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

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

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

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

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

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

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

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Table of contents

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

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

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

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

Language choices

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

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

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

Narrative voice

Ask yourself:

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

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

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

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

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

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

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

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

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

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

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

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

Essay question example

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

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

Thesis statement example

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

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

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

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

Your initial answer might be a surface-level description:

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

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

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

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

Finding textual evidence

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

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

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

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

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

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

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

The introduction

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

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

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

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

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

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

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

Paragraph structure

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

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

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

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

Topic sentences

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

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

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

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

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

Using textual evidence

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

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

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

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

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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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How to Write an Essay Introduction (with Examples)   

The introduction of an essay plays a critical role in engaging the reader and providing contextual information about the topic. It sets the stage for the rest of the essay, establishes the tone and style, and motivates the reader to continue reading. 

Table of Contents

What is an essay introduction , what to include in an essay introduction, how to create an essay structure , step-by-step process for writing an essay introduction , how to write an introduction paragraph , how to write a hook for your essay , how to include background information , how to write a thesis statement .

• Argumentative Essay Introduction Example: 
• Expository Essay Introduction Example 

Literary Analysis Essay Introduction Example

Check and revise – checklist for essay introduction , key takeaways , frequently asked questions .

An introduction is the opening section of an essay, paper, or other written work. It introduces the topic and provides background information, context, and an overview of what the reader can expect from the rest of the work. 1 The key is to be concise and to the point, providing enough information to engage the reader without delving into excessive detail. 

The essay introduction is crucial as it sets the tone for the entire piece and provides the reader with a roadmap of what to expect. Here are key elements to include in your essay introduction: 

• Hook : Start with an attention-grabbing statement or question to engage the reader. This could be a surprising fact, a relevant quote, or a compelling anecdote. 
• Background information : Provide context and background information to help the reader understand the topic. This can include historical information, definitions of key terms, or an overview of the current state of affairs related to your topic. 
• Thesis statement : Clearly state your main argument or position on the topic. Your thesis should be concise and specific, providing a clear direction for your essay. 

Before we get into how to write an essay introduction, we need to know how it is structured. The structure of an essay is crucial for organizing your thoughts and presenting them clearly and logically. It is divided as follows: 2  

• Introduction:  The introduction should grab the reader’s attention with a hook, provide context, and include a thesis statement that presents the main argument or purpose of the essay.  
• Body:  The body should consist of focused paragraphs that support your thesis statement using evidence and analysis. Each paragraph should concentrate on a single central idea or argument and provide evidence, examples, or analysis to back it up.  
• Conclusion:  The conclusion should summarize the main points and restate the thesis differently. End with a final statement that leaves a lasting impression on the reader. Avoid new information or arguments. 

Here’s a step-by-step guide on how to write an essay introduction: 

• Start with a Hook : Begin your introduction paragraph with an attention-grabbing statement, question, quote, or anecdote related to your topic. The hook should pique the reader’s interest and encourage them to continue reading. 
• Provide Background Information : This helps the reader understand the relevance and importance of the topic. 
• State Your Thesis Statement : The last sentence is the main argument or point of your essay. It should be clear, concise, and directly address the topic of your essay. 
• Preview the Main Points : This gives the reader an idea of what to expect and how you will support your thesis. 
• Keep it Concise and Clear : Avoid going into too much detail or including information not directly relevant to your topic. 
• Revise : Revise your introduction after you’ve written the rest of your essay to ensure it aligns with your final argument. 

Here’s an example of an essay introduction paragraph about the importance of education: 

Education is often viewed as a fundamental human right and a key social and economic development driver. As Nelson Mandela once famously said, “Education is the most powerful weapon which you can use to change the world.” It is the key to unlocking a wide range of opportunities and benefits for individuals, societies, and nations. In today’s constantly evolving world, education has become even more critical. It has expanded beyond traditional classroom learning to include digital and remote learning, making education more accessible and convenient. This essay will delve into the importance of education in empowering individuals to achieve their dreams, improving societies by promoting social justice and equality, and driving economic growth by developing a skilled workforce and promoting innovation. 

This introduction paragraph example includes a hook (the quote by Nelson Mandela), provides some background information on education, and states the thesis statement (the importance of education). 

This is one of the key steps in how to write an essay introduction. Crafting a compelling hook is vital because it sets the tone for your entire essay and determines whether your readers will stay interested. A good hook draws the reader in and sets the stage for the rest of your essay.  

• Avoid Dry Fact : Instead of simply stating a bland fact, try to make it engaging and relevant to your topic. For example, if you’re writing about the benefits of exercise, you could start with a startling statistic like, “Did you know that regular exercise can increase your lifespan by up to seven years?” 
• Avoid Using a Dictionary Definition : While definitions can be informative, they’re not always the most captivating way to start an essay. Instead, try to use a quote, anecdote, or provocative question to pique the reader’s interest. For instance, if you’re writing about freedom, you could begin with a quote from a famous freedom fighter or philosopher. 
• Do Not Just State a Fact That the Reader Already Knows : This ties back to the first point—your hook should surprise or intrigue the reader. For Here’s an introduction paragraph example, if you’re writing about climate change, you could start with a thought-provoking statement like, “Despite overwhelming evidence, many people still refuse to believe in the reality of climate change.” 

Including background information in the introduction section of your essay is important to provide context and establish the relevance of your topic. When writing the background information, you can follow these steps: 

• Start with a General Statement:  Begin with a general statement about the topic and gradually narrow it down to your specific focus. For example, when discussing the impact of social media, you can begin by making a broad statement about social media and its widespread use in today’s society, as follows: “Social media has become an integral part of modern life, with billions of users worldwide.” 
• Define Key Terms : Define any key terms or concepts that may be unfamiliar to your readers but are essential for understanding your argument. 
• Provide Relevant Statistics:  Use statistics or facts to highlight the significance of the issue you’re discussing. For instance, “According to a report by Statista, the number of social media users is expected to reach 4.41 billion by 2025.” 
• Discuss the Evolution:  Mention previous research or studies that have been conducted on the topic, especially those that are relevant to your argument. Mention key milestones or developments that have shaped its current impact. You can also outline some of the major effects of social media. For example, you can briefly describe how social media has evolved, including positives such as increased connectivity and issues like cyberbullying and privacy concerns. 
• Transition to Your Thesis:  Use the background information to lead into your thesis statement, which should clearly state the main argument or purpose of your essay. For example, “Given its pervasive influence, it is crucial to examine the impact of social media on mental health.” 

A thesis statement is a concise summary of the main point or claim of an essay, research paper, or other type of academic writing. It appears near the end of the introduction. Here’s how to write a thesis statement: 

• Identify the topic:  Start by identifying the topic of your essay. For example, if your essay is about the importance of exercise for overall health, your topic is “exercise.” 
• State your position:  Next, state your position or claim about the topic. This is the main argument or point you want to make. For example, if you believe that regular exercise is crucial for maintaining good health, your position could be: “Regular exercise is essential for maintaining good health.” 
• Support your position:  Provide a brief overview of the reasons or evidence that support your position. These will be the main points of your essay. For example, if you’re writing an essay about the importance of exercise, you could mention the physical health benefits, mental health benefits, and the role of exercise in disease prevention. 
• Make it specific:  Ensure your thesis statement clearly states what you will discuss in your essay. For example, instead of saying, “Exercise is good for you,” you could say, “Regular exercise, including cardiovascular and strength training, can improve overall health and reduce the risk of chronic diseases.” 

Examples of essay introduction 

Here are examples of essay introductions for different types of essays: 

Argumentative Essay Introduction Example:  

Topic: Should the voting age be lowered to 16? 

“The question of whether the voting age should be lowered to 16 has sparked nationwide debate. While some argue that 16-year-olds lack the requisite maturity and knowledge to make informed decisions, others argue that doing so would imbue young people with agency and give them a voice in shaping their future.” 

Expository Essay Introduction Example  

Topic: The benefits of regular exercise 

“In today’s fast-paced world, the importance of regular exercise cannot be overstated. From improving physical health to boosting mental well-being, the benefits of exercise are numerous and far-reaching. This essay will examine the various advantages of regular exercise and provide tips on incorporating it into your daily routine.” 

Text: “To Kill a Mockingbird” by Harper Lee 

“Harper Lee’s novel, ‘To Kill a Mockingbird,’ is a timeless classic that explores themes of racism, injustice, and morality in the American South. Through the eyes of young Scout Finch, the reader is taken on a journey that challenges societal norms and forces characters to confront their prejudices. This essay will analyze the novel’s use of symbolism, character development, and narrative structure to uncover its deeper meaning and relevance to contemporary society.” 

• Engaging and Relevant First Sentence : The opening sentence captures the reader’s attention and relates directly to the topic. 
• Background Information : Enough background information is introduced to provide context for the thesis statement. 
• Definition of Important Terms : Key terms or concepts that might be unfamiliar to the audience or are central to the argument are defined. 
• Clear Thesis Statement : The thesis statement presents the main point or argument of the essay. 
• Relevance to Main Body : Everything in the introduction directly relates to and sets up the discussion in the main body of the essay. 

Writing a strong introduction is crucial for setting the tone and context of your essay. Here are the key takeaways for how to write essay introduction: 3  

• Hook the Reader : Start with an engaging hook to grab the reader’s attention. This could be a compelling question, a surprising fact, a relevant quote, or an anecdote. 
• Provide Background : Give a brief overview of the topic, setting the context and stage for the discussion. 
• Thesis Statement : State your thesis, which is the main argument or point of your essay. It should be concise, clear, and specific. 
• Preview the Structure : Outline the main points or arguments to help the reader understand the organization of your essay. 
• Keep it Concise : Avoid including unnecessary details or information not directly related to your thesis. 
• Revise and Edit : Revise your introduction to ensure clarity, coherence, and relevance. Check for grammar and spelling errors. 
• Seek Feedback : Get feedback from peers or instructors to improve your introduction further. 

The purpose of an essay introduction is to give an overview of the topic, context, and main ideas of the essay. It is meant to engage the reader, establish the tone for the rest of the essay, and introduce the thesis statement or central argument.  

An essay introduction typically ranges from 5-10% of the total word count. For example, in a 1,000-word essay, the introduction would be roughly 50-100 words. However, the length can vary depending on the complexity of the topic and the overall length of the essay.

An essay introduction is critical in engaging the reader and providing contextual information about the topic. To ensure its effectiveness, consider incorporating these key elements: a compelling hook, background information, a clear thesis statement, an outline of the essay’s scope, a smooth transition to the body, and optional signposting sentences.  

The process of writing an essay introduction is not necessarily straightforward, but there are several strategies that can be employed to achieve this end. When experiencing difficulty initiating the process, consider the following techniques: begin with an anecdote, a quotation, an image, a question, or a startling fact to pique the reader’s interest. It may also be helpful to consider the five W’s of journalism: who, what, when, where, why, and how.   For instance, an anecdotal opening could be structured as follows: “As I ascended the stage, momentarily blinded by the intense lights, I could sense the weight of a hundred eyes upon me, anticipating my next move. The topic of discussion was climate change, a subject I was passionate about, and it was my first public speaking event. Little did I know , that pivotal moment would not only alter my perspective but also chart my life’s course.” 

Crafting a compelling thesis statement for your introduction paragraph is crucial to grab your reader’s attention. To achieve this, avoid using overused phrases such as “In this paper, I will write about” or “I will focus on” as they lack originality. Instead, strive to engage your reader by substantiating your stance or proposition with a “so what” clause. While writing your thesis statement, aim to be precise, succinct, and clear in conveying your main argument.  

To create an effective essay introduction, ensure it is clear, engaging, relevant, and contains a concise thesis statement. It should transition smoothly into the essay and be long enough to cover necessary points but not become overwhelming. Seek feedback from peers or instructors to assess its effectiveness. 

References  

• Cui, L. (2022). Unit 6 Essay Introduction.  Building Academic Writing Skills . 
• West, H., Malcolm, G., Keywood, S., & Hill, J. (2019). Writing a successful essay.  Journal of Geography in Higher Education ,  43 (4), 609-617. 
• Beavers, M. E., Thoune, D. L., & McBeth, M. (2023). Bibliographic Essay: Reading, Researching, Teaching, and Writing with Hooks: A Queer Literacy Sponsorship. College English, 85(3), 230-242. 

