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how to write an analysis in research

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Data Analysis in Research: Types & Methods

data-analysis-in-research

Content Index

Why analyze data in research?

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

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

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

LEARN ABOUT: Research Process Steps

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

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

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

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

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

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

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

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

Phase I: Data Validation

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

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

Phase II: Data Editing

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

Phase III: Data Coding

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

LEARN ABOUT: Steps in Qualitative Research

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

Descriptive statistics

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

Measures of Frequency

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

Measures of Central Tendency

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

Measures of Dispersion or Variation

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

Measures of Position

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

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

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

Inferential statistics

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

Here are two significant areas of inferential statistics.

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

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

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

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

LEARN ABOUT: Best Data Collection Tools

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

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

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How to Write an Analysis

Last Updated: January 31, 2023 Fact Checked

This article was co-authored by Christopher Taylor, PhD and by wikiHow staff writer, Megaera Lorenz, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. There are 14 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 288,211 times.

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

Gathering Information and Building Your Argument

Step 1 Review your assignment carefully.

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

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

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

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

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

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

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

Step 5 Make a list of your main arguments.

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

Step 6 Gather evidence and examples to support your arguments.

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

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

Organizing and Drafting Your Analysis

Step 1 Write a brief...

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

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

Step 2 Create an outline...

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

Step 3 Draft an introductory paragraph.

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

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

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

Step 5 Compose a conclusion...

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

Step 6 Avoid presenting your personal opinions on the document.

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

Polishing Your Analysis

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

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

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

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

Step 3 Cut out any irrelevant passages.

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

Step 4 Proofread your writing and fix any errors.

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

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

Sample Analysis Outline and Conclusion

how to write an analysis in research

Expert Q&A

Christopher Taylor, PhD

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Write

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

About This Article

Christopher Taylor, PhD

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

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Content Analysis | A Step-by-Step Guide with Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

  • Books, newspapers, and magazines
  • Speeches and interviews
  • Web content and social media posts
  • Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyse.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

  • Finding correlations and patterns in how concepts are communicated
  • Understanding the intentions of an individual, group, or institution
  • Identifying propaganda and bias in communication
  • Revealing differences in communication in different contexts
  • Analysing the consequences of communication content, such as the flow of information or audience responses

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  • Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

  • Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

  • Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

  • Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

  • The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
  • The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
  • The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

  • The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
  • The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context, and audience of the texts.

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8.5 Writing Process: Creating an Analytical Report

Learning outcomes.

By the end of this section, you will be able to:

  • Identify the elements of the rhetorical situation for your report.
  • Find and focus a topic to write about.
  • Gather and analyze information from appropriate sources.
  • Distinguish among different kinds of evidence.
  • Draft a thesis and create an organizational plan.
  • Compose a report that develops ideas and integrates evidence from sources.
  • Give and act on productive feedback to works in progress.

You might think that writing comes easily to experienced writers—that they draft stories and college papers all at once, sitting down at the computer and having sentences flow from their fingers like water from a faucet. In reality, most writers engage in a recursive process, pushing forward, stepping back, and repeating steps multiple times as their ideas develop and change. In broad strokes, the steps most writers go through are these:

  • Planning and Organization . You will have an easier time drafting if you devote time at the beginning to consider the rhetorical situation for your report, understand your assignment, gather ideas and information, draft a thesis statement, and create an organizational plan.
  • Drafting . When you have an idea of what you want to say and the order in which you want to say it, you’re ready to draft. As much as possible, keep going until you have a complete first draft of your report, resisting the urge to go back and rewrite. Save that for after you have completed a first draft.
  • Review . Now is the time to get feedback from others, whether from your instructor, your classmates, a tutor in the writing center, your roommate, someone in your family, or someone else you trust to read your writing critically and give you honest feedback.
  • Revising . With feedback on your draft, you are ready to revise. You may need to return to an earlier step and make large-scale revisions that involve planning, organizing, and rewriting, or you may need to work mostly on ensuring that your sentences are clear and correct.

Considering the Rhetorical Situation

Like other kinds of writing projects, a report starts with assessing the rhetorical situation —the circumstance in which a writer communicates with an audience of readers about a subject. As the writer of a report, you make choices based on the purpose of your writing, the audience who will read it, the genre of the report, and the expectations of the community and culture in which you are working. A graphic organizer like Table 8.1 can help you begin.

Summary of Assignment

Write an analytical report on a topic that interests you and that you want to know more about. The topic can be contemporary or historical, but it must be one that you can analyze and support with evidence from sources.

The following questions can help you think about a topic suitable for analysis:

  • Why or how did ________ happen?
  • What are the results or effects of ________?
  • Is ________ a problem? If so, why?
  • What are examples of ________ or reasons for ________?
  • How does ________ compare to or contrast with other issues, concerns, or things?

Consult and cite three to five reliable sources. The sources do not have to be scholarly for this assignment, but they must be credible, trustworthy, and unbiased. Possible sources include academic journals, newspapers, magazines, reputable websites, government publications or agency websites, and visual sources such as TED Talks. You may also use the results of an experiment or survey, and you may want to conduct interviews.

Consider whether visuals and media will enhance your report. Can you present data you collect visually? Would a map, photograph, chart, or other graphic provide interesting and relevant support? Would video or audio allow you to present evidence that you would otherwise need to describe in words?

Another Lens. To gain another analytic view on the topic of your report, consider different people affected by it. Say, for example, that you have decided to report on recent high school graduates and the effect of the COVID-19 pandemic on the final months of their senior year. If you are a recent high school graduate, you might naturally gravitate toward writing about yourself and your peers. But you might also consider the adults in the lives of recent high school graduates—for example, teachers, parents, or grandparents—and how they view the same period. Or you might consider the same topic from the perspective of a college admissions department looking at their incoming freshman class.

Quick Launch: Finding and Focusing a Topic

Coming up with a topic for a report can be daunting because you can report on nearly anything. The topic can easily get too broad, trapping you in the realm of generalizations. The trick is to find a topic that interests you and focus on an angle you can analyze in order to say something significant about it. You can use a graphic organizer to generate ideas, or you can use a concept map similar to the one featured in Writing Process: Thinking Critically About a “Text.”

Asking the Journalist’s Questions

One way to generate ideas about a topic is to ask the five W (and one H) questions, also called the journalist’s questions : Who? What? When? Where? Why? How? Try answering the following questions to explore a topic:

Who was or is involved in ________?

What happened/is happening with ________? What were/are the results of ________?

When did ________ happen? Is ________ happening now?

Where did ________ happen, or where is ________ happening?

Why did ________ happen, or why is ________ happening now?

How did ________ happen?

For example, imagine that you have decided to write your analytical report on the effect of the COVID-19 shutdown on high-school students by interviewing students on your college campus. Your questions and answers might look something like those in Table 8.2 :

Asking Focused Questions

Another way to find a topic is to ask focused questions about it. For example, you might ask the following questions about the effect of the 2020 pandemic shutdown on recent high school graduates:

  • How did the shutdown change students’ feelings about their senior year?
  • How did the shutdown affect their decisions about post-graduation plans, such as work or going to college?
  • How did the shutdown affect their academic performance in high school or in college?
  • How did/do they feel about continuing their education?
  • How did the shutdown affect their social relationships?

Any of these questions might be developed into a thesis for an analytical report. Table 8.3 shows more examples of broad topics and focusing questions.

Gathering Information

Because they are based on information and evidence, most analytical reports require you to do at least some research. Depending on your assignment, you may be able to find reliable information online, or you may need to do primary research by conducting an experiment, a survey, or interviews. For example, if you live among students in their late teens and early twenties, consider what they can tell you about their lives that you might be able to analyze. Returning to or graduating from high school, starting college, or returning to college in the midst of a global pandemic has provided them, for better or worse, with educational and social experiences that are shared widely by people their age and very different from the experiences older adults had at the same age.

Some report assignments will require you to do formal research, an activity that involves finding sources and evaluating them for reliability, reading them carefully, taking notes, and citing all words you quote and ideas you borrow. See Research Process: Accessing and Recording Information and Annotated Bibliography: Gathering, Evaluating, and Documenting Sources for detailed instruction on conducting research.

Whether you conduct in-depth research or not, keep track of the ideas that come to you and the information you learn. You can write or dictate notes using an app on your phone or computer, or you can jot notes in a journal if you prefer pen and paper. Then, when you are ready to begin organizing your report, you will have a record of your thoughts and information. Always track the sources of information you gather, whether from printed or digital material or from a person you interviewed, so that you can return to the sources if you need more information. And always credit the sources in your report.

Kinds of Evidence

Depending on your assignment and the topic of your report, certain kinds of evidence may be more effective than others. Other kinds of evidence may even be required. As a general rule, choose evidence that is rooted in verifiable facts and experience. In addition, select the evidence that best supports the topic and your approach to the topic, be sure the evidence meets your instructor’s requirements, and cite any evidence you use that comes from a source. The following list contains different kinds of frequently used evidence and an example of each.

Definition : An explanation of a key word, idea, or concept.

The U.S. Census Bureau refers to a “young adult” as a person between 18 and 34 years old.

Example : An illustration of an idea or concept.

The college experience in the fall of 2020 was starkly different from that of previous years. Students who lived in residence halls were assigned to small pods. On-campus dining services were limited. Classes were small and physically distanced or conducted online. Parties were banned.

Expert opinion : A statement by a professional in the field whose opinion is respected.

According to Louise Aronson, MD, geriatrician and author of Elderhood , people over the age of 65 are the happiest of any age group, reporting “less stress, depression, worry, and anger, and more enjoyment, happiness, and satisfaction” (255).

Fact : Information that can be proven correct or accurate.

According to data collected by the NCAA, the academic success of Division I college athletes between 2015 and 2019 was consistently high (Hosick).

Interview : An in-person, phone, or remote conversation that involves an interviewer posing questions to another person or people.

During our interview, I asked Betty about living without a cell phone during the pandemic. She said that before the pandemic, she hadn’t needed a cell phone in her daily activities, but she soon realized that she, and people like her, were increasingly at a disadvantage.

Quotation : The exact words of an author or a speaker.

In response to whether she thought she needed a cell phone, Betty said, “I got along just fine without a cell phone when I could go everywhere in person. The shift to needing a phone came suddenly, and I don’t have extra money in my budget to get one.”

Statistics : A numerical fact or item of data.

The Pew Research Center reported that approximately 25 percent of Hispanic Americans and 17 percent of Black Americans relied on smartphones for online access, compared with 12 percent of White people.

Survey : A structured interview in which respondents (the people who answer the survey questions) are all asked the same questions, either in person or through print or electronic means, and their answers tabulated and interpreted. Surveys discover attitudes, beliefs, or habits of the general public or segments of the population.

A survey of 3,000 mobile phone users in October 2020 showed that 54 percent of respondents used their phones for messaging, while 40 percent used their phones for calls (Steele).

  • Visuals : Graphs, figures, tables, photographs and other images, diagrams, charts, maps, videos, and audio recordings, among others.

Thesis and Organization

Drafting a thesis.

When you have a grasp of your topic, move on to the next phase: drafting a thesis. The thesis is the central idea that you will explore and support in your report; all paragraphs in your report should relate to it. In an essay-style analytical report, you will likely express this main idea in a thesis statement of one or two sentences toward the end of the introduction.

For example, if you found that the academic performance of student athletes was higher than that of non-athletes, you might write the following thesis statement:

student sample text Although a common stereotype is that college athletes barely pass their classes, an analysis of athletes’ academic performance indicates that athletes drop fewer classes, earn higher grades, and are more likely to be on track to graduate in four years when compared with their non-athlete peers. end student sample text

The thesis statement often previews the organization of your writing. For example, in his report on the U.S. response to the COVID-19 pandemic in 2020, Trevor Garcia wrote the following thesis statement, which detailed the central idea of his report:

student sample text An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths. end student sample text

After you draft a thesis statement, ask these questions, and examine your thesis as you answer them. Revise your draft as needed.

  • Is it interesting? A thesis for a report should answer a question that is worth asking and piques curiosity.
  • Is it precise and specific? If you are interested in reducing pollution in a nearby lake, explain how to stop the zebra mussel infestation or reduce the frequent algae blooms.
  • Is it manageable? Try to split the difference between having too much information and not having enough.

Organizing Your Ideas

As a next step, organize the points you want to make in your report and the evidence to support them. Use an outline, a diagram, or another organizational tool, such as Table 8.4 .

Drafting an Analytical Report

With a tentative thesis, an organization plan, and evidence, you are ready to begin drafting. For this assignment, you will report information, analyze it, and draw conclusions about the cause of something, the effect of something, or the similarities and differences between two different things.

Introduction

Some students write the introduction first; others save it for last. Whenever you choose to write the introduction, use it to draw readers into your report. Make the topic of your report clear, and be concise and sincere. End the introduction with your thesis statement. Depending on your topic and the type of report, you can write an effective introduction in several ways. Opening a report with an overview is a tried-and-true strategy, as shown in the following example on the U.S. response to COVID-19 by Trevor Garcia. Notice how he opens the introduction with statistics and a comparison and follows it with a question that leads to the thesis statement (underlined).

student sample text With more than 83 million cases and 1.8 million deaths at the end of 2020, COVID-19 has turned the world upside down. By the end of 2020, the United States led the world in the number of cases, at more than 20 million infections and nearly 350,000 deaths. In comparison, the second-highest number of cases was in India, which at the end of 2020 had less than half the number of COVID-19 cases despite having a population four times greater than the U.S. (“COVID-19 Coronavirus Pandemic,” 2021). How did the United States come to have the world’s worst record in this pandemic? underline An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths end underline . end student sample text

For a less formal report, you might want to open with a question, quotation, or brief story. The following example opens with an anecdote that leads to the thesis statement (underlined).

student sample text Betty stood outside the salon, wondering how to get in. It was June of 2020, and the door was locked. A sign posted on the door provided a phone number for her to call to be let in, but at 81, Betty had lived her life without a cell phone. Betty’s day-to-day life had been hard during the pandemic, but she had planned for this haircut and was looking forward to it; she had a mask on and hand sanitizer in her car. Now she couldn’t get in the door, and she was discouraged. In that moment, Betty realized how much Americans’ dependence on cell phones had grown in the months since the pandemic began. underline Betty and thousands of other senior citizens who could not afford cell phones or did not have the technological skills and support they needed were being left behind in a society that was increasingly reliant on technology end underline . end student sample text

Body Paragraphs: Point, Evidence, Analysis

Use the body paragraphs of your report to present evidence that supports your thesis. A reliable pattern to keep in mind for developing the body paragraphs of a report is point , evidence , and analysis :

  • The point is the central idea of the paragraph, usually given in a topic sentence stated in your own words at or toward the beginning of the paragraph. Each topic sentence should relate to the thesis.
  • The evidence you provide develops the paragraph and supports the point made in the topic sentence. Include details, examples, quotations, paraphrases, and summaries from sources if you conducted formal research. Synthesize the evidence you include by showing in your sentences the connections between sources.
  • The analysis comes at the end of the paragraph. In your own words, draw a conclusion about the evidence you have provided and how it relates to the topic sentence.

The paragraph below illustrates the point, evidence, and analysis pattern. Drawn from a report about concussions among football players, the paragraph opens with a topic sentence about the NCAA and NFL and their responses to studies about concussions. The paragraph is developed with evidence from three sources. It concludes with a statement about helmets and players’ safety.

student sample text The NCAA and NFL have taken steps forward and backward to respond to studies about the danger of concussions among players. Responding to the deaths of athletes, documented brain damage, lawsuits, and public outcry (Buckley et al., 2017), the NCAA instituted protocols to reduce potentially dangerous hits during football games and to diagnose traumatic head injuries more quickly and effectively. Still, it has allowed players to wear more than one style of helmet during a season, raising the risk of injury because of imperfect fit. At the professional level, the NFL developed a helmet-rating system in 2011 in an effort to reduce concussions, but it continued to allow players to wear helmets with a wide range of safety ratings. The NFL’s decision created an opportunity for researchers to look at the relationship between helmet safety ratings and concussions. Cocello et al. (2016) reported that players who wore helmets with a lower safety rating had more concussions than players who wore helmets with a higher safety rating, and they concluded that safer helmets are a key factor in reducing concussions. end student sample text

Developing Paragraph Content

In the body paragraphs of your report, you will likely use examples, draw comparisons, show contrasts, or analyze causes and effects to develop your topic.

