research essay analytical

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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Writing a Research Paper

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The pages in this section provide detailed information about how to write research papers including discussing research papers as a genre, choosing topics, and finding sources.

The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

• Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
• Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
• Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
• Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

How to Write an Analysis Essay: Examples + Writing Guide

An analysis / analytical essay is a standard assignment in college or university. You might be asked to conduct an in-depth analysis of a research paper, a report, a movie, a company, a book, or an event. In this article, you’ll find out how to write an analysis paper introduction, thesis, main body, and conclusion, and analytical essay example.

Our specialists will write a custom essay specially for you!

So, what is an analytical essay? This type of assignment implies that you set up an argument and analyze it using a range of claims. The claims should be supported by appropriate empirical evidence. Note that you need to explore both the positive and negative sides of the issue fully.

Analytical skills are the key to getting through your academic career. Moreover, they can be useful in many real-life situations. Keep reading this article by Custom-writing experts to learn how to write an analysis!

❓ What Is an Analytical Essay?

• 🤔 Getting Started

📑 Analytical Essay Outline

• 📔 Choosing a Title
• 💁 Writing an Introduction
• 🏋 Writing a Body
• 🏁 Writing a Conclusion

🔗 References

Before you learn how to start an analysis essay, you should understand some fundamentals of writing this type of paper. It implies that you analyze an argument using a range of claims supported by facts . It is essential to understand that in your analysis essay, you’ll need to explore the negative sides of the issue and the positive ones. That’s what distinguishes an analytical essay from, say, a persuasive one.

These are the steps to write an academic paper :

• Review the literature . Before starting any paper, you should familiarize yourself with what has already been written in the field. And the analytical essay is no exception. The easiest way is to search on the web for the information.
• Brainstorm ideas. After you’ve done your search, it is time for a brainstorm! Make a list of topics for your analysis essay, and then choose the best one. Generate your thesis statement in the same way.
• Prepare an outline . Now, when you’ve decided on the topic and the thesis statement of your analytical essay, think of its structure. Below you will find more detailed information on how your paper should be structured.
• Write the first draft. You’ve done a lot of work by now. Congratulations! Your next goal is to write the first version of your analysis essay, using all the notes that you have. Remember, you don’t need to make it perfect!
• Polish your draft. Now take your time to polish and edit your draft to transform it into the paper’s final version.

You are usually assigned to analyze an article, a book, a movie, or an event. If you need to write your analytical essay on a book or an article, you’ll have to analyze the style of the text, its main points, and the author’s purported goals.

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🤔 Analytical Essay: Getting Started

The key to writing an analysis paper is to choose an argument that you will defend throughout it. For example: maybe you are writing a critical analysis paper on George Orwell’s Animal Farm The first and imperative task is to think about your thesis statement. In the case of Animal Farm , the argument could be:

In Orwell’s Animal Farm , rhetoric and language prove to be more effective ways to keep social control than physical power.

The University of North Carolina at Chapel Hill gives a great explanation of the thesis statement , how to create one, and what its function is.

But that’s not all. Once you have your thesis statement, you need to break down how you will approach your analysis essay to prove your thesis. To do this, follow these steps:

• Define the main goal(s) of your analysis . Remember that it is impossible to address each and every aspect in a single paper. Know your goal and focus on it.
• Conduct research , both online and offline, to clarify the issue contained within your thesis statement.
• Identify the main parts of the issue by looking at each part separately to see how it works.
• Try to clearly understand how each part works.
• Identify the links between the various aspects of the topic .
• By using the information you found, try to solve your main problem .

At this point, you should have a clear understanding of both the topic and your thesis statement. You should also have a clear direction for your analysis paper firmly planted in your mind and recorded in writing.

This will give you what you need to produce the paper’s outline.

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An outline is the starting point for your work. A typical analytical essay features the usual essay structure. A 500-word essay should consist of a one-paragraph introduction, a three-paragraph body, and a one-paragraph conclusion. Find below a great analytical essay outline sample. Feel free to use it as an example when doing your own work!

Analysis Essay: Introduction

• Start with a startling statement or provocative question.

“All animals are equal, but some animals are more equal”. Animal Farm abounds in ironic and provocative phrases to start an analytical essay.

• Introduce the work and its author.
• Give background information that would help the reader understand your opinion.
• Formulate a thesis statement informing the reader about the purpose of the essay. Essay format does not presuppose telling everything possible on the given topic. Thus, a thesis statement tells what you are going to say, implying what you will not discuss, establishing the limits.

In Animal Farm, Orwell uses different irony types to ridicule totalitarianism to manifest its inability to make every member of society equal and happy.

Analysis Essay: Body

The analytical essay structure requires 2-3 developmental paragraphs, each dedicated to one separate idea confirming your thesis statement. The following template should be used for each of the body paragraphs.

• Start with a topic sentence that supports an aspect of your thesis.

Dramatic irony is used in Animal Farm to point out society’s ignorance.

• Continue with textual evidence (paraphrase, summary, direct quotations, specific details). Use several examples that substantiate the topic sentence.

Animals are unaware of the fact that Boxer was never sent to the hospital. He was sent to the slaughterhouse. However, the reader and writer understand that this is a lie.

• Conclude with an explanation.

By allowing the readers to learn some essential facts before the characters, dramatic irony creates suspense and shows how easy it is to persuade and manipulate the public.

Analysis Essay Conclusion

The next four points will give you a short instruction on how to conclude an analytical essay.

• Never use new information or topics here.
• Restate your thesis in a different formulation.
• Summarize the body paragraphs.
• Comment on the analyzed text from a new perspective.

📔 Choosing a Title for Your Analysis Essay

Choosing a title seems like not a significant step, but it is actually very important. The title of your critical analysis paper should:

• Entice and engage the reader
• Be unique and capture the readers’ attention
• Provide an adequate explanation of the content of the essay in just a few carefully chosen words

In the Animal Farm example, your title could be:

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“How Do the Pigs Manage to Keep Social Control on Animal Farm?”

Analysis Essay Topics

• Analyze the media content.
• Analyze the specifics and history of hip-hop culture.
• Sociological issues in the film Interstellar .
• Discuss the techniques M. Atwood uses to describe social issues in her novel The Handmaid’s Tale .
• Compare and analyze the paintings of Van Gogh and George Seurat.
• Analysis of Edgar Allan Poe’s The Black Cat .
• Examine the juvenile crime rates.
• Describe the influence of different parenting styles on children’s mind.
• Analyze the concept of the Ship of Theseus .
• Compare and analyze the various views on intelligence .
• Analysis of The Yellow Wallpaper by Charlotte Perkins Gilman .
• Discuss the techniques used by W. Shakespeare in A Midsummer Night’s Dream .
• Analyze the biography of Frederic Chopin .
• Manifestation of the Chicano culture in the artwork An Ofrenda for Dolores del Rio .
• Similarities and differences of Roman, Anglo-Saxon, and Spanish Empires .
• Describe the problem of stalking and its impact on human mental health.
• Examine the future of fashion .
• Analyze the topicality of the article Effectiveness of Hand Hygiene Interventions in Reducing Illness Absence .
• Discuss Thomas Paine’s impact on the success of American revolution.
• Meaningful messages in Recitatif by Toni Morrison .
• Explore the techniques used by directors in the film Killing Kennedy .
• Compare the leadership styles of Tang Empress Wu Zetian and the Pharaoh Cleopatra .
• Evaluate the credibility of Kristof’s arguments in his article Remote Learning Is Often an Oxymoron .
• Analyze genetically modified food .
• Examine the influence of Europeans on Indian tribes in The Narrative of the Captivity and Restoration of Mrs. Mary Rowlandson .
• Describe the rhetoric techniques used in The Portrait of Dorian Gray by Oscar Wilde .
• The importance of fighting against violence in communities in the documentary film The Interrupters .
• Analyze indoor and outdoor pollution .
• Analyze the issue of overprotective parenthood .
• Explore the connection between eating habits and advertisement.
• Discuss the urgence of global warming issue .
• Influence of sleep on people’s body and mental health.
• Analyze the relationship between Christianity and sports .
• Discuss the concept of leadership and its significance for company efficiency.
• Analyze the key lessons of the book Rich Dad Poor Dad by Robert Kiyosaki .
• Examine the specifics of nursing ethic .
• The theme of emotional sufferings in the short story A Rose for Emily .
• Analysis of bias in books for children .
• Analyze the rhetoric of the article Public Monuments .
• Describe the main messages in Jean-Paul Sartre’s Nausea .
• Explore the problem of structural racism in healthcare .
• The reasons of tango dance popularity.
• The shortcomings of the American educational system in Waiting for Superman.
• Analyze and compare Erin’s Law and Megan’s Law .
• Analyze the James Madison’s essay Federalist 10 .
• Examine symbols in the movie The Joker .
• Compare the thematic connection and stylistic devices in the poems The Road Not Taken and Find Your Way .
• Describe and analyze the life of Eddie Bernice Johnson .
• Explore the social classes in America .
• Crucial strengths and weaknesses of the main translation theories .

💁 Writing Your Analytical Essay Introduction

You must understand how to compose an introduction to an analysis paper. The University of Wollongong describes the introduction as a “map” of any writing. When writing the introduction, follow these steps:

• Provide a lead-in for the reader by offering a general introduction to the topic of the paper.
• Include your thesis statement , which shifts the reader from the generalized introduction to the specific topic and its related issues to your unique take on the essay topic.
• Present a general outline of the analysis paper.

Watch this great video for further instructions on how to write an introduction to an analysis essay.

Example of an Analytical Essay Introduction

“Four legs good, two legs bad” is one of the many postulates invented by George Orwell for his characters in Animal Farm to vest them with socialist ideology and control over the animal population. The social revolution on Manor Farm was built on language instruments, first for the collective success of the animals, and later for the power consolidation by the pigs. The novel was written in 1945 when the transition from limitless freedoms of socialist countries transformed into dictatorship. Through his animal protagonists, the author analyzes the reasons for peoples’ belief in the totalitarian regime. In Orwell’s Animal Farm , rhetoric and language prove to be more effective ways to keep social control than physical power.

🏋 Writing Your Analytical Essay Body

The body of the paper may be compared to its heart. This is the part where you show off your talent for analysis by providing convincing, well-researched, and well-thought-out arguments to support your thesis statement. You have already gathered the information, and now all you may start crafting your paper.

To make the body of an analytical essay, keep the following in mind:

• Discuss one argument per paragraph , although each argument can relate to multiple issues
• Strike a balance between writing in an unbiased tone, while expressing your personal opinion
• Be reasonable when making judgments regarding any of the problems you discuss
• Remember to include the opposing point of view to create a balanced perspective

The bottom line is: you want to offer opposing views, but you must pose your arguments so they will counter those opposing views and prove your point of view. Follow these steps when constructing each body paragraph:

• Choose the main sentence. The main or topic sentence will be the first line in your essay. The topic sentence is responsible for presenting the argument you will discuss in the paragraph and demonstrate how this argument relates to the thesis statement.
• Provide the context for the topic sentence , whether it relates to a quote, a specific incident in society, or something else. Offer evidence on who, what, where, when, why, and how.
• Give your analysis of the argument and how it adequately proves your thesis.
• Write a closing sentence that sums up the paragraph and provides a transition to the following paragraph.

Example of an Analytical Essay Body

Literacy can grant power, provided that there are animals who cannot read or write. In the beginning, the animals’ literacy and intellect are relatively the same. Old Major is the cleverest pig; he is the kind old philosopher, like Karl Marx or Vladimir Lenin. During his retirement, he develops a theory that all humans are the root of evil. His speech was the foundation for the pigs’ assumption of power. They refined his ideas into a new ideology and called it Animalism. They also learned how to read. It allowed the pigs to declare themselves the “mind workers.” Therefore, the pigs’ literacy assured the illiterate animals in their objective superiority.

Meanwhile, as the pigs were the intellectual elite, they were not supposed to work, which raised their social status by itself. Snowball tried to promote education among all the animals, but most of them failed to master the alphabet. This is a metaphor for the general public being predominantly ignorant and easy to manipulate. At the same time, Boxer and other animals that spend most of the day in hard work merely have no time to develop their intellect. Thus, the pigs’ intention to build a school for pig children was highly efficient. Unequal access to education and unequal ability to express one’s thoughts in perspective reinforce the social divide, making the pigs smarter and more powerful and undermining other animals’ self-esteem.

At this point, the pigs resort to propaganda and rhetoric. Squealer uses his oratorical gift to refine the pigs’ message to the other animals. Upon Napoleon’s order, he breaks the Seven Commandments of farm governance. At night, he climbs the ladder to change them, and once even falls from the ladder trying to change the commandment on alcohol. The “proletarian” animals soon forget what the Seven Commandments were like in the first place and are unsure if they have ever been altered. Further on, Minimus writes a poem praising Napoleon. Finally, Squealer replaces the Commandments with a single assertion: “All animals are equal, but some animals are more equal than others.” Language is no longer used to convince. It is used to control and manipulate.

🏁 Writing Your Analytical Essay Conclusion

The conclusion is short and sweet. It summarizes everything you just wrote in the essay and wraps it up with a beautiful shiny bow. Follow these steps to write a convincing conclusion:

• Repeat the thesis statement and summarize your argument. Even when using the best summary generator for the task, reread it to make sure all the crucial points are included.
• Take your argument beyond what is simply stated in your paper. You want to show how it is essential in terms of the bigger picture. Also, you may dwell on the influence on citizens of the country.

Example of an Analytical Essay Conclusion

Because of everything mentioned above, it becomes clear that language and rhetoric can rise to power, establish authority, and manipulate ordinary people. Animal Farm is the simplified version of a communist society. It shows how wise philosophers’ good intentions can be used by mean leaders to gain unopposed power and unconditional trust. Unfortunately, this can lead to the death of many innocent animals, i.e., people, as totalitarianism has nothing to do with people’s rule. Therefore, language and oratory are potent tools that can keep people oppressed and weak, deprive them of any chance for improvement and growth, and make them think that there is no other possible existence.

Now you are ready to write an analysis essay! See, it’s easier than you thought.

Of course, it’s always helpful to see other analysis essay examples. The University of Arkansas at Little Rock provides some great examples of an analytical paper .

✏️ Analysis Essay FAQ

A great analytical paper should be well-structured, cohesive, and logically consistent. Each part of the essay should be in its place, creating a smooth and easy-to-read text. Most importantly, the statements should be objective and backed by arguments and examples.

It is a paper devoted to analyzing a certain topic or subject. An analysis essay is all about reviewing certain details of the subject and interpreting them. For example, such an analysis for a poem includes a description of artistic means that helped the poet convey the idea.

Writing an analytical essay on a book/movie/poem start with an outline. Point out what catches the eye when reviewing the subject. See how these details can be interpreted. Make sure that you refer to the main idea/message. Add an appropriate introduction and a logical conclusion.

Being more analytical in writing can be essential for a student. This is a skill that can be self-taught: try to start noticing subtle details and describe them. As you write, interpret the facts and strive to draw conclusions. Try to be as objective as possible.

• Elements of Analysis
• How Can I Create Stronger Analysis?
• How to Write a Literary Analysis Essay: Bucks.edu
• Essay Structure | – Harvard College Writing Center
• Analytical Writing: Looking Closely (Colostate.edu)
• Analytical Thesis Statements – University of Arizona
• Writing an analytic essay – UTSC – University of Toronto
• Organizing Your Analysis // Purdue Writing Lab
• How to Write an Analytical Essay: 15 Steps (with Pictures)
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How to Write an Analytical Essay

Last Updated: February 2, 2024 Fact Checked

This article was co-authored by Megan Morgan, PhD . Megan Morgan is a Graduate Program Academic Advisor in the School of Public & International Affairs at the University of Georgia. She earned her PhD in English from the University of Georgia in 2015. There are 9 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 3,985,764 times.

Writing an analytical essay can seem daunting, especially if you've never done it before. Don't worry! Take a deep breath, buy yourself a caffeinated beverage, and follow these steps to create a well-crafted analytical essay.

Prewriting for Your Essay

• For example, "Stanley Kubrick's The Shining uses a repeating motif of Native American culture and art to comment on America's history of colonizing Native Americans' lands" is an analytical thesis. It is analyzing a particular text and setting forth an argument about it in the form of a thesis statement.

• If you're writing an analytical essay about a work of fiction, you could focus your argument on what motivates a specific character or group of characters. Or, you could argue why a certain line or paragraph is central to the work as a whole. For example: Explore the concept of vengeance in the epic poem Beowulf .
• If you're writing about a historical event, try focusing on the forces that contributed to what happened.
• If you're writing about scientific research or findings, follow the scientific method to analyze your results.