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• Literary Terms

Essay Introduction

I. what is an introduction.

An introduction is the opening of an essay. Its purpose is to inform your audience about the topic of your essay, and to state your opinion or stance (if any) about the stated topic. Your introduction is your essay’s ‘first impression’ on your audience, and as such, it is very important!

II. Examples of Introductions

This section provides three models of successful introductions. We will be using these models to provide examples of the parts of an introduction, which are defined in section III.

We all have had enough of environmental disasters. From oil spills to coal mine explosions, our use of fossil fuels has cost us and our natural world too much. Fortunately, many companies are turning to other energy sources. I support this trend whole-heartedly because I know that using solar, wind, or tidal power instead of fossil fuels means we will have a cleaner environment. However, I am concerned that people are putting too much hope in one of these sources: solar energy. The fact is, solar energy is too slow and too unpredictable to do what many people think it can do. After examining its drawbacks, I am sure you will agree that solar power is not the answer to our energy needs.

There I was, an ant among elephants, knowing I was about to be stepped on. It was August, 2015, and I was at my first day of high school football tryouts. I was a skinny freshman about to take my first run through a line of enormous varsity players. I knew I was small, but I was also fast and I like to win. The next two weeks were the hardest of my life, but when they were over every player on the team knew my name.

Choosing the right source of clean energy is essential for every large business in the 21 st century. Many companies are investing in other energy sources in order to minimize their impact on the environment. Investing in new sources of energy can cost millions of dollars; it is therefore essential that business owners choose the right kind of energy for their companies. Currently, the best choices are solar, wind, and tidal energy. In order to choose the best energy source, a company must compare the benefits and costs for each of these energy sources. Knowing the right source of energy means more money saved and less impact on the environment.

III. Parts of Introduction

Sometimes known as a ‘hook’ or a ‘lead’, the purpose of an opening is to get your reader’s interest and have them connect to the content of the essay. A strong opening may be surprising, vivid, or thought-provoking. It’s really important because it helps the audience decide whether they want to keep reading. In most cases, the more interesting or relatable the opening is, the more likely the rest of your essay will be read, so make it good!

Example 1 (model 1)

“We all have had enough of environmental disasters.”

This is a successful opening because it makes a statement that is easy for readers to connect to.

Example 2 (model 2)

“There I was, an ant among elephants, knowing I was about to be stepped on.”

This opening is effective because it creates a vivid image through use of a metaphor. By comparing himself to an ant, the narrator helps the audience imagine his experience, which also helps the audience connect to the essay.

b. Statement of topic

An essential job of the introduction is to identify the topic for the reader. The topic may be a single sentence or a clause in a larger sentence.

“Fortunately, many companies are turning to other energy sources.”

The topic here is clearly stated for the reader. The reader can expect to read more about companies switching to other energy sources.

“I was at my first day of high school football tryouts.”

This example lets the reader know that the topic of the narrative is the writer’s experience at football tryouts.

c. Thesis (opinion or stance)

The thesis is a statement that is supported or proven in the body of the essay. An introduction must include a thesis. It is often placed at the beginning or end of the introduction.

“I am sure you will agree that solar power is not the answer to our energy needs.”

The thesis statement here makes it clear that the writer is taking a stance against solar power. It is placed at the end of the introduction after the writer has given the audience “context” for the essay (explained below).

Example 2 (model 3)

“Choosing the right source of clean energy is essential for every large business in the 21 st century.”

This thesis lets the reader know that the author believes that businesses need to choose their sources of energy carefully. Placed at the beginning of the introduction, this thesis informs readers what the opinion is right from the start.

d. Context or purpose

An introduction needs to help the reader understand why the topic is important.  The introduction must give enough information for the audience to make a connection and create interest.

“From oil spills to coal mine explosions, our use of fossil fuels has cost us and our natural world too much. [. . . ] I know that using solar, wind, or tidal power instead of fossil fuels means we will have a cleaner environment.”

This introduction puts the topic of energy sources in the context of safety and environmental protection. Safety and environmental protection are interesting to most people, and something that connects to nearly everyone’s lives.

“In order to choose the best energy source, a company must compare the benefits and costs for each of these energy sources. Knowing the right source of energy means more money saved and less impact on the environment.”

The context in this introduction lets business owners know that the topic involves profit (money earned) and minimizing the effects or harm to the environment – two reasons for the audience to be interested in the essay.

e. Identification of Main Points

A detailed introduction will include information that helps the reader anticipate or predict the main ideas in the essay. This is often accomplished by listing subtopics, reasons, or evidence that will be explained in the body paragraphs.

Example (model 1)

“The fact is, solar energy is too slow and too unpredictable to do what many people think it can do.”

Based on this information in the introduction, the reader can expect the essay’s main points to discuss why solar energy is too slow and unpredictable.

Example (model 3)

“Currently, the best choices are solar, wind, and tidal energy.”

This example is a simple list that introduces three kinds of energy sources. Readers can expect to find details about these three main ideas in the body of the essay.

IV. How to Write an Introduction

Know your topic.

You must do adequate research before writing your introduction. Organize your thoughts until you have a detailed picture of what you want to write about. You need to know enough about your topic for you to define it clearly for your audience.

Set the tone

The tone of a piece sets how formal or informal it will be.

• If you are introducing formal writing (such as for academics, business, or law), the tone should be polite and unemotional. Information is the focus, not emotion. Careful attention to grammar and writing conventions is essential.
• On the other hand, If you are writing an introduction for an informal piece (such as for friends, a personal blog, or a journal entry), the tone will have more emotion. You may use fewer ‘fancy’ words, and choose slang or figures of speech instead.

The tone of an introduction also shows the kind of relationship between the writer and the reader. If the writer and the reader know each other personally, an informal tone works well. However, if the writer is not already on close terms with the reader, then an informal tone is best.

For example, model 3 has a formal tone. The introduction is focused on determining facts. In contrast, Model 1 has informal tone. The introduction focuses on the emotions of the author and the audience.

State your purpose and provide context

A strong introduction provides context and direction for the reader. It must include why you are writing about the topic, and what you are going to focus on. Provide information that tells the reader why the essay is important or interesting enough to read.

Take a clear point of view

An introduction must express the relationship between you (the writer) and the topic. You must state what you think, or how you feel about the topic. A clear introduction does this in a single sentence: the thesis. (See section III, part 3). It’s a good idea to put your thesis statement at either the beginning or the end of the introduction; readers tend to focus on these parts of a paragraph.

Lead the reader

Let the reader know what to expect in the body of your essay. State your main ideas in the introduction so that the reader can look for them in your following paragraphs. You may also encourage them to agree with your point of view.

List of Terms

• Alliteration
• Amplification
• Anachronism
• Anthropomorphism
• Antonomasia
• APA Citation
• Aposiopesis
• Autobiography
• Bildungsroman
• Characterization
• Circumlocution
• Cliffhanger
• Comic Relief
• Connotation
• Deus ex machina
• Deuteragonist
• Doppelganger
• Double Entendre
• Dramatic irony
• Equivocation
• Extended Metaphor
• Figures of Speech
• Flash-forward
• Foreshadowing
• Intertextuality
• Juxtaposition
• Literary Device
• Malapropism
• Onomatopoeia
• Parallelism
• Pathetic Fallacy
• Personification
• Point of View
• Polysyndeton
• Protagonist
• Red Herring
• Rhetorical Device
• Rhetorical Question
• Science Fiction
• Self-Fulfilling Prophecy
• Synesthesia
• Turning Point
• Understatement
• Urban Legend
• Verisimilitude
• Essay Guide
• Cite This Website

• How to Cite
• Language & Lit
• Rhyme & Rhythm
• The Rewrite
• Search Glass

How to Make a Strong Introduction for a Literary Analysis Essay

The introduction is the first thing your reader will encounter in your literary analysis essay, so it's essential that you write clearly and concisely. Literary analysis requires the writer to carefully follow a theme, motif, character development or stylistic element and examine its importance within the context of the book. Because literary analysis depends on the writer's interpretation of the text, it's often necessary to convince the reader of your point of view. Writing a strong introduction to your essay will help launch your reader into your main points.

Begin writing the introduction after you have completed your literary analysis essay. This may seem counter-intuitive, but once you have finished enumerating and explaining your main points, you'll be better able to identify what they share in common, which you can introduce in the first paragraph of your essay. You can also begin writing the introduction after completing your in-depth outline of the essay, where you lay out your main points and organize your paper before you begin writing.

Start your introduction with a grabber. In a literary analysis essay, an effective grabber can be a short quote from the text you're analyzing that encapsulates some aspect of your interpretation. Other good grabbers are quotes from the book's author regarding your paper's topic or another aspect relevant to the text and how you interpreted it. Place the quote in quotation marks as the first sentence of the introductory paragraph. Your next sentence should identify the speaker and context of the quotation, as well as briefly describing how the quote relates to your literary analysis.

Keep the body of your introduction relatively short. A paragraph in a literary analysis essay should be between eight and 12 sentences long. In the introduction, write three to four sentences generally describing the topic of your paper and explaining why it is interesting and important to the book you read. These three or four sentences will make up the bulk of your introductory paragraph. Use these sentences to sketch the main points that you describe in greater detail in the body of your essay.

Finish your introductory paragraph with your thesis statement. The thesis statement clearly states the main point of your paper as a whole. It can be one sentence long or span two sentences, but it should always be the very last part of the introductory paragraph. For a five-paragraph essay with three body paragraphs, write one sentence identifying your paper's main point. In the second sentence, called the blueprint, identify the three main topics of each body paragraph and how they support your thesis. For more advanced literary analysis essays, it's not always necessary to enumerate explicitly the main point of each body paragraph as part of your thesis statement. Focus instead on clearly and concisely stating the driving force behind your paper's organization and development.

• It can be useful to finish writing your paper, including your concluding paragraph, before you tackle the introduction. The conclusion and the introduction should contain the same content, stated differently. In the conclusion, you can sum up the main points of your essay and explain how and why they are important to the book and to your interpretation of the text. Your introduction can then be a reworked paraphrasing of your conclusion, and you can rest assured that you haven't left anything out.

Things You'll Need

• City Colleges of Chicago: A Proper Introduction
• BookRags: How to Write a Five Paragraph Essay

Goody Clairenstein has been a writer since 2004. She has sat on the editorial board of several non-academic journals and writes about creative writing, editing and languages. She has worked in professional publishing and news reporting in print and broadcast journalism. Her poems have appeared in "Small Craft Warnings." Clairenstein earned her Bachelor of Arts in European languages from Skidmore College.

Literary Analysis Essay

Literary Analysis Essay Writing

Last updated on: May 21, 2023

Literary Analysis Essay - Ultimate Guide By Professionals

By: Cordon J.

Reviewed By: Rylee W.

Published on: Dec 3, 2019

A literary analysis essay specifically examines and evaluates a piece of literature or a literary work. It also understands and explains the links between the small parts to their whole information.

It is important for students to understand the meaning and the true essence of literature to write a literary essay.

One of the most difficult assignments for students is writing a literary analysis essay. It can be hard to come up with an original idea or find enough material to write about. You might think you need years of experience in order to create a good paper, but that's not true.

This blog post will show you how easy it can be when you follow the steps given here.Writing such an essay involves the breakdown of a book into small parts and understanding each part separately. It seems easy, right?

Trust us, it is not as hard as good book reports but it may also not be extremely easy. You will have to take into account different approaches and explain them in relation with the chosen literary work.

It is a common high school and college assignment and you can learn everything in this blog.

Continue reading for some useful tips with an example to write a literary analysis essay that will be on point. You can also explore our detailed article on writing an analytical essay .

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What is a Literary Analysis Essay?

A literary analysis essay is an important kind of essay that focuses on the detailed analysis of the work of literature.

The purpose of a literary analysis essay is to explain why the author has used a specific theme for his work. Or examine the characters, themes, literary devices , figurative language, and settings in the story.

