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
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 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. 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...”].
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?
- 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:
- 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?
- 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.
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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- How to Do Thematic Analysis | Step-by-Step Guide & Examples
How to Do Thematic Analysis | Step-by-Step Guide & Examples
Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.
Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.
There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.
This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.
Table of contents
When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.
Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .
Some types of research questions you might use thematic analysis to answer:
- How do patients perceive doctors in a hospital setting?
- What are young women’s experiences on dating sites?
- What are non-experts’ ideas and opinions about climate change?
- How is gender constructed in high school history teaching?
To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.
However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.
Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.
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Once you’ve decided to use thematic analysis, there are different approaches to consider.
There’s the distinction between inductive and deductive approaches:
- An inductive approach involves allowing the data to determine your themes.
- A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.
Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?
There’s also the distinction between a semantic and a latent approach:
- A semantic approach involves analyzing the explicit content of the data.
- A latent approach involves reading into the subtext and assumptions underlying the data.
Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?
After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .
The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.
This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.
Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.
Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:
In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.
At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.
After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.
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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.
Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:
At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.
Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.
Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.
Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?
If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.
For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.
Now that you have a final list of themes, it’s time to name and define each of them.
Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.
Naming themes involves coming up with a succinct and easily understandable name for each theme.
For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.
Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.
We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.
The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.
In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Normal distribution
- Measures of central tendency
- Chi square tests
- Confidence interval
- Quartiles & Quantiles
- Cluster sampling
- Stratified sampling
- Discourse analysis
- Cohort study
- Peer review
- Implicit bias
- Cognitive bias
- Conformity bias
- Hawthorne effect
- Availability heuristic
- Attrition bias
- Social desirability bias
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Caulfield, J. (2023, June 22). How to Do Thematic Analysis | Step-by-Step Guide & Examples. Scribbr. Retrieved November 12, 2023, from https://www.scribbr.com/methodology/thematic-analysis/
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How to Analyze an Article
Last Updated: September 26, 2023 Fact Checked
This article was reviewed by Gerald Posner . Gerald Posner is an Author & Journalist based in Miami, Florida. With over 35 years of experience, he specializes in investigative journalism, nonfiction books, and editorials. He holds a law degree from UC College of the Law, San Francisco, and a BA in Political Science from the University of California-Berkeley. He’s the author of thirteen books, including several New York Times bestsellers, the winner of the Florida Book Award for General Nonfiction, and has been a finalist for the Pulitzer Prize in History. He was also shortlisted for the Best Business Book of 2020 by the Society for Advancing Business Editing and Writing. There are 9 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 80,975 times.
Learning to analyze and think critically is a valuable skill. Not only will it help with schoolwork, but it will also allow you to judge the validity of news articles and conduct thoughtful research for the rest of your life. A good analysis requires a summary, annotation, and examination of an article and its writer.
Summarizing an Article
Annotating an Article
- Ensure you have page numbers, so that you can cite the article correctly in your analysis.
- If you are reading a scientific paper, look for methods, evidence, and results. This is the accepted structure of most scientific papers.
Analyzing an Article
- State whether you believe the author could be guilty of a bias.  X Research source In media-related articles, you should state whether the author was able to stay somewhat objective as they relayed news to the audience.
- Refer back to your annotations to find quotations or questions about the validity of an argument.
Video . By using this service, some information may be shared with YouTube.
- Always proof your work for content, spelling, and grammatical errors before you turn it in. Although an article analysis can be done fairly quickly it should be edited at least once. Thanks Helpful 0 Not Helpful 0
Things You'll Need
- Word processor/paper
- Works Cited page
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- ↑ https://writingcenter.uconn.edu/wp-content/uploads/sites/593/2014/06/How_to_Summarize_a_Research_Article1.pdf
- ↑ https://www.trentu.ca/academicskills/how-guides/how-write-university/how-approach-any-assignment/writing-article-summaries
- ↑ https://www.lbcc.edu/sites/main/files/file-attachments/summarizingparagraph.pdf
- ↑ https://owl.purdue.edu/owl/research_and_citation/using_research/quoting_paraphrasing_and_summarizing/index.html
- ↑ http://www.ncbi.nlm.nih.gov/pubmed/15827843
- ↑ https://researchguides.njit.edu/eng352/summarize
- ↑ https://www.psychologytoday.com/us/basics/bias
- ↑ https://pitt.libguides.com/citationhelp
About This Article
To analyze an article, start by reading it carefully and highlighting or underlining key concepts and themes that reoccur in the text. Next, highlight the thesis of the article to understand the author's purpose for writing it. Then, determine how successfully the author proves the thesis by noting specific examples and using in-text citations. Finally, consider stating your opinion about the topic as long as the article isn't scientific in nature. For tips on writing formatting a formal analysis for an assignment, read on! Did this summary help you? Yes No
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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.
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 ].
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.
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.
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 ]
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.
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.
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.
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 ].
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 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).
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.
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.
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.
How to Write an Article Analysis
What Are the Five Parts of an Argumentative Essay?
As you write an article analysis, focus on writing a summary of the main points followed by an analytical critique of the author’s purpose.
Knowing how to write an article analysis paper involves formatting, critical thinking of the literature, a purpose of the article and evaluation of the author’s point of view. In an article analysis critique, you integrate your perspective of the author about a specific topic into a mix of reasoning and arguments. So, you develop an argumentative approach to the point of view of the author. However, a careful distinction occurs between summary and analysis.
When presenting your findings of the article analysis, you might want to summarize the main points, which allows you to formulate a thesis statement. Then, inform the readers about the analytical aspects the author presents in his arguments. Most likely, developing ideas on how to write an article analysis entails a meticulous approach to the critical thinking of the author.
Writing Steps for an Article Analysis
As with any formal paper, you want to begin by quickly reading the article to get the main points. Once you generate a general idea of the point of view of the author, start analyzing the main ideas of each paragraph. An ideal way to take notes based on the reading is to jot them down in the margin of the article. If that's not possible, include notes on your computer or a separate piece of paper. Interact with the text you're reading.
Becoming an active reader helps you decide the relevant information the author intends to communicate. At this point, you might want to include a summary of the main ideas. After you finish writing down the main points, read them to yourself and decide on a concise thesis statement. To do so, begin with the author’s name followed by the title of the article. Next, complete the sentence with your analytical perspective.
Ideally, you want to use outlines, notes and concept mapping to draft your copy. As you progress through the body of the critical part of the paper, include relevant information such as literature references and the author’s purpose for the article. Formal documents, such as an article analysis, also use in-text citation and proofreading. Any academic paper includes a grammar, spelling and mechanics proofreading. Make sure you double-check your paper before submission.
When you write the summary of the article, focus on the purpose of the paper and develop ideas that inform the reader in an unbiased manner. One of the most crucial parts of an analysis essay is the citation of the author and the title of the article. First, introduce the author by first and last name followed by the title of the article. Add variety to your sentence structure by using different formats. For example, you can use “Title,” author’s name, then a brief explanation of the purpose of the piece. Also, many sentences might begin with the author, “Title,” then followed by a description of the main points. By implementing active, explicit verbs into your sentences, you'll show a clear understanding of the material.