• Look for repeated imagery, metaphors, phrases, or ideas. Things that repeat are often important. See if you can decipher why these things are so crucial. Do they repeat in the same way each time, or differently?
• How does the text work? If you're writing a rhetorical analysis, for example, you might analyze how the author uses logical appeals to support her argument and decide whether you think the argument is effective. If you're analyzing a creative work, consider things like imagery, visuals in a film, etc. If you're analyzing research, you may want to consider the methods and results and analyze whether the experiment is a good design.
• A mind map can be helpful to some people. Start with your central topic, and arrange smaller ideas around it in bubbles. Connect the bubbles to identify patterns and how things are related.
• Good brainstorming can be all over the place. In fact, that can be a good way to start off! Don't discount any ideas just yet. Write down any element or fact that you think of as you examine your topic.

• This is an analytical thesis because it examines a text and makes a particular claim.
• The claim is "arguable," meaning it's not a statement of pure fact that nobody could contest. An analytical essay takes a side and makes an argument.
• Make sure your thesis is narrow enough to fit the scope of your assignment. "Revenge in Beowulf could be a PhD dissertation, it's so broad. It's probably much too big for a student essay. However, arguing that one character's revenge is more honorable than another's is manageable within a shorter student essay. [3] X Research source
• Unless instructed to write one, avoid the "three-prong" thesis that presents three points to be discussed later. These thesis statements usually limit your analysis too much and give your argument a formulaic feel. It's okay to state generally what your argument will be.

• Example of supporting evidence : To support a claim that the dragon’s vengeance was more righteous than Grendel's mother's, look at the passages in the poem that discuss the events leading up to each monster’s attack, the attacks themselves, as well as the reactions to those attacks. Don't: ignore or twist evidence to fit your thesis. Do: adjust your thesis to a more nuanced position as you learn more about the topic.

• If you're not quite sure how all your evidence fits together, don't worry! Making an outline can help you figure out how your argument should progress.
• You can also make a more informal outline that groups your ideas together in large groups. From there, you can decide what to talk about where.
• Your essay will be as long as it needs to be to adequately discuss your topic. A common mistake students make is to choose a large topic and then allow only 3 body paragraphs to discuss it. This makes essays feel shallow or rushed. Don't be afraid to spend enough time discussing each detail!

Writing Your Essay

• Example introduction : Revenge was a legally recognized right in ancient Anglo-Saxon culture. The many revenges in the epic poem Beowulf show that retribution was an essential part of the Anglo-Saxon age. However, not all revenges are created alike. The poet's portrayal of these revenges suggests that the dragon was more honorable in his act of revenge than Grendel's mother.
• This introduction gives your readers information they should know to understand your argument, and then presents an argument about the complexity of a general topic (revenge) in the poem. This type of argument can be interesting because it suggests that the reader needs to think about the text very carefully and not take it at face value. Don't: include filler and fluff sentences beginning with "In modern society" or "Throughout time." Do: briefly mention the title, author, and publication date of the text you're analyzing.

• Example topic sentence : The key to differentiating between the two attacks is the notion of excessive retribution.
• Example analysis : Grendel's mother does not simply want vengeance, as per the Medieval concept of ‘an eye for an eye.’ Instead, she wants to take a life for a life while also throwing Hrothgar’s kingdom into chaos.
• Example evidence : Instead of simply killing Aeschere, and thus enacting just revenge, she “quickly [snatches] up” that nobleman and, with him “tight in her clutches,” she leaves for the fen (1294). She does this to lure Beowulf away from Heorot so she can kill him as well.
• The formula "CEE" may help you remember: Claim-Evidence-Explanation. Whenever you present a claim, make sure you present evidence to support that claim and explain how the evidence relates to your claim.

• Example of a quote : Instead of simply killing Aeschere, and thus enacting just revenge, she “quickly [snatches] up” that nobleman and, with him “tight in her clutches,” she leaves for the fen (1294).
• Example of a paraphrased sentence : The female Grendel enters Heorot, snatches up one of the men sleeping inside it, and runs away to the fen (1294).

• Example conclusion : The concept of an ‘eye for an eye’ was very present in the early Medieval world. However, by comparing the attacks of both Grendel's mother and the dragon, the medieval world’s perception of righteous vengeance versus unjust revenge is made clear. While the dragon acts out in the only way he knows how, Grendel's mother attacks with evil intent.
• Example conclusion with a ‘bigger world connection’: The concept of an ‘eye for an eye’ was very present in the early Medieval world. However, by comparing the attacks of both Grendel's mother and the dragon, the medieval world’s perception of righteous vengeance versus unjust revenge is made clear. While the dragon acts out in the only way he knows how, Grendel's mother attacks with evil intent. As we saw from the study of other characters, these portrayals may tie into an early Medieval perception that women had greater potential for evil.

Finalizing Your Essay

• Make sure to also format your essay correctly. For example, using a 12-pt standard font (like Arial or Times New Roman) and 1" margins is standard.

• If you are analyzing a film, look up the list of characters online. Check two or three sources to make sure that you have the correct spelling.

Analytical Essay Writing Help

Community Q&A

• Ask yourself "What am I trying to prove?" The answer should be in your thesis. If not, go back and fix it. Thanks Helpful 0 Not Helpful 0
• If you are writing a formal analysis or critique, then avoid using colloquial writing . Though informal language may bring some color to a paper, you do not want to risk weakening your argument by influencing it with verbal slang. Thanks Helpful 0 Not Helpful 0
• Avoid being too vague. Vagueness leaves room for misinterpretation and in a coherent, analytical essay, leaving room for misinterpretation decreases the effectiveness of your argument. Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ https://www.stetson.edu/other/writing-center/media/Handout%20-%20Analytical%20Essay.pdf
• ↑ https://www.bucks.edu/media/bcccmedialibrary/pdf/HOWTOWRITEALITERARYANALYSISESSAY_10.15.07_001.pdf
• ↑ http://writing2.richmond.edu/writing/wweb/rsrchppr.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-can-i-create-stronger-analysis-.html
• ↑ https://academics.umw.edu/writing-fredericksburg/files/2011/09/Basic-Outlines.pdf
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-write-an-intro--conclusion----body-paragraph.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-incorporate-quotes-.html
• ↑ https://owl.purdue.edu/owl/general_writing/the_writing_process/proofreading/proofreading_suggestions.html
• ↑ https://academicguides.waldenu.edu/writingcenter/writingprocess/proofreading

About This Article

To write an analytical essay, first write an introduction that gives your reader background information and introduces your thesis. Then, write body paragraphs in support of your thesis that include a topic sentence, an analysis of some part of the text, and evidence from the text that supports your analysis. You can use direct quotes from the text that support your point of view or paraphrase if you’re trying to summarize information. Finally, complete your essay with a conclusion that reiterates your thesis and your primary support for it. To learn from our English reviewer how to come up with your thesis statement and find evidence that supports it, read on! Did this summary help you? Yes No

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How To Write An Analytical Essay A Full Guide

Crafting an impeccable analytical essay is an art form that demands precision, insight, and a structured approach. Whether you’re delving into literature, dissecting historical events, or unraveling scientific theories, the ability to present a compelling analysis is pivotal. Here’s a comprehensive guide to navigate the intricate path of writing a flawless analytical essay.

What Is An Analytical Essay

An analytical essay is a type of academic writing that delves deeply into a topic, idea, or piece of literature. Unlike descriptive or narrative essays, which focus on providing a vivid description or telling a story, an analytical essay aims to examine and dissect its subject matter.

The primary objective of an analytical essay is to present a thorough analysis or interpretation of the subject, often breaking it down into its constituent parts and scrutinizing how they contribute to the whole.

Why Analytical Essay Is Important

Analytical essays play a pivotal role in developing critical thinking skills and fostering a deeper understanding of complex subjects. Through the meticulous examination and interpretation of information, these essays teach individuals how to dissect arguments, evaluate evidence, and form well-supported conclusions. They serve as a platform for honing analytical prowess, enabling individuals to engage with diverse perspectives, challenge assumptions, and articulate their insights effectively. Moreover, mastering the art of analytical essays equips individuals with invaluable skills applicable across various disciplines, fostering a capacity for logical reasoning, problem-solving, and persuasive communication—a skill set indispensable in academia, professional endeavors, and everyday life.

Tips For Writing A Good Analytical Essay

Understanding the essence.

To excel in analytical writing, one must comprehend the essence of analysis itself. It’s not merely about summarizing or narrating; it’s about deconstructing the core components, scrutinizing their significance, and synthesizing perspectives to derive insightful conclusions.

Devising a Strategic Blueprint

Begin with a comprehensive understanding of your subject matter. Formulate a thesis statement —a succinct encapsulation of your perspective—which serves as the guiding beacon throughout your essay. Craft an outline delineating key sections and their respective arguments, ensuring a logical flow that seamlessly connects each point.

The Pinnacle of Research

A sturdy analytical essay is built upon a foundation of rigorous research. Delve into reputable sources, be it scholarly articles, books, or credible online repositories. Gather diverse perspectives and data to fortify your arguments, but always uphold the standards of credibility and relevance.

Structure: The Backbone of Brilliance

A well-structured essay is akin to an architectural marvel. The introduction should entice readers with a gripping hook, provide context, and introduce the thesis statement. The body paragraphs, each beginning with a topic sentence, should expound on individual arguments supported by evidence and analysis. Finally, the conclusion should reaffirm the thesis while offering a nuanced synthesis of the essay’s core ideas.

The Art of Analysis

Here’s where the magic unfolds. Analyze, dissect, and interpret the data and evidence gathered. Scrutinize underlying themes, dissect intricate details, and juxtapose contrasting viewpoints. Employ analytical tools pertinent to your subject, such as literary devices for literature analyses, statistical methods for scientific inquiries, or historical frameworks for historical essays.

Precision in Language and Style

The language employed in an analytical essay should be precise, articulate, and tailored to convey complex ideas clearly. Utilize a formal tone, vary sentence structures, and employ transitions to ensure a seamless progression of ideas. Embrace clarity and coherence as your allies in elucidating intricate analyses.

Revisiting and Refining

Revision is the crucible wherein a good essay transforms into a great one. Review your work meticulously—check for coherence, refine arguments, ensure logical transitions, and verify the alignment of evidence with your thesis. Seek feedback from peers or mentors to gain diverse perspectives and refine your essay further.

Conclusion: A Culmination of Mastery

In conclusion, a perfect analytical essay isn’t merely a collection of facts and opinions; it’s an orchestrated symphony of critical thinking, analysis, and eloquent expression. Embrace the journey of discovery, relish the complexities, and let your essay resonate as a testament to your mastery of analytical prowess.

Best Place To Avail Analytical Essay Service

At Allessaywriter.com, excellence meets expertise in crafting exceptional analytical essay services . Our platform is your gateway to top-tier service, offering a seamless experience to elevate your academic journey. With a team of seasoned writers dedicated to precision and depth in analysis, we ensure tailored essays that reflect critical thinking and comprehensive understanding. Trust us for meticulous research, compelling arguments, and impeccable structure, all aimed at delivering the finest analytical essays that exceed expectations.

An analytical essay service encapsulates the culmination of rigorous analysis, insightful interpretation, and concise articulation. It serves as the pinnacle of intellectual prowess, combining critical thinking with eloquent expression to offer a profound understanding of complex subjects. So if you are still wondering about analytical essay writing then ask our writers and get our do my essay help services.

Meet Kathy, the mindful mind behind the words at minimalistfocus.com. With an innate ability to distill the essence of life down to its purest form, Kathy's writing resonates with those seeking clarity in a cluttered world.

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Types of research papers.

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Although research paper assignments may vary widely, there are essentially two basic types of research papers. These are argumentative and analytical .

Argumentative

In an argumentative research paper, a student both states the topic they will be exploring and immediately establishes the position they will argue regarding that topic in a thesis statement . This type of paper hopes to persuade its reader to adopt the view presented.

 Example : a paper that argues the merits of early exposure to reading for children would be an argumentative essay.

An analytical research paper states the topic that the writer will be exploring, usually in the form of a question, initially taking a neutral stance. The body of the paper will present multifaceted information and, ultimately, the writer will state their conclusion, based on the information that has unfolded throughout the course of the essay. This type of paper hopes to offer a well-supported critical analysis without necessarily persuading the reader to any particular way of thinking.

Example : a paper that explores the use of metaphor in one of Shakespeare's sonnets would be an example of an analytical essay.

*Please note that this LibGuide will primarily be concerning itself with argumentative or rhetorical research papers.

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The Ultimate Guide to Analytical Essay Writing: How to Craft an A-Grade Paper?

25 January, 2021

17 minutes read

Author:  Kate Smith

An analytical essay is often considered the most challenging piece of writing. However, those who have dealt with it at least once are a step closer to calling themselves masters of essay writing. This type of paper requires plenty of analytical skills to carry out an in-depth analysis of the assigned topic. Yet, the main goal of an analytical essay is not only to demonstrate your ability to learn the basics of the theme.

You also need to think critically, analyze facts, express your standpoint, and clearly show a deep understanding of key concepts. In short, your main task as an author is to prove the validity of your views by coming up with strong arguments that do not beg any questions.

The given guide provides a full analytical essay definition, as well as specifies its features and structural aspects. The following information will help you properly start your paper, choose a relevant topic, and come up with compelling conclusions. 

What is an Analytical Essay?

An analytical essay is a piece of writing aimed to provide a thorough analysis of a definite phenomenon using persuasive arguments and supporting assertions. Analysis in the analytical essay writing process stands for a method of research that allows one to study specific features of an object. Analytical papers also have to do with analysis of a specific problem; that is consideration of the problem itself and identification of its key patterns. The subject matter of analysis can be a well-known or little-studied scientific phenomenon, artistic work, historical event, social problem, etc.

The content of an analytical essay will totally depend on the object that has been chosen for analysis. Thus, when shedding light on any kind of scientific work, an analytical essay can be devoted to the analysis of research credibility, its relevance, or the adequacy of conclusions. When considering a work of art, an essay writer can focus on the analysis of the author’s artistic techniques or issues raised in the book. For this reason, it is essential to accurately determine the topic and subject matter of your future analytical essay.

Steps to Take Before Writing

The preparational stage of analytical essay writing cannot be omitted. It lays the basis for the A-grade paper and should be carefully completed. If you don’t know how to start an analytical essay, read a few handy tips that will ensure a solid foundation for your paper.  

Define a subject matter

You first need to clearly understand the issue you will base your essay on. Since analytical essays imply an in-depth analysis of a specific problem, you need to define its core. Try to split the analysis into several components and provide arguments taken either from a book, a research, a scientific work, or a movie (depending on the subject matter of your analysis), and support your views comprehensively.

Decide on the content of your analytical essay

If you are a student who was given an analytical essay topic, read the task several times before you are 100% sure that you clearly understand the requirements as to the analytical essay format. In case you were lucky to choose the topic of the analytical paper by yourself, make sure the theme you will be dealing with is familiar or at least seems interesting to you. 

Remember that different subject matters require a different approach to their analysis. If you examine some literature work, you can prove your opinion based on the deeds of a certain or several characters. But if you have been assigned the task to elaborate on some historic events, analyze their main causes, driving forces that have affected their course, and their global consequences.  

Take care of the proper start

Don’t forget to start your analytical essay with a thesis statement. It is a sentence or a couple of sentences that aim to summarize the key statements of your paper. A thesis statement should provide readers with a preliminary idea of what your essay is all about.  

Find extra reasoning

Make sure your thesis is supported by compelling arguments. To find enough evidence, you should carry out a thorough analysis of the assigned topic. List the crucial points of your research and ponder over the ways they can be used to prove your final opinion. 

Elaborate the outline

A sound outline elaborated at the preparation stage will help you ensure a proper analytical essay structure and make the overall writing process easier. As a rule, an analytical essay consists of an introduction, three body paragraphs, and a conclusion. Your outline plan should include the key arguments you want to discuss in each paragraph. 

Analytical Essay Thesis

A thesis statement represents the central idea of your paper and must serve as strong proof of your standpoint. While elaborating your thesis statement, it is crucial to include it at the end of the first paragraph and thus set a direction for the overall paper. 

Analytical Essay Outline

An outline is not a required element of analytical essays writing and should not be included in the text, but it can greatly facilitate the whole process of paper writing.

The analytical essay structure looks as follows:

Introduction

In the introduction of an analytical essay, you will need to identify your paper’s subject matter. Mention the purpose of your work and specify its scope of research. Don’t forget to include a thesis to let readers know what your work is about.

Body Section

As has already been mentioned, the body section covers three or more main paragraphs, each being supported with arguments and details. Besides, you need to provide a small conclusion to each statement to make your essay sound professional and persuasive. 