This type of essay encourages students to think about how the book or the short story has been written. And why the author has created this work.

The method used in the literary analysis essay differs from other types of essays. It primarily focuses on the type of work and literature that is being analyzed.

Mostly, you will be going to break down the work into various parts. In order to develop a better understanding of the idea being discussed, each part will be discussed separately.

The essay should explain the choices of the author and point of view along with your answers and personal analysis.

How To Write A Literary Analysis Essay

So how to start a literary analysis essay? The answer to this question is quite simple.

The following sections are required to write an effective literary analysis essay. By following the guidelines given in the following sections, you will be able to craft a winning literary analysis essay.

Introduction

The aim of the introduction is to establish a context for readers. You have to give a brief on the background of the selected topic.

It should contain the name of the author of the literary work along with its title. The introduction should be effective enough to grab the reader’s attention.

In the body section, you have to retell the story that the writer has narrated. It is a good idea to create a summary as it is one of the important tips of literary analysis.

Other than that, you are required to develop ideas and disclose the observed information related to the issue. The ideal length of the body section is around 1000 words.

To write the body section, your observation should be based on evidence and your own style of writing.

It would be great if the body of your essay is divided into three paragraphs. Make a strong argument with facts related to the thesis statement in all of the paragraphs in the body section.

Start writing each paragraph with a topic sentence and use transition words when moving to the next paragraph.

Summarize the important points of your literary analysis essay in this section. It is important to compose a short and strong conclusion to help you make a final impression of your essay.

Pay attention that this section does not contain any new information. It should provide a sense of completion by restating the main idea with a short description of your arguments. End the conclusion with your supporting details.

You have to explain why the book is important. Also, elaborate on the means that the authors used to convey her/his opinion regarding the issue.

For further understanding, here is a downloadable literary analysis essay outline. This outline will help you structure and format your essay properly and earn an A easily.

DOWNLOADABLE LITERARY ANALYSIS ESSAY OUTLINE (PDF)

Types of Literary Analysis Essay

• Close reading - This method involves attentive reading and detailed analysis. No need for a lot of knowledge and inspiration to write an essay that shows your creative skills.
• Theoretical - In this type, you will rely on theories related to the selected topic.
• Historical - This type of essay concerns the discipline of history. Sometimes historical analysis is required to explain events in detail.
• Applied - This type involves analysis of a specific issue from a practical perspective.
• Comparative - This type of writing is based on when two or more alternatives are compared

Examples of Literary Analysis Essay

Examples are great to understand any concept, especially if it is related to writing. Below are some great literary analysis essay examples that showcase how this type of essay is written.

A ROSE FOR EMILY LITERARY ANALYSIS ESSAY

TO KILL A MOCKINGBIRD LITERARY ANALYSIS ESSAY

THE GREAT GATSBY LITERARY ANALYSIS ESSAY

THE YELLOW WALLPAPER LITERARY ANALYSIS ESSAY

If you do not have experience in writing essays, this will be a very chaotic process for you. In that case, it is very important for you to conduct good research on the topic before writing.

There are two important points that you should keep in mind when writing a literary analysis essay.

First, remember that it is very important to select a topic in which you are interested. Choose something that really inspires you. This will help you to catch the attention of a reader.

The selected topic should reflect the main idea of writing. In addition to that, it should also express your point of view as well.

Another important thing is to draft a good outline for your literary analysis essay. It will help you to define a central point and division of this into parts for further discussion.

Literary Analysis Essay Topics

Literary analysis essays are mostly based on artistic works like books, movies, paintings, and other forms of art. However, generally, students choose novels and books to write their literary essays.

Some cool, fresh, and good topics and ideas are listed below:

• Role of the Three Witches in flaming Macbeth’s ambition.
• Analyze the themes of the Play Antigone,
• Discuss Ajax as a tragic hero.
• The Judgement of Paris: Analyze the Reasons and their Consequences.
• Oedipus Rex: A Doomed Son or a Conqueror?
• Describe the Oedipus complex and Electra complex in relation to their respective myths.
• Betrayal is a common theme of Shakespearean tragedies. Discuss
• Identify and analyze the traits of history in T.S Eliot’s ‘Gerontion’.
• Analyze the theme of identity crisis in The Great Gatsby.
• Analyze the writing style of Emily Dickinson.

If you are still in doubt then there is nothing bad in getting professional writers’ help.

We at 5StarEssays.com can help you get a custom paper as per your specified requirements with our do essay for me service.

Our essay writers will help you write outstanding literary essays or any other type of essay. Such as compare and contrast essays, descriptive essays, rhetorical essays. We cover all of these.

So don’t waste your time browsing the internet and place your order now to get your well-written custom paper.

Frequently Asked Questions

What should a literary analysis essay include.

A good literary analysis essay must include a proper and in-depth explanation of your ideas. They must be backed with examples and evidence from the text. Textual evidence includes summaries, paraphrased text, original work details, and direct quotes.

What are the 4 components of literary analysis?

Here are the 4 essential parts of a literary analysis essay;

No literary work is explained properly without discussing and explaining these 4 things.

How do you start a literary analysis essay?

Start your literary analysis essay with the name of the work and the title. Hook your readers by introducing the main ideas that you will discuss in your essay and engage them from the start.

How do you do a literary analysis?

In a literary analysis essay, you study the text closely, understand and interpret its meanings. And try to find out the reasons behind why the author has used certain symbols, themes, and objects in the work.

Why is literary analysis important?

It encourages the students to think beyond their existing knowledge, experiences, and belief and build empathy. This helps in improving the writing skills also.

What is the fundamental characteristic of a literary analysis essay?

Interpretation is the fundamental and important feature of a literary analysis essay. The essay is based on how well the writer explains and interprets the work.

Law, Finance Essay

Cordon. is a published author and writing specialist. He has worked in the publishing industry for many years, providing writing services and digital content. His own writing career began with a focus on literature and linguistics, which he continues to pursue. Cordon is an engaging and professional individual, always looking to help others achieve their goals.

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Literary Analysis Essay

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Literature is an art that can inspire, challenge, and transform us. But how do we analyze literature in a way that truly captures its essence? 

That's where a literary analysis essay comes in. 

Writing a literary analysis essay allows you to delve into the themes, characters, and symbols of a literary work. It's a chance to engage with literature on a deeper level and to discover new insights. 

In this comprehensive guide, we will take you through the process of writing a literary analysis essay, step by step. Plus, you’ll get to read some great examples to help you out!

So let’s dive in!

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What is a Literary Analysis Essay?

Literary analysis is a process of examining a literary work in detail to uncover its meaning and significance. 

It involves breaking down the various elements of a work, such as plot, character, setting, and theme. And then analyzing how they work together to create a specific effect on the reader.

In other words, literary analysis is an exercise in interpretation. The reader of a work asks questions about what the author means to say, how they are saying it, and why. 

A literary analysis essay is an essay where you explore such questions in depth and offer your own insights.

What is the Purpose of a Literary Analysis Essay?

In general, the purpose of a literary analysis essay is as follows: 

• To gain a greater understanding and appreciation of the work.
• To be able to think critically and analytically about a text. 

Content of a Literary Analysis 

A literary analysis essay delves deep into the various aspects of a literary work to examine its meaning, symbolism, themes, and more. Here are the key elements to include in your literary analysis essay:

Plot Analysis 

Plot refers to the sequence of events that make up the storyline of a literary work. It encompasses the main events, conflicts, and resolutions that drive the narrative forward. 

Elements of Plot Analysis 

The elements of a plot typically include:

• Exposition: The introduction of the story that establishes the setting, characters, and initial circumstances.
• Rising action: A set of events or actions that sets the main conflict into motion, often occurring early in the story.
• Conflict: The series of events that build tension and develop the conflict, leading to the story's climax.
• Climax: The turning point of the story, where the conflict reaches its peak and the outcome hangs in the balance.
• Falling Action: The events that occur after the climax, leading towards the resolution of the conflict.
• Resolution: The point in the story where the conflict is resolved, providing closure to the narrative.

Character Analysis 

Character analysis involves studying the role, development, and motivations of the characters in a literary work. It explores how characters contribute to the overall narrative and themes of the story.

Elements of Character Analysis 

• Identification of major and minor characters.
• Examination of their traits, behaviors, and relationships.
• Analysis of character development and changes throughout the story.
• Evaluation of the character's role in advancing the plot or conveying themes.

Symbolism and Imagery Analysis 

Symbolism and imagery analysis focuses on the use of symbols, objects, or images in a work. It analyzes and explores the use of literary devices to convey deeper meanings and evoke emotions. 

Elements of Symbolism and Imagery Analysis 

• Identification of key symbols or recurring motifs.
• Interpretation of their symbolic significance.
• Analysis of how imagery is used to create vivid mental pictures and enhance the reader's understanding and emotional experience.

Theme Analysis 

Analyzing the theme involves exploring the central ideas or messages conveyed in a literary work. It examines the underlying concepts, or messages that the author wants to convey through the story.

Elements of Theme Analysis 

• Identification of the main themes or central ideas explored in the text.
• Analysis of how the themes are developed and reinforced throughout the story.
• Exploration of the author's perspective and the intended message behind the themes.

Setting Analysis 

The Setting of a story includes the time, place, and social context in which the story takes place. Analyzing the setting involves how the setting influences the characters, plot, and overall atmosphere of the work.

Elements of Setting Analysis 

• Description and analysis of the physical, cultural, and historical aspects of the setting.
• Examination of how the setting contributes to the mood, atmosphere, and themes of the work.
• Evaluation of how the setting shapes the characters' actions and motivations.

Structure and Style Analysis 

Structure and style analysis involves studying the organization, narrative techniques, and literary devices employed by the author. It explores how the structure and style contribute to the overall impact and effectiveness of the work.

Elements of Structure and Style Analysis 

• Analysis of the narrative structure, such as the use of flashbacks, nonlinear timelines, or multiple perspectives.
• Examination of the author's writing style, including the use of language, tone, and figurative language.
• Evaluation of literary devices, such as foreshadowing, irony, or allusion, and their impact on the reader's interpretation.

Paper due? Why Suffer? That's our job.

How to Write a Literary Analysis Essay?  

Writing a great literary analysis piece requires you to follow certain steps. Here's what you need to do to write a literary essay:

Preparing for Your Essay 

The pre-writing process for writing a literary analysis essay includes the following:

• Choosing a literary work to analyze
• Reading and analyzing the work
• Taking notes and organizing your thoughts
• Creating an outline for your essay

Choosing a Work to Analyze 

As a student, you would most probably be assigned a literary piece to analyze. It could be a short story, a novel, or a poem.  However, sometimes you get to choose it yourself.

In such a case, you should choose a work that you find interesting and engaging. This will make it easier to stay motivated as you analyze the work and write your essay.

Moreover, you should choose a work that has some depth and complexity. This will give you plenty of material to analyze and discuss in your essay. Finally, make sure that your choice fits within the scope of the assignment and meets the expectations of your instructor.

Reading and Analyzing 

Once you've chosen a literary work, it's time to read the work with careful attention. There are several key elements to consider when reading and analyzing a literary work:

• Plot - The sequence of events that make up the story. Analyzing the plot involves examining the structure of the story, including its exposition, rising action, climax, falling action, and resolution.
• Characters - The people or entities that populate the story. Analyzing characters involves examining their motivations, personalities, relationships, and development over the course of the story.

Want to learn more about character analysis? Head to our blog about how to conduct character analysis and learn easy steps with examples.

• Setting - The time, place, and environment in which the story takes place. Analyzing the setting involves examining how the atmosphere contributes to the story's overall meaning.
• Theme - The underlying message or meaning of the story. Analyzing themes involves examining the work's central ideas and how they are expressed through the various elements of the story.

Moreover, it's important to consider the following questions while analyzing:

• What is the central theme or main point the author is trying to make?
• What literary devices and techniques has the author used?
• Why did the author choose to write this particular work?
• What themes and ideas are present in the work?

These questions will help you dive deeper into the work you are writing about.