Paragraphs developed with Example are common in reports. The paragraph below, adapted from a report by student John Zwick on the mental health of soldiers deployed during wartime, draws examples from three sources.

student sample text Throughout the Vietnam War, military leaders claimed that the mental health of soldiers was stable and that men who suffered from combat fatigue, now known as PTSD, were getting the help they needed. For example, the New York Times (1966) quoted military leaders who claimed that mental fatigue among enlisted men had “virtually ceased to be a problem,” occurring at a rate far below that of World War II. Ayres (1969) reported that Brigadier General Spurgeon Neel, chief American medical officer in Vietnam, explained that soldiers experiencing combat fatigue were admitted to the psychiatric ward, sedated for up to 36 hours, and given a counseling session with a doctor who reassured them that the rest was well deserved and that they were ready to return to their units. Although experts outside the military saw profound damage to soldiers’ psyches when they returned home (Halloran, 1970), the military stayed the course, treating acute cases expediently and showing little concern for the cumulative effect of combat stress on individual soldiers. end student sample text

When you analyze causes and effects , you explain the reasons that certain things happened and/or their results. The report by Trevor Garcia on the U.S. response to the COVID-19 pandemic in 2020 is an example: his report examines the reasons the United States failed to control the coronavirus. The paragraph below, adapted from another student’s report written for an environmental policy course, explains the effect of white settlers’ views of forest management on New England.

student sample text The early colonists’ European ideas about forest management dramatically changed the New England landscape. White settlers saw the New World as virgin, unused land, even though indigenous people had been drawing on its resources for generations by using fire subtly to improve hunting, employing construction techniques that left ancient trees intact, and farming small, efficient fields that left the surrounding landscape largely unaltered. White settlers’ desire to develop wood-built and wood-burning homesteads surrounded by large farm fields led to forestry practices and techniques that resulted in the removal of old-growth trees. These practices defined the way the forests look today. end student sample text

Compare and contrast paragraphs are useful when you wish to examine similarities and differences. You can use both comparison and contrast in a single paragraph, or you can use one or the other. The paragraph below, adapted from a student report on the rise of populist politicians, compares the rhetorical styles of populist politicians Huey Long and Donald Trump.

student sample text A key similarity among populist politicians is their rejection of carefully crafted sound bites and erudite vocabulary typically associated with candidates for high office. Huey Long and Donald Trump are two examples. When he ran for president, Long captured attention through his wild gesticulations on almost every word, dramatically varying volume, and heavily accented, folksy expressions, such as “The only way to be able to feed the balance of the people is to make that man come back and bring back some of that grub that he ain’t got no business with!” In addition, Long’s down-home persona made him a credible voice to represent the common people against the country’s rich, and his buffoonish style allowed him to express his radical ideas without sounding anti-communist alarm bells. Similarly, Donald Trump chose to speak informally in his campaign appearances, but the persona he projected was that of a fast-talking, domineering salesman. His frequent use of personal anecdotes, rhetorical questions, brief asides, jokes, personal attacks, and false claims made his speeches disjointed, but they gave the feeling of a running conversation between him and his audience. For example, in a 2015 speech, Trump said, “They just built a hotel in Syria. Can you believe this? They built a hotel. When I have to build a hotel, I pay interest. They don’t have to pay interest, because they took the oil that, when we left Iraq, I said we should’ve taken” (“Our Country Needs” 2020). While very different in substance, Long and Trump adopted similar styles that positioned them as the antithesis of typical politicians and their worldviews. end student sample text

The conclusion should draw the threads of your report together and make its significance clear to readers. You may wish to review the introduction, restate the thesis, recommend a course of action, point to the future, or use some combination of these. Whichever way you approach it, the conclusion should not head in a new direction. The following example is the conclusion from a student’s report on the effect of a book about environmental movements in the United States.

student sample text Since its publication in 1949, environmental activists of various movements have found wisdom and inspiration in Aldo Leopold’s A Sand County Almanac . These audiences included Leopold’s conservationist contemporaries, environmentalists of the 1960s and 1970s, and the environmental justice activists who rose in the 1980s and continue to make their voices heard today. These audiences have read the work differently: conservationists looked to the author as a leader, environmentalists applied his wisdom to their movement, and environmental justice advocates have pointed out the flaws in Leopold’s thinking. Even so, like those before them, environmental justice activists recognize the book’s value as a testament to taking the long view and eliminating biases that may cloud an objective assessment of humanity’s interdependent relationship with the environment. end student sample text

Citing Sources

You must cite the sources of information and data included in your report. Citations must appear in both the text and a bibliography at the end of the report.

The sample paragraphs in the previous section include examples of in-text citation using APA documentation style. Trevor Garcia’s report on the U.S. response to COVID-19 in 2020 also uses APA documentation style for citations in the text of the report and the list of references at the end. Your instructor may require another documentation style, such as MLA or Chicago.

Peer Review: Getting Feedback from Readers

You will likely engage in peer review with other students in your class by sharing drafts and providing feedback to help spot strengths and weaknesses in your reports. For peer review within a class, your instructor may provide assignment-specific questions or a form for you to complete as you work together.

If you have a writing center on your campus, it is well worth your time to make an online or in-person appointment with a tutor. You’ll receive valuable feedback and improve your ability to review not only your report but your overall writing.

Another way to receive feedback on your report is to ask a friend or family member to read your draft. Provide a list of questions or a form such as the one in Table 8.5 for them to complete as they read.

Revising: Using Reviewers’ Responses to Revise your Work

When you receive comments from readers, including your instructor, read each comment carefully to understand what is being asked. Try not to get defensive, even though this response is completely natural. Remember that readers are like coaches who want you to succeed. They are looking at your writing from outside your own head, and they can identify strengths and weaknesses that you may not have noticed. Keep track of the strengths and weaknesses your readers point out. Pay special attention to those that more than one reader identifies, and use this information to improve your report and later assignments.

As you analyze each response, be open to suggestions for improvement, and be willing to make significant revisions to improve your writing. Perhaps you need to revise your thesis statement to better reflect the content of your draft. Maybe you need to return to your sources to better understand a point you’re trying to make in order to develop a paragraph more fully. Perhaps you need to rethink the organization, move paragraphs around, and add transition sentences.

Below is an early draft of part of Trevor Garcia’s report with comments from a peer reviewer:

student sample text To truly understand what happened, it’s important first to look back to the years leading up to the pandemic. Epidemiologists and public health officials had long known that a global pandemic was possible. In 2016, the U.S. National Security Council (NSC) published a 69-page document with the intimidating title Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents . The document’s two sections address responses to “emerging disease threats that start or are circulating in another country but not yet confirmed within U.S. territorial borders” and to “emerging disease threats within our nation’s borders.” On 13 January 2017, the joint Obama-Trump transition teams performed a pandemic preparedness exercise; however, the playbook was never adopted by the incoming administration. end student sample text

annotated text Peer Review Comment: Do the words in quotation marks need to be a direct quotation? It seems like a paraphrase would work here. end annotated text

annotated text Peer Review Comment: I’m getting lost in the details about the playbook. What’s the Obama-Trump transition team? end annotated text

student sample text In February 2018, the administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention; cuts to other health agencies continued throughout 2018, with funds diverted to unrelated projects such as housing for detained immigrant children. end student sample text

annotated text Peer Review Comment: This paragraph has only one sentence, and it’s more like an example. It needs a topic sentence and more development. end annotated text

student sample text Three months later, Luciana Borio, director of medical and biodefense preparedness at the NSC, spoke at a symposium marking the centennial of the 1918 influenza pandemic. “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no.” end student sample text

annotated text Peer Review Comment: This paragraph is very short and a lot like the previous paragraph in that it’s a single example. It needs a topic sentence. Maybe you can combine them? end annotated text

annotated text Peer Review Comment: Be sure to cite the quotation. end annotated text

Reading these comments and those of others, Trevor decided to combine the three short paragraphs into one paragraph focusing on the fact that the United States knew a pandemic was possible but was unprepared for it. He developed the paragraph, using the short paragraphs as evidence and connecting the sentences and evidence with transitional words and phrases. Finally, he added in-text citations in APA documentation style to credit his sources. The revised paragraph is below:

student sample text Epidemiologists and public health officials in the United States had long known that a global pandemic was possible. In 2016, the National Security Council (NSC) published Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents , a 69-page document on responding to diseases spreading within and outside of the United States. On January 13, 2017, the joint transition teams of outgoing president Barack Obama and then president-elect Donald Trump performed a pandemic preparedness exercise based on the playbook; however, it was never adopted by the incoming administration (Goodman & Schulkin, 2020). A year later, in February 2018, the Trump administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention, leaving key positions unfilled. Other individuals who were fired or resigned in 2018 were the homeland security adviser, whose portfolio included global pandemics; the director for medical and biodefense preparedness; and the top official in charge of a pandemic response. None of them were replaced, leaving the White House with no senior person who had experience in public health (Goodman & Schulkin, 2020). Experts voiced concerns, among them Luciana Borio, director of medical and biodefense preparedness at the NSC, who spoke at a symposium marking the centennial of the 1918 influenza pandemic in May 2018: “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no” (Sun, 2018, final para.). end student sample text

A final word on working with reviewers’ comments: as you consider your readers’ suggestions, remember, too, that you remain the author. You are free to disregard suggestions that you think will not improve your writing. If you choose to disregard comments from your instructor, consider submitting a note explaining your reasons with the final draft of your report.

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Grad Coach

Narrative Analysis 101

Everything you need to know to get started

By: Ethar Al-Saraf (PhD)| Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to research, the host of qualitative analysis methods available to you can be a little overwhelming. In this post, we’ll  unpack the sometimes slippery topic of narrative analysis . We’ll explain what it is, consider its strengths and weaknesses , and look at when and when not to use this analysis method. 

Overview: Narrative Analysis

  • What is narrative analysis (simple definition)
  • The two overarching approaches  
  • The strengths & weaknesses of narrative analysis
  • When (and when not) to use it
  • Key takeaways

What Is Narrative Analysis?

Simply put, narrative analysis is a qualitative analysis method focused on interpreting human experiences and motivations by looking closely at the stories (the narratives) people tell in a particular context.

In other words, a narrative analysis interprets long-form participant responses or written stories as data, to uncover themes and meanings . That data could be taken from interviews, monologues, written stories, or even recordings. In other words, narrative analysis can be used on both primary and secondary data to provide evidence from the experiences described.

That’s all quite conceptual, so let’s look at an example of how narrative analysis could be used.

Let’s say you’re interested in researching the beliefs of a particular author on popular culture. In that case, you might identify the characters , plotlines , symbols and motifs used in their stories. You could then use narrative analysis to analyse these in combination and against the backdrop of the relevant context.

This would allow you to interpret the underlying meanings and implications in their writing, and what they reveal about the beliefs of the author. In other words, you’d look to understand the views of the author by analysing the narratives that run through their work.

Simple definition of narrative analysis

The Two Overarching Approaches

Generally speaking, there are two approaches that one can take to narrative analysis. Specifically, an inductive approach or a deductive approach. Each one will have a meaningful impact on how you interpret your data and the conclusions you can draw, so it’s important that you understand the difference.

First up is the inductive approach to narrative analysis.

The inductive approach takes a bottom-up view , allowing the data to speak for itself, without the influence of any preconceived notions . With this approach, you begin by looking at the data and deriving patterns and themes that can be used to explain the story, as opposed to viewing the data through the lens of pre-existing hypotheses, theories or frameworks. In other words, the analysis is led by the data.

For example, with an inductive approach, you might notice patterns or themes in the way an author presents their characters or develops their plot. You’d then observe these patterns, develop an interpretation of what they might reveal in the context of the story, and draw conclusions relative to the aims of your research.

Contrasted to this is the deductive approach.

With the deductive approach to narrative analysis, you begin by using existing theories that a narrative can be tested against . Here, the analysis adopts particular theoretical assumptions and/or provides hypotheses, and then looks for evidence in a story that will either verify or disprove them.

For example, your analysis might begin with a theory that wealthy authors only tell stories to get the sympathy of their readers. A deductive analysis might then look at the narratives of wealthy authors for evidence that will substantiate (or refute) the theory and then draw conclusions about its accuracy, and suggest explanations for why that might or might not be the case.

Which approach you should take depends on your research aims, objectives and research questions . If these are more exploratory in nature, you’ll likely take an inductive approach. Conversely, if they are more confirmatory in nature, you’ll likely opt for the deductive approach.

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Strengths & Weaknesses

Now that we have a clearer view of what narrative analysis is and the two approaches to it, it’s important to understand its strengths and weaknesses , so that you can make the right choices in your research project.

A primary strength of narrative analysis is the rich insight it can generate by uncovering the underlying meanings and interpretations of human experience. The focus on an individual narrative highlights the nuances and complexities of their experience, revealing details that might be missed or considered insignificant by other methods.

Another strength of narrative analysis is the range of topics it can be used for. The focus on human experience means that a narrative analysis can democratise your data analysis, by revealing the value of individuals’ own interpretation of their experience in contrast to broader social, cultural, and political factors.

All that said, just like all analysis methods, narrative analysis has its weaknesses. It’s important to understand these so that you can choose the most appropriate method for your particular research project.

The first drawback of narrative analysis is the problem of subjectivity and interpretation . In other words, a drawback of the focus on stories and their details is that they’re open to being understood differently depending on who’s reading them. This means that a strong understanding of the author’s cultural context is crucial to developing your interpretation of the data. At the same time, it’s important that you remain open-minded in how you interpret your chosen narrative and avoid making any assumptions .

A second weakness of narrative analysis is the issue of reliability and generalisation . Since narrative analysis depends almost entirely on a subjective narrative and your interpretation, the findings and conclusions can’t usually be generalised or empirically verified. Although some conclusions can be drawn about the cultural context, they’re still based on what will almost always be anecdotal data and not suitable for the basis of a theory, for example.

Last but not least, the focus on long-form data expressed as stories means that narrative analysis can be very time-consuming . In addition to the source data itself, you will have to be well informed on the author’s cultural context as well as other interpretations of the narrative, where possible, to ensure you have a holistic view. So, if you’re going to undertake narrative analysis, make sure that you allocate a generous amount of time to work through the data.

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When To Use Narrative Analysis

As a qualitative method focused on analysing and interpreting narratives describing human experiences, narrative analysis is usually most appropriate for research topics focused on social, personal, cultural , or even ideological events or phenomena and how they’re understood at an individual level.

For example, if you were interested in understanding the experiences and beliefs of individuals suffering social marginalisation, you could use narrative analysis to look at the narratives and stories told by people in marginalised groups to identify patterns , symbols , or motifs that shed light on how they rationalise their experiences.

In this example, narrative analysis presents a good natural fit as it’s focused on analysing people’s stories to understand their views and beliefs at an individual level. Conversely, if your research was geared towards understanding broader themes and patterns regarding an event or phenomena, analysis methods such as content analysis or thematic analysis may be better suited, depending on your research aim .

how to write an analysis in research

Let’s recap

In this post, we’ve explored the basics of narrative analysis in qualitative research. The key takeaways are:

  • Narrative analysis is a qualitative analysis method focused on interpreting human experience in the form of stories or narratives .
  • There are two overarching approaches to narrative analysis: the inductive (exploratory) approach and the deductive (confirmatory) approach.
  • Like all analysis methods, narrative analysis has a particular set of strengths and weaknesses .
  • Narrative analysis is generally most appropriate for research focused on interpreting individual, human experiences as expressed in detailed , long-form accounts.

If you’d like to learn more about narrative analysis and qualitative analysis methods in general, be sure to check out the rest of the Grad Coach blog here . Alternatively, if you’re looking for hands-on help with your project, take a look at our 1-on-1 private coaching service .

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Research aims, research objectives and research questions

Thanks. I need examples of narrative analysis

Derek Jansen

Here are some examples of research topics that could utilise narrative analysis:

Personal Narratives of Trauma: Analysing personal stories of individuals who have experienced trauma to understand the impact, coping mechanisms, and healing processes.

Identity Formation in Immigrant Communities: Examining the narratives of immigrants to explore how they construct and negotiate their identities in a new cultural context.

Media Representations of Gender: Analysing narratives in media texts (such as films, television shows, or advertisements) to investigate the portrayal of gender roles, stereotypes, and power dynamics.

Yvonne Worrell

Where can I find an example of a narrative analysis table ?

Belinda

Please i need help with my project,

Mst. Shefat-E-Sultana

how can I cite this article in APA 7th style?

Towha

please mention the sources as well.

Bezuayehu

My research is mixed approach. I use interview,key_inforamt interview,FGD and document.so,which qualitative analysis is appropriate to analyze these data.Thanks

Which qualitative analysis methode is appropriate to analyze data obtain from intetview,key informant intetview,Focus group discussion and document.

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How to conduct a meta-analysis in eight steps: a practical guide

  • Open access
  • Published: 30 November 2021
  • Volume 72 , pages 1–19, ( 2022 )

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  • Christopher Hansen 1 ,
  • Holger Steinmetz 2 &
  • Jörn Block 3 , 4 , 5  

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1 Introduction

“Scientists have known for centuries that a single study will not resolve a major issue. Indeed, a small sample study will not even resolve a minor issue. Thus, the foundation of science is the cumulation of knowledge from the results of many studies.” (Hunter et al. 1982 , p. 10)

Meta-analysis is a central method for knowledge accumulation in many scientific fields (Aguinis et al. 2011c ; Kepes et al. 2013 ). Similar to a narrative review, it serves as a synopsis of a research question or field. However, going beyond a narrative summary of key findings, a meta-analysis adds value in providing a quantitative assessment of the relationship between two target variables or the effectiveness of an intervention (Gurevitch et al. 2018 ). Also, it can be used to test competing theoretical assumptions against each other or to identify important moderators where the results of different primary studies differ from each other (Aguinis et al. 2011b ; Bergh et al. 2016 ). Rooted in the synthesis of the effectiveness of medical and psychological interventions in the 1970s (Glass 2015 ; Gurevitch et al. 2018 ), meta-analysis is nowadays also an established method in management research and related fields.

