaadofadaahaaWcbeqaaiaadwgaaaaa aOaeaaaaaaaaa8qacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacc kacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiO aiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGc GaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaaccka caGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOai aacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGa aiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckaca GGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaa cckapaWaaeWaaeaapeGaaGynaaWdaiaawIcacaGLPaaaaaa@851E@

  • W MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4vaaaa@36D3@ is the equivalent income per person
  • D MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiraaaa@36C0@  is the reference family’s disposable income
  • S MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uaaaa@36CF@  is the target family size
  • e MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaaaa@36E1@  is the equivalence elasticity.

e = 0 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaiabg2 da9iaaicdaaaa@38A1@  then W = D MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4vaiabg2 da9iaadseaaaa@38A2@ , which would represent complete economies of scale between family sizes (i.e., no adjustment is needed for family size).

e = 1 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaiabg2 da9iaaigdaaaa@38A2@  then W = D S MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4vaiabg2 da9maaliaabaGaamiraaqaaiaadofaaaaaaa@398C@ , which would represent no economies of scale between family sizes (i.e., income divided by family size would yield income per capita).

Using the terminology introduced in the previous section, equation (3) is simply a special form of equation (5), where S i = 1 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uamaaBa aaleaacaWGPbaabeaakiabg2da9iaaigdaaaa@39B3@  and e = 1 / 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaiabg2 da9maalyaabaGaaGymaaqaaiaaikdaaaaaaa@3973@ .

Rearranging the terms in equation (5) gives a formula for calculating the equivalence elasticity:

e = ( ln D − ln W ) − ln S                       ( 6 ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaiabg2 da9maaliaabaWaaeWaaeaaciGGSbGaaiOBaiaadseacqGHsislciGG SbGaaiOBaiaadEfaaiaawIcacaGLPaaaaeaacqGHsislciGGSbGaai OBaiaadofaaaaeaaaaaaaaa8qacaGGGcGaaiiOaiaacckacaGGGcGa aiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckaca GGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaa cckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaai iOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGG GcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacc kacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiO aiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGc WdamaabmaabaWdbiaaiAdaa8aacaGLOaGaayzkaaaaaa@8BB0@

Using equation (6), various equivalization schemes can be easily compared.

Since equation (5) has an implied reference family size of one, equation (6) would need to be adjusted slightly when using other family sizes in the following way:

  • S r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uamaaBa aaleaacaWGYbaabeaaaaa@37F2@ is the reference family size
  • S i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uamaaBa aaleaacaWGPbaabeaaaaa@37E8@ is the target family size

In their analysis, the authors found that varying the size of e MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaaaa@36E1@  from low to high values could lead to differences in inequality measures. At the same time, a value of e = 1 / 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaiabg2 da9maalyaabaGaaGymaaqaaiaaikdaaaaaaa@3973@ , which yields the “square root scale,” was seen as delivering a “compromise” level of equivalence between complete and no economies of scale and yielded fairly similar equivalence elasticities as other scales commonly in use. For example, they estimated the e MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaaaa@36E1@  for Canada’s low-income cut-offs (LICOs) to be 0.56—very close to the square root scale.

In Table 2, the MBM poverty thresholds based on different equivalence factors in the 2018 reference year are presented for Halifax, Nova Scotia. When , there are complete economies of scale. Under this scenario, quantities and costs of necessities do not change based on family size (i.e., the threshold for the reference family would be appropriate for all family sizes). When , there are no economies of scale. Under this scenario, a family of one would need only one-quarter the income of the reference family. Since these are extreme examples, the table also presents results using different equivalence elasticity values, including the implied LICO adjustment factor of 0.56. This value (0.56) implies slightly lower economies of scale than the square root value (0.5). It also implies an 8% reduction in the poverty threshold for a family of one and a 4% reduction for a family of two. Fewer small families in poverty would have the effect of tilting the composition of poverty away from smaller families (e.g., seniors) towards larger families (e.g., families with children). In addition, the poverty rate could also change.