Take Notes and Gather Material 

As you read and analyze the literary work, it's important to take notes so you don't forget important details and ideas. This also helps you identify patterns and connections between different elements of the piece.

One effective way to take notes is to list important elements of the work, such as characters, setting, and theme. You can also use sticky notes, highlighters, or annotations to mark important passages and write down your ideas.

Writing Your Literary Analysis Essay 

Once you have read a piece of literature and taken notes, you have all the material you need to write an essay. Follow the simple steps below to write an effective literary analysis essay.

Create an Outline for Your Essay 

Firstly, creating an outline is necessary. This will help you to organize your thoughts and ideas and ensure that your essay flows logically and coherently.

This is what your literary essay outline would look like: 

Writing the Introduction 

Writing your essay introduction involves the three following parts:

• Begin the introductory paragraph with an engaging hook statement that captures the readers' attention. An effective hook statement can take many different forms, such as a provocative quote, an intriguing question, or a surprising fact. 

Make sure that your hook statement is relevant to the literary work you are writing about. Here are a few examples of effective hooks:

• Afterward, present the necessary background information and context about the literary work. For instance, 
• Talk about the author of the work or when and where it was written. 
• Give an overview of the work or why it is significant. 
• Provide readers with sufficient context so they can know what the work is generally about.
• Finally, end the introduction with a clear thesis statement . Your thesis statement should be a concise statement that clearly states the argument you will be making in your essay. It should be specific and debatable, and it should provide a roadmap for the rest of your essay.

For example, a thesis statement for an essay on "Hamlet" might be: 

Watch this video to learn more about writing an introduction for a literary analysis essay:

Writing the Body 

Here are the steps to follow when writing a body paragraph for a literary analysis essay:

• Start with a topic sentence: 

The topic sentence should introduce the main point or argument you will be making in the paragraph. It should be clear and concise and should indicate what the paragraph is about.

• Provide evidence: 

After you have introduced your main point, provide evidence from the text to support your analysis. This could include quotes, paraphrases, or summaries of the text.

• Explain and discuss the evidence:

Explain how the evidence supports your main point or argument or how it connects back to your thesis statement.

• Conclude the paragraph: 

End the paragraph by relating your main point to the thesis and discussing its significance. You should also use transitions to connect the paragraph to your next point or argument.

Writing the Conclusion 

The conclusion of a literary analysis essay provides closure to your analysis and reinforces your thesis statement. Here's what a conclusion includes:

• Restate your thesis statement: 

Start by restating your thesis statement in a slightly different way than in your introduction. This will remind the reader of the argument you made and the evidence you provided to support it.

• Summarize your main points: 

Briefly summarize the main points you made in your essay's body paragraphs. This will help tie everything together and provide closure to your analysis.

• Personal reflections:

The conclusion is the best place to provide some personal reflections on the literary piece. You can also explain connections between your analysis and the larger context. This could include connections to other literary works, your personal life, historical events, or contemporary issues.

• End with a strong statement: 

End your conclusion with a strong statement that leaves a lasting impression on the reader. This could be a thought-provoking question, a call to action, or a final insight into the significance of your analysis.

Finalizing your Essay

You've completed the first draft of your literary analysis essay. Congratulations!

However, it's not over just yet. You need some time to polish and improve the essay before it can be submitted. Here's what you need to do:

Proofread and Revise your Essay 

After completing your draft, you should proofread your essay. You should look out for the following aspects:

• Check for clarity: 

Make sure that your ideas are expressed clearly and logically. You should also take a look at your structure and organization. Rearrange your arguments if necessary to make them clearer.

• Check for grammar and spelling errors: 

Use spelling and grammar check tools online to identify and correct any basic errors in your essay. 

• Verify factual information:

You must have included information about the work or from within the work in your essay. Recheck and verify that it is correct and verifiable. 

• Check your formatting: 

Make sure that your essay is properly formatted according to the guidelines provided by your instructor. This includes requirements for font size, margins, spacing, and citation style.

Helpful Tips for Revising a Literary Essay 

Here are some tips below that can help you proofread and revise your essay better:

• Read your essay out loud:

Reading your essay out loud makes it easier to identify awkward phrasing, repetitive language, and other issues.

• Take a break: 

It can be helpful to step away from your essay for a little while before starting the editing process. This can help you approach your essay with fresh eyes and a clearer perspective.

• Be concise:

Remove any unnecessary words or phrases that do not add to your argument. This can help to make your essay more focused and effective.

• Let someone else proofread and get feedback: 

You could ask a friend or a teacher to read your essay and provide feedback. This way, you can get some valuable insights on what you could include or catch mistakes that you might have missed.

Literary Analysis Essay Examples 

Reading a few good examples helps to understand literary analysis essays better. So check out these examples below and read them to see what a well-written essay looks like. 

How to Write a Literary Analysis Essay

Literary Analysis Essay Example

Sample Literary Analysis Essay

Lord of the Rings Literary Analysis

The Great Gatsby Literary Analysis

Literary Analysis Example for 8th Grade

Literary Analysis Essay Topics 

Need a topic for your literary analysis essay? You can pick any aspect of any work of literature you like. Here are some example topics that will help you get inspired:

• The use of symbolism in "The Great Gatsby" by F. Scott Fitzgerald.
• The theme of isolation in "The Catcher in the Rye" by J.D. Salinger.
• The portrayal of social class in "Pride and Prejudice" by Jane Austen.
• The use of magical realism in "One Hundred Years of Solitude" by Gabriel Garcia Marquez.
• The role of women in "The Handmaid's Tale" by Margaret Atwood.
• The use of foreshadowing in "Lord of the Flies" by William Golding.
• The portrayal of race and identity in "Invisible Man" by Ralph Ellison.
• The use of imagery in "The Road" by Cormac McCarthy.
• The theme of forgiveness in "The Kite Runner" by Khaled Hosseini.
• The use of allegory in "Animal Farm" by George Orwell.

To conclude,

Writing a literary analysis essay can be a rewarding experience for any student or writer, But it's not easy. However, by following the steps you learned in this guide, you can successfully produce a well-written literary analysis essay. 

Also, you have got some examples of essays to read and topic ideas to get creative inspiration. With these resources, you have all you need to craft an engaging piece. So don't hesitate to start writing your essay and come back to this blog whenever you need.

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Frequently Asked Questions

What are the 4 components of literary analysis.

The four main components of literary analysis are: 

• Conflict 
• Characters 
• Setting 

What is the fundamental characteristic of a literary analysis essay?

Interpretive is the fundamental characteristic of a literary analysis essay. 

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Essay Introduction Examples

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Always have a road map for an essay introduction . Having a strong essay introduction structure is critical to a successful paper. It sets the tone for the reader and interests them in your work. It also tells them what the essay is about and why they should read it at all.

It shouldn't leave the reader confused with a cliffhanger at the end. Instead, it should generate interest and guide the reader to Chapter One. Using the right parts of an essay introduction can help with this.

Check out an effective essay introduction structure below. It’s a road map for writing an essay—just like the parts of essay introductions are road maps for readers.

Essay Introduction Structure

Attention-grabbing start

Outline of argument

Thesis statement

Some academics find the beginning the most difficult part of writing an essay , so our editors have created some examples of good essay introductions to guide you. Let's take a look at the samples below to see how the essay introduction structures come together. 

If you are unsure about your paper, our essay editors would love to give you some feedback on how to write an essay introduction. 

[1] According to Paul Ratsmith, the tenuous but nonetheless important relationship between pumpkins and rats is little understood: "While I've always been fascinated by this natural kinship, the connection between pumpkins and rats has been the subject of few, if any, other studies" (2008). [2] Ratsmith has been studying this connection, something he coined "pumpkinology," since the early 1990s. He is most well known for documenting the three years he spent living in the wild among pumpkins and rats. [3] Though it is a topic of little recent interest, the relationship has been noted in several ancient texts and seems to have been well understood by the Romans. Critics of Ratsmith have cited poor science and questionable methodology when dismissing his results, going so far as to call pumpkinology "rubbish" (de Vil, 2009), "stupid" (Claw, 2010), and "quite possibly made up" (Igthorn, 2009). [4] Despite these criticisms, there does appear to be a strong correlation between pumpkin patches and rat populations, with Ratsmith documenting numerous pumpkin–rat colonies across North America, leading to the conclusion that pumpkins and rats are indeed "nature's best friends" (2008).

Let's break down this example of a good essay introduction structure. The beginning hooks our attention from the get-go in section one. This is because it piques our curiosity. What is this strange relationship? Why has no one studied it? Then, section two gives us context for the topic. Ratsmith is an expert in a controversial field: pumpkinology. It's the study of the connection between pumpkins and rats. 

The second half of the paragraph also demonstrates why this is a good essay introduction example. Section three gives us the main argument: the topic is rarely studied because critics think Ratsmith's work is "rubbish," but the relationship between pumpkins and rats has ancient roots. Then section four gives us the thesis statement: Ratsmith's work has some merit.

The parts of an essay introduction help us chart a course through the topic. We know the paper will take us on a journey. It's all because the author practiced how to write an essay introduction. 

Let’s take a look at another example of a good essay introduction.

[1] Societies have long believed that if a black cat crosses one's path, one might have bad luck—but it wasn't until King Charles I's black cat died that the ruler's bad luck began (Pemberton, 2018). [2] Indeed, for centuries, black cats have been seen as the familiars of witches—as demonic associates of Satan who disrespect authority (Yuko, 2021). Yet, they have also been associated with good luck, from England's rulers to long-distance sailors (Cole, 2021). [3] This essay shows how outdated the bad luck superstition really is. It provides a comprehensive history of the belief and then provides proof that this superstition has no place in today's modern society. [4] It argues that despite the prevailing belief that animals cause bad luck, black cats often bring what seems to be "good luck" and deserve a new reputation.

This example of a good essay introduction pulls us in right away. This is because section one provides an interesting fact about King Charles I. What is the story there, and what bad luck did he experience after his cat passed away? Then, section two provides us with general information about the current status of black cats. We understand the context of the essay and why the topic is controversial.

Section three then gives us a road map that leads us through the main arguments. Finally, section four gives us the essay's thesis: "black cats often bring what seems to be 'good luck' and deserve a new reputation."

Still feeling unsure about how to write an essay introduction? Here's another example using the essay introduction structure we discussed earlier.

[1] When the Lutz family moved into a new house in Amityville, New York, they found themselves terrorized by a vengeful ghost (Labianca, 2021). Since then, their famous tale has been debunked by scientists and the family themselves (Smith, 2005). [2] Yet ghost stories have gripped human consciousness for centuries (History, 2009). Scientists, researchers, and theorists alike have argued whether ghosts are simply figments of the imagination or real things that go bump in the night. In considering this question, many scientists have stated that ghosts may actually exist. [3] Lindley (2017) believes the answer may be in the quantum world, which "just doesn’t work the way the world around us works," but "we don’t really have the concepts to deal with it." Scientific studies on the existence of ghosts date back hundreds of years (History, 2009), and technology has undergone a vast evolution since then (Lamey, 2018). State-of-the-art tools and concepts can now reveal more about ghosts than we've ever known (Kane, 2015). [4] This essay uses these tools to provide definitive proof of the existence of ghosts in the quantum realm. 

This example of a good essay introduction uses a slightly different strategy than the others. To hook the reader, it begins with an interesting anecdote related to the topic. That pulls us in, making us wonder what really happened to the Lutzs. Then, section two provides us with some background information about the topic to help us understand. Many people believe ghosts aren't real, but some scientists think they are.

This immediately flows into section three, which charts a course through the main arguments the essay will make. Finally, it ends with the essay's thesis: there is definitive proof of the existence of ghosts in the quantum realm. It all works because the author used the parts of an essay introduction well.

For attention-grabbing introductions, an understanding of essay introduction structure and how to write an essay introduction is required.

Our essay introduction examples showing the parts of an essay introduction will help you craft the beginning paragraph you need to start your writing journey on the right foot.

If you'd like more personalized attention to your essay, consider sending it for Essay Editing by Scribendi. We can help you ensure that your essay starts off strong.