The increasing importance of meta-analysis in management research has resulted in the publication of guidelines in recent years that discuss the merits and best practices in various fields, such as general management (Bergh et al. 2016 ; Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ), international business (Steel et al. 2021 ), economics and finance (Geyer-Klingeberg et al. 2020 ; Havranek et al. 2020 ), marketing (Eisend 2017 ; Grewal et al. 2018 ), and organizational studies (DeSimone et al. 2020 ; Rudolph et al. 2020 ). These articles discuss existing and trending methods and propose solutions for often experienced problems. This editorial briefly summarizes the insights of these papers; provides a workflow of the essential steps in conducting a meta-analysis; suggests state-of-the art methodological procedures; and points to other articles for in-depth investigation. Thus, this article has two goals: (1) based on the findings of previous editorials and methodological articles, it defines methodological recommendations for meta-analyses submitted to Management Review Quarterly (MRQ); and (2) it serves as a practical guide for researchers who have little experience with meta-analysis as a method but plan to conduct one in the future.

2 Eight steps in conducting a meta-analysis

2.1 step 1: defining the research question.

The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed. When defining the research question, two hurdles might develop. First, when defining an adequate study scope, researchers must consider that the number of publications has grown exponentially in many fields of research in recent decades (Fortunato et al. 2018 ). On the one hand, a larger number of studies increases the potentially relevant literature basis and enables researchers to conduct meta-analyses. Conversely, scanning a large amount of studies that could be potentially relevant for the meta-analysis results in a perhaps unmanageable workload. Thus, Steel et al. ( 2021 ) highlight the importance of balancing manageability and relevance when defining the research question. Second, similar to the number of primary studies also the number of meta-analyses in management research has grown strongly in recent years (Geyer-Klingeberg et al. 2020 ; Rauch 2020 ; Schwab 2015 ). Therefore, it is likely that one or several meta-analyses for many topics of high scholarly interest already exist. However, this should not deter researchers from investigating their research questions. One possibility is to consider moderators or mediators of a relationship that have previously been ignored. For example, a meta-analysis about startup performance could investigate the impact of different ways to measure the performance construct (e.g., growth vs. profitability vs. survival time) or certain characteristics of the founders as moderators. Another possibility is to replicate previous meta-analyses and test whether their findings can be confirmed with an updated sample of primary studies or newly developed methods. Frequent replications and updates of meta-analyses are important contributions to cumulative science and are increasingly called for by the research community (Anderson & Kichkha 2017 ; Steel et al. 2021 ). Consistent with its focus on replication studies (Block and Kuckertz 2018 ), MRQ therefore also invites authors to submit replication meta-analyses.

2.2 Step 2: literature search

2.2.1 search strategies.

Similar to conducting a literature review, the search process of a meta-analysis should be systematic, reproducible, and transparent, resulting in a sample that includes all relevant studies (Fisch and Block 2018 ; Gusenbauer and Haddaway 2020 ). There are several identification strategies for relevant primary studies when compiling meta-analytical datasets (Harari et al. 2020 ). First, previous meta-analyses on the same or a related topic may provide lists of included studies that offer a good starting point to identify and become familiar with the relevant literature. This practice is also applicable to topic-related literature reviews, which often summarize the central findings of the reviewed articles in systematic tables. Both article types likely include the most prominent studies of a research field. The most common and important search strategy, however, is a keyword search in electronic databases (Harari et al. 2020 ). This strategy will probably yield the largest number of relevant studies, particularly so-called ‘grey literature’, which may not be considered by literature reviews. Gusenbauer and Haddaway ( 2020 ) provide a detailed overview of 34 scientific databases, of which 18 are multidisciplinary or have a focus on management sciences, along with their suitability for literature synthesis. To prevent biased results due to the scope or journal coverage of one database, researchers should use at least two different databases (DeSimone et al. 2020 ; Martín-Martín et al. 2021 ; Mongeon & Paul-Hus 2016 ). However, a database search can easily lead to an overload of potentially relevant studies. For example, key term searches in Google Scholar for “entrepreneurial intention” and “firm diversification” resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ). Addressing the challenge of identifying relevant articles in the growing number of database publications, (semi)automated approaches using text mining and machine learning (Bosco et al. 2017 ; O’Mara-Eves et al. 2015 ; Ouzzani et al. 2016 ; Thomas et al. 2017 ) can also be promising and time-saving search tools in the future. Also, some electronic databases offer the possibility to track forward citations of influential studies and thereby identify further relevant articles. Finally, collecting unpublished or undetected studies through conferences, personal contact with (leading) scholars, or listservs can be strategies to increase the study sample size (Grewal et al. 2018 ; Harari et al. 2020 ; Pigott and Polanin 2020 ).

2.2.2 Study inclusion criteria and sample composition

Next, researchers must decide which studies to include in the meta-analysis. Some guidelines for literature reviews recommend limiting the sample to studies published in renowned academic journals to ensure the quality of findings (e.g., Kraus et al. 2020 ). For meta-analysis, however, Steel et al. ( 2021 ) advocate for the inclusion of all available studies, including grey literature, to prevent selection biases based on availability, cost, familiarity, and language (Rothstein et al. 2005 ), or the “Matthew effect”, which denotes the phenomenon that highly cited articles are found faster than less cited articles (Merton 1968 ). Harrison et al. ( 2017 ) find that the effects of published studies in management are inflated on average by 30% compared to unpublished studies. This so-called publication bias or “file drawer problem” (Rosenthal 1979 ) results from the preference of academia to publish more statistically significant and less statistically insignificant study results. Owen and Li ( 2020 ) showed that publication bias is particularly severe when variables of interest are used as key variables rather than control variables. To consider the true effect size of a target variable or relationship, the inclusion of all types of research outputs is therefore recommended (Polanin et al. 2016 ). Different test procedures to identify publication bias are discussed subsequently in Step 7.

In addition to the decision of whether to include certain study types (i.e., published vs. unpublished studies), there can be other reasons to exclude studies that are identified in the search process. These reasons can be manifold and are primarily related to the specific research question and methodological peculiarities. For example, studies identified by keyword search might not qualify thematically after all, may use unsuitable variable measurements, or may not report usable effect sizes. Furthermore, there might be multiple studies by the same authors using similar datasets. If they do not differ sufficiently in terms of their sample characteristics or variables used, only one of these studies should be included to prevent bias from duplicates (Wood 2008 ; see this article for a detection heuristic).

In general, the screening process should be conducted stepwise, beginning with a removal of duplicate citations from different databases, followed by abstract screening to exclude clearly unsuitable studies and a final full-text screening of the remaining articles (Pigott and Polanin 2020 ). A graphical tool to systematically document the sample selection process is the PRISMA flow diagram (Moher et al. 2009 ). Page et al. ( 2021 ) recently presented an updated version of the PRISMA statement, including an extended item checklist and flow diagram to report the study process and findings.

2.3 Step 3: choice of the effect size measure

2.3.1 types of effect sizes.

The two most common meta-analytical effect size measures in management studies are (z-transformed) correlation coefficients and standardized mean differences (Aguinis et al. 2011a ; Geyskens et al. 2009 ). However, meta-analyses in management science and related fields may not be limited to those two effect size measures but rather depend on the subfield of investigation (Borenstein 2009 ; Stanley and Doucouliagos 2012 ). In economics and finance, researchers are more interested in the examination of elasticities and marginal effects extracted from regression models than in pure bivariate correlations (Stanley and Doucouliagos 2012 ). Regression coefficients can also be converted to partial correlation coefficients based on their t-statistics to make regression results comparable across studies (Stanley and Doucouliagos 2012 ). Although some meta-analyses in management research have combined bivariate and partial correlations in their study samples, Aloe ( 2015 ) and Combs et al. ( 2019 ) advise researchers not to use this practice. Most importantly, they argue that the effect size strength of partial correlations depends on the other variables included in the regression model and is therefore incomparable to bivariate correlations (Schmidt and Hunter 2015 ), resulting in a possible bias of the meta-analytic results (Roth et al. 2018 ). We endorse this opinion. If at all, we recommend separate analyses for each measure. In addition to these measures, survival rates, risk ratios or odds ratios, which are common measures in medical research (Borenstein 2009 ), can be suitable effect sizes for specific management research questions, such as understanding the determinants of the survival of startup companies. To summarize, the choice of a suitable effect size is often taken away from the researcher because it is typically dependent on the investigated research question as well as the conventions of the specific research field (Cheung and Vijayakumar 2016 ).

2.3.2 Conversion of effect sizes to a common measure

After having defined the primary effect size measure for the meta-analysis, it might become necessary in the later coding process to convert study findings that are reported in effect sizes that are different from the chosen primary effect size. For example, a study might report only descriptive statistics for two study groups but no correlation coefficient, which is used as the primary effect size measure in the meta-analysis. Different effect size measures can be harmonized using conversion formulae, which are provided by standard method books such as Borenstein et al. ( 2009 ) or Lipsey and Wilson ( 2001 ). There also exist online effect size calculators for meta-analysis. Footnote 2

2.4 Step 4: choice of the analytical method used

Choosing which meta-analytical method to use is directly connected to the research question of the meta-analysis. Research questions in meta-analyses can address a relationship between constructs or an effect of an intervention in a general manner, or they can focus on moderating or mediating effects. There are four meta-analytical methods that are primarily used in contemporary management research (Combs et al. 2019 ; Geyer-Klingeberg et al. 2020 ), which allow the investigation of these different types of research questions: traditional univariate meta-analysis, meta-regression, meta-analytic structural equation modeling, and qualitative meta-analysis (Hoon 2013 ). While the first three are quantitative, the latter summarizes qualitative findings. Table 1 summarizes the key characteristics of the three quantitative methods.

2.4.1 Univariate meta-analysis

In its traditional form, a meta-analysis reports a weighted mean effect size for the relationship or intervention of investigation and provides information on the magnitude of variance among primary studies (Aguinis et al. 2011c ; Borenstein et al. 2009 ). Accordingly, it serves as a quantitative synthesis of a research field (Borenstein et al. 2009 ; Geyskens et al. 2009 ). Prominent traditional approaches have been developed, for example, by Hedges and Olkin ( 1985 ) or Hunter and Schmidt ( 1990 , 2004 ). However, going beyond its simple summary function, the traditional approach has limitations in explaining the observed variance among findings (Gonzalez-Mulé and Aguinis 2018 ). To identify moderators (or boundary conditions) of the relationship of interest, meta-analysts can create subgroups and investigate differences between those groups (Borenstein and Higgins 2013 ; Hunter and Schmidt 2004 ). Potential moderators can be study characteristics (e.g., whether a study is published vs. unpublished), sample characteristics (e.g., study country, industry focus, or type of survey/experiment participants), or measurement artifacts (e.g., different types of variable measurements). The univariate approach is thus suitable to identify the overall direction of a relationship and can serve as a good starting point for additional analyses. However, due to its limitations in examining boundary conditions and developing theory, the univariate approach on its own is currently oftentimes viewed as not sufficient (Rauch 2020 ; Shaw and Ertug 2017 ).

2.4.2 Meta-regression analysis

Meta-regression analysis (Hedges and Olkin 1985 ; Lipsey and Wilson 2001 ; Stanley and Jarrell 1989 ) aims to investigate the heterogeneity among observed effect sizes by testing multiple potential moderators simultaneously. In meta-regression, the coded effect size is used as the dependent variable and is regressed on a list of moderator variables. These moderator variables can be categorical variables as described previously in the traditional univariate approach or (semi)continuous variables such as country scores that are merged with the meta-analytical data. Thus, meta-regression analysis overcomes the disadvantages of the traditional approach, which only allows us to investigate moderators singularly using dichotomized subgroups (Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ). These possibilities allow a more fine-grained analysis of research questions that are related to moderating effects. However, Schmidt ( 2017 ) critically notes that the number of effect sizes in the meta-analytical sample must be sufficiently large to produce reliable results when investigating multiple moderators simultaneously in a meta-regression. For further reading, Tipton et al. ( 2019 ) outline the technical, conceptual, and practical developments of meta-regression over the last decades. Gonzalez-Mulé and Aguinis ( 2018 ) provide an overview of methodological choices and develop evidence-based best practices for future meta-analyses in management using meta-regression.

2.4.3 Meta-analytic structural equation modeling (MASEM)

MASEM is a combination of meta-analysis and structural equation modeling and allows to simultaneously investigate the relationships among several constructs in a path model. Researchers can use MASEM to test several competing theoretical models against each other or to identify mediation mechanisms in a chain of relationships (Bergh et al. 2016 ). This method is typically performed in two steps (Cheung and Chan 2005 ): In Step 1, a pooled correlation matrix is derived, which includes the meta-analytical mean effect sizes for all variable combinations; Step 2 then uses this matrix to fit the path model. While MASEM was based primarily on traditional univariate meta-analysis to derive the pooled correlation matrix in its early years (Viswesvaran and Ones 1995 ), more advanced methods, such as the GLS approach (Becker 1992 , 1995 ) or the TSSEM approach (Cheung and Chan 2005 ), have been subsequently developed. Cheung ( 2015a ) and Jak ( 2015 ) provide an overview of these approaches in their books with exemplary code. For datasets with more complex data structures, Wilson et al. ( 2016 ) also developed a multilevel approach that is related to the TSSEM approach in the second step. Bergh et al. ( 2016 ) discuss nine decision points and develop best practices for MASEM studies.

2.4.4 Qualitative meta-analysis

While the approaches explained above focus on quantitative outcomes of empirical studies, qualitative meta-analysis aims to synthesize qualitative findings from case studies (Hoon 2013 ; Rauch et al. 2014 ). The distinctive feature of qualitative case studies is their potential to provide in-depth information about specific contextual factors or to shed light on reasons for certain phenomena that cannot usually be investigated by quantitative studies (Rauch 2020 ; Rauch et al. 2014 ). In a qualitative meta-analysis, the identified case studies are systematically coded in a meta-synthesis protocol, which is then used to identify influential variables or patterns and to derive a meta-causal network (Hoon 2013 ). Thus, the insights of contextualized and typically nongeneralizable single studies are aggregated to a larger, more generalizable picture (Habersang et al. 2019 ). Although still the exception, this method can thus provide important contributions for academics in terms of theory development (Combs et al., 2019 ; Hoon 2013 ) and for practitioners in terms of evidence-based management or entrepreneurship (Rauch et al. 2014 ). Levitt ( 2018 ) provides a guide and discusses conceptual issues for conducting qualitative meta-analysis in psychology, which is also useful for management researchers.

2.5 Step 5: choice of software

Software solutions to perform meta-analyses range from built-in functions or additional packages of statistical software to software purely focused on meta-analyses and from commercial to open-source solutions. However, in addition to personal preferences, the choice of the most suitable software depends on the complexity of the methods used and the dataset itself (Cheung and Vijayakumar 2016 ). Meta-analysts therefore must carefully check if their preferred software is capable of performing the intended analysis.

Among commercial software providers, Stata (from version 16 on) offers built-in functions to perform various meta-analytical analyses or to produce various plots (Palmer and Sterne 2016 ). For SPSS and SAS, there exist several macros for meta-analyses provided by scholars, such as David B. Wilson or Andy P. Field and Raphael Gillet (Field and Gillett 2010 ). Footnote 3 Footnote 4 For researchers using the open-source software R (R Core Team 2021 ), Polanin et al. ( 2017 ) provide an overview of 63 meta-analysis packages and their functionalities. For new users, they recommend the package metafor (Viechtbauer 2010 ), which includes most necessary functions and for which the author Wolfgang Viechtbauer provides tutorials on his project website. Footnote 5 Footnote 6 In addition to packages and macros for statistical software, templates for Microsoft Excel have also been developed to conduct simple meta-analyses, such as Meta-Essentials by Suurmond et al. ( 2017 ). Footnote 7 Finally, programs purely dedicated to meta-analysis also exist, such as Comprehensive Meta-Analysis (Borenstein et al. 2013 ) or RevMan by The Cochrane Collaboration ( 2020 ).

2.6 Step 6: coding of effect sizes

2.6.1 coding sheet.

The first step in the coding process is the design of the coding sheet. A universal template does not exist because the design of the coding sheet depends on the methods used, the respective software, and the complexity of the research design. For univariate meta-analysis or meta-regression, data are typically coded in wide format. In its simplest form, when investigating a correlational relationship between two variables using the univariate approach, the coding sheet would contain a column for the study name or identifier, the effect size coded from the primary study, and the study sample size. However, such simple relationships are unlikely in management research because the included studies are typically not identical but differ in several respects. With more complex data structures or moderator variables being investigated, additional columns are added to the coding sheet to reflect the data characteristics. These variables can be coded as dummy, factor, or (semi)continuous variables and later used to perform a subgroup analysis or meta regression. For MASEM, the required data input format can deviate depending on the method used (e.g., TSSEM requires a list of correlation matrices as data input). For qualitative meta-analysis, the coding scheme typically summarizes the key qualitative findings and important contextual and conceptual information (see Hoon ( 2013 ) for a coding scheme for qualitative meta-analysis). Figure  1 shows an exemplary coding scheme for a quantitative meta-analysis on the correlational relationship between top-management team diversity and profitability. In addition to effect and sample sizes, information about the study country, firm type, and variable operationalizations are coded. The list could be extended by further study and sample characteristics.

figure 1

Exemplary coding sheet for a meta-analysis on the relationship (correlation) between top-management team diversity and profitability

2.6.2 Inclusion of moderator or control variables

It is generally important to consider the intended research model and relevant nontarget variables before coding a meta-analytic dataset. For example, study characteristics can be important moderators or function as control variables in a meta-regression model. Similarly, control variables may be relevant in a MASEM approach to reduce confounding bias. Coding additional variables or constructs subsequently can be arduous if the sample of primary studies is large. However, the decision to include respective moderator or control variables, as in any empirical analysis, should always be based on strong (theoretical) rationales about how these variables can impact the investigated effect (Bernerth and Aguinis 2016 ; Bernerth et al. 2018 ; Thompson and Higgins 2002 ). While substantive moderators refer to theoretical constructs that act as buffers or enhancers of a supposed causal process, methodological moderators are features of the respective research designs that denote the methodological context of the observations and are important to control for systematic statistical particularities (Rudolph et al. 2020 ). Havranek et al. ( 2020 ) provide a list of recommended variables to code as potential moderators. While researchers may have clear expectations about the effects for some of these moderators, the concerns for other moderators may be tentative, and moderator analysis may be approached in a rather exploratory fashion. Thus, we argue that researchers should make full use of the meta-analytical design to obtain insights about potential context dependence that a primary study cannot achieve.