At this stage, you need to summarize the points elucidated in your paper and make sure there is a smooth and logical transition from the body section to the concluding part of the text. If you don’t know how to conclude an analytical essay, try to restate the thesis statement without copying it word for word.  

Analytical Essay Examples

Writing an analytical essay may seem to be a thorny way. If you are still not sure how to properly craft one, try to find some examples that will help you go in the right direction. Below, there are some great examples of analytical essays. Take a look at their structure and try to write something similar based on your views and ideas:

https://drive.google.com/file/d/1JeR4i4RIZIj448W3KVFyHP-eS3QPN7gW/view

https://stlcc.edu/docs/student-support/academic-support/college-writing-center/rhetorical-analysis-sample-essay.pdf

https://www.germanna.eduhttp://handmadewriting.com/wp-content/uploads/tutoring/handouts/Literary-Analysis-Sample-Paper.pdf

30 Analytical Essay Topics

If you were allowed to choose the theme for your paper by yourself, check on the following analytical essay topics. Each of them can bring you the highest score:

General topics

• The influence of social networks on the life of teens
• Are salaries of football players too high?
• Wearing uniforms in schools should be banned
• A person in society: the problems of loneliness and privacy
• Sociology of corporate relationships
• Does the observation of space need more investments?
• Should the voting age in the UK be decreased?
• Reasons why capital punishment should be brought back in the UK
• A world with no rules: a new human era or a road to the global collapse?
• Life without technologies: will modern people survive?
• Should scientists test drugs on animals to fight cancer?
• The problem of keeping the balance between career and family life
• The importance of listening to your body 
• Problems caused by the lack of communication
• Food addiction and the problems it causes
• Problems of vaccination in the XXI century
• Does evil really rule the world?
• How does body size affect life quality?
• Pros and cons of video games 
• The role of a family model in the life and career of a person

Analytical Essay Topics on Literature

• “Robinson Crusoe”: fantasy vs reality
• Observation of the artistic uniqueness in the comedy by W. Shakespeare “A Midsummer Night’s Dream” 
• Observe the social problems in the novel by John Steinbeck “The Grapes of Wrath”
• Convulsions and death of the “little man” in the networks of impersonal, alienated forces in the novel “The Metamorphosis”
• Observation of the problems of a man on a plagued land in the novel “The Plague”
• Revolt of the protagonist in the novel by J. Salinger “The Catcher in the Rye”
• Observation of friendship and love in the fate of humanity in the XX century
• The triumph of immorality in the novel by F. Sagan “Hello Sadness”
• Observation of the personality of an American student in the novel by J. Salinger “The Catcher in the Rye”
• Eternal tragedies of humanity in the tragedy by W. Shakespeare “The Tragedy of Hamlet, Prince of Denmark”

How to Write a Well-Structured Analytical Essay With a Solid Argument

Writing an analytical essay with a clear structure might be challenging unless you are thoroughly prepared. We decided to help you out and create a detailed guide listing the main things to consider when creating an analytical essay outline. You need to explain your main idea in a concise way to bring your point across. As analytical writing has high requirements, it pays off to find an analytical essay example and analyze how this text was written. It will allow you to understand the analytical essay format better and learn how to provide substantive analysis on various topics. Read on to learn how to write a top-level analytical paper and submit it on time.

Main Tips for Writing an Analytical Essay

An analytical essay should provide a comprehensive analysis of a chosen topic. What makes an analysis essay different from other assignments is that it includes a personal opinion of an author. This is why analytical writing should be persuasive.

Below, we have rounded up the key tips you need to follow when producing an analytical essay outline and the main body of your text. Read on to learn more about the analytical essay format and create a text that will fully meet the requirements.

Select an Analytical Essay Topic

Before creating an analytical essay outline, make sure to pick a topic that you are interested in. It should be provocative enough to engage your readers. A widely-debated topic will help you write an analytical essay that grabs the attention of a wide audience.

Consider your goals and conduct thorough research to see if you have enough sources to support the main thesis of your analysis essay.

Come Up With a Strong Analytical Thesis Statement

When writing an analytical essay, start by formulating a thesis statement that includes the topic and the main goal of your text. It will help you create an analytical essay outline and show your readers what you will discuss in your analysis essay.

Add it to the last paragraph of your analytical essay introduction. Due to this, your analytical essay outline will look better structured. Look at any analytical essay example to see how you can introduce your subject. In most cases, one sentence will suffice to state your analysis essay’s goal. However, a complex analytical essay outline might require you to use two sentences for a thesis statement.

Write an Analytical Essay Body with a Clear Structure

Your analytical essay outline should include 3-4 paragraphs. However, a literary analysis essay usually consists of 5 paragraphs. When it comes to analytical writing, it is important to cover a different point in each section of the main body of an analysis paper.

After writing an analytical essay, check whether each paragraph contains an introduction and the main point. Besides, it should contain evidence. An expertly written analytical essay outline will help you reach out to your target audience more effectively.

Conduct Research Before Writing an Analytical Essay Outline

While this step is preparatory, it is a must for those who want to write a well-grounded analytical paper.

• First, select the best ideas for your essay
• Then, emphasize the problems with works written by other researchers
• Finally, write your analytical essay outline to demonstrate what approach you want to take

Examine the context and find examples to illustrate the scope of the issue. You may draw parallels to emphasize your point and make your topic more relatable.

Analyze the Implications of the Evidence

After listing your pieces of evidence and demonstrating how it is related to your thesis, show why it is important. You need to explore it deeply and use it to support your argument. It will make your analytical essay outline well-grounded facts.

Write an Analytical Essay Conclusion

Whether you write a literary analysis essay or other types of assignments, there is no need to add any new data at the end of your analysis paper. Instead, summarize the arguments you mentioned in your analytical essay outline. The conclusion of your analysis essay should be short and clear. Here, you need to demonstrate that you have achieved your goals.

Analytical Essay Writing Tips

If you want to get the highest grade for your analytical essay, you need to know a little bit more than just the basics of paper writing. Read these handy tips to write a perfect essay you will be proud of:

• Double-check your paper for spelling and grammar mistakes. In case your essay contains too many errors, neither an in-depth analysis nor the elaborate writing style will make it look any better. Situations when essays of great value in terms of research and a message they convey are poorly assessed because of the abundance of mistakes are not rare. Make sure you have enough time to proofread your paper before submission. Also, you may consider asking somebody to take a fresh look at your essay and check it for you.
• Reading your analytical essay out loud helps you discover all types of errors or weak phrases. This method might seem a bit uncomfortable, but it has proved to be very effective for many students. Note that silent reading of your paper isn’t even half as helpful as reading it aloud. 
• Another great idea to check on the rhythm and flow of your paper is to ask someone to read it for you. While listening to the text, you could perceive it from another perspective and discover even more inconsistencies and mistakes.  
• Double-check the facts you use in your analytical essay. The names of people, books, research, publications, as well as dates of historical events are too important to be misspelled. Things like these show your professionalism and the way you treat your readers.

Write an Analytical Essay with HandmadeWriting

Writing an analytical essay requires time, strong writing skills, great attention to detail, and a huge interest in the assigned topic. However, life can be unpredictable sometimes, and students might find themselves at risk of failing their creative assignments. Stress, family issues, poor health, and even unwillingness to work on a certain topic may become significant obstacles on their way to the A-grade work.

If you have similar problems, there is no need to compromise your reputation and grades. You can always refer to HandmadeWriting professionals who are ready to help you with a paper of any type and complexity. They will understand your individual style and totally devote themselv

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Analytical Essay: Tips, Structure, Examples

Analytical essays could be perfect for you if you enjoy immersing yourself in tasks and excel at thinking creatively. By conducting thorough analysis and employing innovative writing techniques, you can discover new viewpoints and enhance your understanding of the subject.

In this article, our research paper writer will explain what it entails, learn how to structure your paper for top marks, snag some snazzy topic ideas, and glean practical examples. This guide has got all the essentials you need for writing success!

What Is an Analytical Essay

To write an effective analytical essay, it is important to understand its purpose and method. In basic terms, it requires using textual evidence to logically support the author's arguments rather than relying on emotions or personal anecdotes. ‍ Compared to persuasive essays that advocate for one specific viewpoint, a quality analytical essay example should delve into all aspects of the subject. This involves examining different perspectives, dissecting arguments, and assessing evidence thoughtfully. ‍ Ultimately, you will have to express your viewpoint after conducting your analysis. This requires combining your research and determining if you agree with the conclusions made or have a different interpretation.

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How to Structure an Analytical Essay

Now it's time to ace the process of crafting an excellent paper. To make writing easier, organize your thoughts and structure your arguments clearly. An analytical essay needs the introduction, body, and conclusion of an outline, which acts as a guide from start to finish. Here's the simple breakdown of an analytical essay outline:

‍Introduction‍

• Background information
• Thesis statement

Body paragraph 1‍

• Topic sentence
• Supporting evidence
• Transition to the body paragraph to‍

Body paragraph 2‍

• Transition to body paragraph 3

Body paragraph 3

• Transition to conclusion
• Summary of major points
• Restate the thesis
• Key takeaways

Introduction: To begin your essay successfully (see our analytical essay example), captivate the reader's interest from the very start and clearly outline the topic. A good beginning should give some background information, outline the essay's purpose, and suggest the main arguments that will be made. Grab the reader's attention with an engaging and pertinent opening sentence, such as a surprising fact, a funny story, or a challenging question. After that, introduce your thesis statement, which summarizes your main point in the essay.

Body Paragraphs: In an analysis essay, each paragraph begins with a topic sentence that helps guide the reader. The paragraph then provides evidence to support the thesis, concentrating on a single issue. By summarizing the main point at the end of each paragraph, the essay flows smoothly from one idea to the next, maintaining clarity and coherence in the argument.

Conclusion: The final paragraph of an analytical essay usually adheres to a specific structure:

• Reiterating the main argument
• Outlining the main ideas discussed in the essay
• Providing thoughts on the overall significance of the analysis

It is crucial to include your perspective on the topic's relevance and how your analysis adds to the understanding of it. This approach can strongly impact the reader's perception of the essay.

Meanwhile, you might also be interested in how to write a reflection paper , so check out the article for more information!

How to Write an Analytical Essay

Once you understand the structure, there are some steps you can take to make writing an analytical essay easier. Preparing beforehand can simplify the process and improve the overall flow and structure of your essay. Here are some tips from our experts. Meanwhile, you can also request from our experts - write my essay for me . And we'll take care of it right away.

• Think Ahead : Before writing, a good analytical essay writer should spend some time thinking about the topic. Make a list of ideas or themes related to it. This helps you find interesting angles for your essay.
• Create a Thesis Statement : Develop a clear and concise thesis statement that outlines the main argument of your essay. This will guide you in how to write an analytical essay introduction and keep your writing focused.
• Visualize Information : Use graphs or charts to organize your thoughts visually. This makes your research easier to understand. For example, you can compare ideas with a chart.
• Consider Different Views : Address opposing viewpoints in your essay. This shows you've thought about different perspectives and strengthens your argument.
• Use Original Sources : Include interviews, presentations, or original documents in your research. They give a unique insight into your topic. For instance, old letters can offer personal views on historical events.
• Analyze Cause and Effect : Explore the cause-and-effect relationships within your topic. Analyze how different factors contribute to certain outcomes or phenomena.

Analytical Essay Topics

Choosing the right one among lots of analytical essay topic ideas is crucial when tackling your essay. Here's a guide to help you pick wisely:

• Find a topic that piques your interest. It's easier to dive into analysis when you're passionate about the subject.
• Look for topics that catch the reader's eye. Think about what would make someone stop and want to read more.
• Avoid topics that are too broad. Focus on something specific that you can thoroughly analyze within the scope of your essay.
• Ensure there's enough quality research available to support your analysis. You'll need evidence to back up your points.
• Your topic should raise questions worth exploring. Aim for something that sparks curiosity and has significance.

Now, here's a mix of engaging topics from our dissertation services to consider:

• Analyzing the Impact of Blue Light from Screens on Sleep Quality
• Exploring the Environmental Effects of Microplastics in Ocean Ecosystems
• Understanding the Benefits of Deep Breathing Exercises for Anxiety Relief
• Analyzing the Influence of Instagram Filters on Body Image Perception
• Exploring the Health Risks of Artificial Sweeteners in Diet Soda
• Understanding the Psychological Effects of High-Intensity Interval Training (HIIT)
• Analyzing the Environmental Impact of Fast Food Packaging Waste
• Exploring the Effects of Social Media Validation on Self-Esteem
• Understanding the Benefits of Indoor Plants for Air Quality Improvement
• Analyzing the Relationship Between Sugar Consumption and Dental Health
• Exploring the Psychological Effects of Online Shopping Addiction
• Understanding the Environmental Impact of Fast Fashion Textile Dyes
• Analyzing the Effects of Smartphone Notifications on Focus and Productivity
• Exploring the Benefits of Outdoor Exercise for Vitamin D Production
• Understanding the Relationship Between Noise Pollution and Cardiovascular Health
• Analyzing the Psychological Effects of Colorful Food Presentation on Appetite
• Exploring the Impact of Petting Therapy Dogs on Stress Reduction
• Understanding the Benefits of Classical Music for Concentration While Studying
• Analyzing the Effects of Lavender Aromatherapy on Sleep Quality
• Exploring the Health Risks of Prolonged Sitting and Sedentary Lifestyles

Analytical Essay Examples

Here is our analytical writing sample to see theory in action. Notice how analytical thinking applies to real-world situations, improving your understanding of concepts. By studying these examples, you'll learn to analyze complex issues, build strong arguments, and sharpen your critical thinking skills.

Final Thoughts

With our tips on how to write an analytical essay and examples, you're ready to boost your writing skills and craft essays that captivate your audience. With practice, you'll become a pro at analytical writing, ready to tackle any topic with confidence.

And hey, if you need help to buy essay online , just drop us a line saying ' do my homework for me ' and we'll jump right in!

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How to Write an Analytical Essay?

What is an analytical essay, what is the purpose of an analytical essay, related articles.

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Guide to writing an analytical essay, carla johnson.

• June 13, 2023
• Essay Topics and Ideas , How to Guides

An analytical essay is a type of academic writing in which a complicated topic or idea is broken down into smaller parts, analyzed and looked at, and a well-structured argument or evaluation is given. The main purpose of an analytical essay is to show that the writer has a deep understanding of the topic and can also think critically about it. Analytical essays are important in many fields, such as literature, history, science, and sociology. They require a deep knowledge of the topic and the ability to think critically and objectively about the information given. Analytical essays include things like a literary analysis, a research paper , or an analysis of a piece of rhetoric. Writing an analytical essay is useful because it helps the writer improve their analytical and critical thinking skills. In analytical essays, students must look at and evaluate different sources, find patterns and relationships, and come to meaningful conclusions. When students write analytical essays, they also improve their research skills because they have to find relevant information from multiple sources and put it all together in a logical argument.

Also, analytical essays are important in academic writing because they help students understand ideas, theories, and concepts that are hard to understand. By breaking a topic down into smaller parts, students can better understand it and figure out what the main ideas and themes are. Analytical essays help students get better at writing by making them present their arguments in a way that is clear, concise, and well-organized. Writing an analytical essay is a skill that students need to learn if they want to do well in school and in their careers. For analytical essays, you need to be able to think critically , do research, and write well, all of which are important for success in many fields. By getting better at these skills, students can become better writers and thinkers, which will help them reach their academic and career goals.

What You'll Learn

Understanding the Basics of Analytical Essays

There are a few main things that set an analytical essay apart from other types of essays. One of the most important things about it is that it requires a thorough look at the subject. An analytical essay isn’t just a description of a topic or a point of view. Instead, it calls for a thorough look at the subject, breaking it down into its different parts and evaluating each one carefully.

Argumentative essays try to convince the reader to agree with a certain point of view. Descriptive essays, on the other hand, try to give a detailed description of a topic. Analytical essays, on the other hand, require the writer to look at the topic objectively and judge it, as well as use evidence from different sources to back up their claims. In an analytical essay, you can’t say enough about how important analysis is. Analysis is the process of breaking down big ideas or thoughts into smaller, more manageable pieces. By analyzing the topic, the writer can find the main ideas, patterns, and connections, which can then be used to back up their arguments.

Also, analysis lets the author draw conclusions that make sense based on the evidence given. If there wasn’t any analysis in an analytical essay, it would just be a list of facts and opinions. Analysis is what gives depth and substance to an analytical essay and lets the writer make a well-reasoned, evidence-based argument.