In the paper Equivalence scales: An empirical validation , the Centre d’étude sur la pauvreté et l’exclusion (CEPE) evaluated the appropriateness of using the square root scale in the context of the MBM in 2010. Using expenditure patterns drawn from the Survey of Household Spending, MBM inputs and other reasonable strategies, the CEPE estimated equivalence factors for families of unattached people in Quebec. If the equivalence factor derived empirically was close to the value of 2, this would support the use of the square root scale. According to the CEPE , the expenditure patterns of Quebec singles relative to Quebec parents with two children closely conformed (Table 3) to those approximated by the square root equivalence scale. A value of E = 2.07 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraiabg2 da9iaaikdacaGGUaGaaGimaiaaiEdaaaa@3AB0@ suggests slightly lower economies of scale for necessities in Quebec compared with the values the square root scale would have produced. Therefore, it would have yielded a lower threshold for singles, although the differences would be small.

It is also of note that the CEPE paper reported values of E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ for the food, clothing, shelter and other necessities components of the MBM for Quebec. Note According to the analysis in that paper, the equivalence coefficients for the clothing and other necessities components were the highest, reflecting relatively low economies of scale for these types of goods and services (i.e., a relatively low possibility of sharing these items). The food component’s equivalence coefficient was the second lowest, implying that food items have relatively higher economies of scale compared with clothing. Finally, the shelter component had the lowest equivalence coefficient, suggesting that housing costs can be more easily shared. The fact that some components have high economies of scale while others have low economies of scale created a balancing effect. As a result, the MBM equivalencies based upon expenditure patterns closely replicated the square root method.

A common criticism of the MBM is that it fails to properly equivalize shelter. Note Critics point to the disparity in shelter costs between single-bedroom units and three-bedroom units (the type needed by the reference family), noting that the rent for a one-bedroom apartment is more than half that for a three-bedroom apartment. Based on expenditure patterns, the CEPE shows that housing costs for unattached people were 79% of those of the reference family. Furthermore, items with high economies of scale, like housing, when combined with items with low economies of scale, like food and clothing, create a balance, resulting in the “compromise” square root method, which accurately captures these differences when the individual components are summed to the total threshold values.

This section will present the results from a similar analysis by Statistics Canada, which builds upon the CEPE study. Unlike the CEPE , Statistics Canada has access to all the component production data used by the MBM to create the basket for the reference family. Like the CEPE analysis, the Statistics Canada analysis selected different family types, including those with different numbers of children. In doing this, MBM -like thresholds can be directly estimated for different family sizes and compositions, and the appropriateness of the square root method for adjusting can be tested. More detail on the Statistics Canada analysis is presented in Appendix C.

In Appendix D of the paper, considerations are raised for creating equivalencies to adjust the MBM thresholds for different types of families.

As suggested, to compute the thresholds for different family sizes, the MBM methodology used for the reference family was modified to match the standards set out by experts in their fields for the selected family types. The following is a brief overview of the adjustments made to the components of the MBM .

Food component

Health Canada provided Statistics Canada the food quantity requirements that meet individual basic nutritional needs for a female aged 25 to 49, a male aged 25 to 49, a female aged 9 to 13 and a male aged 9 to 13. Note With these specified quantities, Statistics Canada used the prices from the 2018-base MBM to calculate the total food basket costs for various family compositions.

Clothing and footwear component

Statistics Canada used the clothing and footwear items, replacement schedule, and prices that were used for each member of the reference family, while changing the number of clothing and footwear items based on different family compositions. Note

Transportation component

Statistics Canada used the same basket of compact cars for the private transportation subcomponent, while making certain adjustments (e.g., the number of licences, insurance requirements). For the public transportation subcomponent, the number of required public transit passes was altered according to the family composition.

Shelter component

The Canada Mortgage and Housing Corporation’s National Occupancy Standard was maintained when determining the shelter needs for a selected family composition. Single people, for whom the standard is met using multiple different dwelling configurations, were assigned the average cost of renting a studio or one-bedroom unit.

Other necessities component

Unique “other necessities” multipliers were estimated for different family sizes (e.g., one to five), and cellphone expenses relevant to each family size were also computed. Note

The benefit of this analysis is that the costs of market baskets, based on the selected family compositions, reflect as much as possible the same methods used when costing out the basket for the MBM reference family.