2.6.3 Treatment of multiple effect sizes in a study

A long-debated issue in conducting meta-analyses is whether to use only one or all available effect sizes for the same construct within a single primary study. For meta-analyses in management research, this question is fundamental because many empirical studies, particularly those relying on company databases, use multiple variables for the same construct to perform sensitivity analyses, resulting in multiple relevant effect sizes. In this case, researchers can either (randomly) select a single value, calculate a study average, or use the complete set of effect sizes (Bijmolt and Pieters 2001 ; López-López et al. 2018 ). Multiple effect sizes from the same study enrich the meta-analytic dataset and allow us to investigate the heterogeneity of the relationship of interest, such as different variable operationalizations (López-López et al. 2018 ; Moeyaert et al. 2017 ). However, including more than one effect size from the same study violates the independency assumption of observations (Cheung 2019 ; López-López et al. 2018 ), which can lead to biased results and erroneous conclusions (Gooty et al. 2021 ). We follow the recommendation of current best practice guides to take advantage of using all available effect size observations but to carefully consider interdependencies using appropriate methods such as multilevel models, panel regression models, or robust variance estimation (Cheung 2019 ; Geyer-Klingeberg et al. 2020 ; Gooty et al. 2021 ; López-López et al. 2018 ; Moeyaert et al. 2017 ).

2.7 Step 7: analysis

2.7.1 outlier analysis and tests for publication bias.

Before conducting the primary analysis, some preliminary sensitivity analyses might be necessary, which should ensure the robustness of the meta-analytical findings (Rudolph et al. 2020 ). First, influential outlier observations could potentially bias the observed results, particularly if the number of total effect sizes is small. Several statistical methods can be used to identify outliers in meta-analytical datasets (Aguinis et al. 2013 ; Viechtbauer and Cheung 2010 ). However, there is a debate about whether to keep or omit these observations. Anyhow, relevant studies should be closely inspected to infer an explanation about their deviating results. As in any other primary study, outliers can be a valid representation, albeit representing a different population, measure, construct, design or procedure. Thus, inferences about outliers can provide the basis to infer potential moderators (Aguinis et al. 2013 ; Steel et al. 2021 ). On the other hand, outliers can indicate invalid research, for instance, when unrealistically strong correlations are due to construct overlap (i.e., lack of a clear demarcation between independent and dependent variables), invalid measures, or simply typing errors when coding effect sizes. An advisable step is therefore to compare the results both with and without outliers and base the decision on whether to exclude outlier observations with careful consideration (Geyskens et al. 2009 ; Grewal et al. 2018 ; Kepes et al. 2013 ). However, instead of simply focusing on the size of the outlier, its leverage should be considered. Thus, Viechtbauer and Cheung ( 2010 ) propose considering a combination of standardized deviation and a study’s leverage.

Second, as mentioned in the context of a literature search, potential publication bias may be an issue. Publication bias can be examined in multiple ways (Rothstein et al. 2005 ). First, the funnel plot is a simple graphical tool that can provide an overview of the effect size distribution and help to detect publication bias (Stanley and Doucouliagos 2010 ). A funnel plot can also support in identifying potential outliers. As mentioned above, a graphical display of deviation (e.g., studentized residuals) and leverage (Cook’s distance) can help detect the presence of outliers and evaluate their influence (Viechtbauer and Cheung 2010 ). Moreover, several statistical procedures can be used to test for publication bias (Harrison et al. 2017 ; Kepes et al. 2012 ), including subgroup comparisons between published and unpublished studies, Begg and Mazumdar’s ( 1994 ) rank correlation test, cumulative meta-analysis (Borenstein et al. 2009 ), the trim and fill method (Duval and Tweedie 2000a , b ), Egger et al.’s ( 1997 ) regression test, failsafe N (Rosenthal 1979 ), or selection models (Hedges and Vevea 2005 ; Vevea and Woods 2005 ). In examining potential publication bias, Kepes et al. ( 2012 ) and Harrison et al. ( 2017 ) both recommend not relying only on a single test but rather using multiple conceptionally different test procedures (i.e., the so-called “triangulation approach”).

2.7.2 Model choice

After controlling and correcting for the potential presence of impactful outliers or publication bias, the next step in meta-analysis is the primary analysis, where meta-analysts must decide between two different types of models that are based on different assumptions: fixed-effects and random-effects (Borenstein et al. 2010 ). Fixed-effects models assume that all observations share a common mean effect size, which means that differences are only due to sampling error, while random-effects models assume heterogeneity and allow for a variation of the true effect sizes across studies (Borenstein et al. 2010 ; Cheung and Vijayakumar 2016 ; Hunter and Schmidt 2004 ). Both models are explained in detail in standard textbooks (e.g., Borenstein et al. 2009 ; Hunter and Schmidt 2004 ; Lipsey and Wilson 2001 ).

In general, the presence of heterogeneity is likely in management meta-analyses because most studies do not have identical empirical settings, which can yield different effect size strengths or directions for the same investigated phenomenon. For example, the identified studies have been conducted in different countries with different institutional settings, or the type of study participants varies (e.g., students vs. employees, blue-collar vs. white-collar workers, or manufacturing vs. service firms). Thus, the vast majority of meta-analyses in management research and related fields use random-effects models (Aguinis et al. 2011a ). In a meta-regression, the random-effects model turns into a so-called mixed-effects model because moderator variables are added as fixed effects to explain the impact of observed study characteristics on effect size variations (Raudenbush 2009 ).

2.8 Step 8: reporting results

2.8.1 reporting in the article.

The final step in performing a meta-analysis is reporting its results. Most importantly, all steps and methodological decisions should be comprehensible to the reader. DeSimone et al. ( 2020 ) provide an extensive checklist for journal reviewers of meta-analytical studies. This checklist can also be used by authors when performing their analyses and reporting their results to ensure that all important aspects have been addressed. Alternative checklists are provided, for example, by Appelbaum et al. ( 2018 ) or Page et al. ( 2021 ). Similarly, Levitt et al. ( 2018 ) provide a detailed guide for qualitative meta-analysis reporting standards.

For quantitative meta-analyses, tables reporting results should include all important information and test statistics, including mean effect sizes; standard errors and confidence intervals; the number of observations and study samples included; and heterogeneity measures. If the meta-analytic sample is rather small, a forest plot provides a good overview of the different findings and their accuracy. However, this figure will be less feasible for meta-analyses with several hundred effect sizes included. Also, results displayed in the tables and figures must be explained verbally in the results and discussion sections. Most importantly, authors must answer the primary research question, i.e., whether there is a positive, negative, or no relationship between the variables of interest, or whether the examined intervention has a certain effect. These results should be interpreted with regard to their magnitude (or significance), both economically and statistically. However, when discussing meta-analytical results, authors must describe the complexity of the results, including the identified heterogeneity and important moderators, future research directions, and theoretical relevance (DeSimone et al. 2019 ). In particular, the discussion of identified heterogeneity and underlying moderator effects is critical; not including this information can lead to false conclusions among readers, who interpret the reported mean effect size as universal for all included primary studies and ignore the variability of findings when citing the meta-analytic results in their research (Aytug et al. 2012 ; DeSimone et al. 2019 ).

2.8.2 Open-science practices

Another increasingly important topic is the public provision of meta-analytical datasets and statistical codes via open-source repositories. Open-science practices allow for results validation and for the use of coded data in subsequent meta-analyses ( Polanin et al. 2020 ), contributing to the development of cumulative science. Steel et al. ( 2021 ) refer to open science meta-analyses as a step towards “living systematic reviews” (Elliott et al. 2017 ) with continuous updates in real time. MRQ supports this development and encourages authors to make their datasets publicly available. Moreau and Gamble ( 2020 ), for example, provide various templates and video tutorials to conduct open science meta-analyses. There exist several open science repositories, such as the Open Science Foundation (OSF; for a tutorial, see Soderberg 2018 ), to preregister and make documents publicly available. Furthermore, several initiatives in the social sciences have been established to develop dynamic meta-analyses, such as metaBUS (Bosco et al. 2015 , 2017 ), MetaLab (Bergmann et al. 2018 ), or PsychOpen CAMA (Burgard et al. 2021 ).

3 Conclusion

This editorial provides a comprehensive overview of the essential steps in conducting and reporting a meta-analysis with references to more in-depth methodological articles. It also serves as a guide for meta-analyses submitted to MRQ and other management journals. MRQ welcomes all types of meta-analyses from all subfields and disciplines of management research.

Gusenbauer and Haddaway ( 2020 ), however, point out that Google Scholar is not appropriate as a primary search engine due to a lack of reproducibility of search results.

One effect size calculator by David B. Wilson is accessible via: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php .

The macros of David B. Wilson can be downloaded from: http://mason.gmu.edu/~dwilsonb/ .

The macros of Field and Gillet ( 2010 ) can be downloaded from: https://www.discoveringstatistics.com/repository/fieldgillett/how_to_do_a_meta_analysis.html .

The tutorials can be found via: https://www.metafor-project.org/doku.php .

Metafor does currently not provide functions to conduct MASEM. For MASEM, users can, for instance, use the package metaSEM (Cheung 2015b ).

The workbooks can be downloaded from: https://www.erim.eur.nl/research-support/meta-essentials/ .

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Ten simple rules for carrying out and writing meta-analyses

Diego a. forero.

1 Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia

2 PhD Program in Health Sciences, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia

Sandra Lopez-Leon

3 Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, United States of America

Yeimy González-Giraldo

4 Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá., Colombia

Pantelis G. Bagos

5 Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

Introduction

In the context of evidence-based medicine, meta-analyses provide novel and useful information [ 1 ], as they are at the top of the pyramid of evidence and consolidate previous evidence published in multiple previous reports [ 2 ]. Meta-analysis is a powerful tool to cumulate and summarize the knowledge in a research field [ 3 ]. Because of the significant increase in the published scientific literature in recent years, there has also been an important growth in the number of meta-analyses for a large number of topics [ 4 ]. It has been found that meta-analyses are among the types of publications that usually receive a larger number of citations in the biomedical sciences [ 5 , 6 ]. The methods and standards for carrying out meta-analyses have evolved in recent years [ 7 – 9 ].

Although there are several published articles describing comprehensive guidelines for specific types of meta-analyses, there is still the need for an abridged article with general and updated recommendations for researchers interested in the development of meta-analyses. We present here ten simple rules for carrying out and writing meta-analyses.

Rule 1: Specify the topic and type of the meta-analysis

Considering that a systematic review [ 10 ] is fundamental for a meta-analysis, you can use the Population, Intervention, Comparison, Outcome (PICO) model to formulate the research question. It is important to verify that there are no published meta-analyses on the specific topic in order to avoid duplication of efforts [ 11 ]. In some cases, an updated meta-analysis in a topic is needed if additional data become available. It is possible to carry out meta-analyses for multiple types of studies, such as epidemiological variables for case-control, cohort, and randomized clinical trials. As observational studies have a larger possibility of having several biases, meta-analyses of these types of designs should take that into account. In addition, there is the possibility to carry out meta-analyses for genetic association studies, gene expression studies, genome-wide association studies (GWASs), or data from animal experiments. It is advisable to preregister the systematic review protocols at the International Prospective Register of Systematic Reviews (PROSPERO; https://www.crd.york.ac.uk/Prospero ) database [ 12 ]. Keep in mind that an increasing number of journals require registration prior to publication.

Rule 2: Follow available guidelines for different types of meta-analyses

There are several available general guidelines. The first of such efforts were the Quality of Reports of Meta-analyses of Randomized Controlled Trials (QUORUM) [ 13 ] and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) statements [ 14 ], but currently, the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) [ 15 ] has been broadly cited and used. In addition, there have been efforts to develop specific guidelines regarding meta-analyses for clinical studies (Cochrane Handbook; https://training.cochrane.org/handbook ), genetic association studies [ 16 ], genome-wide expression studies [ 17 ], GWASs [ 18 ], and animal studies [ 19 ].

Rule 3: Establish inclusion criteria and define key variables

You should establish in advance the inclusion (such as type of study, language of publication, among others) and exclusion (such as minimal sample size, among others) criteria. Keep in mind that the current consensus advises against strict criteria concerning language or sample size. You should clearly define the variables that will be extracted from each primary article. Broad inclusion criteria increase heterogeneity between studies, and narrow inclusion criteria can make it difficult to find studies; therefore, a compromise should be found. Prospective meta-analyses, which usually are carried out by international consortia, have the advantage of the possibility of including individual-level data [ 20 ].

Rule 4: Carry out a systematic search in different databases and extract key data

You can carry out your systematic search in several bibliographic databases, such as PubMed, Embase, The Cochrane Central Register of Controlled Trials, Scopus, Web of Science, and Google Scholar [ 21 ]. Usually, searching in several databases helps to minimize the possibility of failing to identify all published studies [ 22 ]. In some specific areas, searching in specialized databases is also worth doing (such as BIOSIS, Cumulative index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Sociological Abstracts, and EconLit, among others). Moreover, in other cases, direct search for the data is also advisable (i.e., Gene Expression Omnibus [GEO] database for gene expression studies) [ 23 ]. Usually, the bibliography of review articles might help to identify additional articles and data from other types of documents (such as theses or conference proceedings) that might be included in your meta-analysis. The Web of Science database can be used to identify publications that have cited key articles. Adequate extraction and recording of key data from primary articles are fundamental for carrying out a meta-analysis. Quality assessment of the included studies is also an important issue; it can be used for determining inclusion criteria, sensitivity analysis, or differential weighting of the studies. For example the Jadad scale [ 24 ] is frequently used for randomized clinical trials, the Newcastle–Ottawa scale [ 25 ] for nonrandomized studies, and QUADAS-2 for the Quality Assessment of Diagnostic Accuracy Studies [ 26 ]. It is recommended that these steps be carried out by two researchers in parallel and that discrepancies be resolved by consensus. Nevertheless, the reader must be aware that quality assessment has been criticized, especially when it reduces the studies to a single “quality” score [ 27 , 28 ]. In any case, it is important to avoid the confusion of using guidelines for the reporting of primary studies as scales for the assessment of the quality of included articles [ 29 , 30 ].

Rule 5: Contact authors of primary articles to ask for missing data

It is common that key data are not available in the main text or supplementary files of primary articles [ 31 ], leading to the need to contact the authors to ask for missing data. However, the rate of response from authors is lower than expected. There are multiple standards that promote the availability of primary data in published articles, such as the minimum information about a microarray experiment (MIAME) [ 32 ] and the STrengthening the REporting of Genetic Association Studies (STREGA) [ 33 ]. In some areas, such as genetics, in which it was shown that it is possible to identify an individual using the aggregated statistics from a particular study [ 34 ], strict criteria are imposed for data sharing, and specialized permissions might be needed.

Rule 6: Select the best statistical models for your question

For cases in which there is enough primary data of adequate quality for a quantitative summary, there is the option to carry out a meta-analysis. The potential analyst must be warned that in many cases the data are reported in noncompatible forms, so one must be ready to perform various types of transformations. Thankfully, there are methods available for extracting and transforming data regarding continuous variables [ 35 – 37 ], 2 × 2 tables [ 38 , 39 ], or survival data [ 40 ]. Frequently, meta-analyses are based on fixed-effects or random-effects statistical models [ 20 ]. In addition, models based on combining ranks or p -values are also available and can be used in specific cases [ 41 – 44 ]. For more complex data, multivariate methods for meta-analysis have been proposed [ 45 , 46 ]. Additional statistical examinations involve sensitivity analyses, metaregressions, subgroup analyses, and calculation of heterogeneity metrics, such as Q or I 2 [ 20 ]. It is fundamental to assess and, if present, explain the possible sources of heterogeneity. Although random-effects models are suitable for cases of between-studies heterogeneity, the sources of between-studies variation should be identified, and their impact on effect size should be quantified using statistical tests, such as subgroup analyses or metaregression. Publication bias is an important aspect to consider [ 47 ], since in many cases negative findings have less probability of being published. Other types of bias, such as the so-called “Proteus phenomenon” [ 48 ] or “winner’s curse” [ 49 ], are common in some scientific fields, such as genetics, and the approach of cumulative meta-analysis is suggested in order to identify them.