Analytical essays are different from other types of essays because they focus on analysis and evaluation. They require a thorough look at a subject, breaking it down into its different parts and judging each one objectively. Students can get the skills they need to do well in school and in the workplace by learning what makes an analytical essay unique and what role analysis plays in this type of writing.

Choosing a Topic for Your Analytical Essay

It can be hard to decide what to write about in an analytical essay, but there are several ways to come up with ideas. One way to do this is to make a list of possible topics based on your interests, your schoolwork, or what’s going on in the world right now. You can also find possible topics by reading articles, books, or other materials in your field of study. When choosing a topic for your analytical essay, you should think about a few things. First and foremost, the topic must fit with the needs of the course or assignment. It should also be narrow enough to allow for a detailed analysis while still having enough information for research . Also, the topic should be something you’re interested in as a writer. This will make the writing process more interesting and fun.

To help you get started, here are some examples of potential analytical essay topics:

1. The impact of social media on mental health

2. Analyzing the themes of race and identity in Toni Morrison’s “Beloved”

3. The role of technology in modern education

4. An analysis of the effectiveness of the Affordable Care Act

5. Examining the causes and consequences of income inequality in the United States

6. The portrayal of gender roles in Shakespeare’s plays

7. Analyzing the impact of climate change on global food production

8. A critical analysis of the role of the media in shaping public opinion

9. A comparison of different political ideologies and their impact on society

10. An analysis of the ethical implications of gene editing technology.

A topic for an analytical essay must be chosen with care, taking into account several factors such as relevance, scope, and personal interest. By brainstorming ideas, researching different sources, and applying these criteria, you can choose a topic that is both interesting and informative, allowing you to write a well-researched and well-argued analytical essay.

Conducting Research for Your Analytical Essay

In order to write an analytical essay, you need to do research. It lets the writer gather relevant information, find patterns and relationships, and come to conclusions that make sense. If you didn’t do research for your analytical essay, it wouldn’t have much substance or credibility, and the arguments you made would be weak and not backed up. You can gather information for your analytical essay from a number of different places. There are many examples, such as books, academic journals, online databases, government reports, and reputable news sources. When choosing sources, think about how relevant, reliable, and trustworthy they are. Academic sources like peer-reviewed journals and scholarly books are more reliable and credible than popular sources like blogs and social media posts.

To conduct effective research for your analytical essay, here are some tips to keep in mind:

1. Start early: Give yourself plenty of time to conduct research, as it can be a time-consuming process.

2. Use multiple sources: Gather information from a variety of sources to ensure you have a well-rounded understanding of the topic.

3. Take notes: Keep detailed notes on the information you gather, including the source and page number, to make it easier to cite your sources later.

4. Evaluate your sources: Assess the reliability and credibility of your sources, looking for biases or conflicts of interest that may affect the information presented.

5. Organize your research: Create a system for organizing your research, such as using annotated bibliographies or note-taking apps, to keep track of your sources and ideas.

You can conduct effective research for your analytical essay by following these tips, gathering reliable and credible information that supports your arguments and improves the overall quality of your writing.

Developing a Thesis Statement for Your Analytical Essay

A thesis statement is a short sentence that sums up the main argument or point of an essay. It gives the reader a clear idea of where the author stands on the subject and acts as a road map. In an analytical essay, the thesis statement is very important because it sets the tone for the whole essay and shows the writer how to analyze and evaluate the subject . In an analytical essay, you can’t say enough about how important a strong thesis statement is. A well-written thesis statement states the writer’s main point in a clear and concise way, making it easier for the reader to follow the writer’s thought process and understand the purpose of the essay. A strong thesis statement also helps the writer focus their analysis and evaluation, making sure that each paragraph supports the main point and builds on it.

To develop a strong thesis statement for your analytical essay, here are some tips to consider:

1. Start with a question: Ask yourself a question related to your topic and use the answer to develop your thesis statement.

2. Be specific: Your thesis statement should be specific and focused on the main argument of your essay .

3. Use evidence: Support your thesis statement with evidence from your research, such as quotes or statistics, to give it more credibility and strength.

4. Be original: Your thesis statement should be unique and original, providing a fresh perspective on the topic.

5. Revise as needed: As you write your essay , revisit your thesis statement and revise it if necessary to ensure it remains relevant and accurate.

By following these tips, you can develop a strong thesis statement for your analytical essay, providing a clear and concise statement of your main argument and guiding the reader through your analysis and evaluation of the topic.

Analytical Essay Structure

An analytical essay is made up of an introduction, body paragraphs, and a conclusion, just like a regular essay. The purpose of the introduction is to give background on the topic, introduce the thesis statement, and get the reader interested. Through analysis and evaluation of the topic, the body paragraphs should support the thesis statement with evidence and examples . The conclusion should sum up the main points of the essay and restate the thesis statement in a new way that makes sense.

Here is a more detailed breakdown of the structure of an analytical essay:

1. Introduction: The introduction should set the tone for the essay by providing background information on the subject as well as a clear thesis statement. It should also engage the reader and convey the writer’s point of view on the subject.

2. Body paragraphs: The body of the essay should be divided into several paragraphs, each focusing on a different aspect of the topic. Each paragraph should start with a clear topic sentence that supports the thesis statement and then proceed to an analysis and evaluation of the subject matter, using evidence and examples to support the writer’s argument.

3. Conclusion: The conclusion should summarize the essay’s main points and restate the thesis statement in a new and meaningful way. It should also provide the reader with a sense of closure, leaving them with a clear understanding of the writer’s point of view on the subject.

To organize your ideas in an analytical essay, here are some tips to consider:

1. Create an outline: Before you start writing your essay , create an outline that organizes your ideas and supports your thesis statement. This will help you to organize your thoughts and ensure that each paragraph supports the main argument.

2. Use topic sentences: Each paragraph should begin with a clear topic sentence that supports the thesis statement and provides a roadmap for the reader.

3. Use evidence: Use evidence and examples to support each point you make in your essay , ensuring that each paragraph reinforces the main argument.

4. Use transitions: Use transitional phrases and sentences to connect your ideas and ensure that your essay flows smoothly from one paragraph to the next.

By following these tips, you can organize your ideas effectively in an analytical essay, producing a well-structured and well-supported piece of writing.

Writing the Introduction Paragraph

An analytical essay’s introduction paragraph serves as a road map for the reader, providing background information on the topic, introducing the thesis statement, and engaging the reader. The introduction’s purpose is to set the tone for the essay , grab the reader’s attention, and provide a clear understanding of the writer’s viewpoint on the topic.

To write an engaging introduction paragraph for your analytical essay, here are some tips to consider:

1. Start with a hook: Begin your introduction with a hook that captures the reader’s attention and makes them want to keep reading. This could be a surprising statistic, a provocative question, or a striking image.

2. Provide background information: Provide some context for the topic by providing background information and explaining why it is important.

3. Introduce the thesis statement: Clearly state your thesis statement in the introduction, providing a roadmap for the reader and guiding the rest of your essay.

4. Be concise: Keep your introduction concise and to the point, avoiding unnecessary information or tangents.

5. Revise as needed: As you write your essay, revisit your introduction and revise it as needed to ensure that it remains relevant and engaging.

Here are some examples of effective introduction paragraphs for an analytical essay:

1. “Throughout history, the concept of justice has been a subject of debate and controversy. From the ancient Greeks to modern-day philosophers, the definition and application of justice have been explored in depth. In this essay, I will examine the concept of justice in the context of criminal justice reform and argue that a more restorative approach is needed to address the root causes of crime and reduce recidivism.

2. The rise of social media has had a profound impact on our society, transforming the way we communicate, share information, and interact with the world around us. However, this transformation has not been without its challenges. In this essay, I will explore the impact of social media on mental health and argue that we need to take a more proactive approach to addressing the negative effects of social media on our well-being.

3. “Climate change is one of the most pressing issues of our time, affecting everything from the environment to the economy. Despite the overwhelming scientific evidence, there are still those who deny the reality of climate change or refuse to take action. In this essay, I will analyze the reasons for this denial and argue that we need to take urgent action to address the threat of climate change before it’s too late.”

Writing the Body Paragraphs

An analytical essay’s body paragraphs are where the writer presents their analysis and evaluation of the topic. The purpose of the body paragraphs is to provide evidence and examples to support the thesis statement, while using clear and concise language to make the argument as persuasive as possible.

To write clear and concise body paragraphs for your analytical essay, here are some tips to consider:

1. Use topic sentences: Each body paragraph should begin with a clear topic sentence that supports the thesis statement and provides a roadmap for the reader.

2. Provide evidence: Use evidence and examples to support each point you make in your essay , ensuring that each paragraph reinforces the main argument. The evidence should be relevant, reliable, and credible.

3. Use analysis and evaluation: Analyze and evaluate the evidence you present to demonstrate how it supports your argument. This shows the reader that you have thought deeply about the topic and considered multiple perspectives.

4. Be clear and concise: Use clear and concise language to make your argument as persuasive as possible. Avoid using jargon, complex sentences, or overly technical language that may confuse the reader.

5. Use transitions: Use transitional phrases and sentences to connect your ideas smoothly and ensure that your essay flows from one paragraph to the next.

Here are some examples of effective body paragraphs for an analytical essay:

1. The first reason why a restorative approach to criminal justice reform is necessary is that it addresses the root causes of crime. Rather than simply punishing offenders, restorative justice encourages dialogue between the offender and the victim, allowing both parties to understand the harm that has been caused and work together to find a solution. This approach has been shown to reduce recidivism rates and promote healing within communities.”

2. One of the most significant negative effects of social media on mental health is the increase in anxiety and depression. Studies have shown that social media use is associated with feelings of isolation, comparison, and low self-esteem, all of which contribute to poor mental health . In order to address this issue, we need to take a more proactive approach to promoting mental health and well-being, such as limiting social media use, encouraging face-to-face interactions, and providing mental health resources for those in need.

3. One of the main reasons why climate change denial persists is due to the influence of special interest groups and political ideology . These groups use their resources to spread misinformation and discredit the overwhelming scientific consensus on climate change. In order to combat this, we need to prioritize education and awareness, promote scientific literacy, and hold those who spread misinformation accountable for their actions.”

Writing the Conclusion Paragraph

The conclusion paragraph of an analytical essay summarizes the author’s main points and emphasizes the significance of the thesis statement. The conclusion’s goal is to leave a lasting impression on the reader by bringing the essay to a close and reinforcing the main argument .

To write a strong conclusion for your analytical essay, here are some tips to consider:

1. Restate the thesis statement: The conclusion should restate the thesis statement in a new and meaningful way, emphasizing its importance and relevance to the topic.

2. Summarize the main points: Summarize the main points of the essay, highlighting the evidence and examples that support the thesis statement .

3. Provide a final thought: End the essay with a final thought or reflection on the topic, leaving the reader with something to think about or consider.

4. Be concise: Keep the conclusion concise and to the point, avoiding any new information or arguments.

5. Make it memorable: Use language and phrasing that is memorable and impactful, leaving a lasting impression on the reader.

Here are some examples of effective conclusion paragraphs for an analytical essay:

1. “In conclusion, a restorative approach to criminal justice reform is not only necessary but essential. By addressing the root causes of crime, promoting dialogue and understanding, and fostering healing within communities, we can create a more just and equitable society for all.”

2. In order to address the negative effects of social media on mental health , we need to take a more proactive approach to promoting well-being. By limiting social media use, encouraging face-to-face interactions, and providing mental health resources, we can create a healthier and more connected society.

3. “Climate change is one of the most pressing issues of our time, and we must take urgent action to address it. By promoting education and awareness, prioritizing scientific literacy, and holding those who spread misinformation accountable, we can create a more sustainable and equitable future for generations to come.”

Editing and Proofreading Your Analytical Essay

The editing and proofreading stages of the essay writing process are critical. It ensures that the essay is clear, concise, and error-free, improving overall writing quality and the credibility of the arguments presented.

To edit and proofread your analytical essay, here are some tips to consider:

1. Take a break: After completing your essay, take a break before editing and proofreading it. This will allow you to approach the essay with fresh eyes and a clear mind.

2. Read it out loud: Reading the essay out loud can help you to identify awkward phrasing, grammatical errors, and other issues.

3. Be consistent: Ensure that you are consistent in your use of language, formatting, and citation styles throughout the essay.

4. Use a checklist: Use a checklist to ensure that you have addressed all the necessary components of the essay, such as the thesis statement, evidence, and analysis.

5. Get feedback: Ask a friend or colleague to read your essay and provide feedback on areas that need improvement.

Common mistakes to avoid in analytical essays include:

1. Using overly complicated language or jargon that may confuse the reader.

2. Failing to provide evidence or examples to support the thesis statement.

3. Neglecting to address counterarguments or alternative perspectives on the topic.

4. Making unsupported claims or presenting opinions as facts.

5. Failing to proofread and edit the essay thoroughly, leading to grammatical errors and typos.

By following these tips and avoiding common mistakes, you can edit and proofread your analytical essay effectively, producing a well-written and error-free piece of writing.

Analytical Essay Examples

Here are some examples of successful analytical essays:

1. “The Symbolism of the Green Light in The Great Gatsby” by F. Scott Fitzgerald: This essay analyzes the symbolism of the green light in the novel, arguing that it represents Gatsby’s hopes and dreams and his ultimate failure to achieve them.

2. “The Rhetoric of Malcolm X” by Malcolm X: In this essay, Malcolm X analyzes his own rhetorical strategies, explaining how he uses language and persuasion to achieve his goals.

3. “The Role of Women in Shakespeare’s Macbeth” by William Shakespeare: This essay analyzes the role of women in the play, arguing that they are often marginalized and oppressed by the male characters.

Each of these essays follows a clear and well-structured format, with a strong thesis statement, supporting evidence, and a clear conclusion. The writers use analysis and evaluation to present their arguments, using evidence and examples to support their claims.

When using analytical essay examples to improve your writing, here are some tips to consider:

1. Choose examples that are relevant to your topic or subject matter.

2. Read the example essays carefully, paying attention to the structure, language, and evidence used.

3. Identify the thesis statement and main arguments of the essay.

4. Analyze the evidence used to support the arguments, evaluating its relevance and credibility.

5. Consider how the writer uses language and rhetoric to persuade the reader.

6. Use The examples as a guide for structuring your own essay, but be sure to use your own unique ideas and perspective.

7. Practice writing your own analytical essays and seek feedback from others to improve your writing skills.

8. Avoid copying or plagiarizing the example essays, as this can lead to serious academic consequences.

Frequently Asked Questions

1. what is an analytical essay.

An analytical essay is a type of academic writing that requires the writer to analyze and evaluate a specific topic or subject matter. The writer presents an argument or thesis statement and supports it with evidence and examples, using analysis and evaluation to persuade the reader.

2. What are the main characteristics of an analytical essay?

The main characteristics of an analytical essay include:

– A clear and concise thesis statement that presents the writer’s argument

– Use of evidence and examples to support the argument

– Analysis and evaluation of the evidence presented

– Clear and logical structure, with well-developed paragraphs and transitions between them

– Use of formal and academic language

– Objective and impartial tone

3. What are some tips for writing an analytical essay?

Here are some tips for writing an analytical essay:

– Choose a topic that interests you and that you can analyze in depth

– Develop a clear and concise thesis statement that presents your argument

– Use evidence and examples to support your argument, ensuring that they are relevant, reliable, and credible

– Analyze and evaluate the evidence presented, demonstrating your critical thinking skills

– Use a clear and logical structure, with well-developed paragraphs and transitions between them

– Use formal and academic language, avoiding slang and colloquialisms

– Edit and proofread your essay thoroughly, ensuring that it is error-free and well-written.

To summarize, writing an effective analytical essay necessitates a thorough understanding of the subject matter, a well-developed thesis statement, strong evidence and analysis, and a clear and logical structure. To summarize the main points covered in this guide:

– The writer of an analytical essay must analyze and evaluate a specific topic or subject matter.

– A clear thesis statement, evidence and examples to support the argument, analysis and evaluation of the evidence, a clear and logical structure, formal and academic language, and an objective tone are the main characteristics of an analytical essay.

– Choosing a relevant topic, developing a clear thesis statement, using credible evidence and examples, analyzing and evaluating the evidence, using a clear and logical structure, using formal and academic language, and thoroughly editing and proofreading the essay are all tips for writing a successful analytical essay.

An effective analytical essay demonstrates critical thinking skills as well as the ability to analyze and evaluate complex issues. It is an important skill for both academic and professional success.

Finally, practicing writing on a regular basis, seeking feedback from others, and reading and analyzing examples of successful analytical essays can all help you improve your own writing skills.