A notable caveat is that only “per-unit” prices for food for the MBM baskets (i.e., on a price-per-kilogram basis) were available to Statistics Canada. Therefore, quantity discounts available to consumers cannot be factored in. This could affect the degree of economies of scale observed in the food and other necessities components. Other economies of scale may have also been missed.

Because the MBM was specifically designed for the composition of the reference family, drawing overly strong conclusions from experimental calculations for other family types would be unreasonable. Therefore, the results shown here are intended for evaluating the square root method for different family sizes and should not be used for other purposes.

Once the basket costs for different family sizes and compositions are established, the equivalence factors ( E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ ) and the equivalence elasticity values ( e MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaaaa@36E1@ ) can be estimated and compared with those produced by the square root method. Table 4 shows the average values as computed for MBM thresholds across all regions in Canada.

According to the results in Table 4, thresholds calculated using the square root method are slightly higher than those calculated directly using the MBM components. For example, the threshold for an unattached individual using this method was $19,663, compared with the threshold of $21,394 derived using the square root method. Comparing the values of the equivalization factors ( E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ ), the value was 2.00 from the square root method and 2.18 for an unattached male. Recall that the CEPE found that the value for a one-person family was 2.07. Therefore, the direct MBM method of calculation and the results from the CEPE suggest that the economies of scale for the MBM basket are smaller than those suggested by the square root scale, a result confirmed by the estimate of 0.57 for e MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwB Ln hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaaaa@36E1@ . With these methods, a single male needs 46% of the income of the reference family to be equally well off, compared with 50% as implied by the square root method.

The method also allows for components of the MBM to be evaluated, with component threshold values for equivalization values ( E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ ) and equivalization elasticities ( e MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyzaaaa@36E1@ ) also computable. Results are shown in Table 5.

Notably, none of the equivalization factors ( E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ ) for a single male are near 2. Transportation and shelter are less than 2, reflecting relatively high economies of scale in these components, while food and clothing are greater than 2, reflecting lower economies of scale in these components. The results underscore the conclusion made by the CEPE and elsewhere that the square root method should not be used to create equivalent values for MBM components.

To conclude this portion of the analysis, the evidence is relatively weak for the argument that the square root method does a poor job creating equivalent levels of poverty thresholds for different family sizes. Based on existing research, the square root method may create poverty thresholds that are higher than those obtained by directly pricing baskets for smaller family sizes. This should address concerns that the poverty thresholds may be too low for smaller families because of the poor performance of the square root method. However, this observation comes with important caveats, including the fact that the approach used in this section could not account for quantity discounts in food and potentially some other aspects of economies of scale. Given the absence of perfect accounting for economies of scale across some items, it is reasonable to agree with previous research by CEPE and conclude that the square root method produces valid standard of living adjustments by family size for the MBM ’s poverty thresholds. Note

This discussion paper describes why equivalization methods are used, followed by an explanation of the square root scale and the motivations for using it. The paper concludes by providing a summary of the assessments of the efficiency of the square root scale and evaluates the results.

As with the other products in this series, this paper aims to foster engagement and debate with the public and stakeholders to explore research topics that could help inform discussions for the next comprehensive review of the MBM , improve the understanding of the MBM methodology, and potentially expand analytical tools that involve or rely on the MBM . Users are welcome to ask questions, provide feedback and make suggestions for future work on any topics relevant to the MBM .

Those who are interested in contacting us are encouraged to email statcan.market.basket.measure-mesure.du.panier.de.consommation.statcan@statcan.gc.ca .

An alternative approach to costing regional-specific MBM thresholds would be to calculate the threshold for a reference family in one area and use a spatial price deflator to yield the thresholds for other regions. However, a price deflator with at the level of detail needed for the MBM does not currently exist. Nevertheless, given the set of thresholds for the 66 MBM regions, equivalence factors can also be expressed for each region. In Table B, regional equivalences (relative to Toronto) would range from a low of 0.779 for the Quebec communities with a population between 30,000 and 99,999 to a high of 2.150 for Iqaluit in Nunavut. Through this method, some researchers have used the MBM thresholds as a practical spatial price deflator. Note

This appendix includes an additional description of how Statistics Canada evaluated the square root scale for this study.