Much like any formal paper, consider the most substantial points as your main ideas followed by evidence and facts from the author’s persuasive text. Remember to use transition words to guide your readers in the writing. Those transition phrases or words encourage readers to understand your perspective of the author’s purpose in the article. More importantly, as you write the body of the analysis essay, use the author’s name and article title at the beginning of a paragraph.
When you write your evidence-based arguments, keep the author’s last name throughout the paper. Besides writing your critique of the author’s purpose, remember the audience. The readers relate to your perspective based on what you write. So, use facts and evidence when making inferences about the author’s point of view.
Description of an Article Analysis Essay
When you analyze an article based on the argumentative evidence, generate ideas that support or not the author’s point of view. Although the author’s purpose to communicate the intentions of the article may be clear, you need to evaluate the reasons for writing the piece. Since the basis of your analysis consists of argumentative evidence, elaborate a concise and clear thesis. However, don't rely on the thesis to stay the same as you research the article.
At many times, you'll find that you'll change your argument when you see new facts. In this way, you might want to use text, reader, author, context and exigence approaches. You don't need elaborate ideas. Just use the author’s text so that the reader understands the point of views. However, evaluate the strong tone of the author and the validity of the claims in the article. So, use the context of the article.
Then, ask yourself if the author explains the purpose of his or her persuasive reasons. As you discern the facts and evidence of the article, analyze the point of views carefully. Look for assumptions without basis and biased ideas that aren't valid. An analysis example paragraph easily includes your perspective of the author’s purpose and whether you agree or not. Don't be surprised if your critique changes as you research other authors about the article.
Consequently, your response might end up agreeing, disagreeing or being somewhat in between despite your efforts of finding supporting evidence. Regardless of the consequences of your research of the literature and the perspective of the author’s point of view, maintain a definite purpose in writing. Don't fluctuate from agreement and disagreement. Focusing your analysis on presenting the points of view of the author so readers understand it and disseminating that critique is the basis of your paper.
When reading the text carefully, analyze the main points and explain the reasons of the author. Also, as you describe the document, offer evidence and facts to eliminate any biases. In an argumentative analysis, the focus of the writer can quickly shift. Avoid ineffective ways of approaching the author’s point of view that make the writing vague and lack supporting evidence. A clear way to stay away from biases is to use quotes from the author. However, using excessive amounts of quotes is counterproductive. Use author quotes sparingly.
As you develop your own ideas about the author’s viewpoints, use deductive reasoning to analyze the various aspects of the article. Often, you'll find the historical background influenced the author or persuaded the author to challenge the ideals of the time. Distinguishing between writing a summary and an analysis paper is crucial to your essay. You might find that using a review at the beginning of your article indicates a clear perspective to your analysis. Hence, a summary explains the main points of a paper in a concise manner.
You condense the original text, describe the main points, write your thesis and form no opinion about the article. On the other hand, an analysis is the breakdown of the author’s arguments that you use to derive the purpose of the author. When analyzing an article, you're dissecting the main points to draw conclusions about the persuasive ideas of the author. Furthermore, you offer argumentative evidence, strengths and weaknesses of the main points. More importantly, you don't give your opinion. Rather than providing comments on the author’s point of views, you compile evidence of how the author persuades readers to think about a particular topic and whether the author elaborates it adequately.
Examples of an Article Analysis
A summary and analysis essay example illustrates the arguments the author makes and how those claims are valid. For instance, a sample article analysis of “Sex, Lies, and Conversation; Why Is It So Hard for Men and Women to Talk to Each Other” by Deborah Tannen begins with a summary of the main critical points followed by an analytical perspective. One of the precise ways to summarize is to focus on the main ideas that Tannen uses to distinguish between men and women.
The writer of the summary also clearly states how one idea correlates to the other without presenting biases or opinions. Also, the writer doesn't take any sides on whether men or women are to blame for miscommunication. Instead, the summary points to the communication differences between men and women. In the analytical section of the sample, the writer immediately takes a transparent approach to the article and the author. The analysis shows apparent examples from the article with quotes and refers back to the article connecting miscommunication with misinterpretation. Finally, the writer poses various questions that Tannen didn't address, such as strategies for effective communication. However, the writer gives the reader the purpose of Tannen’s article and the reasons the author wrote it.
Another example of an article analysis is “The Year That Changed Everything” by Lance Morrow. The writer presents a concise summary of the elected government positions of Nixon, Kennedy and Johnson. Furthermore, the writer distinguishes between the three elected men's positions and discusses the similarities. The summary tends to lean toward a more powerful tone but effectively explains the author’s point of view for each one of these men. Then, the writer further describes the ideals of the period between morality and immoral values. The analytical aspect of the sample shows the reader the author’s powerful message.
The writer immediately lets the reader know about the persuasive nature of the article and the relevance of the time. For instance, the writer shows the reader in various parts of the article by suggesting examples in specific paragraph numbers. The writer also makes a powerful impact with the use of quotes embedded into the text. The writer uses transition words and active verbs, such as more examples, links, uncovering and secrets, and backs this claim up to describe Morrow’s purpose for the article. The analysis has the audience in mind.
The writer points out the specific details of the time era that only people of the time would relate. More importantly, the writer analyzes Morrow’s ideas as critical to formulating an opinion about Nixon, Kennedy and Johnson. However, the writer points out the assumptions Morrow makes between personal lifestyle and how it affects the political arena. Moreover, the writer suggests that Morrow’s claims aren't entirely valid just because the author mentions historical events. Unlike Tannen’s analytical example, the writer lets the readers know the misconnection between moral value and the lifestyle of many people at the time.
Both article analyses show a clear way to present different persuasive points of view. Unlike a summary, an analysis approach offers the reader an explicit representation of the author’s viewpoints without any opinions. The writers of each sample focus on providing evidence, facts and reasonable statements. Consequently, each example demonstrates the proper use of the critical analysis of the literature and evaluates the purpose of the author. Without seeking an agreement or not, the writer clearly distinguishes between a summary and an analysis of each article.
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Barbara earned a B. S. in Biochemistry and Chemistry from the Univ. of Houston and the Univ. of Central Florida, respectively. Besides working as a chemist for the pharmaceutical and water industry, she pursued her degree in secondary science teaching. Barbara now writes and researches educational content for blogs and higher-ed sites.
How to Critically Analyse an Article
Critical analysis refers to the skill required to evaluate an author’s work. Students are frequently asked to critically analyse a particular journal. The analysis is designed to enhance the reader’s understanding of the thesis and content of the article, and crucially is subjective, because a piece of critical analysis writing is a way for the writer to express their opinions, analysis, and evaluation of the article in question. In essence, the article needs to be broken down into parts, each one analysed separately and then brought together as one piece of critical analysis of the whole.