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Linking essay-writing tests using many-facet models and neural automated essay scoring

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• Masaki Uto   ORCID: orcid.org/0000-0002-9330-5158 1 &
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For essay-writing tests, challenges arise when scores assigned to essays are influenced by the characteristics of raters, such as rater severity and consistency. Item response theory (IRT) models incorporating rater parameters have been developed to tackle this issue, exemplified by the many-facet Rasch models. These IRT models enable the estimation of examinees’ abilities while accounting for the impact of rater characteristics, thereby enhancing the accuracy of ability measurement. However, difficulties can arise when different groups of examinees are evaluated by different sets of raters. In such cases, test linking is essential for unifying the scale of model parameters estimated for individual examinee–rater groups. Traditional test-linking methods typically require administrators to design groups in which either examinees or raters are partially shared. However, this is often impractical in real-world testing scenarios. To address this, we introduce a novel method for linking the parameters of IRT models with rater parameters that uses neural automated essay scoring technology. Our experimental results indicate that our method successfully accomplishes test linking with accuracy comparable to that of linear linking using few common examinees.

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Introduction

The growing demand for assessing higher-order skills, such as logical reasoning and expressive capabilities, has led to increased interest in essay-writing assessments (Abosalem, 2016 ; Bernardin et al., 2016 ; Liu et al., 2014 ; Rosen & Tager, 2014 ; Schendel & Tolmie, 2017 ). In these assessments, human raters assess the written responses of examinees to specific writing tasks. However, a major limitation of these assessments is the strong influence that rater characteristics, including severity and consistency, have on the accuracy of ability measurement (Bernardin et al., 2016 ; Eckes, 2005 , 2023 ; Kassim, 2011 ; Myford & Wolfe, 2003 ). Several item response theory (IRT) models that incorporate parameters representing rater characteristics have been proposed to mitigate this issue (Eckes, 2023 ; Myford & Wolfe, 2003 ; Uto & Ueno, 2018 ).

The most prominent among them are many-facet Rasch models (MFRMs) (Linacre, 1989 ), and various extensions of MFRMs have been proposed to date (Patz & Junker, 1999 ; Patz et al., 2002 ; Uto & Ueno, 2018 , 2020 ). These IRT models have the advantage of being able to estimate examinee ability while accounting for rater effects, making them more accurate than simple scoring methods based on point totals or averages.

However, difficulties can arise when essays from different groups of examinees are evaluated by different sets of raters, a scenario often encountered in real-world testing. For instance, in academic settings such as university admissions, individual departments may use different pools of raters to assess essays from specific applicant pools. Similarly, in the context of large-scale standardized tests, different sets of raters may be allocated to various test dates or locations. Thus, when applying IRT models with rater parameters to account for such real-world testing cases while also ensuring that ability estimates are comparable across groups of examinees and raters, test linking becomes essential for unifying the scale of model parameters estimated for each group.

Conventional test-linking methods generally require some overlap of examinees or raters across the groups being linked (Eckes, 2023 ; Engelhard, 1997 ; Ilhan, 2016 ; Linacre, 2014 ; Uto, 2021a ). For example, linear linking based on common examinees, a popular linking method, estimates the IRT parameters for shared examinees using data from each group. These estimates are then used to build a linear regression model, which adjusts the parameter scales across groups. However, the design of such overlapping groups can often be impractical in real-world testing environments.

To facilitate test linking in these challenging environments, we introduce a novel method that leverages neural automated essay scoring (AES) technology. Specifically, we employ a cutting-edge deep neural AES method (Uto & Okano, 2021 ) that can predict IRT-based abilities from examinees’ essays. The central concept of our linking method is to construct an AES model using the ability estimates of examinees in a reference group, along with their essays, and then to apply this model to predict the abilities of examinees in other groups. An important point is that the AES model is trained to predict examinee abilities on the scale established by the reference group. This implies that the trained AES model can predict the abilities of examinees in other groups on the ability scale established by the reference group. Therefore, we use the predicted abilities to calculate the linking coefficients required for linear linking and to perform a test linking. In this study, we conducted experiments based on real-world data to demonstrate that our method successfully accomplishes test linking with accuracy comparable to that of linear linking using few common examinees.

It should be noted that previous studies have attempted to employ AES technologies for test linking (Almond, 2014 ; Olgar, 2015 ), but their focus has primarily been on linking tests with varied writing tasks or a mixture of essay tasks and objective items, while overlooking the influence of rater characteristics. This differs from the specific scenarios and goals that our study aims to address. To the best of our knowledge, this is the first study that employs AES technologies to link IRT models incorporating rater parameters for writing assessments without the need for common examinees and raters.

Setting and data

In this study, we assume scenarios in which two groups of examinees respond to the same writing task and their written essays are assessed by two distinct sets of raters following the same scoring rubric. We refer to one group as the reference group , which serves as the basis for the scale, and the other as the focal group , whose scale we aim to align with that of the reference group.

Let \(u^{\text {ref}}_{jr}\) be the score assigned by rater \(r \in \mathcal {R}^{\text {ref}}\) to the essay of examinee \(j \in \mathcal {J}^{\text {ref}}\) , where \(\mathcal {R}^{\text {ref}}\) and \(\mathcal {J}^{\text {ref}}\) denote the sets of raters and examinees in the reference group, respectively. Then, a collection of scores for the reference group can be defined as

where \(\mathcal{K} = \{1,\ldots ,K\}\) represents the rating categories, and \(-1\) indicates missing data.

Similarly, a collection of scores for the focal group can be defined as

where \(u^{\text {foc}}_{jr}\) indicates the score assigned by rater \(r \in \mathcal {R}^{\text {foc}}\) to the essay of examinee \(j \in \mathcal {J}^{\text {foc}}\) , and \(\mathcal {R}^{\text {foc}}\) and \(\mathcal {J}^{\text {foc}}\) represent the sets of raters and examinees in the focal group, respectively.

The primary objective of this study is to apply IRT models with rater parameters to the two sets of data, \(\textbf{U}^{\text {ref}}\) and \(\textbf{U}^{\text {foc}}\) , and to establish IRT parameter linking without shared examinees and raters: \(\mathcal {J}^{\text {ref}} \cap \mathcal {J}^{\text {foc}} = \emptyset \) and \(\mathcal {R}^{\text {ref}} \cap \mathcal {R}^{\text {foc}} = \emptyset \) . More specifically, we seek to align the scale derived from \(\textbf{U}^{\text {foc}}\) with that of \(\textbf{U}^{\text {ref}}\) .

• Item response theory

IRT (Lord, 1980 ), a test theory grounded in mathematical models, has recently gained widespread use in various testing situations due to the growing prevalence of computer-based testing. In objective testing contexts, IRT makes use of latent variable models, commonly referred to as IRT models. Traditional IRT models, such as the Rasch model and the two-parameter logistic model, give the probability of an examinee’s response to a test item as a probabilistic function influenced by both the examinee’s latent ability and the item’s characteristic parameters, such as difficulty and discrimination. These IRT parameters can be estimated from a dataset consisting of examinees’ responses to test items.

However, traditional IRT models are not directly applicable to essay-writing test data, where the examinees’ responses to test items are assessed by multiple human raters. Extended IRT models with rater parameters have been proposed to address this issue (Eckes, 2023 ; Jin and Wang, 2018 ; Linacre, 1989 ; Shin et al., 2019 ; Uto, 2023 ; Wilson & Hoskens, 2001 ).

Many-facet Rasch models and their extensions

The MFRM (Linacre, 1989 ) is the most commonly used IRT model that incorporates rater parameters. Although several variants of the MFRM exist (Eckes, 2023 ; Myford & Wolfe, 2004 ), the most representative model defines the probability that the essay of examinee j for a given test item (either a writing task or prompt) i receives a score of k from rater r as

where \(\theta _j\) is the latent ability of examinee j , \(\beta _{i}\) represents the difficulty of item i , \(\beta _{r}\) represents the severity of rater  r , and \(d_{m}\) is a step parameter denoting the difficulty of transitioning between scores \(m-1\) and m . \(D = 1.7\) is a scaling constant used to minimize the difference between the normal and logistic distribution functions. For model identification, \(\sum _{i} \beta _{i} = 0\) , \(d_1 = 0\) , \(\sum _{m = 2}^{K} d_{m} = 0\) , and a normal distribution for the ability \(\theta _j\) are assumed.

Another popular MFRM is one in which \(d_{m}\) is replaced with \(d_{rm}\) , a rater-specific step parameter denoting the severity of rater r when transitioning from score  \(m-1\) to m . This model is often used to investigate variations in rating scale criteria among raters caused by differences in the central tendency, extreme response tendency, and range restriction among raters (Eckes, 2023 ; Myford & Wolfe, 2004 ; Qiu et al., 2022 ; Uto, 2021a ).

A recent extension of the MFRM is the generalized many-facet model (GMFM) (Uto & Ueno, 2020 ) Footnote 1 , which incorporates parameters denoting rater consistency and item discrimination. GMFM defines the probability \(P_{ijrk}\) as

where \(\alpha _i\) indicates the discrimination power of item i , and \(\alpha _r\) indicates the consistency of rater r . For model identification, \(\prod _{r} \alpha _i = 1\) , \(\sum _{i} \beta _{i} = 0\) , \(d_{r1} = 0\) , \(\sum _{m = 2}^{K} d_{rm} = 0\) , and a normal distribution for the ability \(\theta _j\) are assumed.

In this study, we seek to apply the aforementioned IRT models to data involving a single test item, as detailed in the Setting and data section. When there is only one test item, the item parameters in the above equations become superfluous and can be omitted. Consequently, the equations for these models can be simplified as follows.

MFRM with rater-specific step parameters (referred to as MFRM with RSS in the subsequent sections):

Note that the GMFM can simultaneously capture the following typical characteristics of raters, whereas the MFRM and MFRM with RSS can only consider a subset of these characteristics.

Severity : This refers to the tendency of some raters to systematically assign higher or lower scores compared with other raters regardless of the actual performance of the examinee. This tendency is quantified by the parameter \(\beta _r\) .

Consistency : This is the extent to which raters maintain their scoring criteria consistently over time and across different examinees. Consistent raters exhibit stable scoring patterns, which make their evaluations more reliable and predictable. In contrast, inconsistent raters show varying scoring tendencies. This characteristic is represented by the parameter \(\alpha _r\) .

Range Restriction : This describes the limited variability in scores assigned by a rater. Central tendency and extreme response tendency are special cases of range restriction. This characteristic is represented by the parameter \(d_{rm}\) .

For details on how these characteristics are represented in the GMFM, see the article (Uto & Ueno, 2020 ).

Based on the above, it is evident that both the MFRM and MFRM with RSS are special cases of the GMFM. Specifically, the GMFM with constant rater consistency corresponds to the MFRM with RSS. Moreover, the MFRM with RSS that assumes no differences in the range restriction characteristic among raters aligns with the MFRM.

When the aforementioned IRT models are applied to datasets from multiple groups composed of different examinees and raters, such as \(\textbf{U}^{\text {red}}\) and \(\textbf{U}^{\text {foc}}\) , the scales of the estimated parameters generally differ among them. This discrepancy arises because IRT permits arbitrary scaling of parameters for each independent dataset. An exception occurs when it is feasible to assume equality in between-test distributions of examinee abilities and rater parameters (Linacre, 2014 ). However, real-world testing conditions may not always satisfy this assumption. Therefore, if the aim is to compare parameter estimates between different groups, test linking is generally required to unify the scale of model parameters estimated from each individual group’s dataset.

One widely used approach for test linking is linear linking . In the context of the essay-writing test considered in this study, implementing linear linking necessitates designing two groups so that there is some overlap in examinees between them. With this design, IRT parameters for the shared examinees are estimated individually for each group. These estimates are then used to construct a linear regression model for aligning the parameter scales across groups, thereby rendering them comparable. We now introduce the mean and sigma method  (Kolen & Brennan, 2014 ; Marco, 1977 ), a popular method for linear linking, and illustrate the procedures for parameter linking specifically for the GMFM, as defined in Eq.  7 , because both the MFRM and the MFRM with RSS can be regarded as special cases of the GMFM, as explained earlier.