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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

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Analytical Essay Guide

Analytical Essay Example

15 Analytical Essay Samples to Learn From - Tips Included

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Analytical Essay Guide with Examples & Tips

Interesting Analytical Essay Topics Ideas for Students

Analytical Essay Outline - An Easy Guide

Analytical essay writing can be hard for students because it demands a thorough grasp of a subject and the skill to break it into smaller pieces.

This can lead to stress, lower grades, and a sense of frustration.

No need to fret! MyPerfectWords.com has a solution for you.

In this blog, we'll provide you with excellent analytical examples and easy-to-follow tips for writing outstanding analytical essays.

Are you ready to conquer the analytical essay? Let's dive in!

• 1. Analytical Essay -Definition 
• 2. Analytical Essay Examples
• 3. Analytical Essay Outline Example
• 4. Analytical Essay Topics Examples
• 5. Tips to Write an Effective Analytical Essay

Analytical Essay -Definition 

An analytical essay is a type of academic writing that dives deep into a specific subject, dissecting it into its various components and examining how they interrelate.

It requires the writer to not only present a clear understanding of the topic but also to analyze and evaluate it critically. 

Unlike a descriptive essay , an analytical essay demands a more in-depth exploration, often involving an argument or thesis statement that guides the analysis. 

Analytical Essay Examples

To truly understand the art of analytical essay writing, one of the most effective methods is to examine practical examples. 

Analytical essay examples provide a clear blueprint of how to approach this challenging task successfully.

Take a look at these examples to find inspiration for writing a compelling analytical essay.

Analytical Essay Example (Pdf)

Analytical Essay Examples For High School

Analytical Essay Example College

Analytical Essay Example Sample

Analytical Essay Example Introduction

Analytical Essay Example University

Conclusion For Analytical Essay Example

Short Analytical Essay Example

IHere are some more examples of analytical essays to help you get inspired.

Thesis Statement For An Analytical Essay Example

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Analytical Essay Example On A Book

Analytical Essay Outline Example

When tackling an analytical essay, having a well-structured outline is your key to success. This outline serves as a roadmap, guiding you through the essay-writing process, ensuring you don't miss vital elements.

Let's break down the essential sections of an analytical essay outline :

Introduction - Setting the Stage

In the introduction , your aim is to set the stage for your analysis. This section should introduce the topic, provide context, and present a clear thesis statement that outlines the main argument or focus of your essay.

Body - Analyzing Key Points

The body paragraphs of your analytical essay are where the real analysis takes place. This section can be divided into multiple paragraphs, each addressing a specific point or aspect related to your thesis. 

Here, you should provide evidence, examples, and critical analysis to support your argument.

Conclusion - Summing It Up

As you reach the essay conclusion , it's time to tie it all together. Summarize your main points, restate your thesis statement, and underscore the importance of your analysis. 

Remember, this is not the place to introduce new background information. Instead, offer a succinct and impactful recap of your discoveries.

Here's a sample outline for your reference to simplify the process.

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Analytical Essay Topics Examples

Choosing the right type of essay topic is a crucial first step when writing an analytical essay. The topic you select should be engaging, relevant, and suitable for in-depth analysis. 

Here are some thought-provoking analytical essay topics to consider:

• The Symbolism of the "Green Light" in F. Scott Fitzgerald's "The Great Gatsby"
• The Impact of Social Media on Mental Health
• Analyzing the Causes and Consequences of Income Inequality
• The Role of Nature in Shakespeare's Sonnets
• Analyzing the Historical Significance of the Industrial Revolution
• The Influence of Technology on Education
• The Psychology of Marketing and Consumer Behavior
• The Impact of Globalization on Cultural Identity
• Analyzing the Ethical Dilemmas in Artificial Intelligence
• The Evolution of Environmental Policies and Their Impact on Conservation

Are you still having trouble coming up with a good analytical essay topic? Check out this blog for more than 150 compelling analytical essay topics .

Tips to Write an Effective Analytical Essay

Writing an effective analytical essay requires a structured approach and attention to detail. Here are some valuable tips to help you craft a compelling analytical essay:

• Focus Your Topic : Select a specific topic or aspect for in-depth analysis rather than a broad subject.
• Research Thoroughly : Gather reliable sources and evidence to support your analysis.
• Create a Well-Structured Outline : Plan your essay with a clear introduction, body, and conclusion.
• Analyze, Don't Summarize : Avoid summarizing the subject; instead, critically evaluate and interpret it.
• Use Clear Topic Sentences : Start each paragraph with a clear topic sentence that relates to your thesis.
• Provide Evidence : Support your analysis with quotes, examples, and data from your research.
• Critical Thinking : Engage in critical thinking to question assumptions and explore alternative perspectives.
• Maintain Clarity : Use clear and concise language to convey your points effectively.

In conclusion, the analytical essay stands as a potent tool for honing your skills and conveying your understanding of complex subjects.

Through this guide, we've explored the essence of analytical essays and the essential steps to compose a compelling piece.

If you are assigned to write an analytical essay, take help from the above-mentioned examples. Another option is to get assistance from a professional analytical essay writer. 

The top  analytical essay writing service  at MyPerfectWords.com can assist you and provide customized written essays and papers for every academic level. We offer the best essay writing service.

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Nova Allison is a Digital Content Strategist with over eight years of experience. Nova has also worked as a technical and scientific writer. She is majorly involved in developing and reviewing online content plans that engage and resonate with audiences. Nova has a passion for writing that engages and informs her readers.

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• Knowledge Base
• 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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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

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S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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• D. Rödder   ORCID: orcid.org/0000-0002-6108-1639 1  

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Habitat change and fragmentation are the primary causes of biodiversity loss worldwide. Recent decades have seen a surge of funding, published papers and citations in the field as these threats to biodiversity continue to rise. However, how research directions and agenda are evolving in this field remains poorly understood. In this study, we examined the current state of research on habitat fragmentation (due to agriculture, logging, fragmentation, urbanisation and roads) pertaining to two of the most threatened vertebrate groups, reptiles and amphibians. We did so by conducting a global scale review of geographical and taxonomical trends on the habitat fragmentation types, associated sampling methods and response variables. Our analyses revealed a number of biases with existing research efforts being focused on three continents (e.g., North America, Europe and Australia) and a surplus of studies measuring species richness and abundance. However, we saw a shift in research agenda towards studies utilising technological advancements including genetic and spatial data analyses. Our findings suggest important associations between sampling methods and prevalent response variables but not with the types of habitat fragmentation. These research agendas are found homogeneously distributed across all continents. Increased research investment with appropriate sampling techniques is crucial in biodiversity hotpots such as the tropics where unprecedented threats to herpetofauna exist.

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Introduction

Habitat loss and fragmentation are the predominant causes underlying widespread biodiversity changes in terrestrial ecosystems (Fahrig 2003 ; Newbold et al. 2015 ). These processes may cause population declines by disrupting processes such as dispersal, gene flow, and survival. Over the past 30 years habitat loss and fragmentation have been suggested to have reduced biodiversity by up to 75% in different biomes around the world (Haddad et al. 2015 ). This is mainly due to the clearing of tropical forests, the expansion of agricultural landscapes, the intensification of farmland production, and the expansion of urban areas (FAO and UNEP 2020 ). The rate of deforestation and corresponding land conversions of natural habitats are happening rapidly and will continue to increase in the future at an accelerated rate, particularly in biodiversity hotspots (Deikumah et al. 2014 ; Habel et al. 2019 ; FAO and UNEP 2020 ).

For this reason, habitat fragmentation has been a central research focus for ecologists and conservationists over the past two decades (Fardila et al. 2017 ). However, habitat fragmentation consists of two different processes: loss of habitat and fragmentation of existing habitat (Fahrig 2003 ). The former simply means the removal of habitat, and latter is the transformation of continuous areas into discontinuous patches of a given habitat. In a radical review, Fahrig ( 2003 ) suggested that fragmentation per se, i.e., the breaking up of habitat after controlling for habitat loss, has a weaker or even no effect on biodiversity compared to habitat loss. She further recommended that the effects of these two components should be measured independently (Fahrig 2017 ). Despite being recognised as two different processes, researchers tend not to distinguish between their effects and commonly lump the combined consequences under a single umbrella term “habitat fragmentation” (Fahrig 2003 , 2017 ; Lindenmayer and Fischer 2007 ; Riva and Fahrig 2022 ). Nonetheless, fragmentation has been widely recognised in the literature and describes changes that occur in landscapes, including the loss of habitat (Hadley and Betts 2016 ). Hence, to avoid imprecise or inconsistent use of terminology and provide a holistic view of the effect of modified landscapes, we suggest the term “habitat fragmentation” to indicate any type of landscape change, both habitat loss and fragmentation throughout the current paper.

One main conundrum is that biodiversity decline does not occur homogeneously everywhere nor among all species (Blowes et al. 2019 ). Moreover, we should expect a global disparity in biodiversity responses to habitat fragmentation across different biomes (Newbold et al. 2020 ; Cordier et al. 2021 ). For example, tropical regions are predicted to have higher negative effects of habitat fragmentation than temperate regions. There are two possible reasons: a) higher intensification of land use change in the tropics (Barlow et al. 2018 ), and b) forest animals in the tropics are less likely to cross open areas (Lindell et al. 2007 ). Furthermore, individual species respond to landscape modification differently; some thrive whereas others decline (Fahrig 2003 ). Habitat specialists with broader habitat tolerance and wide-ranging distributions are most likely to benefit from increase landscape heterogeneity and more open and edge habitat (Hamer and McDonnell 2008 ; Newbold et al. 2014 ; Palmeirim et al. 2017 ). Therefore, appropriate response metrics should be used in measuring the effect of habitat fragmentation on biodiversity depending on the taxa group, biome and scale of study as patterns of richness can sometimes be masked by the abundance of generalist species (Riemann et al. 2015 ; Palmeirim et al. 2017 ).

Previous reviews have identified general patterns and responses of reptile and amphibian populations to habitat modification. They have been largely centred around specific types of habitat fragmentation: land use change (Newbold et al. 2020 ), logging (Sodhi et al. 2004 ), fragmentation per se (Fahrig 2017 ), urbanisation (Hamer and McDonnell 2008 ; McDonald et al. 2013 ), fire (Driscoll et al. 2021 ), and roads (Rytwinski and Fahrig 2012 ). Few reviews have, however, attempted a global synthesis of all types of land use changes and even fewer have addressed biases in geographical regions and taxonomical groups (but see Gardner et al. ( 2007 ) and Cordier et al. ( 2021 )). Gardner et al. ( 2007 ) synthesised the extant literature and focused on 112 papers on the consequences of habitat fragmentation on reptiles and amphibians published between 1945 and 2006. They found substantial biases across geographic regions, biomes, types of data collected as well as sampling design and effort. However, failure to report basic statistics by many studies prevented them from performing meta-analyses on research conclusions. More recently, a review by Cordier et al. ( 2021 ) conducted a meta-analysis based on 94 primary studies on the overall effects of land use changes through time and across the globe. Yet, there has been no comprehensive synthesis on the research patterns and agenda of published literature on habitat fragmentation associated with the recent advances of novel research tools and techniques. Therefore, our review may provide new insights of the evolution and biases in the field over the last decades and provide a basis for future research directions. Knowledge gaps caused by these biases could hamper the development of habitat fragmentation research and the implementation of effective strategies for conservation.

We aim to remedy this by examining research patterns for the two vertebrate classes Amphibia and Reptilia, at a global scale. We chose amphibians and reptiles for several reasons. First, habitat fragmentation research has been dominated by birds and mammals (Fardila et al. 2017 ). Reptiles and amphibians, on the other hand, are under-represented; together, they constitute only 10% of the studies (Fardila et al. 2017 ). Second, high proportions of amphibian and reptile species are threatened globally. To date, more than one third of amphibian (40%) and one in five reptile species (21%) are threatened with extinction (Stuart et al. 2004 ; Cox et al. 2022 ). Amphibians are known to be susceptible to land transformation as a result of their cryptic nature, habitat requirements, and reduced dispersal ability (Green 2003 ; Sodhi et al. 2008 ; Ofori‐Boateng et al. 2013 ; Nowakowski et al. 2017 ). Although poorly studied (with one in five species classified as data deficient) (Böhm et al. 2013 ), reptiles face the same threats as those impacting amphibians (Gibbons et al. 2000 ; Todd et al. 2010 ; Cox et al. 2022 ). Reptiles have small distributional ranges with high endemism compared to other vertebrates and as such are likely vulnerable to habitat fragmentation (Todd et al. 2010 ; Meiri et al. 2018 ). Third, both these groups are poikilotherms whose physiology makes them highly dependent on temperature and precipitation levels. Hence, they are very sensitive to changing thermal landscapes (Nowakowski et al. 2017 ).

Here, we first ask how is the published literature distributed across geographic regions and taxa? Is there a bias in the geographic distribution of species studied compared to known species? It is well known that conservation and research efforts are often concentrated in wealthy and English-speaking countries (Fazey et al. 2005 ), but has this bias improved over the years? Second, how are researchers conducting these studies? We assess whether certain sampling methods and response variables are associated to specific types of habitat fragmentation. Over the past decades new tools and techniques are constantly being discovered or developed. Combinations of methodologies are now shedding new light on biodiversity responses and consequences of habitat fragmentation. In particular, genetic techniques are useful in detecting changes in population structure, identifying isolated genetic clusters, and in estimating dispersal (Smith et al. 2016 ). Similarly, habitat occupancy and modelling can also provide powerful insights into dispersal (Driscoll et al. 2014 ). Remote sensing data are now used in analysing effects of area, edge, and isolation (Ray et al. 2002 ). Finally, how are these associations or research agendas distributed across space? We expect to find geographic structure of emerging agendas across the globe. For instance, we predict genetic studies to be located in North America and Europe but also in East Asian countries such as China and Japan as a result of their advancement in genetics (Forero et al. 2016 ). On the other hand, simple biodiversity response indicators which do not require extensive capacity building and application of advanced technologies are likely more used in developing regions of the world (Barber et al. 2014 ). These findings are valuable to evaluate and update the global status of our research on the effects of habitat fragmentation on herpetofauna and to suggest recommendations for conservation plans.

Materials and methods

Data collection.

We conducted the review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Fig.  1 ) (Moher et al. 2009 ). We conducted a comprehensive and exhaustive search using Web of Science to review published studies reporting the consequences of habitat fragmentation on amphibians and reptiles. We consulted the database in November 2019 by using two general search strings: (1) Habitat fragmentation AND (frog* OR amphib* OR salamander* OR tadpole*) (2) Habitat fragmentation AND (reptil* OR snake* OR lizard* OR turtle* OR crocodile*). This returned a total of 869 records from search (1) and 795 from search (2), with 1421 unique records remaining after duplicates were removed. We did not include “habitat loss” in our search term as it would only introduce unrelated articles focusing on biodiversity and conservation management instead of methodology and mechanistic approaches.

PRISMA flow-diagram of the study selection process

Throughout, we use the term papers to refer to individual journal article records. Out of the 1421 papers, we were unfortunately not able to locate seven papers from Acta Oecologica, Zoology: Analysis of Complex Systems, Israel Journal of Ecology and Evolution, Western North American Naturalist, Natural Areas Journal, Ecology, and the Herpetological Journal. We screened all articles from the title through the full text to determine whether they met our criteria for inclusion. To be included, studies needed to fulfil several criteria. First, papers needed to be peer-reviewed journal articles containing data collected on reptiles and/or amphibians at the species level (224 articles rejected because no species-specific data was available). Reviews and metastudies (n = 102) were excluded from the data analysis as they may represent duplicates as they are mainly based on data sets from other papers, but these form an integral part of our discussion. Furthermore, papers which do not provide data on contemporary time scales such as long-term (> 10, 000 years ago) changes on the paleo-spatial patterns (n = 59) were excluded. Because the effects of fragmentation per se have been measured inconsistently by many authors and have not been differentiated from habitat removal (Fahrig 2003 ), we consider any recent anthropogenic habitat degradation, and modification at patch or/and landscape scales during the Holocene as an effect of habitat fragmentation. Only papers which examined direct or indirect effects of habitat fragmentation were included in our analysis, regardless of the magnitude and direction. Papers which did not mention specific types of habitat fragmentation as the focus of their study (n = 338) were excluded.

Geographical and taxonomical distribution

Using the selected papers, we compiled a taxonomic and geographical database for each paper: (a) GPS or georeferenced location of the study site; (b) the focal group investigated (amphibian and/or reptile); (c) taxonomic groups (order, family, genus).

We listed the overall number of species studied covered by selected papers in each continent and compared them to the total number of currently described species. We obtained total described species of both reptiles and amphibians from the following sources: ReptileDatabase (Uetz et al. 2021 ) and AmphibiaWeb (AmphibiaWeb 2021 ). Then, we calculated the proportions of species covered by the selected papers compared to total number of described species for each continent. We did not update species nomenclature from selected papers as the mismatches from these potentially outdated taxonomies would be insignificant in our analyses.