Key point: you need to be aware that when you are analysing an article your goal is to ensure that your readers understand the main points of the paper with ease. This means demonstrating critical thinking skills, judgement, and evaluation to illustrate how you came to your conclusions and opinions on the work. This might sound simple, and it can be, if you follow our guide to critically analyse an article:
- Before you start your essay, you should read through the paper at least three times.
- The first time ensures you understand, the second allows you to examine the structure of the work and the third enables you to pick out the key points and focus of the thesis statement given by the author (if there is one of course!). During these reads and re-reads you can set down bullet points which will eventually frame your outline and draft for the final work.
- Look for the purpose of the article – is the writer trying to inform through facts and research, are they trying to persuade through logical argument, or are they simply trying to entertain and create an emotional response. Examine your own responses to the article and this will guide to the purpose.
- When you start writing your analysis, avoid phrases such as “I think/believe”, “In my opinion”. The analysis is of the paper, not your views and perspectives.
- Ensure you have clearly indicated the subject of the article so that is evident to the reader.
- Look for both strengths and weaknesses in the work – and always support your assertions with credible, viable sources that are clearly referenced at the end of your work.
- Be open-minded and objective, rely on facts and evidence as you pull your work together.
Structure for Critical Analysis of an Article
Remember, your essay should be in three mains sections: the introduction, the main body, and a conclusion.
Your introduction should commence by indicating the title of the work being analysed, including author and date of publication. This should be followed by an indication of the main themes in the thesis statement. Once you have provided the information about the author’s paper, you should then develop your thesis statement which sets out what you intend to achieve or prove with your critical analysis of the article.
Key point: your introduction should be short, succinct and draw your readers in. Keep it simple and concise but interesting enough to encourage further reading.
Overview of the paper
This is an important section to include when writing a critical analysis of an article because it answers the four “w’s”, of what, why, who, when and also the how. This section should include a brief overview of the key ideas in the article, along with the structure, style and dominant point of view expressed. For example,
“The focus of this article is… based on work undertaken… The main thrust of the thesis is that… which is the foundation for an argument which suggests. The conclusion from the authors is that…. However, it can be argued that…
Once you have given the overview and outline, you can then move onto the more detailed analysis.
For each point you make about the article, you should contain this in a separate paragraph. Introduce the point you wish to make, regarding what you see as a strength or weakness of the work, provide evidence for your perspective from reliable and credible sources, and indicate how the authors have achieved, or not their goal in relation to the points made. For each point, you should identify whether the paper is objective, informative, persuasive, and sufficiently unbiased. In addition, identify whether the target audience for the work has been correctly addressed, the survey instruments used are appropriate and the results are presented in a clear and concise way.
If the authors have used tables, figures or graphs do they back up the conclusions made? If not, why not? Again, back up your statements with reliable hard evidence and credible sources, fully referenced at the end of your work.
In the same way that an introduction opens up the analysis to readers, the conclusion should close it. Clearly, concisely and without the addition of any new information not included in the body paragraph.
Key points for a strong conclusion include restating your thesis statement, paraphrased, with a summary of the evidence for the accuracy of your views, combined with identification of how the article could have been improved – in other words, asking the reader to take action.
Key phrases for Critical Analysis of an article
- This article has value because it…
- There is a clear bias within this article based on the focus on…
- It appears that the assumptions made do not correlate with the information presented…
- Aspects of the work suggest that…
- The proposal is therefore that…
- The evidence presented supports the view that…
- The evidence presented however overlooks…
- Whilst the author’s view is generally accurate, it can also be indicated that…
- Closer examination suggests there is an omission in relation to
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10 Easy Steps: How to Write an Analysis for an Article
Step 1: understand the purpose of an article analysis.
An article analysis is a critical examination of a piece of writing that aims to evaluate its strengths and weaknesses, as well as its overall effectiveness. It involves breaking down the article into its key components, analyzing the author's arguments and evidence, and providing your own interpretation and evaluation. By understanding the purpose of an article analysis , you can approach the task with clarity and focus.
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What is the purpose of an article analysis?
An article analysis serves several purposes:
- To assess the quality and credibility of the article
- To identify the main arguments and supporting evidence
- To evaluate the effectiveness of the author's writing style and persuasive techniques
- To provide your own interpretation and evaluation of the article
By conducting a thorough analysis, you can gain a deeper understanding of the article and its implications, as well as develop your critical thinking and analytical skills
Step 2: Read the Article Carefully
Before you can begin analyzing an article , it is essential to read it carefully and attentively. Take your time to understand the main ideas , arguments, and evidence presented by the author. Pay attention to the structure of the article, the language used , and any supporting visuals or examples.
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How to read an article for analysis?
When reading an article for analysis , follow these steps:
- Read the article once to get a general understanding of the content.
- Read the article again, this time highlighting or underlining key points, arguments, and evidence.
- Take notes on the main ideas, supporting details, and any questions or concerns that arise.
- Consider the author's tone, style, and use of language.
- Identify any biases or assumptions made by the author.
By reading the article carefully, you can gather the necessary information and insights to conduct a thorough analysis.
Step 3: Identify the Main Arguments
Once you have read the article, the next step is to identify the main arguments presented by the author. These arguments are the central ideas or claims that the author is making and are supported by evidence and reasoning.
How to identify the main arguments?
To identify the main arguments in an article , follow these steps:
- Look for statements or claims that the author repeatedly emphasizes or supports with evidence.
- Identify any thesis statements or central ideas that the author presents.
- Consider the overall structure of the article and how the arguments are organized.
- Pay attention to any counterarguments or opposing viewpoints that the author addresses.
By identifying the main arguments, you can focus your analysis on evaluating the strength and validity of these claims.
Step 4: Evaluate the Evidence
After identifying the main arguments, the next step is to evaluate the evidence presented by the author. Evidence can include facts, statistics, examples, expert opinions , or research findings that support the author's claims.
How to evaluate the evidence?
To evaluate the evidence in an article, consider the following:
- Is the evidence relevant and directly related to the author's arguments?
- Is the evidence credible and supported by reliable sources ?
- Is the evidence sufficient to support the author's claims?
- Are there any biases or limitations in the evidence presented?
By critically evaluating the evidence , you can determine its strength and reliability, which will contribute to your overall analysis of the article.
Step 5: Analyze the Writing Style and Techniques
In addition to evaluating the arguments and evidence, it is important to analyze the author's writing style and persuasive techniques. The way an article is written can greatly impact its effectiveness and influence on the reader.
What to consider when analyzing the writing style and techniques?
When analyzing the writing style and techniques used in an article, consider the following:
- Is the language clear, concise, and appropriate for the intended audience?
- Does the author use rhetorical devices, such as metaphors or analogies, to enhance their arguments?
- Is the tone of the article objective, subjective, or biased?
- Does the author use persuasive techniques, such as emotional appeals or logical reasoning ?
By analyzing the writing style and techniques, you can gain insights into how the author effectively communicates their ideas and influences the reader.
Step 6: Provide Your Interpretation and Evaluation
After conducting a thorough analysis of the article, it is time to provide your own interpretation and evaluation. This is where you can express your thoughts, opinions, and insights based on the information and analysis you have conducted.