Rule 7: Use available software to carry metastatistics

There are several very user-friendly and freely available programs for carrying out meta-analyses [ 43 , 44 ], either within the framework of a statistical package such as Stata or R or as stand-alone applications. Stata and R [ 50 – 52 ] have dozens of routines, mostly user written, that can handle most meta-analysis tasks, even complex analyses such as network meta-analysis and meta-analyses of GWASs and gene expression studies ( https://cran.r-project.org/web/views/MetaAnalysis.html ; https://www.stata.com/support/faqs/statistics/meta-analysis ). There are also stand-alone packages that can be useful for general applications or for specific areas, such as OpenMetaAnalyst [ 53 ], NetworkAnalyst [ 54 ], JASP [ 55 ], MetaGenyo [ 56 ], Cochrane RevMan ( https://community.cochrane.org/help/tools-and-software/revman-5 ), EpiSheet (krothman.org/episheet.xls), GWAR [ 57 ], GWAMA [ 58 ], and METAL [ 59 ]. Some of these programs are web services or stand-alone software. In some cases, certain programs can present issues when they are run because of their dependency on other packages.

Rule 8: The records and study report must be complete and transparent

Following published guidelines for meta-analyses guarantees that the manuscript will describe the different steps and methods used, facilitating their transparency and replicability [ 15 ]. Data such as search and inclusion criteria, numbers of abstracts screened, and included studies are quite useful, in addition to details of meta-analytical strategies used. An assessment of quality of included studies is also useful [ 60 ]. A spreadsheet can be constructed in which every step in the selection criteria is recorded; this will be helpful to construct flow charts. In this context, a flow diagram describing the progression between the different steps is quite useful and might enhance the quality of the meta-analysis [ 61 ]. Records will be also useful if, in the future, the meta-analysis needs to be updated. Stating the limitations of the analysis is also important [ 62 ].

Rule 9: Provide enough data in your manuscript

A table with complete information about included studies (such as author, year, details of included subjects, DOIs, or PubMed IDs, among others) is quite useful in an article reporting a meta-analysis; it can be included in the main text of the manuscript or as a supplementary file. Software used for carrying out meta-analyses and to generate key graphs, such as forest plots, should be referenced. Summary effect measures, such as a pooled odds ratios or the counts used to generate them, should be always reported, including confidence intervals. It is also possible to generate figures with information from multiple forest plots [ 63 ]. In the case of positive findings, plots from sensitivity analyses are quite informative. In more-complex analyses, it is advisable to include in the supplementary files the scripts used to generate the results [ 64 ].

Rule 10: Provide context for your findings and suggest future directions

The Discussion section is an important scientific component in a manuscript describing a meta-analysis, as the authors should discuss their current findings in the context of the available scientific literature and existing knowledge [ 65 ]. Authors can discuss possible reasons for the positive or negative results of their meta-analysis, provide an interpretation of findings based on available biological or epidemiological evidence, and comment on particular features of individual studies or experimental designs used [ 66 ]. As meta-analyses are usually synthesizing the existing evidence from multiple primary studies, which commonly took years and large amounts of funding, authors can recommend key suggestions for conducting and/or reporting future primary studies [ 67 ].

As open science is becoming more important around the globe [ 68 , 69 ], adherence to published standards, in addition to the evolution of methods for different meta-analytical applications, will be even more important to carry out meta-analyses of high quality and impact.

Funding Statement

YG-G is supported by a PhD fellowship from Centro de Estudios Interdisciplinarios Básicos y Aplicados CEIBA (Rodolfo Llinás Program). DAF is supported by research grants from Colciencias and VCTI. PGB is partially supported by ELIXIR-GR, the Greek Research Infrastructure for data management and analysis in the biosciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

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

Here are several examples of analysis:

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

Analysis research involves several steps:

  • Finding and collecting documents.
  • Specifying criteria or patterns that you are looking for.
  • Analyzing documents for patterns, noting number of occurrences or other factors.
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Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

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Narrative Analysis

Narrative Analysis

Definition:

Narrative analysis is a qualitative research methodology that involves examining and interpreting the stories or narratives people tell in order to gain insights into the meanings, experiences, and perspectives that underlie them. Narrative analysis can be applied to various forms of communication, including written texts, oral interviews, and visual media.

In narrative analysis, researchers typically examine the structure, content, and context of the narratives they are studying, paying close attention to the language, themes, and symbols used by the storytellers. They may also look for patterns or recurring motifs within the narratives, and consider the cultural and social contexts in which they are situated.

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

This type of narrative analysis involves examining the content of a narrative in order to identify themes, motifs, and other patterns. Researchers may use coding schemes to identify specific themes or categories within the text, and then analyze how they are related to each other and to the overall narrative. Content analysis can be used to study various forms of communication, including written texts, oral interviews, and visual media.

Structural Analysis

This type of narrative analysis focuses on the formal structure of a narrative, including its plot, character development, and use of literary devices. Researchers may analyze the narrative arc, the relationship between the protagonist and antagonist, or the use of symbolism and metaphor. Structural analysis can be useful for understanding how a narrative is constructed and how it affects the reader or audience.

Discourse Analysis

This type of narrative analysis focuses on the language and discourse used in a narrative, including the social and cultural context in which it is situated. Researchers may analyze the use of specific words or phrases, the tone and style of the narrative, or the ways in which social and cultural norms are reflected in the narrative. Discourse analysis can be useful for understanding how narratives are influenced by larger social and cultural structures.

Phenomenological Analysis

This type of narrative analysis focuses on the subjective experience of the narrator, and how they interpret and make sense of their experiences. Researchers may analyze the language used to describe experiences, the emotions expressed in the narrative, or the ways in which the narrator constructs meaning from their experiences. Phenomenological analysis can be useful for understanding how people make sense of their own lives and experiences.

Critical Analysis

This type of narrative analysis involves examining the political, social, and ideological implications of a narrative, and questioning its underlying assumptions and values. Researchers may analyze the ways in which a narrative reflects or reinforces dominant power structures, or how it challenges or subverts those structures. Critical analysis can be useful for understanding the role that narratives play in shaping social and cultural norms.

Autoethnography

This type of narrative analysis involves using personal narratives to explore cultural experiences and identity formation. Researchers may use their own personal narratives to explore issues such as race, gender, or sexuality, and to understand how larger social and cultural structures shape individual experiences. Autoethnography can be useful for understanding how individuals negotiate and navigate complex cultural identities.

Thematic Analysis

This method involves identifying themes or patterns that emerge from the data, and then interpreting these themes in relation to the research question. Researchers may use a deductive approach, where they start with a pre-existing theoretical framework, or an inductive approach, where themes are generated from the data itself.

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

  • Identify the research question: Narrative analysis begins with identifying the research question or topic of interest. Researchers may want to explore a particular social or cultural phenomenon, or gain a deeper understanding of a particular individual’s experience.
  • Collect the narratives: Researchers then collect the narratives or stories that they will analyze. This can involve collecting written texts, conducting interviews, or analyzing visual media.
  • Transcribe and code the narratives: Once the narratives have been collected, they are transcribed into a written format, and then coded in order to identify themes, motifs, or other patterns. Researchers may use a coding scheme that has been developed specifically for the study, or they may use an existing coding scheme.
  • Analyze the narratives: Researchers then analyze the narratives, focusing on the themes, motifs, and other patterns that have emerged from the coding process. They may also analyze the formal structure of the narratives, the language used, and the social and cultural context in which they are situated.
  • Interpret the findings: Finally, researchers interpret the findings of the narrative analysis, and draw conclusions about the meanings, experiences, and perspectives that underlie the narratives. They may use the findings to develop theories, make recommendations, or inform further research.

Applications of Narrative Analysis

Narrative analysis is a versatile qualitative research method that has applications across a wide range of fields, including psychology, sociology, anthropology, literature, and history. Here are some examples of how narrative analysis can be used:

  • Understanding individuals’ experiences: Narrative analysis can be used to gain a deeper understanding of individuals’ experiences, including their thoughts, feelings, and perspectives. For example, psychologists might use narrative analysis to explore the stories that individuals tell about their experiences with mental illness.
  • Exploring cultural and social phenomena: Narrative analysis can also be used to explore cultural and social phenomena, such as gender, race, and identity. Sociologists might use narrative analysis to examine how individuals understand and experience their gender identity.
  • Analyzing historical events: Narrative analysis can be used to analyze historical events, including those that have been recorded in literary texts or personal accounts. Historians might use narrative analysis to explore the stories of survivors of historical traumas, such as war or genocide.
  • Examining media representations: Narrative analysis can be used to examine media representations of social and cultural phenomena, such as news stories, films, or television shows. Communication scholars might use narrative analysis to examine how news media represent different social groups.
  • Developing interventions: Narrative analysis can be used to develop interventions to address social and cultural problems. For example, social workers might use narrative analysis to understand the experiences of individuals who have experienced domestic violence, and then use that knowledge to develop more effective interventions.

Examples of Narrative Analysis

Here are some examples of how narrative analysis has been used in research:

  • Personal narratives of illness: Researchers have used narrative analysis to examine the personal narratives of individuals living with chronic illness, to understand how they make sense of their experiences and construct their identities.
  • Oral histories: Historians have used narrative analysis to analyze oral histories to gain insights into individuals’ experiences of historical events and social movements.
  • Children’s stories: Researchers have used narrative analysis to analyze children’s stories to understand how they understand and make sense of the world around them.
  • Personal diaries : Researchers have used narrative analysis to examine personal diaries to gain insights into individuals’ experiences of significant life events, such as the loss of a loved one or the transition to adulthood.
  • Memoirs : Researchers have used narrative analysis to analyze memoirs to understand how individuals construct their life stories and make sense of their experiences.
  • Life histories : Researchers have used narrative analysis to examine life histories to gain insights into individuals’ experiences of migration, displacement, or social exclusion.

Purpose of Narrative Analysis

The purpose of narrative analysis is to gain a deeper understanding of the stories that individuals tell about their experiences, identities, and beliefs. By analyzing the structure, content, and context of these stories, researchers can uncover patterns and themes that shed light on the ways in which individuals make sense of their lives and the world around them.

The primary purpose of narrative analysis is to explore the meanings that individuals attach to their experiences. This involves examining the different elements of a story, such as the plot, characters, setting, and themes, to identify the underlying values, beliefs, and attitudes that shape the story. By analyzing these elements, researchers can gain insights into the ways in which individuals construct their identities, understand their relationships with others, and make sense of the world.

Narrative analysis can also be used to identify patterns and themes across multiple stories. This involves comparing and contrasting the stories of different individuals or groups to identify commonalities and differences. By analyzing these patterns and themes, researchers can gain insights into broader cultural and social phenomena, such as gender, race, and identity.

In addition, narrative analysis can be used to develop interventions that address social and cultural problems. By understanding the stories that individuals tell about their experiences, researchers can develop interventions that are tailored to the unique needs of different individuals and groups.

Overall, the purpose of narrative analysis is to provide a rich, nuanced understanding of the ways in which individuals construct meaning and make sense of their lives. By analyzing the stories that individuals tell, researchers can gain insights into the complex and multifaceted nature of human experience.

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

  • Studying life stories: Narrative analysis can be useful in understanding how individuals construct their life stories, including the events, characters, and themes that are important to them.
  • Analyzing cultural narratives: Narrative analysis can be used to analyze cultural narratives, such as myths, legends, and folktales, to understand their meanings and functions.
  • Exploring organizational narratives: Narrative analysis can be helpful in examining the stories that organizations tell about themselves, their histories, and their values, to understand how they shape the culture and practices of the organization.
  • Investigating media narratives: Narrative analysis can be used to analyze media narratives, such as news stories, films, and TV shows, to understand how they construct meaning and influence public perceptions.
  • Examining policy narratives: Narrative analysis can be helpful in examining policy narratives, such as political speeches and policy documents, to understand how they construct ideas and justify policy decisions.

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

  • Focus on stories and narratives: Narrative analysis is concerned with analyzing the stories and narratives that people tell, whether they are oral or written, to understand how they shape and reflect individuals’ experiences and identities.
  • Emphasis on context: Narrative analysis seeks to understand the context in which the narratives are produced and the social and cultural factors that shape them.
  • Interpretive approach: Narrative analysis is an interpretive approach that seeks to identify patterns and themes in the stories and narratives and to understand the meaning that individuals and communities attach to them.
  • Iterative process: Narrative analysis involves an iterative process of analysis, in which the researcher continually refines their understanding of the narratives as they examine more data.
  • Attention to language and form : Narrative analysis pays close attention to the language and form of the narratives, including the use of metaphor, imagery, and narrative structure, to understand the meaning that individuals and communities attach to them.
  • Reflexivity : Narrative analysis requires the researcher to reflect on their own assumptions and biases and to consider how their own positionality may shape their interpretation of the narratives.
  • Qualitative approach: Narrative analysis is typically a qualitative research method that involves in-depth analysis of a small number of cases rather than large-scale quantitative studies.

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

  • Rich and detailed data : Narrative analysis provides rich and detailed data that allows for a deep understanding of individuals’ experiences, emotions, and identities.
  • Humanizing approach: Narrative analysis allows individuals to tell their own stories and express their own perspectives, which can help to humanize research and give voice to marginalized communities.
  • Holistic understanding: Narrative analysis allows researchers to understand individuals’ experiences in their entirety, including the social, cultural, and historical contexts in which they occur.
  • Flexibility : Narrative analysis is a flexible research method that can be applied to a wide range of contexts and research questions.
  • Interpretive insights: Narrative analysis provides interpretive insights into the meanings that individuals attach to their experiences and the ways in which they construct their identities.
  • Appropriate for sensitive topics: Narrative analysis can be particularly useful in researching sensitive topics, such as trauma or mental health, as it allows individuals to express their experiences in their own words and on their own terms.
  • Can lead to policy implications: Narrative analysis can provide insights that can inform policy decisions and interventions, particularly in areas such as health, education, and social policy.

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

  • Subjectivity : Narrative analysis relies on the interpretation of researchers, which can be influenced by their own biases and assumptions.
  • Limited generalizability: Narrative analysis typically involves in-depth analysis of a small number of cases, which limits its generalizability to broader populations.
  • Ethical considerations: The process of eliciting and analyzing narratives can raise ethical concerns, particularly when sensitive topics such as trauma or abuse are involved.
  • Limited control over data collection: Narrative analysis often relies on data that is already available, such as interviews, oral histories, or written texts, which can limit the control that researchers have over the quality and completeness of the data.
  • Time-consuming: Narrative analysis can be a time-consuming research method, particularly when analyzing large amounts of data.
  • Interpretation challenges: Narrative analysis requires researchers to make complex interpretations of data, which can be challenging and time-consuming.
  • Limited statistical analysis: Narrative analysis is typically a qualitative research method that does not lend itself well to statistical analysis.

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Literature review

A general guide on how to conduct and write a literature review.

Please check course or programme information and materials provided by teaching staff, including your project supervisor, for subject-specific guidance.

What is a literature review?

A literature review is a piece of academic writing demonstrating knowledge and understanding of the academic literature on a specific topic placed in context.  A literature review also includes a critical evaluation of the material; this is why it is called a literature review rather than a literature report. It is a process of reviewing the literature, as well as a form of writing.

To illustrate the difference between reporting and reviewing, think about television or film review articles.  These articles include content such as a brief synopsis or the key points of the film or programme plus the critic’s own evaluation.  Similarly the two main objectives of a literature review are firstly the content covering existing research, theories and evidence, and secondly your own critical evaluation and discussion of this content. 

Usually a literature review forms a section or part of a dissertation, research project or long essay.  However, it can also be set and assessed as a standalone piece of work.

What is the purpose of a literature review?

…your task is to build an argument, not a library. Rudestam, K.E. and Newton, R.R. (1992) Surviving your dissertation: A comprehensive guide to content and process. California: Sage, p49.

In a larger piece of written work, such as a dissertation or project, a literature review is usually one of the first tasks carried out after deciding on a topic.  Reading combined with critical analysis can help to refine a topic and frame research questions.  Conducting a literature review establishes your familiarity with and understanding of current research in a particular field before carrying out a new investigation. After doing a literature review, you should know what research has already been done and be able to identify what is unknown within your topic.

When doing and writing a literature review, it is good practice to:

  • summarise and analyse previous research and theories;
  • identify areas of controversy and contested claims;
  • highlight any gaps that may exist in research to date.

Conducting a literature review

Focusing on different aspects of your literature review can be useful to help plan, develop, refine and write it.  You can use and adapt the prompt questions in our worksheet below at different points in the process of researching and writing your review.  These are suggestions to get you thinking and writing.

Developing and refining your literature review (pdf)

Developing and refining your literature review (Word)

Developing and refining your literature review (Word rtf)

Writing a literature review has a lot in common with other assignment tasks.  There is advice on our other pages about thinking critically, reading strategies and academic writing.  Our literature review top tips suggest some specific things you can do to help you submit a successful review.

Literature review top tips (pdf)

Literature review top tips (Word rtf)

Our reading page includes strategies and advice on using books and articles and a notes record sheet grid you can use.

Reading at university

The Academic writing page suggests ways to organise and structure information from a range of sources and how you can develop your argument as you read and write.

Academic writing

The Critical thinking page has advice on how to be a more critical researcher and a form you can use to help you think and break down the stages of developing your argument.