Categorisation of papers

Each paper is classified into three main types of data collected: forms of habitat fragmentation, sampling methods, and response variables (Online Appendix 1). A paper can be classified into one or multiple categories in each type of data. The types of data and their following categories were:

Forms of habitat fragmentation

We recorded different types of habitat fragmentation from the selection of studies: (1) “Fragmentation” (includes patch isolation, edge and area effects); (2) “Agriculture” (includes any form of commercial and subsistence farming such as monocultures, plantations, and livestock farming); (3) “Logging” (e.g., agroforestry and silviculture); (4) “Mining” (presence of mining activities); (5) “Urbanisation” (includes presence of cities, towns or villages and parks created for recreational purposes); (6) “Road” (includes any vehicle roadway such as railways and highways) and (7) “Other types of habitat fragmentation” (e.g., fire, river dams, ditches, diseases, desertification etc.). Many studies deal with more than one type of habitat fragmentation. However, we made sure the selection for fragmentation forms is mainly based on the focus and wordings in the methodology section.

Sampling methods

We report trends in the design and sampling methods among the compiled studies over the last three decades. Due to the substantial variability in the level of sample design information reported by different studies, we narrowed them down into six general categories representing common sampling methods. Common methods used in estimating herpetofauna diversity (e.g., visual transect surveys, acoustic monitoring and trapping methods) were not included in the analyses due to their omnipresence in the data. The categories are:

(1) “Genetics” studies documented any use of codominant markers (i.e., allozymes and microsatellites), dominant markers [i.e., DNA sequences, random amplified polymorphic DNA (RAPDs) and amplified fragment length polymorphism (AFLPs)] to analyse genetic variability and gene diversity respectively. (2) “Direct tracking methods” studies measured potential dispersal distances or species movement patterns by means of radio telemetry, mark-recapture methods, or fluorescent powder tracking. (3) “Aerial photographs” studies reported the use of aerial photographs while (4) “GIS/Satellite image” studies described the use of satellite imagery and land cover data (i.e., Landsat) and GIS programs (e.g., QGIS and ArcGIS, etc.) in analysing spatial variables. (5) “Experimental” studies involved predictions tested through empirical studies, regardless if they occur naturally or artificially; in a natural or a captive environment. (6) “Prediction/simulation models” studies made use of techniques such as ecological niche models, habitat suitability (i.e., occurrence and occupancy models) and simulations for probability of survival and population connectivity.

Response variables

To further conceptualise how the effects of habitat fragmentation are measured, we assigned 12 biodiversity or ecological response variables. We recorded the type of data that was used in all selected studies: (1) “Species richness or diversity” which are measures of species richness, evenness or diversity (such as the Shannon–Wiener index) (Colwell 2009 ); (2) “Functional richness or species guilds” describes diversity indices based on functional traits (such as body size, reproductive modes, microhabitat association or taxonomic groups); (3) “Presence/absence” or species occupancy; (4) “Population” includes an estimation of population size or density (only when measured specifically in the paper). It includes genetic variation and divergence within and between populations; (5) “Abundance” or counts of individuals for comparison between habitat fragmentation type or species; (6) “Dispersal” takes into account any displacement or movement and can include indirect measurements of dispersal using genetic techniques; (7) “Breeding sites” which measures available breeding or reproduction sites; (8) “Fitness measure” are records of any physiological, ecological or behavioural changes; (9) “Interspecific interaction” depicts any interaction between species including competition and predation; (10) “Extinction or colonisation rate” counts the number of population extinctions or colonisations within a time period; (11) “Microhabitat preference” includes any direct observation made on an individual’s surrounding environmental features (substrate type, perch height, vegetation type, distance to cover etc.); (12) “Generalist or specialist comparison” involves any comparison made between generalist and specialist species. Generalists are able to thrive in various environments whereas specialists occupy a much narrower niche; (13) “Other response variables” can include road kill mortality counts, infection rate of diseases, injury, or any effect from introduced animals and a variety of other responses.

Data analysis

All statistical analyses were conducted in the open source statistical software package R 4.1.0 (R Core Team 2021 ). To gain a broad insight into our understanding of the complexity of habitat fragmentation we applied a Multiple Correspondence Analysis (MCA) (Roux and Rouanet 2004 ) and Hierarchical Clustering on Principle Components (HCPC) (Ward 1963 ) to investigate potential interactions between forms of habitat fragmentation, sampling methods and response variables. MCA is ideal for investigation of datasets with multiple categorical variables and exploration of unbiased relationships between these variables.

We first separate the dataset into papers concerning amphibians or reptiles. The MCA was performed using the MCA function from FactoMineR package of R version 3.1 (Lê et al. 2008 ). To identify subgroups (cluster) of similar papers within our dataset, we performed cluster analysis on our MCA results using HCPC. The cluster results are then visualised in factor map and dendrogram for easier interpretation using factoextra package. This allows us to identify categorical variables which have the highest effect within each cluster. Statistical analyses were considered significant at α = 0.05, while a p between 0.10 and 0.05 was considered as a tendency. The p-value is less than 5% when one category is significantly linked to other categories. The V tests show whether the category is over-expressed (positive values) or under-expressed (negative values) in the cluster (Lebart et al. 1995 ).

Results from the literature review were also analysed with VOSviewer, freeware for constructing and visualising bibliometric networks ( http://www.vosviewer.com/ ). The program uses clustering techniques to analyse co-authors, co-occurrence of keywords, citations, or co-citations (van Eck and Waltman 2014 ). First, we analyse co-authorships of countries to provide a geographical representation of groups of authors in various countries over the past 30 years. Each circle represents an author’s country and the size represents the collaboration frequency with other countries. The lines between the nodes represent the collaboration networks between the countries while the thickness of the lines indicates the collaboration intensities between them. Lastly, to complement the MCA and HCPC, we used VOSviewer to analyse a clustering solution of categories at an aggregate level. Aggregate clustering is a meta-clustering method to improve the robustness of clustering and does not require a priori information about the number of clusters. In this case, instead of author’s keywords, we used the co-occurrence of categories associated to each selected paper as input to run the software.

We identified a total of 698 papers published between January 1991 and November 2019 reporting consequences of habitat fragmentations corresponding to our selection criteria (Fig.  1 ). The complete list of studies included (hereafter termed “selected papers”) is available in Online Appendix 2. The distribution of these selected papers between focal groups and among continents was non-homogeneous (Fig.  2 ). Selected papers reviewed were predominantly studies which were conducted in North America 310 (44%) and Europe 131 (19%), but also from Oceania 104 (15%), South America 85 (12%), Asia 37 (5%) and Africa 31 (5%). For co-authorships between countries based on VOSviewer, the minimum document number of a country was set as 5 and a total of 21 and 14 countries met the threshold for amphibians and reptiles respectively (Fig.  3 ). For amphibians, countries in the American continent such as United States of America or USA (178 articles), Brazil (38 articles) and Canada (35 articles) have the largest research weight (Fig.  3 a). Authors from the USA have the largest international cooperation network, followed by Brazil. Australia and other European countries such as Germany, France and England also have high collaboration relationships with other countries. In contrast, reptile studies were mainly concentrated around two countries: the USA (139 articles) and Australia (86 articles) (Fig.  3 b). No other country from the rest of the world has more than 20 articles. While both the USA and Australia have the largest collaboration networks, Canada, Spain and Mexico are also highly cooperative with authors from other countries.

Map of study locations for a amphibians and b reptiles with each circle representing the study location of papers included in the review. The colour scale of the continents ranging from 0 – 0.9 indicates the proportions of amphibian and reptile species represented in the reviewed papers when compared to known species in the world (obtained from AmphibiaWeb and ReptileDatabase): a Europe (0.73), Africa (0.23), North America (0.23), South America (0.18), Oceania (0.07) and Asia (0.06) and b Europe (0.27), Oceania (0.18), Africa (0.12), North America (0.11), South America (0.09) and Asia (0.02)

Co-authorship map of countries involved in habitat fragmentation research in a amphibians and b reptiles. The colours represent the continents countries belong to. Each circle represents an author’s country and the size represents the collaboration frequency with other countries. The lines between the nodes represent the collaboration networks between the countries while the thickness of the lines indicates the collaboration intensities between them. Category co-occurrence network maps for c amphibians and d reptiles. The colour represents the different cluster groups each category belongs to. Abbreviations for the categories in forms of habitat change: fragmentation (FGM), agriculture (AGR), Logging (LOG), Mining (MIN), Urbanisation (URB), road (RD), other habitat fragmentation (OHC); sampling methods: genetics (GEN), direct tracking method (DTM), aerial photographs (APT), GIS/ Satellite images (GIS), experimental (EXP), prediction/ simulation models (PSM) and response variables: species richness/ diversity (SPR), functional richness/ species guild (FCR), presence/ absence (PAS), population (POP), abundance (ABD), dispersal (DSP), breeding sites (BRD), fitness measure (FIT), interspecific interaction (INT),extinction/ colonisation rate (ECR), microhabitat preference (MHP), comparison between generalist and specialist (CGS), other response varialbes (ORV) (see also Online Appendix 1). Maps are created in VOSviewer

Overall, over half of all selected papers included only amphibians (376 papers; 54%), whilst 276 papers (39%) included only reptiles and 46 papers (7%) assessed both reptiles and amphibians. In relation to species richness, we identified 1490 amphibian species and 1199 reptile species across all papers; among which 141 taxa were not identified to species level but were still included in our analyses as taxonomic units analogous to species (Online Appendix 2). Among these species, more than half of the studied amphibians were found in South America (537; 38%) and North America (328; 23%), followed by Africa (297; 21%), Asia (137; 10%), Europe (77; 5%), and Oceania (51; 3%). Half of the reptile species studied were from North America (302; 25%) and Africa (278; 23%), with the other half consisting of species from Oceania (276; 23%), South America (200; 17%), Europe (76; 6%), and Asia (67; 6%).

When compared to the known species richness in the world, large portions of European species are studied while species from other continents were severely under-represented (Fig.  2 ). The proportions of amphibian species represented in papers were the highest in Europe (73%), while the proportions are much lower for Africa (23%), North America (23%), South America (18%), Oceania (7%) and Asia (6%) (Fig.  2 a). Among reptiles, Europe represents again the highest proportion of studied species (27%), followed by Oceania (18%), Africa (12%), North America (11%) and South America (8.9%) (Fig.  2 b). In contrast, of all Asian reptile species, only a mere 1.73% were included in the selected papers. The species coverage in our selected papers does not seem optimistic. Amphibians and reptiles each have only six families with more than half of the species covered (including three reptilian families containing one species in total). Meanwhile, 23 and 25 families remain fully neglected for amphibians and reptiles respectively (Figs.  4 – 5 ).

Species coverage for each taxonomic family in selected papers of amphibians. The numbers on each row indicate the total number of species known in its respective family (obtained from AmphibiaWeb 2021 )

Species coverage for each taxonomic family in selected papers of reptiles. The numbers on each row indicate the total number of species known in its respective family (obtained from ReptileDatabase)

Multiple correspondence analysis provided important insights into underlying patterns in our data allowing us to visualise the relationship between forms of habitat fragmentation (Median = 1 [1–4]), sampling methods (Median = 1 [0–5]) and response variables (Median = 2 [1–6]). Percentage of variance (or eigenvalues) from MCA output represents the contribution of each dimension in explaining the observed patterns. The top ten new dimensions identified by MCA explained a total of 61.64% and 61.16% of the total variance for amphibians and reptiles respectively. The two dimensions with the highest variance percentages explained were found in the first (Dim1, 12.55%) and second (Dim2, 9.13%) dimensions in amphibians (Online Appendix 3–4). Genetics (sampling method; 13.73%) and population (response variable; 12.39%) contributed the most to Dim1, together with species richness (response variable;10.41%) and dispersal (response variable; 9.20%). For Dim2, experimental (sampling method; 14.38%) was the dominant variable, the rest was determined by GIS/Satellite images (sampling method; 9.71%), fitness measure (response variable; 9.12%) and urbanisation (form of fragmentation; 8.94%). For reptiles, the two dimensions explaining the most variation were the first (Dim1, 11.34%) and second (Dim2, 8.28%) dimensions (Online Appendix 3–4). The variables contributing the most to Dim1 were species richness (response variable; 15.51%), abundance (response variable; 10.11%), presence/absence (response variable; 6.97%) and genetics (sampling method; 6.39%). On the other hand, Dim2 was determined by interspecific interaction (response variable; 13.49%), genetics (12.79%), experimental methods (sampling method; 11.21%) and fitness measure (response variable; 10.94%). The contribution of each category to the definition of the dimensions is reported in Online Appendix 3. The categories identified in the MCA dimensions are subsequently used for building the distance matrix in the clustering analysis.

The HCPC analysis identified three clusters of variables for amphibians and reptiles (Online Appendix 5–6). The output of the HCPC analysis is reported in Online Appendix 7. V test represent the influence of variables in the cluster composition. In general, three clusters for both amphibians and reptiles appeared to be uniquely similar by definition of categories (Fig.  6 ). For amphibians, cluster 1 was defined by studies on species richness (p < 0.05, V test = 14.30) and presence/absence (p < 0.05, V test = 13.42), while cluster 2 was determined by experimental studies (p < 0.05, V test = 10.95) and fitness measures (p < 0.05, V test = 9.77). Cluster 3 was defined by genetics (p < 0.05, V test = 18.44) and population studies (p < 0.05, V test = 17.73) (Online Appendix 7). Abundance and functional richness were also unique to cluster 1; other response variables and direct tracking methods were important to cluster 2 and dispersal was present in cluster 3 even though these variables are less expressed (Fig.  6 a).

Percentage contribution of the categories contributing to the uniqueness of each cluster in amphibians (Dark green = 1, Bright green = 2, Bright yellow = 3) and reptiles (Dark red = 1, Orange = 2, Dark yellow = 3) based on the Cla/Mod results of HCPC (see Online Appendix 7). Abbreviations for the categories can be found in Fig.  3 and in Online Appendix 1

For reptiles, cluster 1 was represented by species richness (p < 0.05, V test = 14.26), abundance (p < 0.05, V test = 11.22) and presence absence (p < 0.05, V test = 8.55) papers, whereas cluster 2 was determined by papers on fitness measures (p < 0.05, V test = 10.99), direct tracking methods (p < 0.05, V test = 8.64) and interspecific interaction (p < 0.05, V test = 7.86), and cluster 3 was defined by genetics (p < 0.05, V test = 12.79), population (p < 0.05, V test = 9.95) and prediction/simulation models (p < 0.05, V test = 7.68) papers (Online Appendix 7). Although slightly less expressed in the clusters, papers using comparisons between generalist and specialist species and papers on functional richness were also unique to cluster 1; experimental methods and other response variables were heavily present in cluster 2, while dispersal studies were distinct to cluster 3 (Fig.  6 b).

Results from VOSviewer categories of both amphibians and reptiles appear to be similar to each other (Fig.  3 c, d). The clustering of the categories in the co-occurrence network maps confirms what we observed in the HCPC results (Fig.  6 ). In addition to geographical representation of study locations in (1), the corresponding clusters of selected papers are also mapped in Figs.  7 and 8 to investigate the spatial grouping patterns for the three clusters (see Online Appendix 8–9 for geographical representation for each category). We also plotted the temporal trend in Online Appendix 10 and 11. Overall, the three clusters are distributed homogeneously across the globe, but concentrated in the USA, Europe and south eastern Australia. Cluster 1 papers were found to be the most predominant cluster in amphibians (57% papers) across all continents (see Online Appendix 12; Fig.  7 ). When compared to other clusters, studies from this cluster are often conducted in Afrotropics, particularly Madagascar (100% papers), central (Costa Rica (60% papers) and Mexico (92% papers) and south America (80% papers) (Online Appendix 12, Figs.  7 , 8 ). On the other hand, cluster 2 papers appear to be more prevalent for reptile studies compared to amphibian studies, with a higher number of studies conducted across North America (65 to 51) and Australia (22 to 2) (Figs.  7 , 8 ). Lastly, a vast majority of cluster 3 papers were located in North America and Europe (both contributing to 79% of the papers) for amphibians and North America and Australia (both contributing to 84% of the papers) for reptiles (Online Appendix 12, Figs.  7 , 8 ). Publications from this cluster started to gain popularity from 2005 onwards, following similar increasing trends as cluster 2 (Online Appendix 10–11). Overall, except for cluster 1 in South America, most of the clusters in Asia and Africa appear to experience very little or no increase in publications over the years (Online Appendix 10–11).