How to provide your interpretation and evaluation?
When providing your interpretation and evaluation of an article, consider the following:
- Summarize the main arguments and evidence presented by the author.
- State your own interpretation of the article's main ideas and their implications.
- Evaluate the strengths and weaknesses of the author's arguments and evidence.
- Provide supporting examples or counterarguments to strengthen your evaluation.
By providing your interpretation and evaluation, you can contribute to the ongoing discussion and analysis of the article.
Step 7: Revise and Edit Your Analysis
Once you have written your analysis, it is important to revise and edit it for clarity, coherence, and accuracy. This step ensures that your analysis is well-structured, easy to understand, and free from grammatical and spelling errors.
How to revise and edit your analysis?
When revising and editing your analysis, consider the following:
- Read your analysis aloud to check for any awkward or unclear sentences.
- Ensure that your analysis is well-organized and follows a logical flow.
- Check for grammatical and spelling errors, and make necessary corrections.
- Ask a friend or colleague to review your analysis and provide feedback.
By revising and editing your analysis, you can improve its overall quality and readability.
Step 8: Include Proper Citations and References
When writing an analysis for an article, it is important to include proper citations and references to acknowledge the original author and sources of information. This not only gives credit to the original work but also allows readers to verify the information and conduct further research if desired.
How to include proper citations and references?
When including citations and references in your analysis, follow these guidelines:
- Use the appropriate citation style , such as APA, MLA, or Chicago.
- Cite direct quotes, paraphrases, and summaries of the original article .
- Include a reference list or bibliography at the end of your analysis.
- Ensure that all citations and references are accurate and complete.
By including proper citations and references, you demonstrate academic integrity and provide readers with the necessary information to locate the original article.
Step 9: Seek Feedback and Revise if Necessary
After completing your analysis, it can be beneficial to seek feedback from others, such as your instructor, peers, or writing center tutors. Feedback can provide valuable insights and suggestions for improvement, helping you refine your analysis and make it even stronger.
How to seek feedback and revise your analysis?
When seeking feedback and revising your analysis, consider the following:
- Share your analysis with others and ask for their honest opinions and suggestions.
- Consider the feedback received and identify areas for improvement.
- Revise your analysis based on the feedback and make necessary changes.
- Proofread your revised analysis to ensure its quality and accuracy.
By seeking feedback and revising your analysis, you can enhance its overall quality and effectiveness.
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What is the purpose of writing an analysis for an article?
The purpose of writing an analysis for an article is to critically examine and evaluate the content, structure, and effectiveness of the article. It involves identifying the main arguments, supporting evidence, and the overall message conveyed by the author.
What are the key steps in writing an analysis for an article?
The key steps in writing an analysis for an article include reading the article multiple times to gain a thorough understanding, taking notes on important points and evidence, identifying the main thesis or argument, evaluating the author's use of evidence and reasoning, and organizing your analysis into a coherent and logical structure.
How should I structure my analysis for an article?
A typical structure for an analysis of an article includes an introduction that provides background information and a clear thesis statement, body paragraphs that discuss different aspects of the article in detail, and a conclusion that summarizes the main findings and offers a critical evaluation of the article's strengths and weaknesses.
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- A Research Guide
- Writing Guide
- Article Writing
How to Analyze an Article
- What is an article analysis
- Outline and structure
- Step-by-step writing guide
- Article analysis format
- Analysis examples
- Article analysis template
What Is an Article Analysis?
- Summarize the main points in the piece – when you get to do an article analysis, you have to analyze the main points so that the reader can understand what the article is all about in general. The summary will be an overview of the story outline, but it is not the main analysis. It just acts to guide the reader to understand what the article is all about in brief.
- Proceed to the main argument and analyze the evidence offered by the writer in the article – this is where analysis begins because you must critique the article by analyzing the evidence given by the piece’s author. You should also point out the flaws in the work and support where it needs to be; it should not necessarily be a positive critique. You are free to pinpoint even the negative part of the story. In other words, you should not rely on one side but be truthful about what you are addressing to the satisfaction of anyone who would read your essay.
- Analyze the piece’s significance – most readers would want to see why you need to make article analysis. It is your role as a writer to emphasize the importance of the article so that the reader can be content with your writing. When your audience gets interested in your work, you will have achieved your aim because the main aim of writing is to convince the reader. The more persuasive you are, the more your article stands out. Focus on motivating your audience, and you will have scored.
Outline and Structure of an Article Analysis
What do you need to write an article analysis, how to write an analysis of an article, step 1: analyze your audience, step 2: read the article.
- The evidence : identify the evidence the writer used in the article to support their claim. While looking into the evidence, you should gauge whether the writer brings out factual evidence or it is personal judgments.
- The argument’s validity: a writer might use many pieces of evidence to support their claims, but you need to identify the sources they use and determine whether they are credible. Credible sources are like scholarly articles and books, and some are not worth relying on for research.
- How convictive are the arguments? You should be able to judge the writer’s persuasion of the audience. An article is usually informative and therefore has to be persuasive to the readers to be considered worthy. If it does not achieve this, you should be able to critique that and illustrate the same.
Step 3: Make the plan
Step 4: write a critical analysis of an article, step 5: edit your essay, article analysis format, article analysis example, what didn’t you know about the article analysis template.
- Read through the piece quickly to get an overview.
- Look for confronting words in the article and note them down.
- Read the piece for the second time while summarizing major points in the literature piece.
- Reflect on the paper’s thesis to affirm and adhere to it in your writing.
- Note the arguments and the evidence used.
- Evaluate the article and focus on your audience.
- Give your opinion and support it to the satisfaction of your audience.
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Table of Contents
Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics , businesses today are working with slimmer margins for error.
Companies that want to stay in business and thrive can improve their odds of success by making smart choices while answering the question: “What is data analysis?” And how does an individual or organization make these choices? They collect as much useful, actionable information as possible and then use it to make better-informed decisions!
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This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on bad data. Organizations need information; they need data. This is where data analysis or data analytics enters the picture.
The job of understanding data is currently one of the growing industries in today's day and age, where data is considered as the 'new oil' in the market. Now, before getting into the details about the data analysis methods, let us first answer the question, what is data analysis?
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What Is Data Analysis?
Although many groups, organizations, and experts have different ways of approaching data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.
A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.
It’s not uncommon to hear the term “ big data ” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “ What is data ?”
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Why is Data Analysis Important?
Here is a list of reasons why data analysis is crucial to doing business today.
- Better Customer Targeting: You don’t want to waste your business’s precious time, resources, and money putting together advertising campaigns targeted at demographic groups that have little to no interest in the goods and services you offer. Data analysis helps you see where you should be focusing your advertising and marketing efforts.
- You Will Know Your Target Customers Better: Data analysis tracks how well your products and campaigns are performing within your target demographic. Through data analysis, your business can get a better idea of your target audience’s spending habits, disposable income, and most likely areas of interest. This data helps businesses set prices, determine the length of ad campaigns, and even help project the number of goods needed.