Alternate MBM thresholds were produced for different family sizes, from one-person families to five-person families, with different family compositions (i.e., combinations of adults and children), including the four-person reference family (Table C.1).

The family types were chosen to reflect different, common family sizes and types, but are not intended to be exhaustive. As in the MBM , adults are aged 18 or older.

The analysis was conducted for the 53 MBM regions in the Canadian provinces. The territorial regions, where the MBM calculation methodologies are slightly different, were excluded. This paper produces MBM thresholds for nine family types for each of the 53 regions. Statistics presented in the main paper are based on a weighted average of the 53 regions, with weights derived from the 2016 Census regional population shares.

Alternate 2018 MBM thresholds by family size were created by repurposing the data points originally used in the 2018-base MBM methodology. Readers of this paper are expected to have a basic understanding of how the thresholds were constructed for the reference family in the 2018-base MBM . Note

Modifications that were incorporated to reflect unique needs of different family sizes and compositions are presented in Table C.2.

The analysis presented in paper demonstrated how Statistics Canada created poverty thresholds for various family sizes using equivalence scales. However, as mentioned earlier, equivalence factors can be computed for any family characteristic and used to adjust poverty thresholds. Indeed, critics of the MBM sometimes focus on a particular group of people at risk of poverty and argue that a new set of poverty lines should be developed to recognize that group’s additional costs of living. For example, Griffin and Tabbara (2023) argue the MBM does not properly capture seniors’ poverty in Canada and that the development of a seniors-specific measure of income adequacy is therefore necessary. Also, Scott, Berrigan, Kneebone and Zwicker (2022) document caregiving services, assistive devices and aids, and other out-of-pocket expenses incurred by people with disabilities that may not be fully captured in the MBM methodology.

The fact that costs of living are sensitive to differences in family characteristics, beyond region and family size, is not under debate. However, the MBM is a statistical tool used to examine the effect of changes in family income, prices and government policy on poverty. It is not intended to be the final word on the cost of living for different groups at risk of poverty, nor is it meant to determine program eligibility or set a minimum income. Rather, MBM thresholds and the poverty rate are to be used with other statistics and knowledge to make informed decisions.

If one wishes to calculate alternative thresholds for families that consider specific characteristics, this paper has demonstrated this could be achieved by determining an equivalization factor ( E MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyraaaa@36C1@ ) for a given characteristic. This could be done by examining the differences in expenditure patterns between families with those characteristics and those without. The information on expenditure patterns could come from a survey or another expert source.

Buhman, Brigitte, Lee Rainwater, Guenther Schmaus and Timothy M. Smeeding (1988). “ Equivalence scales, well-being, inequality, and poverty: sensitivity estimates across ten countries using the Luxembourg income study (LIS) database .” The review of income and wealth.

Chen, Wen-Hao (2008). “ Comparing Low Income of Canada’s Regions: A Stochastic Dominance Approach .” Catalogue no. 75F0002M - No. 006.

Devin, Nancy, Eric Dugas, Burton Gustajtis, Sarah McDermott and José Mendoza Rodríguez (2023). “ Launch of the Third Comprehensive Review of the Market Basket Measure .” Catalogue no. 75F0002M.

Djidel, Samir, Burton Gustajtis, Andrew Heisz, Keith Lam, Isabelle Marchand and Sarah McDermott (2020). “ Report on the second comprehensive review of the Market Basket Measure .” Catalogue no. 75F0002M.

Fréchet, Guy, Pierre Lanctôt, Alexandre Morin and Frédéric Savard (2010). “ EQUIVALENCE SCALES: AN EMPIRICAL VALIDATION ." Centre d’étude sur la pauvreté et l’exclusion. Catalogue no. 978-2-550-59521-2

Griffin, Paloma and Mohy-Dean Tabbara (2023). “ A fine line: Finding the right seniors’ poverty measure in Canada .” Maytree.

Gustajtis, Burton and Andrew Heisz (2022). “ Market Basket Measure Technical Paper: The other necessities component .” Catalogue no. 75F0002M2022006.