Map of the individual selected papers belonging to each cluster groups (Dark green = 1, Bright green = 2, Bright yellow = 3) for amphibians, with each circle representing the study location. The colour scale of the continents ranging from 0 to 0.9 indicates the proportions of amphibian species represented in the reviewed papers when compared to known species in the world (obtained from AmphibiaWeb)

Map of the individual selected papers belonging to each cluster groups (Dark red=1, Orange=2, Dark yellow=3) for reptiles, with each circle representing the study location. The colour scale of the continents ranging from 0.0 – 0.9 indicates the proportions of reptile species represented in the reviewed papers when compared to known species in the world (obtained from ReptileDatabase).

Our review found no improvement in the geographical and taxonomic bias in habitat fragmentation studies for both reptiles and amphibians compared to earlier studies (Fardila et al. 2017 ). Yet, our study has made an effective contribution towards identifying major spatial gaps in habitat fragmentation studies over the past three decades (updating reviews such as Cushman 2006 ; Gardner et al. 2007 )). In particular, we found an overall increase in the number of studies measuring species richness and abundance throughout the years while population-level and genetics studies are still lacking in developing countries. Here, we discuss the issues of (1) biogeographical bias, (2) the extent and focus of habitat fragmentation research and (3) the limitations and knowledge gaps in habitat fragmentation research in herpetology and provide recommendations for future research.

Biogeographical bias

Geographic bias in research papers.

Given the research effort in relatively wealthy countries (Holmgren and Schnitzer 2004 ; Fazey et al. 2005 ) it is not surprising that more than half the papers concern North America and Europe, where there is strong prevalence of herpetological research. This pattern is also evident in other taxonomic groups and biological areas including invasion biology (Pyšek et al. 2008 ), biodiversity conservation (Trimble and Aarde 2012 ; Christie et al. 2020 ), and habitat fragmentation (Fardila et al. 2017 ). The USA alone contributed more than a third of the publications in terms of both authors and location of study (Fazey et al. 2005 ; Melles et al. 2019 ). English speaking countries including the USA, the United Kingdom, and Australia have dominated research output over the last 30 years (Melles et al. 2019 ). These patterns were reflected in the collaboration network maps generated by VOSviewer (Fig.  3 ). Similar hotspots found between who does the research (Fig.  3 ) and the study locations (Fig.  2 ) suggest that authors tend not to move much and only to study ecosystems near to where they are based (Meyer et al. 2015 ). One reason for this bias is the distance to field sites accentuated by the costs and time of travelling.

However, the near absence of studies from many parts of the world that are currently under extreme pressures of habitat loss and degradation are of great concern (Habel et al. 2019 ). We feel that the level of threat associated with habitat fragmentation in these continents is not proportional to the level of research attention required. Naturally biodiverse but less economically developed Southeast Asian and sub-Saharan countries will suffer greatest diversity losses in the next century (Newbold et al. 2015 ). If this persists at the current rate, biodiverse areas will likely disappear before new discoveries in those hotspots are made (Moura and Jetz 2021 ). Although conversely our study found that among other developing countries Brazil is currently conducting relatively more in-country amphibian studies and collaboration with other countries. However, how much of this information reaches decision makers and practitioners remains unknown. This is largely due to the lack of intermediary evidence bridges (Kadykalo et al. 2021 ). These intermediaries identify evidence summaries based on research and priorities and distribute them to practitioners, facilitating exchange of knowledge between and among researchers and practitioners (Holderegger et al. 2019 ; Kadykalo et al. 2021 ).

Geographic bias in focal groups

Congruent to results reported in Gardner et al. ( 2007 ), studies on amphibians were more abundant than studies on reptiles. Over the past years, there has been a strong focus on amphibian population declines. This was catalysed by the emergence of chytridiomycosis and global decline of amphibians (Fisher and Garner 2020 ). Amphibians, and predominantly frogs, are the principal focus of herpetological research, with the highest allocation of resources and the highest publication rates (Ferronato 2019 ). Another reason for this bias may be that amphibians serve as valuable indicators of environmental stress and degradation owing to their aquatic and terrestrial lifestyle and permeable skin (Green 2003 ). These attributes make them extremely sensitive to changes in temperature and precipitation as well as pollution (Sodhi et al. 2008 ). Lizards, also susceptible to temperature changes, however, are characterised by a high degree of endemism, restricted geographic ranges, late maturity, a long life-span and are thus very susceptible to population declines (Todd et al. 2010 ; Meiri et al. 2018 ). Certain groups of reptiles, such as worm lizards and blind snakes lead cryptic and solitary lives in contrast to the large breeding aggregations and choruses of, for example, frogs. Such characteristics make them difficult for researchers to study as they require large amount of search effort for little data (Thompson 2013 ).

• Taxonomic bias

We found a heightened geographical bias in the taxonomic coverage of studies. Given the sheer number of selected papers investigated, it is not surprising that the continents of North and South America cover more than half of the amphibian species studied whereas North America and Africa cover almost half of the reptile species studied. This trend broadly mirrors the geographic distribution pattern of the global described species in both these taxa (AmphibiaWeb 2021 ; Uetz et al. 2021 ). While a large proportion of the known European and North American families such as Alytidae and Ambystomatidae have been investigated (Fig.  4 ), species from other continents remain severely under-represented. Yet, the European continent represents only 2% of the described species globally. This high research intensity bias in low biodiverse regions of the world has been noted previously (Fazey et al. 2005 ). In general, reptiles and amphibians have been disproportionately poorly studied in the tropics and in developing areas despite that these areas show among the highest rates of deforestation and a corresponding rise in the number of threatened species (Böhm et al. 2013 ; Deikumah et al. 2014 ). These biodiverse areas largely consist of threatened species having restricted home ranges (Meiri et al. 2018 ). Even though we observed a great fraction of the species investigated in the Afrotropics (Vallan 2002 ; Hillers et al. 2008 ; Ofori‐Boateng et al. 2013 ; Riemann et al. 2015 ; Blumgart et al. 2017 ), especially Madagascar (see Mantellidae and Opluridae in Fig.  4 ), it seems insufficient when considering that an estimated 3.94 million hectares of forest area of the continent was cleared yearly over the last century (FAO and UNEP 2020 ). Further, biodiverse hotspots such as the neotropical regions and Indo-Malayan tropics have the highest chances of new species of amphibians and reptiles being discovered (Moura and Jetz 2021 ).

Being herpetofauna diversity hotspots, countries in South America and Asia are indeed understudied. Although Brazil has a high number of amphibian studies, less than one percent of known reptile species was studied in both continents (Fig.  2 ). A number of factors contribute to this lack of representation. First, there is an overwhelming number of new species being discovered every year in these hotspots (Moura et al. 2018 ; Moura and Jetz 2021 ). Furthermore, newly discovered species tend to belong to more secretive groups such as burrowing snakes, worm lizards and caecilians (Colli et al. 2016 ). Yet, these fossorial organisms are clearly neglected in fragmentation studies (see Fig.  4 – 5 ) with researchers focusing on well-known taxonomic groups (Böhm et al. 2013 ). On a positive note, despite having the country (Australia) with the highest reptile diversity (Uetz et al. 2021 ), Oceania represented a fair coverage of reptile diversity compared to other continents. Since 2001, there has been an increase of fragmentation studies in Australia (e.g., Brown 2001 ; Mac Nally and Brown 2001 ; Hazell et al. 2001 ) and there is a continuing increase in research output (Melles et al. 2019 ), contributing 85 out of 89 reviewed studies in Oceania over the last 30 years.

Extent and focus of research

Our findings showed important associations between methods and response metrics but not different forms of habitat fragmentation. This either suggests that researchers were not favouring any sampling method and response variable for evaluating the effects of certain habitat fragmentation or this pattern may occur due to a relatively even split of papers dealing with different forms or combinations of habitat fragmentation in the clusters. In general, species richness or diversity appears to explain most of the variation in our data ( see Online Appendix 4 ). While species richness remains a popular diversity metric employed in conservation biology (Online Appendix 12; also see Gardner et al. 2007 ), we also found an increasing trend in the use of genetic techniques for habitat fragmentation studies. More specifically in recent years, molecular genetics have become popular and are often studied together with population connectivity to capture species responses to habitat fragmentation ( see Online Appendix 4 ) (Keyghobadi 2007 ). The HCPC approach identified three main clusters of research fields which will be referred to as research agendas from here onwards. Contrary to our expectation, we did not find a global spatial pattern of research agendas, but instead a rather homogeneous distribution of papers, possibly due to the lack of selected studies which are found in developing countries outside USA, Europe and Australia (Figs.  7 , 8 ). This nevertheless indicates that different sampling methods are shared and used between leading herpetological experts from different countries and that there are continuing collaborations between countries, particularly in North America and Europe.

Below, we describe the research agendas and their corresponding categories (Fig.  6 ) that have contributed significantly to the study of habitat fragmentation for the past 30 years: (a) Agenda 1: Measures of direct individual species responses, (b) Agenda 2: Physiological and movement ecology, and (c) Agenda 3: Technology advancement in conservation research.

Agenda 1: Measures of direct individual species responses

We found that the majority of studies around the globe evaluated patterns of assemblage richness, species presence/absence, and abundance (Figs.  7 , 8 ). These simple patterns of richness, diversity and abundance are the most common responses measured because they provide a good indication of species response to habitat fragmentation and are easy to calculate (Colwell 2009 ). Although species richness does not consider abundance or biomass but treats each species as of equal importance to diversity, species evenness weighs each species by its relative abundance (Hill 1972). Further, composite measures like species diversity indices (e.g., Simpson’s 1/D or Shannon’s H) combine both richness and evenness in a single measure (Colwell 2009 ), preventing biases in results. However, directly measuring these species responses might not be ecologically relevant as they fail to account for patterns in species assemblage turnover. In fact, few selected papers (38 out of 697) in our study have attempted to categorise species into meaningful functional groups or guilds, despite that the categorisation of ecological functions such as habitat preference, taxonomic family, reproductive mode, and body size can be easily done (but see Knutson et al. 1999 ; Peltzer et al. 2006 ; Moreira and Maltchik 2014 ). Knutson et al.( 1999 ) was the first in our selected papers to group species with similar life-history characteristics into guilds and to examine their responses to landscape features. They observed negative associations between urban land use and anuran guilds. Analyses of guilds or functional groups can reveal contradictory results (but not always, see Moreira and Maltchik 2014 ). For example, the species richness of anurans in logged areas of West Africa is found to be as high as in primary habitat (Ernst et al. 2006 ). Yet, analyses of functional groups indicated significantly higher diversity in primary forest communities (Ernst et al. 2006 ). Similar differences were also observed for species with varying degrees of niche overlaps, habitat specialists, and for different continents (Ernst et al. 2006 ; Seshadri 2014 ). These results underline that species richness alone is a poor indicator of the functional value of species in the ecosystem as the relationships between functional diversity and species richness are inconsistent and can sometimes be redundant (functional diversity remains constant if assemblages are functionally similar; Riemann et al. 2017 ; Palmeirim et al. 2017 ; Silva et al. 2022 ). The results of some species richness studies may consequently provide misleading inferences regarding consequences of habitat fragmentation and conservation management (Gardner et al. 2007 ).

Although not substantially greater than the agendas 2 and 3, the measure of individual species responses has always been popular across the globe but also increasingly popular in the tropical and subtropical regions (e.g., South America and Africa; Online Appendix 10–11). For example, a research team led by Mark-Oliver Roedel from Germany has conducted numerous studies on Afrotropical amphibian communities (Hillers et al. 2008 ; Ofori‐Boateng et al. 2013 ; Riemann et al. 2017 ). Due to the higher biodiversity and species rarity in these regions compared to temperate areas, it is reasonable to expect a greater level of sampling effort in patterns of species richness, abundance, and guild assemblage to obtain comparisons of diversity with sufficient statistical power across different land use changes (Gardner et al. 2007 ). Access to highly specific expertise and most up to date methods and technology may not be available in these regions, and as such, study designs are limited to multispecies survey addressing simple patterns of diversity and species assemblages (Hetu et al. 2019 ). Unfortunately at the same time, these forest biomes holding the highest richness and abundance of amphibians and reptiles have showed consistent negative responses to land use changes (Cordier et al. 2021 ).

Agenda 2: physiological and movement ecology

We did not observe a strong association between occupancy and dispersal in our study. Perhaps this is because only a few papers investigated dispersal via habitat occupancy compared to the overwhelming proportions of papers examining the presence of species in response to habitat fragmentation in research agenda 1. Similarly, few studies measure dispersal with direct tracking methods, with the majority that discussed dispersal being based on indirect inferences, such as genetic divergence (see Fig.  3 c, d; Driscoll et al. 2014 ). Genetic approaches can be effective in situations where more direct approaches are not possible (Lowe and Allendorf 2010 ). For instance, using microsatellites and mitochondrial DNA, Buckland et al. ( 2014 ) found no migration occurring between isolated subpopulations of a forest day gecko ( Phelsuma guimbeaui ) in a fragmented forest and predicted a dramatic decrease in survival and allelic diversity in the next 50 years if no migration occurs (Buckland et al. 2014 ). In some cases, molecular markers also allow direct dispersal studies by assigning individuals to their parents or population of origin (Manel et al. 2005 ). However, there are limitations on when these techniques can be applied. Assignment tests require appropriate choices of molecular markers and sampling design to permit quantification of indices of dispersal (Broquet and Petit 2009 ; Lowe and Allendorf 2010 ). Parent–offspring analysis is constrained by the uncertainty in assessing whether offspring dispersal is completed at the time of sampling and sample size (Broquet and Petit 2009 ). Genetic tools may thus be best applied in combination with direct approaches because they contain complementary information (Lowe and Allendorf 2010 ; Safner et al. 2011 ; Smith et al. 2016 ).

Traditional approaches in habitat fragmentation research like radiotracking or capture-mark-recapture of animals can be effective in evaluating dispersal and ecological connectivity between populations. For example, based on mark-recapture data over a nine year period, facultative dispersal rates in an endangered amphibian ( Bombina variegata ) were found to be sex biased and relatively low from resulting patch loss (Cayuela et al. 2018 ). In our case, direct tracking methods are more commonly and effectively used in examining the impacts of habitat modification on changes in ecology directly relating to fitness (Fig.  6 ): home ranges (Price-Rees et al. 2013 ), foraging grounds (MacDonald et al. 2012 ) and survival rates (Breininger et al. 2012 ). Yet, such routine movements associated with resource exploitation do not reflect the biological reality and evolutionary consequences of how organisms change as landscape changes (Van Dyck and Baguette 2005 ). Instead, directed behavioural movements affecting dispersal processes (emigration, displacement or immigration) are crucial in determining the functional connectivity between populations in a fragmented landscape (Bonte et al. 2012 ). In one study, spotted salamanders Ambystoma maculatum tracked with fluorescent powder exhibited strong edge mediated behaviour when dispersing across borders between forest and field habitats and can perceive forest habitats from some distance (Pittman and Semlitsch 2013 ). Knowing such behaviour rules can improve predictions of the effects of habitat configuration on survival and dispersal. However, ongoing conversion of natural ecosystems to human modified land cover increases the need to consider various cover types that may be permeable to animal movements. As such, experimental approaches can be effective in examining the effect of matrix type on species movements as seen in our results (Fig.  6 ) (Rothermel and Semlitsch 2002 ; Mazerolle and Desrochers 2005 ). For example, researchers conducted experimental releases of post-metamorphic individuals of forest amphibians into different substrates and mapped the movements of paths and performance (Cline and Hunter Jr 2016 ). They showed that non-forest matrices with lower structural complexity influence the ability of frogs to travel across open cover and to orient themselves towards the forest from distances greater than 40–55 m. Therefore, it is inaccurate to assume matrix permeability to be uniform across all open-matrix types, particularly in amphibians (Cline and Hunter 2014 , 2016 ).

In addition, the ability to move and disperse is highly dependent on the range of external environments and internal physiological limits (Bonte et al. 2012 ), especially in reptiles and amphibians (Nowakowski et al. 2017 ). The study of physiological effects on movement was seen throughout our selected studies (Fig.  6 ). For example, higher temperatures and lower soil moisture in open habitats could increase evaporative water loss in salamanders (Rothermel and Semlitsch 2002 ). Other tests including interaction effects between landscape configuration and physiological constraints (e.g., dehydration rate Rothermel and Semlitsch 2002 ; Watling and Braga 2015 ); body size (Doherty et al. 2019 ) can be useful to better understand fitness and population persistence. We argue here that multidisciplinary projects examining movement physiology, behaviour and environmental constraints in addition to measuring distance moved are needed to progress this field.