To elucidate this, let us assume that the datasets corresponding to the reference and focal groups, denoted as \(\textbf{U}^{\text {ref}}\) and \(\textbf{U}^{\text {foc}}\) , contain overlapping sets of examinees. Furthermore, let us assume that \(\hat{\varvec{\theta }}^{\text {foc}}\) , \(\hat{\varvec{\alpha }}^{\text {foc}}\) , \(\hat{\varvec{\beta }}^{\text {foc}}\) , and \(\hat{\varvec{d}}^{\text {foc}}\) are the GMFM parameters estimated from \(\textbf{U}^{\text {foc}}\) . The mean and sigma method aims to transform these parameters linearly so that their scale aligns with those estimated from \(\textbf{U}^{\text {ref}}\) . This transformation is guided by the equations

where \(\tilde{\varvec{\theta }}^{\text {foc}}\) , \(\tilde{\varvec{\alpha }}^{\text {foc}}\) , \(\tilde{\varvec{\beta }}^{\text {foc}}\) , and \(\tilde{\varvec{d}}^{\text {foc}}\) represent the scale-transformed parameters for the focal group. The linking coefficients are defined as

where \({\mu }^{\text {ref}}\) and \({\sigma }^{\text {ref}}\) represent the mean and standard deviation (SD) of the common examinees’ ability values estimated from \(\textbf{U}^{\text {ref}}\) , and \({\mu }^{\text {foc}}\) and \({\sigma }^{\text {foc}}\) represent those values obtained from \(\textbf{U}^{\text {foc}}\) .

This linear linking method is applicable when there are common examinees across different groups. However, as discussed in the introduction, arranging for multiple groups with partially overlapping examinees (and/or raters) can often be impractical in real-world testing environments. To address this limitation, we aim to facilitate test linking without the need for common examinees and raters by leveraging AES technology.

Automated essay scoring models

Many AES methods have been developed over recent decades and can be broadly categorized into either feature-engineering or automatic feature extraction approaches (Hussein et al., 2019 ; Ke & Ng, 2019 ). The feature-engineering approach predicts essay scores using either a regression or classification model that employs manually designed features, such as essay length and the number of spelling errors (Amorim et al., 2018 ; Dascalu et al., 2017 ; Nguyen & Litman, 2018 ; Shermis & Burstein, 2002 ). The advantages of this approach include greater interpretability and explainability. However, it generally requires considerable effort in developing effective features to achieve high scoring accuracy for various datasets. Automatic feature extraction approaches based on deep neural networks (DNNs) have recently attracted attention as a means of eliminating the need for feature engineering. Many DNN-based AES models have been proposed in the last decade and have achieved state-of-the-art accuracy (Alikaniotis et al., 2016 ; Dasgupta et al., 2018 ; Farag et al., 2018 ; Jin et al., 2018 ; Mesgar & Strube, 2018 ; Mim et al., 2019 ; Nadeem et al., 2019 ; Ridley et al., 2021 ; Taghipour & Ng, 2016 ; Uto, 2021b ; Wang et al., 2018 ). In the next section, we introduce the most widely used DNN-based AES model, which utilizes Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2019 ).

BERT-based AES model

BERT, a pre-trained language model developed by Google’s AI language team, achieved state-of-the-art performance in various natural language processing (NLP) tasks in 2019 (Devlin et al., 2019 ). Since then, it has frequently been applied to AES (Rodriguez et al., 2019 ) and automated short-answer grading (Liu et al., 2019 ; Lun et al., 2020 ; Sung et al., 2019 ) and has demonstrated high accuracy.

BERT is structured as a multilayer bidirectional transformer network, where the transformer is a neural network architecture designed to handle ordered sequences of data using an attention mechanism. See Ref. (Vaswani et al., 2017 ) for details of transformers.

BERT undergoes training in two distinct phases, pretraining and fine-tuning . The pretraining phase utilizes massive volumes of unlabeled text data and is conducted through two unsupervised learning tasks, specifically, masked language modeling and next-sentence prediction . Masked language modeling predicts the identities of words that have been masked out of the input text, while next-sequence prediction predicts whether two given sentences are adjacent.

Fine-tuning is required to adapt a pre-trained BERT model for a specific NLP task, including AES. This entails retraining the BERT model using a task-specific supervised dataset after initializing the model parameters with pre-trained values and augmenting with task-specific output layers. For AES applications, the addition of a special token, [CLS] , at the beginning of each input is required. Then, BERT condenses the entire input text into a fixed-length real-valued hidden vector referred to as the distributed text representation , which corresponds to the output of the special token [CLS]  (Devlin et al., 2019 ). AES scores can thus be derived by feeding the distributed text representation into a linear layer with sigmoid activation , as depicted in Fig.  1 . More formally, let \( \varvec{h} \) be the distributed text representation. The linear layer with sigmoid activation is defined as \(\sigma (\varvec{W}\varvec{h}+\text{ b})\) , where \(\varvec{W}\) is a weight matrix and \(\text{ b }\) is a bias, both learned during the fine-tuning process. The sigmoid function \(\sigma ()\) maps its input to a value between 0 and 1. Therefore, the model is trained to minimize an error loss function between the predicted scores and the gold-standard scores, which are normalized to the [0, 1] range. Moreover, score prediction using the trained model is performed by linearly rescaling the predicted scores back to the original score range.

BERT-based AES model architecture. \(w_{jt}\) is the t -th word in the essay of examinee j , \(n_j\) is the number of words in the essay, and \(\hat{y}_{j}\) represents the predicted score from the model

Problems with AES model training

As mentioned above, to employ BERT-based and other DNN-based AES models, they must be trained or fine-tuned using a large dataset of essays that have been graded by human raters. Typically, the mean-squared error (MSE) between the predicted and the gold-standard scores serves as the loss function for model training. Specifically, let \(y_{j}\) be the normalized gold-standard score for the j -th examinee’s essay, and let \(\hat{y}_{j}\) be the predicted score from the model. The MSE loss function is then defined as

where J denotes the number of examinees, which is equivalent to the number of essays, in the training dataset.

Here, note that a large-scale training dataset is often created by assigning a few raters from a pool of potential raters to each essay to reduce the scoring burden and to increase scoring reliability. In such cases, the gold-standard score for each essay is commonly determined by averaging the scores given by multiple raters assigned to that essay. However, as discussed in earlier sections, these straightforward average scores are highly sensitive to rater characteristics. When training data includes rater bias effects, an AES model trained on that data can show decreased performance as a result of inheriting these biases (Amorim et al., 2018 ; Huang et al., 2019 ; Li et al., 2020 ; Wind et al., 2018 ). An AES method that uses IRT has been proposed to address this issue (Uto & Okano, 2021 ).

AES method using IRT

The main idea behind the AES method using IRT (Uto & Okano, 2021 ) is to train an AES model using the ability value \(\theta _j\) estimated by IRT models with rater parameters, such as MFRM and its extensions, from the data given by multiple raters for each essay, instead of a simple average score. Specifically, AES model training in this method occurs in two steps, as outlined in Fig.  2 .

Estimate the IRT-based abilities \(\varvec{\theta }\) from a score dataset, which includes scores given to essays by multiple raters.

Train an AES model given the ability estimates as the gold-standard scores. Specifically, the MSE loss function for training is defined as

where \(\hat{\theta }_j\) represents the AES’s predicted ability of the j -th examinee, and \(\theta _{j}\) is the gold-standard ability for the examinee obtained from Step 1. Note that the gold-standard scores are rescaled into the range [0, 1] by applying a linear transformation from the logit range \([-3, 3]\) to [0, 1]. See the original paper (Uto & Okano, 2021 ) for details.

Architecture of a BERT-based AES model that uses IRT

A trained AES model based on this method will not reflect bias effects because IRT-based abilities \(\varvec{\theta }\) are estimated while removing rater bias effects.

In the prediction phase, the score for an essay from examinee \(j^{\prime }\) is calculated in two steps.

Predict the IRT-based ability \(\theta _{j^{\prime }}\) for the examinee using the trained AES model, and then linearly rescale it to the logit range \([-3, 3]\) .

Calculate the expected score \(\mathbb {E}_{r,k}\left[ P_{j^{\prime }rk}\right] \) , which corresponds to an unbiased original-scaled score, given \(\theta _{j'}\) and the rater parameters. This is used as a predicted essay score in this method.

This method originally aimed to train an AES model while mitigating the impact of varying rater characteristics present in the training data. A key feature, however, is its ability to predict an examinee’s IRT-based ability from their essay texts. Our linking approach leverages this feature to enable test linking without requiring common examinees and raters.

Outline of our proposed method, steps 1 and 2

Outline of our proposed method, steps 3–6

Proposed method

The core idea behind our method is to develop an AES model that predicts examinee ability using score and essay data from the reference group, and then to use this model to predict the abilities of examinees in the focal group. These predictions are then used to estimate the linking coefficients for a linear linking. An outline of our method is illustrated in Figs.  3 and 4 . The detailed steps involved in the procedure are as follows.

Estimate the IRT model parameters from the reference group’s data \(\textbf{U}^{\text {ref}}\) to obtain \(\hat{\varvec{\theta }}^{\text {ref}}\) indicating the ability estimates of the examinees in the reference group.

Use the ability estimates \(\hat{\varvec{\theta }}^{\text {ref}}\) and the essays written by the examinees in the reference group to train the AES model that predicts examinee ability.

Use the trained AES model to predict the abilities of examinees in the focal group by inputting their essays. We designate these AES-predicted abilities as \(\hat{\varvec{\theta }}^{\text {foc}}_{\text {pred}}\) from here on. An important point to note is that the AES model is trained to predict ability values on the parameter scale aligned with the reference group’s data, meaning that the predicted abilities for examinees in the focal group follow the same scale.

Estimate the IRT model parameters from the focal group’s data \(\textbf{U}^{\text {foc}}\) .

Calculate the linking coefficients A and K using the AES-predicted abilities \(\hat{\varvec{\theta }}^{\text {foc}}_{\text {pred}}\) and the IRT-based ability estimates \(\hat{\varvec{\theta }}^{\text {foc}}\) for examinees in the focal group as follows.

where \({\mu }^{\text {foc}}_{\text {pred}}\) and \({\sigma }^{\text {foc}}_{\text {pred}}\) represent the mean and the SD of the AES-predicted abilities \(\hat{\varvec{\theta }}^{\text {foc}}_{\text {pred}}\) , respectively. Furthermore, \({\mu }^{\text {foc}}\) and \({\sigma }^{\text {foc}}\) represent the corresponding values for the IRT-based ability estimates \(\hat{\varvec{\theta }}^{\text {foc}}\) .

Apply linear linking based on the mean and sigma method given in Eq.  8 using the above linking coefficients and the parameter estimates for the focal group obtained in Step 4. This procedure yields parameter estimates for the focal group that are aligned with the scale of the parameters of the reference group.

As described in Step 3, the AES model used in our method is trained to predict examinee abilities on the scale derived from the reference data \(\textbf{U}^{\text {ref}}\) . Therefore, the abilities predicted by the trained AES model for the examinees in the focal group, denoted as \(\hat{\varvec{\theta }}^{\text {foc}}_{\text {pred}}\) , also follow the ability scale derived from the reference data. Consequently, by using the AES-predicted abilities, we can infer the differences in the ability distribution between the reference and focal groups. This enables us to estimate the linking coefficients, which then allows us to perform linear linking based on the mean and sigma method. Thus, our method allows for test linking without the need for common examinees and raters.

It is important to note that the current AES model for predicting examinees’ abilities does not necessarily offer sufficient prediction accuracy for individual ability estimates. This implies that their direct use in mid- to high-stakes assessments could be problematic. Therefore, we focus solely on the mean and SD values of the ability distribution based on predicted abilities, rather than using individual predicted ability values. Our underlying assumption is that these AES models can provide valuable insights into differences in the ability distribution across various groups, even though the individual predictions might be somewhat inaccurate, thereby substantiating their utility for test linking.

Experiments

In this section, we provide an overview of the experiments we conducted using actual data to evaluate the effectiveness of our method.