Critical thinking

As with other forms of academic writing, your literature review needs to demonstrate good academic practice by following the Code of Student Conduct and acknowledging the work of others through citing and referencing your sources.  

Good academic practice

As with any writing task, you will need to review, edit and rewrite sections of your literature review.  The Editing and proofreading page includes tips on how to do this and strategies for standing back and thinking about your structure and checking the flow of your argument.

Editing and proofreading

Guidance on literature searching from the University Library

The Academic Support Librarians have developed LibSmart I and II, Learn courses to help you develop and enhance your digital research skills and capabilities; from getting started with the Library to managing data for your dissertation.

Searching using the library’s DiscoverEd tool: DiscoverEd

Finding resources in your subject: Subject guides

The Academic Support Librarians also provide one-to-one appointments to help you develop your research strategies.

1 to 1 support for literature searching and systematic reviews

Advice to help you optimise use of Google Scholar, Google Books and Google for your research and study: Using Google

Managing and curating your references

A referencing management tool can help you to collect and organise and your source material to produce a bibliography or reference list. 

Referencing and reference management

Information Services provide access to Cite them right online which is a guide to the main referencing systems and tells you how to reference just about any source (EASE log-in may be required).

Cite them right

Published study guides

There are a number of scholarship skills books and guides available which can help with writing a literature review.  Our Resource List of study skills guides includes sections on Referencing, Dissertation and project writing and Literature reviews.

Study skills guides

Kimberley Garcia and Rachael Benavidez

Using Says/Does Analysis in the Writing Process

Adapted from Dr. Joe Bizup

What is it?

A Says/Does analysis asks you to write a single sentence describing what each paragraph of an essay says and a single sentence describing what it does.

For the “says” part, write a single sentence that fully summarizes the “main point” of the paragraph. Ideally, this will be the topic sentence of the paragraph.

For the “does” part, write a single sentence that describes what a paragraph is doing in terms of PAS (presenting/analyzing/synthesizing) and what it is PAS-ing (an exhibit, idea, argument, theory, etc.).

How do you use Says-Does analysis to improve your writing?

This is primarily a paragraphing tool (but it can also help you section an essay).

  • If two paragraphs “say” the same thing (even if they “do” different things), then one of those paragraphs can usually go.
  • If it is hard to determine the “main point” of a paragraph, then your paragraph might be saying too many things and you will need to separate your ideas into multiple paragraphs. Alternatively, the problem may be that you have no explicit topic sentence; thus, you need to write one into the paragraph.
  • If a paragraph contains information unrelated to its “main point,” then you need to cut extraneous information and refocus the paragraph.
  • If two consecutive paragraphs “do” the same thing (with the same source), then you should probably think about condensing and combining them into a single paragraph.
  • If it is hard to figure out whether a paragraph is P, A, or S, then you should consider breaking up the content of the paragraph by job/function.
  • If it’s hard to figure out how the source in the paragraph is being used (as context, exhibit, argument, or theory), then you need to figure it out and make sure your intended use of the source is clear to both you and your reader.
  • If you find that you are presenting for more than two paragraphs in a row, then you need to add a paragraph of analysis. If you have more than two analysis paragraphs in a row, then you should think adding a paragraph of synthesis.

When do you use it?

Use the Says/Does analysis on:

  • Exploratory drafts to help identify potential ideas that can form the basis of future paragraphs.
  • On ALL non-exploratory drafts (in your other classes too). For this class it’s obvious that you need presentation (of sources), for example, but check your essays for other courses with Says/Does and you may find yourself rather light on presentation. It is ALWAYS necessary. This technique can help you make sure you have included all of the elements a reader needs to understand the content of your essay.

You can also use this says/does analysis as a reading strategy, especially to help with understanding of challenging texts.

Writing About Literature Copyright © by Kimberley Garcia and Rachael Benavidez is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What the Data Says About Pandemic School Closures, Four Years Later

The more time students spent in remote instruction, the further they fell behind. And, experts say, extended closures did little to stop the spread of Covid.

Sarah Mervosh

By Sarah Mervosh ,  Claire Cain Miller and Francesca Paris

Four years ago this month, schools nationwide began to shut down, igniting one of the most polarizing and partisan debates of the pandemic.

Some schools, often in Republican-led states and rural areas, reopened by fall 2020. Others, typically in large cities and states led by Democrats, would not fully reopen for another year.

A variety of data — about children’s academic outcomes and about the spread of Covid-19 — has accumulated in the time since. Today, there is broad acknowledgment among many public health and education experts that extended school closures did not significantly stop the spread of Covid, while the academic harms for children have been large and long-lasting.

While poverty and other factors also played a role, remote learning was a key driver of academic declines during the pandemic, research shows — a finding that held true across income levels.

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the COVID-19 Pandemic .” Score changes are measured from 2019 to 2022. In-person means a district offered traditional in-person learning, even if not all students were in-person.

“There’s fairly good consensus that, in general, as a society, we probably kept kids out of school longer than we should have,” said Dr. Sean O’Leary, a pediatric infectious disease specialist who helped write guidance for the American Academy of Pediatrics, which recommended in June 2020 that schools reopen with safety measures in place.

There were no easy decisions at the time. Officials had to weigh the risks of an emerging virus against the academic and mental health consequences of closing schools. And even schools that reopened quickly, by the fall of 2020, have seen lasting effects.

But as experts plan for the next public health emergency, whatever it may be, a growing body of research shows that pandemic school closures came at a steep cost to students.

The longer schools were closed, the more students fell behind.

At the state level, more time spent in remote or hybrid instruction in the 2020-21 school year was associated with larger drops in test scores, according to a New York Times analysis of school closure data and results from the National Assessment of Educational Progress , an authoritative exam administered to a national sample of fourth- and eighth-grade students.

At the school district level, that finding also holds, according to an analysis of test scores from third through eighth grade in thousands of U.S. districts, led by researchers at Stanford and Harvard. In districts where students spent most of the 2020-21 school year learning remotely, they fell more than half a grade behind in math on average, while in districts that spent most of the year in person they lost just over a third of a grade.

( A separate study of nearly 10,000 schools found similar results.)

Such losses can be hard to overcome, without significant interventions. The most recent test scores, from spring 2023, show that students, overall, are not caught up from their pandemic losses , with larger gaps remaining among students that lost the most ground to begin with. Students in districts that were remote or hybrid the longest — at least 90 percent of the 2020-21 school year — still had almost double the ground to make up compared with students in districts that allowed students back for most of the year.

Some time in person was better than no time.

As districts shifted toward in-person learning as the year went on, students that were offered a hybrid schedule (a few hours or days a week in person, with the rest online) did better, on average, than those in places where school was fully remote, but worse than those in places that had school fully in person.

Students in hybrid or remote learning, 2020-21

80% of students

Some schools return online, as Covid-19 cases surge. Vaccinations start for high-priority groups.

Teachers are eligible for the Covid vaccine in more than half of states.

Most districts end the year in-person or hybrid.

Source: Burbio audit of more than 1,200 school districts representing 47 percent of U.S. K-12 enrollment. Note: Learning mode was defined based on the most in-person option available to students.

Income and family background also made a big difference.

A second factor associated with academic declines during the pandemic was a community’s poverty level. Comparing districts with similar remote learning policies, poorer districts had steeper losses.

But in-person learning still mattered: Looking at districts with similar poverty levels, remote learning was associated with greater declines.

A community’s poverty rate and the length of school closures had a “roughly equal” effect on student outcomes, said Sean F. Reardon, a professor of poverty and inequality in education at Stanford, who led a district-level analysis with Thomas J. Kane, an economist at Harvard.

Score changes are measured from 2019 to 2022. Poorest and richest are the top and bottom 20% of districts by percent of students on free/reduced lunch. Mostly in-person and mostly remote are districts that offered traditional in-person learning for more than 90 percent or less than 10 percent of the 2020-21 year.

But the combination — poverty and remote learning — was particularly harmful. For each week spent remote, students in poor districts experienced steeper losses in math than peers in richer districts.

That is notable, because poor districts were also more likely to stay remote for longer .

Some of the country’s largest poor districts are in Democratic-leaning cities that took a more cautious approach to the virus. Poor areas, and Black and Hispanic communities , also suffered higher Covid death rates, making many families and teachers in those districts hesitant to return.

“We wanted to survive,” said Sarah Carpenter, the executive director of Memphis Lift, a parent advocacy group in Memphis, where schools were closed until spring 2021 .

“But I also think, man, looking back, I wish our kids could have gone back to school much quicker,” she added, citing the academic effects.

Other things were also associated with worse student outcomes, including increased anxiety and depression among adults in children’s lives, and the overall restriction of social activity in a community, according to the Stanford and Harvard research .

Even short closures had long-term consequences for children.

While being in school was on average better for academic outcomes, it wasn’t a guarantee. Some districts that opened early, like those in Cherokee County, Ga., a suburb of Atlanta, and Hanover County, Va., lost significant learning and remain behind.

At the same time, many schools are seeing more anxiety and behavioral outbursts among students. And chronic absenteeism from school has surged across demographic groups .

These are signs, experts say, that even short-term closures, and the pandemic more broadly, had lasting effects on the culture of education.

“There was almost, in the Covid era, a sense of, ‘We give up, we’re just trying to keep body and soul together,’ and I think that was corrosive to the higher expectations of schools,” said Margaret Spellings, an education secretary under President George W. Bush who is now chief executive of the Bipartisan Policy Center.

Closing schools did not appear to significantly slow Covid’s spread.

Perhaps the biggest question that hung over school reopenings: Was it safe?

That was largely unknown in the spring of 2020, when schools first shut down. But several experts said that had changed by the fall of 2020, when there were initial signs that children were less likely to become seriously ill, and growing evidence from Europe and parts of the United States that opening schools, with safety measures, did not lead to significantly more transmission.

“Infectious disease leaders have generally agreed that school closures were not an important strategy in stemming the spread of Covid,” said Dr. Jeanne Noble, who directed the Covid response at the U.C.S.F. Parnassus emergency department.

Politically, though, there remains some disagreement about when, exactly, it was safe to reopen school.

Republican governors who pushed to open schools sooner have claimed credit for their approach, while Democrats and teachers’ unions have emphasized their commitment to safety and their investment in helping students recover.

“I do believe it was the right decision,” said Jerry T. Jordan, president of the Philadelphia Federation of Teachers, which resisted returning to school in person over concerns about the availability of vaccines and poor ventilation in school buildings. Philadelphia schools waited to partially reopen until the spring of 2021 , a decision Mr. Jordan believes saved lives.

“It doesn’t matter what is going on in the building and how much people are learning if people are getting the virus and running the potential of dying,” he said.

Pandemic school closures offer lessons for the future.

Though the next health crisis may have different particulars, with different risk calculations, the consequences of closing schools are now well established, experts say.

In the future, infectious disease experts said, they hoped decisions would be guided more by epidemiological data as it emerged, taking into account the trade-offs.

“Could we have used data to better guide our decision making? Yes,” said Dr. Uzma N. Hasan, division chief of pediatric infectious diseases at RWJBarnabas Health in Livingston, N.J. “Fear should not guide our decision making.”

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the Covid-19 Pandemic. ”

The study used estimates of learning loss from the Stanford Education Data Archive . For closure lengths, the study averaged district-level estimates of time spent in remote and hybrid learning compiled by the Covid-19 School Data Hub (C.S.D.H.) and American Enterprise Institute (A.E.I.) . The A.E.I. data defines remote status by whether there was an in-person or hybrid option, even if some students chose to remain virtual. In the C.S.D.H. data set, districts are defined as remote if “all or most” students were virtual.

An earlier version of this article misstated a job description of Dr. Jeanne Noble. She directed the Covid response at the U.C.S.F. Parnassus emergency department. She did not direct the Covid response for the University of California, San Francisco health system.

How we handle corrections

Sarah Mervosh covers education for The Times, focusing on K-12 schools. More about Sarah Mervosh

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Francesca Paris is a Times reporter working with data and graphics for The Upshot. More about Francesca Paris

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2024 NCAA Tournament bracket predictions: March Madness expert picks, favorites to win, winners, upsets

Our experts have filled out their brackets, so check who they predict will be cutting down the nets.

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Has your NCAA Tournament bracket been busted before the second weekend of March Madness has arrived? Take solace, my friend, because you are not alone. In addition to Bijan Robinson's bid for a perfect bracket ending on Friday, even our team of experts – who watched hundreds of games collectively this season – haven't been 100% accurate.

So whether you've watched zero games all season or hundreds like us, keep in mind what we love most about March is the madness synonymous with the sport. Upsets, Cinderellas and shockers are all part of the game, and the only predictable element is unpredictability.

We like to think we've gotten some things right along the way, though. For transparency purposes, we're airing out all the good and the bad together in the space below. You can see each of our brackets and how busted they've already become to help give you comfort ... and perhaps use it as a guide for what to expect in the days ahead.

OK, let's dive into the good stuff: The brackets. ...  

2024 NCAA Tournament bracket predictions

Click each bracket to enlarge.

Gary Parrish

Watching UConn become the first back-to-back national champion since Florida in 2006 and 2007 would be a blast. And let the record show that the Huskies are the betting-market favorites. So I realize picking against them might prove dumb. But, that acknowledged, I'm going to continue to do what I've been doing most of this season and put my faith in the Boilermakers. Wouldn't that be a great story -- Purdue winning the 2024 NCAA Tournament after losing to a No. 16 seed in the opening round of the 2023 NCAA Tournament? Zach Edey holding the championship trophy as a two-time National Player of the Year? Matt Painter shedding his label as the best coach yet to make a Final Four by becoming the first coach to take Purdue to the final weekend of the season since 1980? It's all such good stuff. Just getting to the Final Four will be challenging considering Tennessee, Creighton and Kansas are also in the Midwest Region. But I'm still taking the Boilermakers to make it to Arizona. And then, once they get there, I think they'll win two more games and cut nets on the second Monday in April.

Matt Norlander

A locomotive screaming down the tracks. The 31-3 reigning national champions enter this NCAA Tournament as the strongest team with the best chance to repeat of any squad since Florida in 2007. Dan Hurley's Huskies are led by All-American guard Tristen Newton (15.2 ppg, 7.0 rpg, 6.0 apg), who holds the school record for triple-doubles. In the middle is 7-foot-2 "Cling Kong," Donvan Clingan, a menace of a defender and the type of player you can't simulate in practice. The Huskies boast the nation's most efficient offense (126.6 adjusted points per 100 possessions, via KenPom.com) and overwhelm teams in a variety of ways. Sophomore Alex Karaban (39.5%) and senior Cam Spencer (44.4%) are both outstanding 3-point shooters. The Huskies have been beaten by Kansas, Seton Hall and Creighton, but all of those were road games, and there are no more road games left this season. UConn will try to become the fourth No. 1 overall seed to win the national title, joining 2007 Florida, 2012 Kentucky and 2013 Louisville.

The antagonistic side of me initially picked Purdue over UConn in the title game. But I sat and thought about it and couldn't make any reasonable case to pick any team other than UConn as champion. Of course, that doesn't guarantee the Huskies win it all and become the first repeat champs since Florida in 2007. There's a lot that can happen in the next few weeks. But they have the electric offense, the guard depth, the size down low, the shooting [takes breath] .. the passing and the pizzazz of a team that's best in the country and knows it. Every top team in this field has a high level at which they can play but no one has a top gear like UConn.

Get every pick, every play, every upset and fill out your bracket with our help! Visit SportsLine now to see which teams will make and break your bracket, and see who will cut down the nets , all from the model that nailed a whopping 20 first-round upsets by double-digit seeds.

Purdue is set for redemption after an embarrassing 2023 loss to No. 16 seed Fairleigh Dickinson in the first round. This time around, the Boilermakers are a much better 3-point shooting team and have a more favorable path than No. 1 overall seed UConn. The Huskies were the most dominant team leading up to the Big Dance the East Region bracket is filled with peril.

palm-2024.jpg

This is not the Purdue you have seen the last few years. Braden Smith has made a big jump from last season to this one. Fletcher Loyer is better. Lance Jones gives Purdue defense, shooting and another ball handler. And Zach Edey is better too. This is a team on a mission. This is the year they accomplish it.

Dennis Dodd

What is there not to like? The Heels won the ACC regular season. They beat Tennessee and swept Duke. RJ Davis is an elite guard and ACC Player of the Year. Hubert Davis has settled in after going to the national championship game in his first season and missing the tournament in his second. This is his best team. There will be/and always is pressure to win it all. 

Armando Bacot is not as dominating as previous. Harrison Ingram (Stanford) and Cormac Ryan (Notre Dame) have been big additions in the portal. The West Region is friendly, assuming here that Alabama and Michigan State don't get in the way before the regional in L.A. An interesting regional final against Arizona looms. In the end, sometimes you go with chalk. UNC has been to the most Final Fours (21) and No. 1 seeds (18) all-time. It is tied with Kentucky for the most tournament wins ever (131). This is what the Heels do.