Our results indicate a high bias of agenda 2 papers represented among developed countries, with a strong focus on reptiles compared to amphibians (Price-Rees et al. 2013 ; Doherty et al. 2019 ) (Online Appendix 12, Figs.  7 , 8 ). The adoption of direct tracking as well as genetic methods can be cost prohibitive in developing and poorer regions. However, cheaper and simpler methods to track individuals are increasing (Mennill et al. 2012 ; Cline and Hunter 2014 , 2016 ). Although existing application might not be ideal for reptiles and amphibians, new technologies for tagging and tracking small vertebrates are being developed including acoustic surveys and improved genetic methods (Broquet and Petit 2009 ; Mennill et al. 2012 ; Marques et al. 2013 ). While there are many improvements needed to obtain better quality dispersal data studies on movement ecology, reptiles and amphibians still only account for a mere 2.2% of the studies on dispersal when compared to plants and invertebrates which comprised over half of the studies based on a systematic review (Driscoll et al. 2014 ). Thus, we urge more studies to be conducted on these lesser-known taxa, especially in biodiverse regions. Given the limited dispersal in amphibians and reptiles, having a deeper understanding on their dispersal can be critical for the effective management and conservation of populations and metapopulations (Smith and Green 2005 ).

Agenda 3: technology advancement in conservation research

While community level approaches such as responses in species richness, occupancy, and abundance measure biodiversity response to habitat fragmentation, they are limited in inference because they do not reflect patterns of fitness across environmental gradients and landscape patterns. Instead, genetic structure at the population level can offer a higher resolution of species responses (Manel and Holderegger 2013 ). For instance, genetic erosion heavily affects the rate of species loss in many amphibian species (Allentoft and O’Brien 2010 ; Rivera‐Ortíz et al. 2015 ). Over the past decades we have seen a rapid increase in studies applying genetic analysis to assess the effects of habitat fragmentation (Keyghobadi 2007 ), reflecting the strength of these approaches. This growth is mostly evident in North America and Europe (but also Oceania for reptiles) ( Online Appendix 10–11). The availability of different genetic markers has been increasing, from microsatellites in the 1990s then shifting towards genotyping by sequencing (NGS) technologies that enable rapid genome-wide development (Allendorf et al. 2010 ; Monteiro et al. 2019 ). However, the study of population structure alone can lead to misleading results as environmental changes to species dynamics are not considered. The resistance imposed by landscape features on the dispersal of animals can ultimately shape gene flow and genetic structure (Bani et al. 2015 ; Pilliod et al. 2015 ; Monteiro et al. 2019 ).

To understand this, researchers combine genetic, land cover and climate variables to study the gene flow patterns across heterogeneous and fragmented landscapes (Manel and Holderegger 2013 ). Spatial analyses can be a powerful tool for monitoring biodiversity by quantifying environmental and landscape parameters. The growing interest in both landcover data and the rapid development of computer processing power prompted the development of new prediction methods, primarily in spatial models (Ray et al. 2002 ), ecological niche modelling (Urbina-Cardona and Loyola 2008 ; Tan et al. 2021 ), and landscape connectivity (Cushman et al. 2013 ; Ashrafzadeh et al. 2019 ). In some cases, niche models are useful in assessing the effectiveness of protected areas for endangered species (Urbina-Cardona and Loyola 2008 ; Tan et al. 2021 ).

The integration of genetic data in ecological niche models for recognising possible dispersal movements between populations were observed in our study (Fig.  3 c, d), especially in reptiles (Fig.  6b ). The hallmark of landscape genetics is the ability to estimate functional connectivity among populations and offer empirical approach of adaptive genetic variation in real landscapes to detect environmental factors driving evolutionary adaptation. The most common approach of landscape genetics is determining whether effective distances as determined by the presence of suitable habitat between populations, better predict genetic distances than do Euclidean distances (assuming spatially homogeneous landscape). However, straight-line geographic distance does not normally reflect true patterns of dispersal as landscape barriers or facilitators in a heterogeneous landscape could strongly affect gene flow (Emel and Storfer 2012 ; Fenderson et al. 2020 ). Therefore, in these cases, ecological distances or landscape resistance can often explain a greater deal of genetic variation between fragmented populations (Cushman 2006 ; Bani et al. 2015 ). Using a combination of habitat suitability modelling (e.g., Maxent, Phillips et al. 2017 ), multiple least-cost paths (LCPs) (Adriaensen et al. 2003 ) and the more recent circuit theory analysis (McRae et al. 2008 ) to investigate landscape resistance can be highly effective predicting potential pathways along which dispersal may occur, hence informing conservation management (Emel and Storfer 2012 ; Bani et al. 2015 ; Pilliod et al. 2015 ). To date, landscape genetics has been shown to be particularly useful in studying organisms with complex life histories (Emel and Storfer 2012 ; Shaffer et al. 2015 ). Yet, the applications of landscape genetics have been limited to contemporary patterns using modern genetic data. Few studies have benefitted from the inclusion of temporal genetic data (Fenderson et al. 2020 ). For example, historical DNA samples and heterochronous analyses could allow us to explore how anthropogenic impacts have affected past genetic diversity and population dynamics (Pacioni et al. 2015 ) and identify areas of future suitability of endangered animals in face of climate change (Nogués-Bravo et al. 2016 ). The possibility to investigate migration through spatiotemporal population connectivity can greatly improve the prediction of species responses under future landscape and climate change scenarios (Fenderson et al. 2020 ).

Population genetic and niche modelling studies for both taxa are rarely found in developing regions of the world, especially in Asia and Africa (Figs.  7 , 8 ). Even though conservation priorities are concentrated in these biodiverse regions, invaluable highly specific expertise such as conservation genetics and other contemporary methodologies might not be readily available due to lack of funding and infrastructure (Hetu et al. 2019 ). Thus, we encourage collaborations with the poorer countries initiated by foreign service providers from developed countries. Contrary to expectations, very few studies on conservation genetics were found in China and Japan despite their vast advances in genetic techniques. Fortunately, China has made substantial progress in the last 20 years in understanding human genetic history and interpreting genetic studies of human diseases (Forero et al. 2016 ) as well as biodiversity conservation (Wang et al. 2020 ), yet the same cannot be said for conservation genetics on reptiles and amphibians (Figs.  7 , 8 ), but see Fan et al. ( 2018 ) and Hu et al. ( 2021 ).

Limitations and knowledge gaps

The forms of habitat fragmentation which we categorised may not reflect the ecological impact in the real world as interactions between different habitat fragmentation forms were not accounted for. Although each of these forms of habitat fragmentation possesses serious environmental consequences, their combination could have severe synergistic impacts (Blaustein and Kiesecker 2002 ). For example, a fragmented landscape is not just reduced and isolated, but subject to other anthropogenic disturbances such as hunting, fire, invasive species, and pollution (Laurance and Useche 2009 ; Lazzari et al. 2022 ). Altered climatic conditions and emerging pathogens such as batrachochytrids can also interact with each other, and other threats (Fisher and Garner 2020 ). The use of habitat suitability models based on climatic scenarios, combined with hydrological and urbanisation models, are effective in detecting best to worst case scenarios and local extinctions, as shown for the spotted marsh frog ( Limnodynastes tasmaniensis ) (Wilson et al. 2013 ).

We acknowledge the bias of scientific research introduced from the limitation of search term to English-speaking literature on the geographic distribution of the papers we sampled (Konno et al. 2020 ; Angulo et al. 2021 ). In Latin American journals for example, we found a number of papers published in Spanish, but unfortunately, they did not fit the criteria of our selection (see Online Appendix 2). Conservation studies written in languages other than English are often published in local journals which do not normally go through international peer review.

The homogeneous distribution of the research agendas across geographical regions in our study may be explained by the lack of studies found in South America, Asia and Africa, preventing us to see a potentially dichotomous spatial pattern among the clusters. However, this reflects the current state of research and the challenges faced in less developed countries.

(4) Our study did not investigate whether habitat fragmentation has led to an improved or decreased biotic response. Predicting species response to habitat modification has been reviewed countless times (Rytwinski and Fahrig 2012 ; Driscoll et al. 2014 ; Doherty et al. 2020 ; Newbold et al. 2020 ; Cordier et al. 2021 ). Yet, these reviews often yield little or no general patterns (Doherty et al. 2020 ; Cordier et al. 2021 ). Response variables or traits measured are often found to be poor predictors of the impacts of habitat fragmentation. There are two possible explanations for this discrepancy. First, the strength and direction of the responses differs between species, ecophysiological groups (Rothermel and Semlitsch 2002 ), and phylogenetic or functional groups (Mazerolle and Desrochers 2005 ; Nowakowski et al. 2017 ). Second, responses in animals to different types of disturbance may be specific to the ecosystem where they live. Different biogeographic regions or biomes have different characteristics affecting local species (Lindell et al. 2007 ; Blowes et al. 2019 ; Newbold et al. 2020 ; Cordier et al. 2021 ).

Conclusions and recommendations

Our results underline promising research fields and geographic areas and may serve as a guideline or starting point for future habitat fragmentation studies. We suspect similar paradigms of geographic and thematic patterns to occur in other taxonomic groups.

Although studies dealing with habitat fragmentation impacts on mammals and birds are already widely recognised (Fardila et al. 2017 ), research on reptiles and amphibians has been lacking. We argue that amphibians and reptiles need more attention as they are equally or more threatened but highly neglected (Rytwinski and Fahrig 2012 ; Ferronato 2019 ; Cox et al. 2022 ).

Greater investment is required for studies in tropical and subtropical areas (Segovia et al. 2020 ), especially within the Asian continent. These areas are currently experiencing the highest rates of habitat loss (McDonald et al. 2013 ). Tropical specialists are further restricted to smaller geographic range sizes according to Rapoport’s rule which states that there is a positive latitudinal correlation with range size (Stevens 1989 ) (at least for amphibians in the Northern hemisphere where there is higher temperature and precipitation seasonality; Whitton et al. 2012 ). Having a small range size is often associated with negative responses to habitat modification (Doherty et al. 2020 ). Thus, more effort is needed in developing countries where the crisis is greatest and there is lack of funding and strong language barriers (Fazey et al. 2005 ). There is an urgent need to better integrate studies published in languages other than English with the broader international literature. Useful integration actions include training of local conservation biologists and promoting partnerships and research visits in these regions may have greater conservation consequences to understand global patterns of habitat modification (Meyer et al. 2015 ). Doing so will help remediate the sampling bias towards temperate generalists and will shed light on the fate of tropical specialists.

We encourage improved access to intermediary evidence-based conservation data (Kadykalo et al. 2021 ). Even when well-established genetic and genomic analyses have been proven to be promising area in herpetological conservation (Shaffer et al. 2015 ), there is a general lack of the transfer of knowledge between scientists and practitioners (Holderegger et al. 2019 ). As practitioners are generally interested in species monitoring and the evaluation of success of connectivity measures, an establishment of scientist-practitioner community to facilitate a platform for international exchange would help tremendously in future conservation planning and management (Holderegger et al. 2019 ).

Although different study designs and landscape measures have different strengths and limitations depending on the study objectives, we suggest reporting basic data to describe the effect of habitat fragmentation using standardised sampling methods, indices, and design (Holderegger et al. 2019 ). The results will allow future meta-analyses to be performed.

Incorporate remote sensing data, whenever possible, in studies involving habitat change and fragmentation. The use of niche modelling techniques combined with high resolution remote sensing has been instrumental in detecting potentially fragmented populations. With advances in landscape genomics, we are now able to examine the correlation between environmental factors and genomic data in natural populations (Manel and Holderegger 2013 ; Shaffer et al. 2015 ). Adopting such tools would be valuable in understanding how habitat amounts and configurations affect dispersal, survival, and population dynamics as well as the impacts of anthropogenic changes such as climate change (Shaffer et al. 2015 ).

Data availability

The datasets generated during the current study are available in Online Appendix 1. Codes used in the analyses are available from corresponding author on request.

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Tan, W.C., Herrel, A. & Rödder, D. A global analysis of habitat fragmentation research in reptiles and amphibians: what have we done so far?. Biodivers Conserv 32 , 439–468 (2023). https://doi.org/10.1007/s10531-022-02530-6

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Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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We asked teachers about how students are doing at their school. Overall, many teachers hold negative views about students’ academic performance and behavior.

• 48% say the academic performance of most students at their school is fair or poor; a third say it’s good and only 17% say it’s excellent or very good.
• 49% say students’ behavior at their school is fair or poor; 35% say it’s good and 13% rate it as excellent or very good.

Teachers in elementary, middle and high schools give similar answers when asked about students’ academic performance. But when it comes to students’ behavior, elementary and middle school teachers are more likely than high school teachers to say it’s fair or poor (51% and 54%, respectively, vs. 43%).

Teachers from high-poverty schools are more likely than those in medium- and low-poverty schools to say the academic performance and behavior of most students at their school are fair or poor.

The differences between high- and low-poverty schools are particularly striking. Most teachers from high-poverty schools say the academic performance (73%) and behavior (64%) of most students at their school are fair or poor. Much smaller shares of teachers from low-poverty schools say the same (27% for academic performance and 37% for behavior).

In turn, teachers from low-poverty schools are far more likely than those from high-poverty schools to say the academic performance and behavior of most students at their school are excellent or very good.

Lasting impact of the COVID-19 pandemic

Among those who have been teaching for at least a year, about eight-in-ten teachers say the lasting impact of the pandemic on students’ behavior, academic performance and emotional well-being has been very or somewhat negative. This includes about a third or more saying that the lasting impact has been very negative in each area.

Shares ranging from 11% to 15% of teachers say the pandemic has had no lasting impact on these aspects of students’ lives, or that the impact has been neither positive nor negative. Only about 5% say that the pandemic has had a positive lasting impact on these things.

A smaller majority of teachers (55%) say the pandemic has had a negative impact on the way parents interact with teachers, with 18% saying its lasting impact has been very negative.

These results are mostly consistent across teachers of different grade levels and school poverty levels.

Major problems at school

When we asked teachers about a range of problems that may affect students who attend their school, the following issues top the list:

• Poverty (53% say this is a major problem at their school)
• Chronic absenteeism – that is, students missing a substantial number of school days (49%)
• Anxiety and depression (48%)

One-in-five say bullying is a major problem among students at their school. Smaller shares of teachers point to drug use (14%), school fights (12%), alcohol use (4%) and gangs (3%).

Differences by school level

Similar shares of teachers across grade levels say poverty is a major problem at their school, but other problems are more common in middle or high schools:

• 61% of high school teachers say chronic absenteeism is a major problem at their school, compared with 43% of elementary school teachers and 46% of middle school teachers.
• 69% of high school teachers and 57% of middle school teachers say anxiety and depression are a major problem, compared with 29% of elementary school teachers.
• 34% of middle school teachers say bullying is a major problem, compared with 13% of elementary school teachers and 21% of high school teachers.

Not surprisingly, drug use, school fights, alcohol use and gangs are more likely to be viewed as major problems by secondary school teachers than by those teaching in elementary schools.

Differences by poverty level

Teachers’ views on problems students face at their school also vary by school poverty level.

Majorities of teachers in high- and medium-poverty schools say chronic absenteeism is a major problem where they teach (66% and 58%, respectively). A much smaller share of teachers in low-poverty schools say this (34%).

Bullying, school fights and gangs are viewed as major problems by larger shares of teachers in high-poverty schools than in medium- and low-poverty schools.

When it comes to anxiety and depression, a slightly larger share of teachers in low-poverty schools (51%) than in high-poverty schools (44%) say these are a major problem among students where they teach.  

Discipline practices

About two-thirds of teachers (66%) say that the current discipline practices at their school are very or somewhat mild – including 27% who say they’re very mild. Only 2% say the discipline practices at their school are very or somewhat harsh, while 31% say they are neither harsh nor mild.

We also asked teachers about the amount of influence different groups have when it comes to determining discipline practices at their school.

• 67% say teachers themselves don’t have enough influence. Very few (2%) say teachers have too much influence, and 29% say their influence is about right.

• 31% of teachers say school administrators don’t have enough influence, 22% say they have too much, and 45% say their influence is about right.
• On balance, teachers are more likely to say parents, their state government and the local school board have too much influence rather than not enough influence in determining discipline practices at their school. Still, substantial shares say these groups have about the right amount of influence.

Teachers from low- and medium-poverty schools (46% each) are more likely than those in high-poverty schools (36%) to say parents have too much influence over discipline practices.

In turn, teachers from high-poverty schools (34%) are more likely than those from low- and medium-poverty schools (17% and 18%, respectively) to say that parents don’t have enough influence.

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