- Reduce Operational Costs: Data analysis shows you which areas in your business need more resources and money, and which areas are not producing and thus should be scaled back or eliminated outright.
- Better Problem-Solving Methods: Informed decisions are more likely to be successful decisions. Data provides businesses with information. You can see where this progression is leading. Data analysis helps businesses make the right choices and avoid costly pitfalls.
- You Get More Accurate Data: If you want to make informed decisions, you need data, but there’s more to it. The data in question must be accurate. Data analysis helps businesses acquire relevant, accurate information, suitable for developing future marketing strategies, business plans, and realigning the company’s vision or mission.
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What Is the Data Analysis Process?
Answering the question “what is data analysis” is only the first step. Now we will look at how it’s performed. The process of data analysis, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process of data analysis consists of:
Data Requirement Gathering
Ask yourself why you’re doing this analysis, what type of data you want to use, and what data you plan to analyze.
Guided by your identified requirements, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.
Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.
Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python , R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
Now that you have your results, you need to interpret them and come up with the best courses of action based on your findings.
Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.
Types of Data Analysis
A half-dozen popular types of data analysis are available today, commonly employed in the worlds of technology and business. They are:
Descriptive analysis involves summarizing and describing the main features of a dataset. It focuses on organizing and presenting the data in a meaningful way, often using measures such as mean, median, mode, and standard deviation. It provides an overview of the data and helps identify patterns or trends.
Inferential analysis aims to make inferences or predictions about a larger population based on sample data. It involves applying statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. It helps generalize findings from a sample to a larger population.
Exploratory Data Analysis (EDA)
EDA focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations, summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps generate hypotheses for further analysis.
Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of Variance), or correlation analysis are commonly used in diagnostic analysis.
Predictive analysis involves using historical data to make predictions or forecasts about future outcomes. It utilizes statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.
Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize outcomes. It helps in decision-making and resource allocation.
Next, we will get into the depths to understand about the data analysis methods.
Data Analysis Methods
Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.
Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis .
Qualitative Data Analysis
The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:
- Content Analysis, for analyzing behavioral and verbal data.
- Narrative Analysis, for working with data culled from interviews, diaries, surveys.
- Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
Quantitative Data Analysis
Also known as statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:
- Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
- Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
- Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body.
We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools.
How to Analyze Data? Top Data Analysis Techniques to Apply
To analyze data effectively, you can apply various data analysis techniques. Here are some top techniques to consider:
Define Your Objectives
Clearly define the objectives of your data analysis. Understand the questions you want to answer or the insights you want to gain from the data. This will guide your analysis process.
Start by cleaning the data to ensure its quality and reliability. Remove duplicates, handle missing values, and correct any errors or inconsistencies. Data cleaning is crucial for accurate analysis.
Calculate descriptive statistics to understand the main characteristics of the data. Compute measures such as mean, median, mode, standard deviation, and percentiles. These statistics provide insights into the data's central tendency, spread, and distribution.
Create visual representations of the data using charts, graphs, or plots. Visualization helps spot patterns, trends, or outliers that may not be immediately apparent in the raw data. Use appropriate visualizations based on the type of data and the insights you want to convey.
Perform EDA techniques to explore the data deeply. Use data profiling, summary statistics, and visual exploration to identify patterns, relationships, or interesting features within the data. EDA helps generate hypotheses and guides further analysis.
Apply inferential statistics to conclude the larger population based on sample data. Use techniques like hypothesis testing, confidence intervals, and regression analysis to test relationships, make predictions, or assess the significance of findings.
Machine Learning Algorithms
Utilize machine learning algorithms to analyze data and make predictions or classifications. Choose appropriate algorithms based on the nature of your data and the problem you're trying to solve. Train models using historical data and evaluate their performance on new data.
Clustering and Segmentation
Employ clustering techniques to identify groups or segments within your data. Clustering helps in understanding patterns or similarities between data points. It can be useful for customer segmentation, market analysis, or anomaly detection.
Time Series Analysis
If your data is collected over time, apply time series analysis techniques. Study trends, seasonality, and patterns in the data to forecast future values or identify underlying patterns or cycles.
Text Mining and NLP
If working with textual data, employ text mining and natural language processing techniques. Analyze sentiment, extract topics, classify text, or conduct entity recognition to derive insights from unstructured text data.
Remember, the choice of techniques depends on your specific data, objectives, and the insights you seek. It's essential to have a systematic and iterative approach, using multiple techniques to gain a comprehensive understanding of your data.
What Is the Importance of Data Analysis in Research?
A huge part of a researcher’s job is to sift through data. That is literally the definition of “research.” However, today’s Information Age routinely produces a tidal wave of data, enough to overwhelm even the most dedicated researcher. From a birds eye view, data analysis:
1. Plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do to their job.
2. Provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis.
3. Offers researchers better data and better ways to analyze and study said data.
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Top Data Analysis Tools
So, here's a list of the top seven data analysis tools in terms of popularity, learning, and performance.
- Tableau Public
- R Programming
- Apache Spark
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How to Become a Data Analyst
Now that we have answered the question “what is data analysis”, if you want to pursue a career in data analytics , you should start by first researching what it takes to become a data analyst . You can even check out the PG Program in Data Analytics in partnership with Purdue University . This program provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts live. You will get broad exposure to key technologies and skills currently used in data analytics.
According to Forbes, the data analytics profession is exploding . The United States Bureau of Labor Statistics forecasts impressively robust growth for data science jobs skills and predicts that the data science field will grow about 28 percent through 2026. So, if you want a career that pays handsomely and will always be in demand, then check out Simplilearn and get started on your new, brighter future!
1. What is meant by data analysis?
Data analysis refers to the process of inspecting, cleaning, transforming, and interpreting data to discover valuable insights, draw conclusions, and support decision-making. It involves using various techniques and tools to analyze large sets of data and extract meaningful patterns, trends, correlations, and relationships within the data. Data analysis is essential across various industries and disciplines, as it helps uncover valuable information that can be used to optimize processes, solve problems, and make informed decisions.
2. What is the purpose of data analysis?
The purpose of data analysis is to gain meaningful insights from raw data to support decision-making, identify patterns, and extract valuable information. Some of the key objectives of data analysis include: Identifying trends and patterns, Making data-driven decisions, Finding correlations and relationships, Detecting anomalies, Improving performance, and Predictive modeling.
3. What are the types of data analytics?
Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.
4. What are the analytical tools used in data analytics?
The top 10 data analytical tools are Sequentum Enterprise, Datapine, Looker, KNIME, Lexalytics, SAS Forecasting, RapidMiner, OpenRefine, Talend, and NodeXL. The tools aid different data analysis processes, from data gathering to data sorting and analysis.
5. What is the career growth in data analytics?
Starting off as a Data Analysis, you can quickly move into Senior Analyst, then Analytics Manager, Director of Analytics, or even Chief Data Officer (CDO).
6. Why Is Data Analytics Important?
Data Analysis is essential as it helps businesses understand their customers better, improves sales, improves customer targeting, reduces costs, and allows for the creation of better problem-solving strategies.