Hatfield, Michael (2002). “ Constructing the Revised Market Basket Measure .” Applied Research Branch, Strategic Policy, Human Resources Development Canada. Catalogue no. MP32-30/01-1E-IN.

Human Resources Development Canada (2003). “ Understanding the 2000 Low Income Statistics Based on the Market Basket Measure .” Applied Research Branch, Strategic Policy, Human Resources Development Canada. Catalogue no. RH63-1/569-03-03E.

Lewbel, Arthur, and Krishna Pendakur (2006). “ Equivalence Scales .” The New Palgrave Dictionary of Economics. Palgrave Macmillan, London.

Picot, Garnett, and Yuqian Lu (2017). “ Chronic Low Income Among Immigrants in Canada and its Communities .” Catalogue no. 11F0019M, no. 397.

Scott, C.W.M., Berrigan, P., Kneebone, R.D. et al. (2022). “ Disability Considerations for Measuring Poverty in Canada Using the Market Basket Measure .” Soc Indic Res 163, 389–407

United Nations Economic Commission for Europe (2011). “ Canberra Group Handbook on Household Income Statistics ,” Second Edition.

More information

Note of appreciation.

Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued co-operation and goodwill.

Standards of service to the public

Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner. To this end, the Agency has developed standards of service which its employees observe in serving its clients.

Published by authority of the Minister responsible for Statistics Canada.

© His Majesty the King in Right of Canada as represented by the Minister of Industry, 2024

All rights reserved. Use of this publication is governed by the Statistics Canada Open Licence Agreement .

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Health Policy Analysis Webinar Series

Department & Center Event 

The working paper series on health policy analysis is hosted by the Health Systems Program in the Department of International Health. 

We invite you to join the Spring 2024 sessions of the working paper series on health policy analysis in low- and middle-income countries. Each webinar will begin with a 10- to 15-minute presentation by the speaker, followed by 30 minutes of open discussion in response to the presentation.

Friday, January 26, 11–11:55 a.m.

Philanthropy and Technoscience: Effective Altruism's Interventions in Neglected Tropical Diseases Samantha Vanderslott, Associate Professor, Department of Paediatrics, University of Oxford This talk considers how ‘effective altruism’ produces philanthropic value by allocating moral worth to particular causes and interventions. The case of neglected tropical diseases shows how effective altruists produce evidence on neglect by reworking global health categories, and how they assess the efficiency of low-cost interventions.

Friday, March 1, 11–11:55 a.m.

Why Has Development Assistance for Health Been So Much Greater Than That for Education? Clio Dintilhac, Doctoral Student, Johns Hopkins School of Advanced International Studies Today, health official development assistance is 60% larger than education official development assistance. This difference is striking since in the early 2000s, both sectors were at the same level. This most similar system design study explores potential explanations for this puzzle and argues these different trajectories are not explained by the structure of the issues, but by the strategic choices of actors.

Friday, April 12, 11–11:55 a.m.

Advancing Research and Practice to Support Sustainable Health Programs Following Donor Transition Abigail Neel, Research Associate, Department of International Health, Johns Hopkins Bloomberg School of Public Health The talk will cover emerging findings from a qualitative study exploring stakeholder perspectives on long-term HIV control, as well as proposed work to advance theory and practice in this space.

Friday, May 3, 11–11:55 a.m.

Project Learning in the Context of Wicked Problems: A 40-Year Case Study of Health Systems Strengthening in Uttar Pradesh, India Sara Bennett, Professor, Department of International Health; Brian Wahl, Associate Research Professor, Department of International Health; Taran Kaur Deol, Graduate Student, Department of International Health This talk examines the history of health systems strengthening initiatives in Uttar Pradesh, and the extent to which there has been learning from project to project, as well as adaptation to the evolving environment.

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  28. Health Policy Analysis Webinar Series

    Events Calendar. Health Policy Analysis Webinar Series. We invite you to join the Spring 2024 sessions of the working paper series on health policy analysis in low- and middle-income countries. Each webinar will begin with a 10- to 15-minute presentation by the speaker, followed by 30 minutes of open discussion in response to the presentation.