Chip Patterson

The selection committee set up plenty of stumbling blocks for the reigning champs, placing what I believe to be the best No. 1 seed, the best No. 2 seed (Iowa State), the best No. 3 seed (Illinois) and the best No. 4 seed (Auburn) in the Huskies bracket. And if accomplishing a historic feat like the first back-to-back title runs since 2007 is going to require that kind of epic journey, UConn has every skill and tool needed to make it back to the top of the mountain. UConn can win in all different ways, overwhelming teams with their offense in high-scoring track meets or out-executing the opponent in low-possession grinders, and it has a handful of key contributors who could each step up as needed during a title run.

Cameron Salerno

Defense wins championships. That is part of the reason why I'm picking Houston to win it all. The Cougars have the top-ranked scoring defense in the country and terrific guard play on offense to complement it. Jamal Shead is arguably the best point guard in the nation, and J'wan Roberts is an X-Factor on both ends of the floor. Houston's path to the Final Four is favorable. The Cougars weren't able to reach the Final Four in their home state last spring, but this will be the year they run the table and win their first national championship in program history.

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MH370 disappearance 10 years on: can we still find it?

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It has been ten years since Malaysia Airlines passenger flight MH370 disappeared on March 8 2014 . To this day it remains one of the biggest aviation mysteries globally.

It’s unthinkable that a modern Boeing 777-200ER jetliner with 239 people on board can simply vanish without any explanation. Yet multiple searches in the past decade have still not yielded the main wreckage or the bodies of the victims.

At a remembrance event held earlier this week, the Malaysian transport minister announced a renewed push for another search .

If approved by the Malaysian government, the survey will be conducted by United States seabed exploration firm Ocean Infinity, whose efforts were unsuccessful in 2018.

Read more: Lessons to learn, despite another report on missing flight MH370 and still no explanation

What happened to MH370?

The flight was scheduled to fly from Kuala Lumpur to Beijing. Air traffic control lost contact with the aircraft within 60 minutes into the flight over the South China Sea.

Subsequently, it was tracked by military radar crossing the Malay Peninsula and was last located by radar over the Andaman Sea in the northeastern Indian Ocean.

A map of the region showing the initial search areas on 8-16 March.

Later, automated satellite communications between the aircraft and British firm’s Inmarsat telecommunications satellite indicated that the plane ended up in the southeast Indian Ocean along the 7th arc (an arc is a series of coordinates).

This became the basis for defining the initial search areas by the Australian Air Transport Safety Bureau. Initial air searches were conducted in the South China Sea and the Andaman Sea.

To date, we still don’t know what caused the aircraft’s change of course and disappearance.

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What have searches for MH370 found so far?

On March 18 2014, ten days after the disappearance of MH370, a search in the southern Indian Ocean was led by Australia , with participation of aircraft from several countries. This search continued until April 28 and covered an area of 4,500,000 square kilometres of ocean. No debris was found.

Two underwater searches of the Indian Ocean, 2,800km off the coast of Western Australia, have also failed to find any evidence of the main crash site.

The initial seabed search, led by Australia, covered 120,000 square kilometres and extended 50 nautical miles across the 7th arc. It took 1,046 days and was suspended on January 17 2017.

A second search by Ocean Infinity in 2018 covered over 112,000 square kilometres . It was completed in just over three months but also didn’t locate the wreckage.

What about debris?

While the main crash site still hasn’t been found, several pieces of debris have washed up in the years since the flight’s disappearance.

In fact, in June 2015 officials from the Australian Air Transport Safety Bureau determined that debris might arrive in Sumatra, contrary to the ocean currents in the region.

The strongest current in the Indian Ocean is the South Equatorial Current. It flows east to west between northern Australia and Madagascar, and debris would be able to cross it.

Indeed, on July 30 2015 a large piece of debris – a flaperon (moving part of a plane wing) – washed up on Reunion Island in the western Indian Ocean. It was later confirmed to belong to MH370.

Overhead view of tables with several broken pieces of an aircraft with detailed plaques.

Twelve months earlier, using an oceanographic drift model, our University of Western Australia (UWA) modelling team had predicted that any debris originating from the 7th arc would end up in the western Indian Ocean.

In subsequent months, additional aircraft debris was found in the western Indian Ocean in Mauritius, Tanzania, Rodrigues, Madagascar, Mozambique and South Africa.

The UWA drift analysis accurately predicted where floating debris from MH370 would beach in the western Indian Ocean. It also guided American adventurer Blaine Gibson and others to directly recover several dozen pieces of debris, three of which have been confirmed to be from MH370, while several others are deemed likely .

A detailed satellite map showing locations of debris found on the shores of Africa and Madagascar.

To date, these debris finds in the western Indian ocean are the only physical evidence found related to MH370.

It is also independent verification that the crash occurred close to the 7th arc, as any debris would initially flow northwards and then to the west, transported by the prevailing ocean currents. These results are consistent with other drift studies undertaken by independent researchers globally.

Read more: Ocean currents suggest where we should be looking for missing flight MH370

Why a new search for MH370 now?

Unfortunately, the ocean is a chaotic place, and even oceanographic drift models cannot pinpoint the exact location of the crash site.

The proposed new search by Ocean Infinity has significantly narrowed down the target area within latitudes 36°S and 33°S. This is approximately 50km to the south of the locations where UWA modelling indicated the release of debris along the 7th arc. If the search does not locate the wreckage, it could be extended north.

Since the initial underwater searches, technology has tremendously improved. Ocean Infinity is using a fleet of autonomous underwater vehicles with improved resolution. The proposed search will also use remotely controlled surface vessels.

In the area where the search is to take place, the ocean is around 4,000 metres deep. The water temperatures are 1–2°C, with low currents. This means that even after ten years, the debris field would be relatively intact.

Therefore, there is a high probability that the wreckage can still be found. If a future search is successful, this would bring closure not just to the families of those who perished, but also the thousands of people who have been involved in the search efforts.

Read more: MH370: New underwater sound wave analysis suggests alternative travel route and new impact locations

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APT & Targeted Attacks

Earth Krahang Exploits Intergovernmental Trust to Launch Cross-Government Attacks

Since early 2022, we have been monitoring an APT campaign that targets several government entities worldwide, with a strong focus in Southeast Asia, but also seen targeting Europe, America, and Africa.

By: Joseph C Chen, Daniel Lunghi March 18, 2024 Read time:  ( words)

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Introduction

Since early 2022, we have been monitoring an APT campaign that targets several government entities worldwide, with a strong focus in Southeast Asia, but also seen targeting Europe, America, and Africa. The threat actor exploits public-facing servers and sends spear phishing emails to deliver previously unseen backdoors.

Our research allowed us to identify the campaign’s multiple connections with a China-nexus threat actor we track as Earth Lusca . However, since the campaign employs independent infrastructure and unique backdoors, we believe it to be a separate intrusion set that we named Earth Krahang. We will examine these connections, as well as potential links to a Chinese company named I-Soon, in a separate section.

One of the threat actor’s favorite tactics involves using its malicious access to government infrastructure to attack other government entities, abusing the infrastructure to host malicious payloads, proxy attack traffic, and send spear-phishing emails to government-related targets using compromised government email accounts. Earth Krahang also uses other tactics, such as building VPN servers on compromised public-facing servers to establish access into the private network of victims and performing brute-force attacks to obtain email credentials. These credentials are then used to exfiltrate victim emails, with the group’s ultimate goal being cyberespionage.

Due to mistakes on the attacker’s side, we managed to retrieve multiple files from Earth Krahang’s servers, including samples, configuration files, and log files from its attack tools. Combining this information with our telemetry helped us understand the Earth Krahang operation and build a clear view of the threat actor’s victimology and interests. In addition, we will also share their preferred malware families and post-exploitation tools in this report.

Reconnaissance and initial access

Infection chain of an Earth Krahang’s spear-phishing attack (see the MITRE ATT&CK section for the details of each technique ID)

One of the infection vectors used involves the scanning of public-facing servers. Earth Krahang heavily employs open-source scanning tools that perform recursive searches of folders such as .git or .idea . The threat actor also resorts to simply brute-forcing directories to help identify files that may contain sensitive information such as file paths or passwords on the victim’s servers. They also tend to examine the subdomains of their targets to find interesting and possible unmaintained servers. Earth Krahang also conducts vulnerability scanning with tools like sqlmap , nuclei , xray , vscan , pocsuite , and wordpressscan to find web server vulnerabilities  that will allow them to access the server, drop web shells, and install backdoors.

The threat actor abused the following vulnerabilities multiple times:

  • CVE-2023-32315 : command execution on OpenFire
  • CVE-2022-21587 : command execution on Oracle Web Applications Desktop Integrator

Earth Krahang also makes use of spear phishing email to attack its targets. Like most spear phishing attacks, the emails are intended trick their targets into opening attachments or embedded URL links that ultimately lead to the execution of a prepared backdoor file on the victim’s machine. Our telemetry data and some of the group’s backdoors uploaded on VirusTotal revealed that the backdoor filenames are usually related to geopolitical topics, indicating their preferred type of lure:

  • "Plan of Action (POA) - TH-VN - TH_Counterdraft_as of Feb 2022.doc.exe"
  • คำบอกกล่าวคำฟ้อง.rar (translated as “Notice of complaint.rar” )
  • “ร่างสถานะ ครม. รว. ไทย-โรมาเนีย as of 25 Feb 2022.doc.exe” (translated as “Draft Cabinet status of Thailand-Romania as of 25 Feb 2022.doc.exe”)
  • “Malaysian defense minister visits Hungary.Malaysian defense minister visits Hungary.exe”
  • “ICJ public hearings- Guyana vs. Venezuela.ICJ public hearings- Guyana vs. Venezuela.exe”
  • “On the visit of Paraguayan Foreign Minister to Turkmenistan.exe”
  • “pay-slip run persal payslip.pay-slip run persal payslip.docx.exe”

We noticed that Earth Krahang retrieves hundreds of email addresses from their targets during the reconnaissance phase. In one case, the actor used a compromised mailbox from a government entity to send a malicious attachment to 796 email addresses belonging to the same entity. The malicious attachment was a RAR archive containing an LNK file that deployed the Xdealer malware (which we will discuss in the Delivered malware families section) and opened a decoy document (available online) related to the governmental entity. It is likely that the actor discovered the weak credentials of the compromised mailbox using brute-forcing tools.

Earth Krahang abuses the trust between governments to conduct their attacks. We found that the group frequently uses compromised government webservers to host their backdoors and send download links to other government entities via spear phishing emails. Since the malicious link uses a legitimate government domain of the compromised server, it will appear less suspicious to targets and may even bypass some domain blacklists.

In addition, the actor used a compromised government email account to send email to other governments. We noticed the following email subjects being used for spear-phishing emails:

  • Malaysian Ministry of Defense Circular
  • Malaysian defense minister visits Hungary
  • ICJ public hearings- Guyana vs. Venezuela
  • About Guyana Procurement Proposal for Taiwan <redacted>

The Python script used by Earth Krahang to send spear-phishing emails to other governments via a stolen government account (redacted)

Our telemetry also showed that the threat actor compromised a government web server and leveraged it to scan vulnerabilities in other government targets.

Post-exploitation TTPs

The threat actor installs the SoftEther VPN on compromised public-facing servers and uses certutil commands to download and install the SoftEther VPN server. The SoftEther server executable is renamed to either taskllst.exe , tasklist.exe , or tasklist_32.exe for the Windows executable and curl for the Linux executable to make it look like a legitimate file on the installed system. With the VPN server installed, the actor can then connect to the victim’s network to conduct their post-exploitation movements.

Additional post-exploitation movements include:

  • Maintaining backdoor persistence with task scheduling
  • Enabling Remote Desktop connections by modifying the Windows Registry “fDenyTSConnections”
  • Accessing credentials by dumping Local Security Authority Subsystem Service (LSASS) with Mimikatz or ProcDump
  • Accessing credentials by dumping the SAM database ( HKLM/sam ) from the Windows Registry
  • Scanning the network using Fscan
  • Lateral code execution via WMIC
  • Using tools such as BadPotato, SweetPotato, GodPotato, or PrinterNotifyPotato for privilege escalation on Windows systems
  • Exploiting CVE-2021-4034, CVE-2021-22555, and CVE-2016-5195 for privilege escalation on Linux systems

Email exfiltration

We observed Earth Krahang conducting brute force attacks on Exchange servers via their Outlook on the web (formerly known as Outlook Web Access, or OWA) portals of its victims. The threat uses a list of common passwords to test the email accounts on the target’s email server.  We have observed the group using a custom Python script targeting the ActiveSync service on the OWA server to perform their brute-force attack.

We also found the threat actor using the open-source tool ruler to brute force email accounts and passwords. Email accounts using weak passwords can be identified by the attacker, who can then perform email exfiltration or abuse the compromised account to send spear phishing emails (as we discussed earlier).

We also identified another Python script that the actor used to exfiltrate emails from a Zimbra mail server. The script can package the victim’s mailbox via the mail server API using an authenticated cookie stolen by the threat actor. However, our investigation was unable to determine how the authenticated tokens were stolen from the victim’s server.

The Python script used by Earth Krahang to exfiltrate the victim’s mailbox

Delivered malware families

Earth Krahang delivers backdoors to establish access to victim machines. Cobalt Strike and two custom backdoors, RESHELL and XDealer, were employed during the initial stage of attack. We found that these backdoors were delivered either through spear-phishing emails or deployed via web shell on compromised servers.

We found the RESHELL backdoor being used several times in attacks during 2022. It was mentioned being used in a targeted attack against a Southeast Asian government by Palo Alto in a previous research report . RESHELL is a simple .NET backdoor that possesses the basic capabilities of collecting information, dropping files, or executing system commands. Its binaries are packed with ConfuserEX and its command-and-control (C&C) communication is encrypted with the AES algorithm.

Since 2023, the Earth Krahang shifted to another backdoor (named XDealer by TeamT5 and DinodasRAT by ESET). Compared to RESHELL, XDealer provides more comprehensive backdoor capabilities. In addition, we found that the threat actor employed both Windows and Linux versions of XDealer to target different systems.

Each XDealer sample embeds a mark string that represents the backdoor’s version. We observed the following marks:

Table 1. The list of the identified marks embedded on XDealer samples

This finding indicates that the backdoor may have been used in the wild for some time now and is still under active development.

It's worth noting that many early XDealer samples were developed as a DLL file packaged with an installer, a stealer module DLL, a text file contents ID string, and an LNK file. The LNK file executes the installer, which then installs the XDealer DLL and the stealer module DLL on the victim’s machine. The stealer module can take screenshots, steal clipboard data, and log keystrokes.

In one case, we found that the LNK file was replaced with another executable, which is an installer loader (it’s likely that Earth Krahang employed a different execution scheme instead of a standalone executable). Furthermore, we found that some of the XDealer DLL loaders were signed with valid code signing certificates issued by GlobalSign to two Chinese companies. According to public information available on the internet, one is a human resource company, while the other is a game development company. It’s likely that their certificates were stolen and abused to sign malicious executables.

Table 2. The list of packages delivering XDealer DLL and other files

Table 3. The list of certificates abused to sign the XDealer loader

Cobalt Strike was also frequently used during the initial stage of an attack. Interestingly, we found that instead of the typical Cobalt Strike usage, Earth Krahang adds additional protection to their C&C server through the adoption of the open-source project RedGuard , which is basically a proxy that helps red teams hinder the discovery of their Cobalt Strike C&C profile.

The threat actor abused RedGuard to prevent its C&C servers from being identified by blue team Cobalt Strike C&C scanners or search engine web crawlers. It also helps the group monitor who is collecting their C&C profiles. We found that Earth Krahang’s C&C server redirected invalid C&C requests to security vendor websites due to RedGuard’s protections.

Cobalt Strike exploits the DLL side-loading vulnerability. In one case we analyzed, the threat actor dropped three files, fontsets.exe , faultrep.dll , and faultrep.dat . The file fontsets.exe (SHA256: 97c668912c29b8203a7c3bd7d5d690d5c4e5da53) is a legitimate executable that was abused to side-load the DLL file faultrep.dll (SHA256: a94d0e51df6abbc4a7cfe84e36eb8f38bc011f46).

The faultrep.dll  file is a custom shellcode loader that will decode the encoded shellcode — which is Cobalt Strike — stored inside faultrep.dat . We also found another DLL loader with a similar decoding routine, but with different byte values for decoding and loads shellcode from a different filename ( conf.data ).

Using our telemetry data, we found that the threat actor also dropped PlugX and ShadowPad samples in victim environments. The PlugX sample, named fualtrep.dll , is likely used for side-loading, similar to the Cobalt Strike routine mentioned above. The ShadowPad samples had the exact same characteristics as seen in our previous Earth Lusca report .

Victimology

We found approximately 70 different victims (organizations that were confirmed to be compromised) spread across 23 different countries. Since we had access to some of Earth Krahang’s logs, we were also able to identify 116 different targets (including those that were not confirmed to be compromised) in 35 countries.

In total, the threat actor was able to compromise or target victims in 45 different countries spread across different regions, most of them in Asia and America, but also in Europe and Africa.

The map of victims targeted by Earth Krahang (countries in red are those that at least one entity compromised, while countries in yellow are those with at least one entity targeted)

Government organizations seem to be Earth Krahang’s primary targets. As an example, in the case of one country, we found that the threat actor compromised a diverse range of organizations belonging to 11 different government ministries.

We found that at least 48 government organizations were compromised, with a further 49 other government entities being targeted. Foreign Affairs ministries and departments were a top target, compromising 10 such organizations and targeting five others.