7. Who Is Using Data Analytics?
Data Analytics has now been adopted almost across every industry. Regardless of company size or industry popularity, data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services. Data Analytics is prominently used across industries such as Healthcare, Travel, Hospitality, and even FMCG products.
8. Is SQL good for data analysis?
Yes, SQL (Structured Query Language) is an excellent tool for data analysis, especially when dealing with structured data in relational databases. SQL is specifically designed for managing and manipulating structured data, making it a powerful language for data analysis tasks like filtering, sorting, aggregating, and joining datasets. It allows users to retrieve specific subsets of data from large databases efficiently.
While SQL excels at handling structured data, it may not be the best choice for all types of data analysis. For tasks involving more complex data manipulation, statistical analysis, or machine learning, other tools like Python or R may be more suitable.
9. What is data analysis in Excel?
Data analysis in Excel refers to the process of using Microsoft Excel's built-in features and functions to perform data analysis tasks. Excel provides a range of tools for basic data analysis, making it accessible to a wide range of users, including those without advanced programming or statistical knowledge.Some common data analysis tasks in Excel include: Filtering and sorting data, PivotTables and PivotCharts, Formulas and Functions, and Charts and Graphs.
While Excel is useful for basic data analysis, it may become limited when dealing with larger datasets or more complex analyses. In such cases, more advanced tools like Python, R, or dedicated data analysis software might be more suitable.
10. What is data analysis in Python?
Data analysis in Python refers to using Python programming language and its associated libraries to perform various data manipulation, exploration, and analysis tasks. Python has become popular for data analysis due to its simplicity, versatility, and the availability of numerous powerful libraries tailored for data science tasks.
Some commonly used Python libraries for data analysis include: Pandas, NumPy, Matplotlib and Seaborn, SciPy, Scikit-learn Python's flexibility and the extensive range of data analysis libraries make it a powerful tool for handling various data analysis challenges, from exploratory data analysis to sophisticated machine learning tasks.
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About the author.
Karin has spent more than a decade writing about emerging enterprise and cloud technologies. A passionate and lifelong researcher, learner, and writer, Karin is also a big fan of the outdoors, music, literature, and environmental and social sustainability.
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A Step-by-Step Guide to the Data Analysis Process
Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.
In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.
Here are the steps we’ll take you through:
- Defining the question
- Collecting the data
- Cleaning the data
- Analyzing the data
- Sharing your results
- Embracing failure
On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.
Ready? Let’s get started with step one.
1. Step one: Defining the question
The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.
Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.
Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”
Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?
Tools to help define your objective
Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.
2. Step two: Collecting the data
Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.
What is first-party data?
First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.
What is second-party data?
To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.
What is third-party data?
Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .
Tools to help you collect data
Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .
Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .
3. Step three: Cleaning the data
Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:
- Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
- Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
- Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
- Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.
A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.
Carrying out an exploratory analysis
Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.
Tools to help you clean your data
Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?
4. Step four: Analyzing the data
Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.
Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.
Learn more: What is descriptive analytics?
Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!
Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.
Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.
Learn more: What are the different types of data analysis?
5. Step five: Sharing your results
You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.
How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!
Tools for interpreting and sharing your findings
There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!
You can learn more about storytelling with data in this free, hands-on tutorial . We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!
6. Step six: Embrace your failures
The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.
While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.
In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:
- Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
- Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
- Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
- Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
- Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
- Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.
What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.
To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:
- These are the top 9 data analytics tools
- 10 great places to find free datasets for your next project
- How to build a data analytics portfolio
An open-source probabilistic record linkage process for records with family-level information: Simulation study and applied analysis
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- Other Affiliation: University of Southern California
- Affiliation: School of Social Work
- Research with administrative records involves the challenge of limited information in any single data source to answer policy-related questions. Record linkage provides researchers with a tool to supplement administrative datasets with other information about the same people when identified in separate sources as matched pairs. Several solutions are available for undertaking record linkage, producing linkage keys for merging data sources for positively matched pairs of records. In the current manuscript, we demonstrate a new application of the Python RecordLinkage package to family-based record linkages with machine learning algorithms for probability scoring, which we call probabilistic record linkage for families (PRLF). First, a simulation of administrative records identifies PRLF accuracy with variations in match and data degradation percentages. Accuracy is largely influenced by degradation (e.g., missing data fields, mismatched values) compared to the percentage of simulated matches. Second, an application of data linkage is presented to compare regression model estimate performance across three record linkage solutions (PRLF, ChoiceMaker, and Link Plus). Our findings indicate that all three solutions, when optimized, provide similar results for researchers. Strengths of our process, such as the use of ensemble methods, to improve match accuracy are discussed. We then identify caveats of record linkage in the context of administrative data.
- regression model
- machine learning
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- California Health and Human Services Agency
- Conrad N. Hilton Foundation, CNHF
- Reissa Foundation
- Heising-Simons Foundation, HSF
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SACGPT: The Fusion of SAC and Generative AI for Intelligent Decision-making
The emergence of Artificial Intelligence Generated Content (AIGC) marks a significant shift in the field of artificial intelligence. Generative AI has profoundly changed the way people process information and will further fundamentally change the way enterprises operate.
As the world’s leading enterprise management software supplier, SAP has also actively responded and followed this technological trend and proposed a new AI strategy – Business AI . SAP Business AI aims to deeply integrate AI technology into all solutions provided by SAP and make full use of advanced AI technology, especially Generative AI, to provide users with more intelligent and automated applications and promote digital innovation of enterprises. and intelligent decision-making. SAP BTP ( SAP Business Technology Platform) , as the technical base of SAP AI strategy , provides enterprises with powerful capabilities in four aspects: data management and analysis, function expansion, system integration, and AI artificial intelligence services.
This article aims to present SACGPT, a project developed by the Business Technology Platform (BTP) team at SAP Greater China. It will demonstrate how to leverage SAP Analytics Cloud to seamlessly integrate with Generative AI technology. The goal is to develop an AI-driven, user-friendly conversational intelligent analysis application that requires no technical expertise.
SACGPT – AI-driven analysis application
Currently, data analysis systems offer self-service applications that enable effective visual exploration and analysis of data. However, a certain level of technical expertise is still required, especially in the face of the constantly changing market environment. The business users require faster, more user-friendly, and fully intelligent analysis applications that do not require any technical skills.
Generative AI possesses powerful capabilities in natural language processing, text generation, code generation, and more. However, it also has certain limitations, particularly in terms of the inaccuracies or false outputs sometimes produced by Generative AI, which may lead to erroneous outcomes. SACGPT seamlessly integrates the data analytics capabilities of SAP Analytics Cloud with Generative AI, effectively mitigating the hallucinations of Generative AI. It not only supports intelligent analysis based on natural language, but also generates thought processes with business logic by Generative AI, all while ensuring data security.