Actual data

We used the dataset previously collected in Uto and Okano ( 2021 ). It consists of essays written in English by 1805 students from grades 7 to 10 along with scores from 38 raters for these essays. The essays originally came from the ASAP (Automated Student Assessment Prize) dataset, which is a well-known benchmark dataset for AES studies. The raters were native English speakers recruited from Amazon Mechanical Turk (AMT), a popular crowdsourcing platform. To alleviate the scoring burden, only a few raters were assigned to each essay, rather than having all raters evaluate every essay. Rater assignment was conducted based on a systematic links design  (Shin et al., 2019 ; Uto, 2021a ; Wind & Jones, 2019 ) to achieve IRT-scale linking. Consequently, each rater evaluated approximately 195 essays, and each essay was graded by four raters on average. The raters were asked to grade the essays using a holistic rubric with five rating categories, which is identical to the one used in the original ASAP dataset. The raters were provided no training before the scoring process began. The average Pearson correlation between the scores from AMT raters and the ground-truth scores included in the original ASAP dataset was 0.70 with an SD of 0.09. The minimum and maximum correlations were 0.37 and 0.81, respectively. Furthermore, we also calculated the intraclass correlation coefficient (ICC) between the scores from each AMT rater and the ground-truth scores. The average ICC was 0.60 with an SD of 0.15, and the minimum and maximum ICCs were 0.29 and 0.79, respectively. The calculation of the correlation coefficients and ICC for each AMT rater excluded essays that the AMT rater did not assess. Furthermore, because the ground-truth scores were given as the total scores from two raters, we divided them by two in order to align the score scale with the AMT raters’ scores.

For further analysis, we also evaluated the ICC among the AMT raters as their interrater reliability. In this analysis, missing value imputation was required because all essays were evaluated by a subset of AMT raters. Thus, we first applied multiple imputation with predictive mean matching to the AMT raters’ score dataset. In this process, we generated five imputed datasets. For each imputed dataset, we calculated the ICC among all AMT raters. Finally, we aggregated the ICC values from each imputed dataset to calculate the mean ICC and its SD. The results revealed a mean ICC of 0.43 with an SD of 0.01.

These results suggest that the reliability of raters is not necessarily high. This variability in scoring behavior among raters underscores the importance of applying IRT models with rater parameters. For further details of the dataset see Uto and Okano ( 2021 ).

Experimental procedures

Using this dataset, we conducted the following experiment for three IRT models with rater parameters, MFRM, MFRM with RSS, and GMFM, defined by Eqs.  5 , 6 , and 7 , respectively.

We estimated the IRT parameters from the dataset using the No-U-Turn sampler-based Markov chain Monte Carlo (MCMC) algorithm, given the prior distributions \(\theta _j, \beta _r, d_m, d_{rm} \sim N(0, 1)\) , and \(\alpha _r \sim LN(0, 0.5)\) following the previous work (Uto & Ueno, 2020 ). Here, \( N(\cdot , \cdot )\) and \(LN(\cdot , \cdot )\) indicate normal and log-normal distributions with mean and SD values, respectively. The expected a posteriori (EAP) estimator was used as the point estimates.

We then separated the dataset randomly into two groups, the reference group and the focal group, ensuring no overlap of examinees and raters between them. In this separation, we selected examinees and raters in each group to ensure distinct distributions of examinee abilities and rater severities. Various separation patterns were tested and are listed in Table  1 . For example, condition 1 in Table  1 means that the reference group comprised randomly selected high-ability examinees and low-severity raters, while the focal group comprised low-ability examinees and high-severity raters. Condition 2 provided a similar separation but controlled for narrower variance in rater severity in the focal group. Details of the group creation procedures can be found in Appendix  A .

Using the obtained data for the reference and focal groups, we conducted test linking using our method, the details of which are given in the Proposed method section. In it, the IRT parameter estimations were carried out using the same MCMC algorithm as in Step 1.

We calculated the Root Mean Squared Error (RMSE) between the IRT parameters for the focal group, which were linked using our proposed method, and their gold-standard parameters. In this context, the gold-standard parameters were obtained by transforming the scale of the parameters estimated from the entire dataset in Step 1 so that it aligned with that of the reference group. Specifically, we estimated the IRT parameters using data from the reference group and collected those estimated from the entire dataset in Step 1. Then, using the examinees in the reference group as common examinees, we applied linear linking based on the mean and sigma method to adjust the scale of the parameters estimated from the entire dataset to match that of the reference group.

For comparison, we also calculated the RMSE between the focal group’s IRT parameters, obtained without applying the proposed linking, and their gold-standard parameters. This functions as the worst baseline against which the results of the proposed method are compared. Additionally, we examined other baselines that use linear linking based on common examinees. For these baselines, we randomly selected five or ten examinees from the reference group, who were assigned scores by at least two focal group’s raters in the entire dataset. The scores given to these selected examinees by the focal group’s raters were then merged with the focal group’s data, where the added examinees worked as common examinees between the reference and focal groups. Using this data, we examined linear linking using common examinees. Specifically, we estimated the IRT parameters from the data of the focal group with common examinees and applied linear linking based on the mean and sigma method using the ability estimates of the common examinees to align its scale with that of the reference group. Finally, we calculated the RMSE between the linked parameter estimates for the examinees and raters belonging only to the original focal group and their gold-standard parameters. Note that this common examinee approach operates under more advantageous conditions compared with the proposed linking method because it can utilize larger samples for estimating the parameters of raters in the focal group.

We repeated Steps 2–5 ten times for each data separation condition and calculated the average RMSE for four cases: one in which our proposed linking method was applied, one without linking, and two others where linear linkings using five and ten common examinees were applied.

The parameter estimation program utilized in Steps 1, 4, and 5 was implemented using RStan (Stan Development Team, 2018 ). The EAP estimates were calculated as the mean of the parameter samples obtained from 2,000 to 5,000 periods using three independent chains. The AES model was developed in Python, leveraging the PyTorch library Footnote 2 . For the AES model training in Step 3, we randomly selected \(90\%\) of the data from the reference group to serve as the training set, with the remaining \(10\%\) designated as the development set. We limited the maximum number of steps for training the AES model to 800 and set the maximum number of epochs to 800 divided by the number of mini-batches. Additionally, we employed early stopping based on the performance on the development set. The AdamW optimization algorithm was used, and the mini-batch size was set to 8.

MCMC statistics and model fitting

Before delving into the results of the aforementioned experiments, we provide some statistics related to the MCMC-based parameter estimation. Specifically, we computed the Gelman–Rubin statistic \(\hat{R}\)  (Gelman et al., 2013 ; Gelman & Rubin, 1992 ), a well-established diagnostic index for convergence, as well as the effective sample size (ESS) and the number of divergent transitions for each IRT model during the parameter estimation phase in Step 1. Across all models, the \(\hat{R}\) statistics were below 1.1 for all parameters, indicating convergence of the MCMC runs. Furthermore, as shown in the first row of Table  2 , our ESS values for all parameters in all models exceeded the criterion of 400, which is considered sufficiently large according to Zitzmann and Hecht ( 2019 ). We also observed no divergent transitions in any of the cases. These results support the validity of the MCMC-based parameter estimation.

Furthermore, we evaluated the model – data fit for each IRT model during the parameter estimation step in Step 1. To assess this fit, we employed the posterior predictive p  value ( PPP -value) (Gelman et al., 2013 ), a commonly used metric for evaluating the model–data fit in Bayesian frameworks (Nering & Ostini, 2010 ; van der Linden, 2016 ). Specifically, we calculated the PPP -value using an averaged standardized residual, a conventional metric for IRT model fit in non-Bayesian settings, as a discrepancy function, similar to the approach in Nering and Ostini ( 2010 ); Tran ( 2020 ); Uto and Okano ( 2021 ). A well-fitted model yields a PPP -value close to 0.5, while poorly fitted models exhibit extreme low or high values, such as those below 0.05 or above 0.95. Additionally, we calculated two information criteria, the widely applicable information criterion (WAIC) (Watanabe, 2010 ) and the widely applicable Bayesian information criterion (WBIC) (Watanabe, 2013 ). The model that minimizes these criteria is considered optimal.

The last three rows in Table  2 shows the results. We can see that the PPP -value for GMFM is close to 0.5, indicating a good fit to the data. In contrast, the other models exhibit high values, suggesting a poor fit to the data. Furthermore, among the three IRT models evaluated, GMFM exhibits the lowest WAIC and WBIC values. These findings suggest that GMFM offers the best fit to the data, corroborating previous work that investigated the same dataset using IRT models (Uto & Okano, 2021 ). We provide further discussion about the model fit in the Analysis of rater characteristics section given later.

According to these results, the following section focuses on the results for GMFM. Note that we also include the results for MFRM and MFRM with RSS in Appendix  B , along with the open practices statement.

Effectiveness of our proposed linking method

The results of the aforementioned experiments for GMFM are shown in Table  3 . In the table, the Unlinked row represents the average RMSE between the focal group’s IRT parameters without applying our linking method and their gold-standard parameters. Similarly, the Linked by proposed method row represents the average RMSE between the focal group’s IRT parameters after applying our linking method and their gold-standard parameters. The rows labeled Linked by five/ten common examinees represent the results for linear linking using common examinees.

A comparison of the results from the unlinked condition and the proposed method reveals that the proposed method improved the RMSEs for the ability and rater severity parameters, namely, \(\theta _j\) and \(\beta _r\) , which we intentionally varied between the reference and focal groups. The degree of improvement is notably substantial when the distributional differences between the reference and focal groups are large, as is the case in Conditions 1–5. On the other hand, for Conditions 6–8, where the distributional differences are relatively minor, the improvements are also smaller in comparison. This is because the RMSEs for the unlinked parameters are already lower in these conditions than in Conditions 1–5. Nonetheless, it is worth emphasizing that the RMSEs after employing our linking method are exceptionally low in Conditions 6–8.

Furthermore, the table indicates that the RMSEs for the step parameters and rater consistency parameters, namely, \(d_{rm}\) and \(\alpha _r\) , also improved in many cases, while the impact of applying our linking method is relatively small for these parameters compared with the ability and rater severity parameters. This is because we did not intentionally vary their distribution between the reference and focal groups, and thus their distribution differences were smaller than those for the ability and rater severity parameters, as shown in the next section.

Comparing the results from the proposed method and linear linking using five common examinees, we observe that the proposed method generally exhibits lower RMSE values for the ability \(\theta _j\) and the rater severity parameters \(\beta _r\) , except for conditions 2–3. Furthermore, when comparing the proposed method with linear linking using ten common examinees, it achieves superior performance in conditions 4–8 and slightly lower performance in conditions 1–3 for \(\theta _j\) and \(\beta _r\) , while the differences are more minor overall than those observed when comparing the proposed method with the condition of five common examinees. Note that the reasons why the proposed method tends to show lower performance for conditions 1–3 are as follows.

The proposed method utilizes fewer samples to estimate the rater parameters compared with the linear linking method using common examinees.

In situations where distributional differences between the reference and focal groups are relatively large, as in conditions 1–3, constructing an accurate AES model for the focal group becomes challenging due to the limited overlap in the ability value range. We elaborate on this point in the next section.

Furthermore, in terms of the rater consistency parameter \(\alpha _r\) and the step parameter \(d_{rm}\) , the proposed method typically shows lower RMSE values compared with linear linking using common examinees. We attribute this to the fact that the performance of the linking method using common examinees is highly dependent on the choice of common examinees, which can sometimes result in significant errors in these parameters. This issue is also further discussed in the next section.

These results suggest that our method can perform linking with comparable accuracy to linear linking using few common examinees, even in the absence of common examinees and raters. Additionally, as reported in Tables  15 and 16 in Appendix  B , both MFRM and MFRM with RSS also exhibit a similar tendency, further validating the effectiveness of our approach regardless of the IRT models employed.

Detailed analysis

Analysis of parameter scale transformation using the proposed method.