Education is another sector of interest to the threat actor. We found at least two different victims and 12 targets belonging to this sector. The communications industry was also targeted; we found multiple compromised telecommunications providers. Other target organizations and entities include post offices (targeted in at least three different countries), logistics platforms, and job services.

There were other industries targeted, but on a smaller scale, including the following:

  • Finance/Insurance
  • Foundations/NGOs/Thinkthanks
  • Manufacturing
  • Real estate

Attribution

Initially, we had no attribution for this campaign since we found no infrastructure overlaps, and had never seen the RESHELL malware family before. Palo Alto published a report that attributes, with moderate confidence, a particular cluster using RESHELL malware to GALLIUM . However, the assessment is based on a toolset that is shared among many different threat actors, and we were hesitant to use this link for proper attribution.  We also considered the possibility that RESHELL is a shared malware family.

Earth Krahang switched to the XDealer malware family in later campaigns. In a research paper presented by TeamT5, XDealer was shown to be associated with Luoyu , a threat actor with Chinese origins that used the WinDealer and ReverseWindow malware families. Our colleague, who was previously involved in the research of Luoyu, shared with us the insights on this association, particularly the sharing of an encryption key between an old XDealer sample and a SpyDealer sample — suggesting a connection between both malware families. ESET, which named this malware DinodasRAT, wrote an extensive report on its features. However they had no particular attribution apart from the possible China-nexus origin.

While we believe it could be possible that this campaign has links to LuoYu, we found no traces of other malware families used by this threat actor. Also, the encryption key mentioned above is different from the samples we found in this campaign, meaning that this malware family has multiple builders. This could suggest that either the key was changed at some point in development, or that the tool is shared among different groups.

In January 2022, we reported on a China-nexus threat actor we called Earth Lusca , following up with updates on their use of a newly discovered backdoor named SprySOCKS and their recent activities capitalizing on the Taiwanese presidential election. During our investigation, we noticed malware being downloaded from IP addresses we attribute to Earth Lusca (45[.]32[.]33[.]17 and 207[.]148[.]75[.]122, for example) at the lateral movement stage of this campaign. This suggests a strong link between this threat actor and Earth Lusca. We also found infrastructure overlaps between some C&C servers that communicated with malware we found during our investigation, and domain names such as googledatas[.]com that we attribute to Earth Lusca.

While the infrastructure and the preference of the initial stage backdoors look to be very different between this new campaign and the previously reported activities of Earth Lusca, our speculation is that they are two intrusion sets running independently but targeting a similar range of victims, becoming more intertwined as they approach their goal — possibly even being  managed by the same threat group. Due to these characteristics, we decided to give the independent name, Earth Krahang, to this intrusion set.

Our previous report suggests Earth Lusca might be the penetration team behind the Chinese company I-Soon, which had their information leaked on GitHub recently. Using this leaked information, we found that the company organized their penetration team into two different subgroups. This could be the possible reason why we saw two independent clusters of activities active in the wild but with limited association. Earth Krahang could be another penetration team under the same company.

In this report, we shared our investigation on a new campaign we named Earth Krahang. Our findings show that this threat actor focuses its efforts on government entities worldwide and abuses compromised government infrastructure to enable its malicious operations.

We were also able to identify two unique malware families used in Earth Krahang’s attacks while also illustrating the larger picture involving the group’s targets and malicious activities via our telemetry data and the exposed files on their servers.

Our investigation also identified multiple links between Earth Krahang and Earth Lusca. We suspected these two intrusion sets are managed by the same threat actor.

Given the importance of Earth Krahang’s targets and their preference of using compromised government email accounts, we strongly advise organizations to adhere to security best practices, including educating employees and other individuals involved with the organization on how to avoid social engineering attacks, such as developing a healthy skepticism when it involves potential security issues, and developing habits such as refraining from clicking on links or opening attachments without verification from the sender. Given the threat actor’s exploitation of vulnerabilities in its attacks, we also encourage organizations to update their software and systems with the latest security patches to avoid any potential compromise.

Indicators of Compromise

The indicators of compromise for this entry can be found here .

Acknowledgment

Special thanks to Leon M Chang who shared to us insights about the overlap of  the TEA encryption key between XDealer and SpyDealer samples.

MITRE ATT&CK

The listed techniques are a subset of the MITRE ATT&CK list .

Joseph C Chen

Threat Researcher

Daniel Lunghi

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  • How to write a literary analysis essay | A step-by-step guide

How to Write a Literary Analysis Essay | A Step-by-Step Guide

Published on January 30, 2020 by Jack Caulfield . Revised on August 14, 2023.

Literary analysis means closely studying a text, interpreting its meanings, and exploring why the author made certain choices. It can be applied to novels, short stories, plays, poems, or any other form of literary writing.

A literary analysis essay is not a rhetorical analysis , nor is it just a summary of the plot or a book review. Instead, it is a type of argumentative essay where you need to analyze elements such as the language, perspective, and structure of the text, and explain how the author uses literary devices to create effects and convey ideas.

Before beginning a literary analysis essay, it’s essential to carefully read the text and c ome up with a thesis statement to keep your essay focused. As you write, follow the standard structure of an academic essay :

  • An introduction that tells the reader what your essay will focus on.
  • A main body, divided into paragraphs , that builds an argument using evidence from the text.
  • A conclusion that clearly states the main point that you have shown with your analysis.

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Table of contents

Step 1: reading the text and identifying literary devices, step 2: coming up with a thesis, step 3: writing a title and introduction, step 4: writing the body of the essay, step 5: writing a conclusion, other interesting articles.

The first step is to carefully read the text(s) and take initial notes. As you read, pay attention to the things that are most intriguing, surprising, or even confusing in the writing—these are things you can dig into in your analysis.

Your goal in literary analysis is not simply to explain the events described in the text, but to analyze the writing itself and discuss how the text works on a deeper level. Primarily, you’re looking out for literary devices —textual elements that writers use to convey meaning and create effects. If you’re comparing and contrasting multiple texts, you can also look for connections between different texts.

To get started with your analysis, there are several key areas that you can focus on. As you analyze each aspect of the text, try to think about how they all relate to each other. You can use highlights or notes to keep track of important passages and quotes.

Language choices

Consider what style of language the author uses. Are the sentences short and simple or more complex and poetic?

What word choices stand out as interesting or unusual? Are words used figuratively to mean something other than their literal definition? Figurative language includes things like metaphor (e.g. “her eyes were oceans”) and simile (e.g. “her eyes were like oceans”).

Also keep an eye out for imagery in the text—recurring images that create a certain atmosphere or symbolize something important. Remember that language is used in literary texts to say more than it means on the surface.

Narrative voice

Ask yourself:

  • Who is telling the story?
  • How are they telling it?

Is it a first-person narrator (“I”) who is personally involved in the story, or a third-person narrator who tells us about the characters from a distance?

Consider the narrator’s perspective . Is the narrator omniscient (where they know everything about all the characters and events), or do they only have partial knowledge? Are they an unreliable narrator who we are not supposed to take at face value? Authors often hint that their narrator might be giving us a distorted or dishonest version of events.

The tone of the text is also worth considering. Is the story intended to be comic, tragic, or something else? Are usually serious topics treated as funny, or vice versa ? Is the story realistic or fantastical (or somewhere in between)?

Consider how the text is structured, and how the structure relates to the story being told.

  • Novels are often divided into chapters and parts.
  • Poems are divided into lines, stanzas, and sometime cantos.
  • Plays are divided into scenes and acts.

Think about why the author chose to divide the different parts of the text in the way they did.

There are also less formal structural elements to take into account. Does the story unfold in chronological order, or does it jump back and forth in time? Does it begin in medias res —in the middle of the action? Does the plot advance towards a clearly defined climax?

With poetry, consider how the rhyme and meter shape your understanding of the text and your impression of the tone. Try reading the poem aloud to get a sense of this.

In a play, you might consider how relationships between characters are built up through different scenes, and how the setting relates to the action. Watch out for  dramatic irony , where the audience knows some detail that the characters don’t, creating a double meaning in their words, thoughts, or actions.

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Your thesis in a literary analysis essay is the point you want to make about the text. It’s the core argument that gives your essay direction and prevents it from just being a collection of random observations about a text.

If you’re given a prompt for your essay, your thesis must answer or relate to the prompt. For example:

Essay question example

Is Franz Kafka’s “Before the Law” a religious parable?

Your thesis statement should be an answer to this question—not a simple yes or no, but a statement of why this is or isn’t the case:

Thesis statement example

Franz Kafka’s “Before the Law” is not a religious parable, but a story about bureaucratic alienation.

Sometimes you’ll be given freedom to choose your own topic; in this case, you’ll have to come up with an original thesis. Consider what stood out to you in the text; ask yourself questions about the elements that interested you, and consider how you might answer them.

Your thesis should be something arguable—that is, something that you think is true about the text, but which is not a simple matter of fact. It must be complex enough to develop through evidence and arguments across the course of your essay.

Say you’re analyzing the novel Frankenstein . You could start by asking yourself:

Your initial answer might be a surface-level description:

The character Frankenstein is portrayed negatively in Mary Shelley’s Frankenstein .

However, this statement is too simple to be an interesting thesis. After reading the text and analyzing its narrative voice and structure, you can develop the answer into a more nuanced and arguable thesis statement:

Mary Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

Remember that you can revise your thesis statement throughout the writing process , so it doesn’t need to be perfectly formulated at this stage. The aim is to keep you focused as you analyze the text.

Finding textual evidence

To support your thesis statement, your essay will build an argument using textual evidence —specific parts of the text that demonstrate your point. This evidence is quoted and analyzed throughout your essay to explain your argument to the reader.

It can be useful to comb through the text in search of relevant quotations before you start writing. You might not end up using everything you find, and you may have to return to the text for more evidence as you write, but collecting textual evidence from the beginning will help you to structure your arguments and assess whether they’re convincing.

To start your literary analysis paper, you’ll need two things: a good title, and an introduction.

Your title should clearly indicate what your analysis will focus on. It usually contains the name of the author and text(s) you’re analyzing. Keep it as concise and engaging as possible.

A common approach to the title is to use a relevant quote from the text, followed by a colon and then the rest of your title.

If you struggle to come up with a good title at first, don’t worry—this will be easier once you’ve begun writing the essay and have a better sense of your arguments.

“Fearful symmetry” : The violence of creation in William Blake’s “The Tyger”

The introduction

The essay introduction provides a quick overview of where your argument is going. It should include your thesis statement and a summary of the essay’s structure.

A typical structure for an introduction is to begin with a general statement about the text and author, using this to lead into your thesis statement. You might refer to a commonly held idea about the text and show how your thesis will contradict it, or zoom in on a particular device you intend to focus on.

Then you can end with a brief indication of what’s coming up in the main body of the essay. This is called signposting. It will be more elaborate in longer essays, but in a short five-paragraph essay structure, it shouldn’t be more than one sentence.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

Some students prefer to write the introduction later in the process, and it’s not a bad idea. After all, you’ll have a clearer idea of the overall shape of your arguments once you’ve begun writing them!

If you do write the introduction first, you should still return to it later to make sure it lines up with what you ended up writing, and edit as necessary.

The body of your essay is everything between the introduction and conclusion. It contains your arguments and the textual evidence that supports them.

Paragraph structure

A typical structure for a high school literary analysis essay consists of five paragraphs : the three paragraphs of the body, plus the introduction and conclusion.

Each paragraph in the main body should focus on one topic. In the five-paragraph model, try to divide your argument into three main areas of analysis, all linked to your thesis. Don’t try to include everything you can think of to say about the text—only analysis that drives your argument.

In longer essays, the same principle applies on a broader scale. For example, you might have two or three sections in your main body, each with multiple paragraphs. Within these sections, you still want to begin new paragraphs at logical moments—a turn in the argument or the introduction of a new idea.

Robert’s first encounter with Gil-Martin suggests something of his sinister power. Robert feels “a sort of invisible power that drew me towards him.” He identifies the moment of their meeting as “the beginning of a series of adventures which has puzzled myself, and will puzzle the world when I am no more in it” (p. 89). Gil-Martin’s “invisible power” seems to be at work even at this distance from the moment described; before continuing the story, Robert feels compelled to anticipate at length what readers will make of his narrative after his approaching death. With this interjection, Hogg emphasizes the fatal influence Gil-Martin exercises from his first appearance.

Topic sentences

To keep your points focused, it’s important to use a topic sentence at the beginning of each paragraph.

A good topic sentence allows a reader to see at a glance what the paragraph is about. It can introduce a new line of argument and connect or contrast it with the previous paragraph. Transition words like “however” or “moreover” are useful for creating smooth transitions:

… The story’s focus, therefore, is not upon the divine revelation that may be waiting beyond the door, but upon the mundane process of aging undergone by the man as he waits.

Nevertheless, the “radiance” that appears to stream from the door is typically treated as religious symbolism.

This topic sentence signals that the paragraph will address the question of religious symbolism, while the linking word “nevertheless” points out a contrast with the previous paragraph’s conclusion.

Using textual evidence

A key part of literary analysis is backing up your arguments with relevant evidence from the text. This involves introducing quotes from the text and explaining their significance to your point.

It’s important to contextualize quotes and explain why you’re using them; they should be properly introduced and analyzed, not treated as self-explanatory:

It isn’t always necessary to use a quote. Quoting is useful when you’re discussing the author’s language, but sometimes you’ll have to refer to plot points or structural elements that can’t be captured in a short quote.

In these cases, it’s more appropriate to paraphrase or summarize parts of the text—that is, to describe the relevant part in your own words:

The conclusion of your analysis shouldn’t introduce any new quotations or arguments. Instead, it’s about wrapping up the essay. Here, you summarize your key points and try to emphasize their significance to the reader.

A good way to approach this is to briefly summarize your key arguments, and then stress the conclusion they’ve led you to, highlighting the new perspective your thesis provides on the text as a whole:

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

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    2.1 Step 1: defining the research question. The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed.

  15. Ten simple rules for carrying out and writing meta-analyses

    Introduction. In the context of evidence-based medicine, meta-analyses provide novel and useful information [], as they are at the top of the pyramid of evidence and consolidate previous evidence published in multiple previous reports [].Meta-analysis is a powerful tool to cumulate and summarize the knowledge in a research field [].Because of the significant increase in the published ...

  16. Analysis

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

  17. Research Findings

    Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables ...

  18. Narrative Analysis

    Narrative analysis is a qualitative research methodology that involves examining and interpreting the stories or narratives people tell in order to gain insights into the meanings, experiences, and perspectives that underlie them. Narrative analysis can be applied to various forms of communication, including written texts, oral interviews, and ...

  19. How to Write a Research Paper

    Conduct preliminary research. Develop a thesis statement. Create a research paper outline. Write a first draft of the research paper. Write the introduction. Write a compelling body of text. Write the conclusion. The second draft. The revision process.

  20. Literature review

    summarise and analyse previous research and theories; identify areas of controversy and contested claims; highlight any gaps that may exist in research to date. Conducting a literature review. Focusing on different aspects of your literature review can be useful to help plan, develop, refine and write it.

  21. Revision: Using Says/Does Analysis

    Using Says/Does Analysis in the Writing Process. Adapted from Dr. Joe Bizup. What is it? A Says/Does analysis asks you to write a single sentence describing what each paragraph of an essay says and a single sentence describing what it does. Says. For the "says" part, write a single sentence that fully summarizes the "main point" of the ...

  22. What the Data Says About Pandemic School Closures, Four Years Later

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  23. 2024 NCAA Tournament bracket predictions: March Madness expert picks

    The antagonistic side of me initially picked Purdue over UConn in the title game. But I sat and thought about it and couldn't make any reasonable case to pick any team other than UConn as champion.

  24. How to Write a Results Section

    The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share: A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression). A more detailed description of your analysis should go in your methodology section.

  25. MH370 disappearance 10 years on: can we still find it?

    Disclosure statement. Charitha Pattiaratchi receives funding from Integrated Marine Observing System research institute, the Australian Research Council and the West Australian Marine Science ...

  26. Earth Krahang Exploits Intergovernmental Trust to Launch Cross

    One of the infection vectors used involves the scanning of public-facing servers. Earth Krahang heavily employs open-source scanning tools that perform recursive searches of folders such as .git or .idea.The threat actor also resorts to simply brute-forcing directories to help identify files that may contain sensitive information such as file paths or passwords on the victim's servers.

  27. A Beginner's Guide to Starting the Research Process

    Step 4: Create a research design. The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you'll use to collect and analyze it, and the location and timescale of your research. There are often many possible paths you can take to answering ...

  28. IU McKinney Faculty Feature at Rocky Mountain Legal Writing Conference

    IU McKinney Faculty Feature at Rocky Mountain Legal Writing Conference. 03/21/2024. Faculty from IU McKinney's nationally ranked Legal Communication and Analysis (LCA) Program recently presented at the Rocky Mountain Legal Writing Conference.. Professor Brad Desnoyer's presentation, AI vs. TA, compared how different AI platforms performed against teaching assistants when answering the same ...

  29. How to Write a Literary Analysis Essay

    Table of contents. Step 1: Reading the text and identifying literary devices. Step 2: Coming up with a thesis. Step 3: Writing a title and introduction. Step 4: Writing the body of the essay. Step 5: Writing a conclusion. Other interesting articles.