Figure_1 : Comparison of enterprise analysis platform and Generative AI
SACGPT is an innovative analysis solution that combines SAP Analytics Cloud with Generative AI. It supports interactive analysis through natural language, translating users’ natural language commands directly into visual, business logic-driven data analysis charts and reports. This greatly facilitates user data analysis and understanding, enhancing the efficiency of corporate data analysis and the utilization of data.
Figure_2 : SACGPT Architecture
SACGPT transforms user questions into prompts and sends them to the Generative AI. The Generative AI, based on these prompts and the requirements of the thought chain, generates AI analytical approaches and steps with business logic, and formulates analytical commands in accordance with the syntax requirements of SAP Analysis Cloud’s natural language query interface ( JustAsk / Search to Insight). This information is relayed back to SAP Analytics Cloud. Once SAP Analytics Cloud interprets the commands, it can automatically generate analytical charts with business logic, and integrate these charts into an analytical report for user review.
SACGPT has achieved a synergy between SAP Analytics Cloud data analysis technology and Generative AI technology, providing users with deep intelligent analysis applications based on natural language. SACGPT allows users to quickly switch underlying data models to adapt to various business scenario needs. At the same time, SACGPT can also connect with different Generative AIs to meet the needs of different users.
Intelligent Analysis Application
Intelligent Question & Answering
Traditionally users would extract information by finding the correct data and building the appropriate visualizations. This was time consuming and required BI expertise. Whereas now we can provide a new way to interact with your data in order to find usable information to users in order to enable you to make decisions at the right time
Intelligent question-answering can automatically generate visualization charts and analytical summaries with business logic based on users’ questions, providing business users with intelligent analysis without any technical barriers.
Figure_3 : SACGPT Intelligent Question and Answering
Intelligent Q&A allows users to pose questions in natural language in SAP Analytics Cloud (SAC) and send them to the Generative AI platform. The Generative AI firstly determines whether the user’s question is related to data analysis. If it is not, it directly returns the corresponding results. Once determined to be data analysis-related, the Generative AI then judges whether the question is simple or complex to ascertain if the analysis requires one or multiple steps. The Generative AI, following the settings required by SACGPT business thinking chain, provides an AI analysis approach with business logic and continuous related analysis steps, and generates the corresponding SAP Analytics Cloud commands. SAP Analytics Cloud will then automatically generate a series of visual analytic charts with business logic based on these commands, and generate corresponding analysis summaries following the analysis approach. The number of steps in the thinking chain, business logic, the quantity, and types of charts, etc., are autonomously determined by the Generative AI.
It’s worth mentioning that, considering users’ concerns about data security, we have specifically set up related data security protection mechanisms. In the Intelligent Q&A, users can choose not to send their data to the Generative AI to ensure data security. Even in the mode of not transmitting data, SACGPT can still generate AI analysis approach and analysis charts based on metadata.
Intelligent Reporting is based on Intelligent Q&A and adds the function of automatically setting the analysis report page. Intelligent Reporting can automatically determine the analysis ideas and steps according to the user’s open analysis needs, such as “make an income analysis report”, and combine it with the business thought of chain to automatically generate a one-page analysis report. The number of charts, types of charts, and page layout in the report will be automatically set according to the AI analytical approach of the Generative AI.
Figure_4 : SACGPT Intelligent Reporting — displays question window and analysis ideas
Figure_5 : SACGPT Intelligent Reporting
Features of SACGPT
Data analysis based on natural language.
We know that Generative AI can understand natural language and respond in a natural language manner. They can also understand and generate very complex texts. In the application of SACGPT, we fully leverage the natural language processing capabilities of Generative AI to understand users’ data analysis needs. Users can pose their questions in a completely natural language, including complex ones. The Generative AI can fully comprehend these questions and translate them into the necessary analysis instructions for visual data analysis.
Simultaneously, we harness the text-generation capabilities of the Generative AI, allowing it to auto-generate analysis summaries that follow human language habits and logic based on chart information and analysis requirements. The summary content can include completion status of key performance indicators, operational issues, operational analysis conclusions. The generated content is returned to SAP Analytics Cloud to provide the users with a combined graphic-text analysis report.
SACGPT enables users to conduct data analysis in a natural language interactive manner, making complex data analysis tasks more concise and efficient. This greatly enhances the efficiency of data analysis and realizes zero-threshold intelligent analysis applications.
Few-shot Prompt improves accuracy of Generative AI
Few-shot Prompt is a technique that allows Generative AI to learn and predict with a small number of training samples. By providing a small number of input and output examples in prompt, we can guide Generative AI to produce desired outputs based on new inputs .
In SACGPT, we’ve set a small number of prompt words according to the grammar standards of SAP Analytics Cloud Natural Language Query function. This approach allows the Generative AI to learn from grammar examples and generate accurate analytical instructions in accordance with grammar standards, even without a large amount of annotated data. With SAP Analytics Cloud Natural Language Query function, users get quick answers to questions and incorporate these insights into a story while working with indexed models based on acquired and SAP HANA, SAP S/4HANA, SAP Universe, and SAP BW live data.
As a result, it can effectively reduce the hallucinations of the Generative AI and prevent the Generative AI from producing misleading outputs.
AI thought of chain setting
SACGPT is not only to generate an analysis chart, but to generate a series of analysis charts based on business logic to provide complete answers to analysis questions.
In SACGPT, we’ve set it up according to the ‘chain of thought’ method used by the Generative AI, allowing it to transform the user’s problems into a series of connected business thoughts, and the business analysis charts and reports. The number of steps in the thinking chain is determined by the Generative AI based on the understanding of the problem.
Figure_6 : Business thought of chain generated by SACGPT
（The customer’s question is “Identify the two industries with the lowest income and conduct an in-depth analysis”. SACGPT proposed five suggestions with logical continuity. ）
The business thinking chain makes full use of the powerful understanding ability of Generative AI for business analysis. To a certain extent, it can supplement the existing business analysis capabilities of analysts. Taking it a step further, if SACGPT is paired with fine-tuned AIGC models that possess industry knowledge and combined with real-time enterprise data, users can obtain highly professional intelligent business analysis insight.
Enterprise Data Security
The Generative AI itself does not have a mechanism for data access management. Based on SAP Analytics Cloud and the underlying SAP data platform, we can set user permissions in the data model. SACGPT can implement fine-grained data access control: for the same question, it can generate different text and charts based on the set user permissions, achieving content generation access control. This ensures that each user can obtain information within their permission range, safeguarding data security while also improving work efficiency.
Figure_7 : SACGPT generates different content for different users to the same question
SACGPT can seamlessly connect data models in various languages, and can also understand and process data in the language of the questioner, transform the data into clear and easy-to-understand answers in the user’s language , and output analysis results, providing great convenience as an excellent platform for information exchange.
Figure_8 : English and Japanese analysis results generated by SACGPT
open to various Generative AI platforms
SACGPT allows users to openly and conveniently access different AIGC models, meeting a wide range of user needs. At the same time, SACGPT also supports users in quickly switching underlying data models to accommodate various business scenario requirements.
SACGPT effectively harnesses the power of Generative AI technology and combines it with the robust data analytics capabilities of the SAP BTP platform to enable interactive data analysis through natural language. It also utilizes AI business thought chains to generate analytical reports with business logic. While ensuring data security, SACGPT helps users to achieve intelligent insights with zero technical expertise, significantly improving data analysis efficiency and data utilization, quickly responding to business needs, and providing strong decision-making support for enterprise development.
Chang Junling BTP Senior Solution Architect, SAP GC
Yin Haining BTP Centre of Excellence, SAP GC
Disclaimer: SAP notes that posts about potential uses of generative AI and large language models are merely the individual poster’s ideas and opinions, and do not represent SAP’s official position or future development roadmap. SAP has no legal obligation or other commitment to pursue any course of business, or develop or release any functionality, mentioned in any post or related content on this website
Is this already general available for all SAP SAC User? if not, how long shall we wait for this capablility be availiable ?
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As I've mentioned in my article, this is a localized integrated project in China. If your company is based in China, you may contact the relevant SAP sales for details on implementation. This is not part of the standard product features.
The purpose of this blog is merely to highlight some interesting engineering techniques that were used in this project, for the sake of technical discussion.
Hope it may clarify.
Hello Xialong, I do refer you to the two items below if you want to learn more around SAC's product direction.
Our statement of direction https://www.sap.com/products/technology-platform/cloud-analytics/features/generative-ai.html
Our public roadmap explorer (Augmented Analytics category) https://roadmaps.sap.com/board?PRODUCT=67838200100800006884&range=CURRENT-LAST&BC=6EAE8B27FCC11ED892E91CE972E180CC#Q4%202023
I hope this helps, kind regards,
SAC Product Management
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Seahawks have faltered with chances to prove they’re among NFL’s best
Baltimore Ravens safety Geno Stone (26) and cornerback Marlon Humphrey, right, break up a pass intended for Seattle Seahawks wide receiver DK Metcalf (14) during the second half of an NFL football game, Sunday, Nov. 5, 2023, in Baltimore. (AP Photo/Alex Brandon)
Seattle Seahawks head coach Pete Carroll speaks during a news conference after an NFL football game against the Baltimore Ravens, Sunday, Nov. 5, 2023, in Baltimore. The Ravens won 37-3. (AP Photo/Alex Brandon)
Seattle Seahawks quarterback Geno Smith (7) throws a pass over Baltimore Ravens safety Kyle Hamilton (14) during the second half of an NFL football game, Sunday, Nov. 5, 2023, in Baltimore. (AP Photo/Alex Brandon)
Baltimore Ravens running back Keaton Mitchell (34) runs from Seattle Seahawks safety Jamal Adams (33) and cornerback Riq Woolen (27) during a 40-yard touchdown run in the second half of an NFL football game, Sunday, Nov. 5, 2023, in Baltimore. (AP Photo/Nick Wass)
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RENTON, Wash. (AP) — Given the opportunity to prove they could compete with an opponent deemed one of the elite in the league, the Seattle Seahawks failed badly and in the process showed there are major concerns going into the second half of the season despite sitting in a tie for the NFC West lead.
Seattle’s 37-3 loss at Baltimore on Sunday amplified the gap between a good team such as the Seahawks and one such as the Ravens.
Sitting at 5-3 through eight games isn’t a failure for Seattle. The Seahawks are tied for the division lead and should be right in the mix for a playoff spot into the final weeks of the regular season.
But it’s how those three losses have played out that leaves an unsatisfying taste to what the Seahawks accomplished in the first half of the season.
There was the surprising blowout loss at home to the Rams to open the season; the frustrating red zone failures in a four-point loss at Cincinnati; and then Sunday’s romp by the Ravens — the second-worst loss of Pete Carroll’s tenure in Seattle.
It’s clear Seattle has the potential of being good. They won at Detroit. They have a nice win over Cleveland.
But the overall evidence is lacking so far to believe the Seahawks are a team capable of making a run should they get to the postseason.
“I would like that this game was maybe a marker that this is where things shifted, and we came right back and got back on track, and we see us come back to who we are,” Carroll said. “We’ll see. This is a big deal.”
Until Sunday’s effort against Baltimore, the Seahawks defense was one of the highlights of the season.
Seattle started Sunday with the eighth-ranked run defense in the NFL and was one of the best at limiting teams in yards per carry. That all got blown up by the Ravens, who had 515 total yards and 298 yards rushing, the most yards on the ground allowed during Carroll’s tenure.
It was a concerning performance but one Seattle believes is an outlier. The return of Bobby Wagner and Jarran Reed has been as important for the defensive success along with the drafting of rookie cornerback Devon Witherspoon and the signings of Dre’Mont Jones and Mario Edwards Jr.
WHAT NEEDS HELP
Geno Smith needs a bit of a reset. The past five games were far from his best, especially when it comes to taking care of the ball. Smith has thrown six interceptions, lost two fumbles and been sacked 13 times in the past five games.
It’s not all on Smith as he’s been one of the more pressured quarterbacks in the league and a litany of injuries on the offensive line seems to be adding up and causing issues in protection.
But Smith must be less careless and make sure some of the miscommunications with his wide receivers that have turned into interceptions comes to an end.
Edge rusher Boye Mafe is turning into a star in his second season. Carroll noted that Mafe showed continual improvement during his rookie season and felt he had the best offseason of anyone on Seattle’s roster.
It’s showing now in the regular season. Mafe has had a sack in six straight games, tying a franchise record. He missed the Week 2 win over Detroit with a knee injury and has had a sack in every game since.
It’s been a mostly quiet first half for DK Metcalf — at least catching the ball. Metcalf had just one reception for 50 yards against the Ravens. He’s only posted one 100-yard game thus far and hasn’t had more than six receptions in a game. Metcalf missed one game and has been dealing with injuries to his ribs and hip for weeks. His issues with penalties garnered attention early on. The Seahawks would like to see that attention placed on his pass catching moving forward.
Seattle felt it was pretty healthy this past week, all things considered. Shoulder injuries to rookie defensive end Derick Hall and running back/special teams standout DeeJay Dallas suffered Sunday could be troublesome. Carroll said Hall was feeling better on Monday while they’ll have to wait for a better idea on Dallas’ availability.
Most important for Seattle would be clarity on right tackle Abraham Lucas and his possible return. Lucas has been out since Week 1, but Carroll said Monday the hope is he’ll practice next week.
30 — That is Seattle’s ranking league-wide on third downs, both offensively and defensively. Seattle is converting just 31.9% of its third down chances and was 1 of 12 against Baltimore. They are giving up 45.3% conversions on third downs defensively and each of the first three games allowed teams to convert at a 50% or better rate. That combination doesn’t work long term and must improve the second half of the season.
The next two weeks are vitally important to how Seattle’s season goes because of what comes after.
The Seahawks will host Washington (4-5) followed by a trip to face the Rams (3-6).
After that comes four brutal weeks for the Seahawks: home to San Francisco on Thanksgiving, at Dallas on a Thursday night, at San Francisco, home for Philadelphia.
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