In this section, we detail how our method transforms the parameter scale. To demonstrate this, we first summarize the mean and SD values of the gold-standard parameters for both the reference and focal groups in Table  4 . The values in the table are averages calculated from ten repetitions of the experimental procedures. The table shows that the mean and SD values of both examinee ability and rater severity vary significantly between the reference and focal groups following our intended settings, as outlined in Table  1 . Additionally, the mean and SD values for the rater consistency parameter \(\alpha _r\) and the rater-specific step parameters \(d_{rm}\) also differ slightly between the groups, although we did not intentionally alter them.

Second, the averaged values of the means and SDs of the parameters, estimated solely from either the reference or the focal group’s data over ten repetitions, are presented in Table  5 . The table reveals that the estimated parameters for both groups align with a normal distribution centered at nearly zero, despite the actual ability distributions differing between the groups. This phenomenon arises because IRT permits arbitrary scaling of parameters for each independent dataset, as mentioned in the Linking section. This leads to differences in the parameter scale for the focal group compared with their gold-standard values, thereby highlighting the need for parameter linking.

Next, the first two rows of Table  6 display the mean and SD values of the ability estimates for the focal group’s examinees, as predicted by the BERT-based AES model. In the table, the RMSE row indicates the RMSE between the AES-predicted ability values and the gold-standard ability values for the focal groups. The Linking Coefficients row presents the linking coefficients calculated based on the AES-predicted abilities. As with the abovementioned tables, these values are also averages over ten experimental repetitions. According to the table, for Conditions 6–8, where the distributional differences between the groups are relatively minor, both the mean and SD estimates align closely with those of the gold-standard parameters. In contrast, for Conditions 1–5, where the distributional differences are more pronounced, the mean and SD estimates tend to deviate from the gold-standard values, highlighting the challenges of parameter linking under such conditions.

In addition, as indicated in the RMSE row, the AES-predicted abilities may lack accuracy under specific conditions, such as Conditions 1, 2, and 3. This inaccuracy could arise because the AES model, trained on the reference group’s data, could not cover the ability range of the focal group due to significant differences in the ability distribution between the groups. Note that even in cases where the mean and SD estimates are relatively inaccurate, these values are closer to the gold-standard ones than those estimated solely from the focal group’s data. This leads to meaningful linking coefficients, which transform the focal group’s parameters toward the scale of their gold-standard values.

Finally, Table  7 displays the averaged values of the means and SDs of the focal group’s parameters obtained through our linking method over ten repetitions. Note that the mean and SD values of the ability estimates are the same as those reported in Table  6 because the proposed method is designed to align them. The table indicates that the differences in the mean and SD values between the proposed method and the gold-standard condition, shown in Table  4 , tend to be smaller compared with those between the unlinked condition, shown in Table  5 , and the gold-standard. To verify this point more precisely, Table  8 shows the average absolute differences in the mean and SD values of the parameters for the focal groups between the proposed method and the gold-standard condition, as well as those between the unlinked condition and the gold-standard. These values were calculated by averaging the absolute differences in the mean and SD values obtained from each of the ten repetitions, unlike the simple absolute differences in the values reported in Tables  4 and 7 . The table shows that the proposed linking method tends to derive lower values, especially for \(\theta _j\) and \(\beta _r\) , than the unlinked condition. Furthermore, this tendency is prominent for conditions 6–8 in which the distributional differences between the focal and reference groups are relatively small. These trends are consistent with the cases for which our method revealed high linking performance, detailed in the previous section.

In summary, the above analyses suggest that although the AES model’s predictions may not always be perfectly accurate, they can offer valuable insights into scale differences between the reference and focal groups, thereby facilitating successful IRT parameter linking without common examinees and raters.

We now present the distributions of examinee ability and rater severity for the focal group, comparing their gold-standard values with those before and after the application of the linking method. Figures  5 , 6 , 7 , 8 , 9 , 10 , 11 , and 12 are illustrative examples for the eight data-splitting conditions. The gray bars depict the distributions of the gold-standard parameters, the blue bars represent those of the parameters estimated from the focal group’s data, the red bars signify those of the parameters obtained using our linking method, and the green bars indicate the ability distribution as predicted by the BERT-based AES. The upper part of the figure presents results for examinee ability \(\theta _j\) and the lower part presents those for rater severity \(\beta _r\) .

The blue bars in these figures reveal that the parameters estimated from the focal group’s data exhibit distributions with different locations and/or scales compared with their gold-standard values. Meanwhile, the red bars reveal that the distributions of the parameters obtained through our linking method tend to align closely with those of the gold-standard parameters. This is attributed to the fact that the ability distributions for the focal group given by the BERT-based AES model, as depicted by the green bars, were informative for performing linear linking.

Analysis of the linking method based on common examinees

For a detailed analysis of the linking method based on common examinees, Table  9 reports the averaged values of means and SDs of the focal groups’ parameter estimates obtained by the linking method based on five and ten common examinees for each condition. Furthermore, Table  10 shows the average absolute differences between these values and those from the gold standard condition. Table  10 shows that an increase in the number of common examinees tends to lower the average absolute differences, which is a reasonable trend. Furthermore, comparing the results with those of the proposed method reported in Table  8 , the proposed method tends to achieve smaller absolute differences in conditions 4–8 for \(\theta _j\) and \(\beta _r\) , which is consistent with the tendency of the linking performance discussed in the “Effectiveness of our proposed linking method” section.

Note that although the mean and SD values in Table  9 are close to those of the gold-standard parameters shown in Table  4 , this does not imply that linear linking based on five or ten common examinees achieves high linking accuracy for each repetition. To explain this, Table  11 shows the means of the gold-standard ability values for the focal group and their estimates obtained from the proposed method and the linking method based on ten common examinees, for each of ten repetitions under condition 8. This table also shows the absolute differences between the estimated ability means and the corresponding gold-standard means.

Example of ability and rater severity distributions for the focal group under data-splitting condition 1

Example of ability and rater severity distributions for the focal group under data-splitting condition 2

Example of ability and rater severity distributions for the focal group under data-splitting condition 3

Example of ability and rater severity distributions for the focal group under data-splitting condition 4

Example of ability and rater severity distributions for the focal group under data-splitting condition 5

Example of ability and rater severity distributions for the focal group under data-splitting condition 6

Example of ability and rater severity distributions for the focal group under data-splitting condition 7

Example of ability and rater severity distributions for the focal group under data-splitting condition 8

The table shows that the results of the proposed method are relatively stable, consistently revealing low absolute differences for every repetition. In contrast, the results of linear linking based on ten common examinees vary significantly across repetitions, resulting in large absolute differences for some repetitions. These results yield a smaller average absolute difference for the proposed method compared with linear linking based on ten common examinees. However, in terms of the absolute difference in the averaged ability means, linear linking based on ten common examinees shows a smaller difference ( \(|0.38-0.33| = 0.05\) ) compared with the proposed method ( \(|0.38-0.46| = 0.08\) ). This occurs because the results of linear linking based on ten common examinees for ten repetitions fluctuate around the ten-repetition average of the gold standard, thereby canceling out the positive and negative differences. However, this does not imply that linear linking based on ten common examinees achieves high linking accuracy for each repetition. Thus, it is reasonable to interpret the average of the absolute differences calculated for each of the ten repetitions, as reported in Tables  8 and  10 .

This greater variability in performance of the linking method based on common examinees also relates to the tendency of the proposed method to show lower RMSE values for the rater consistency parameter \(\alpha _r\) and the step parameters \(d_{rm}\) compared with linking based on common examinees, as mentioned in the Effectiveness of our proposed linking method section. In that section, we mentioned that this is due to the fact that linear linking based on common examinees is highly dependent on the selection of common examinees, which can sometimes lead to significant errors in these parameters.

To confirm this point, Table  12 displays the SD of RMSEs calculated from ten repetitions of the experimental procedures for both the proposed method and linear linking using ten common examinees. The table indicates that the linking method using common examinees tends to exhibit larger SD values overall, suggesting that this linking method sometimes becomes inaccurate, as we also exemplified in Table  11 . This variability also implies that the estimation of the linking coefficient can be unstable.

Furthermore, the tendency of having larger SD values in the common examinee approach is particularly pronounced for the step parameters at the extreme categories, namely, \(d_{r2}\) and \(d_{r5}\) . We consider this comes from the instability of linking coefficients and the fact that the step parameters for the extreme categories tend to have large absolute values (see Table  13 for detailed estimates). Linear linking multiplies the step parameters by a linking coefficient A , although applying an inappropriate linking coefficient to larger absolute values can have a more substantial impact than when applied to smaller values. We concluded that this is why the RMSEs of the step difficulty parameters in the common examinee approach were deteriorated compared with those in the proposed method. The same reasoning would be applicable to the rater consistency parameter, given that it is distributed among positive values with a mean over one. See Table  13 for details.

Prerequisites of the proposed method

As demonstrated thus far, the proposed method can perform IRT parameter linking without the need for common examinees and raters. As outlined in the Introduction section, certain testing scenarios may encounter challenges or incur significant costs in assembling common examinees or raters. Our method provides a viable solution in these situations. However, it does come with specific prerequisites and inherent costs.

The prerequisites of our proposed method are as follows.

The same essay writing task is offered to both the reference and focal groups, and the written essays for it are scored by different groups of raters using the same rubric.

Raters will function identically across both the reference and focal groups, and the established scales can be adjusted through linear transformations. This implies that there are no systematic differences in scoring that are correlated with the groups but are unrelated to the measured construct, such as differential rater functioning (Leckie & Baird, 2011 ; Myford & Wolfe, 2009 ; Uto, 2023 ; Wind & Guo, 2019 ).

The ability ranges of the reference and focal groups require some overlap because the ability prediction accuracy of the AES decreases as the differences in the ability distributions between the groups increases, as discussed in the Detailed analysis section. This is a limitation of this approach, which requires future studies to overcome.

The reference group consists of a sufficient number of examinees for training AES models using their essays as training data.

Related to the fourth point, we conducted an additional experiment to investigate the number of samples required to train AES models. In this experiment, we assessed the ability prediction accuracy of the BERT-based AES model used in this study by varying the number of training samples. The detailed experimental procedures are outlined below.

Estimate the ability of all 1805 examinees from the entire dataset based on the GMFM.

Randomly split the examinees into 80% (1444) and 20% (361) groups. The 20% subset, consisting of examinees’ essays and their ability estimates, was used as test data to evaluate the ability prediction accuracy of the AES model trained through the following steps.

The 80% subset was further divided into 80% (1155) and 20% (289) groups. Here, the essays and ability estimates of the 80% subset were used as the training data, while those of the 20% served as development data for selecting the optimal epoch.

Train the BERT-based AES model using the training data and select the optimal epoch that minimizes the RMSE between the predicted and gold-standard ability values for the development set.

Use the trained AES model at the optimal epoch to evaluate the RMSE between the predicted and gold-standard ability values for the test data.

Randomly sample 50, 100, 200, 300, 500, 750, and 1000 examinees from the training data created in Step 3.

Train the AES model using each sampled set as training data, and select the optimal epoch using the same development data as before.

Use the trained AES model to evaluate the RMSE for the same test data as before.

Repeat Steps 2–8 five times and calculate the average RMSE for the test data.

Relationship between the number of training samples and the ability prediction accuracy of AES

Item response curves of four representative raters found in experiments using actual data

Figure  13 displays the results. The horizontal axis represents the number of training samples, and the vertical axis shows the RMSE values. Each plot illustrates the average RMSE, with error bars indicating the SD ranges. The results demonstrate that larger sample sizes enhance the accuracy of the AES model. Furthermore, while the RMSE decreases significantly when the sample size is small, the improvements tend to plateau beyond 500 samples. This suggests that, for this dataset, approximately 500 samples would be sufficient to train the AES model with reasonable accuracy. However, note that the required number of samples may vary depending on the essay tasks. A detailed analysis of the relationship between the required number of samples and the characteristics of essay writing tasks is planned for future work.

An inherent cost associated with the proposed method is the computational expense required to construct the BERT-based AES model. Specifically, a computer with a reasonably powerful GPU is necessary to efficiently train the AES model. In this study, for example, we utilized an NVIDIA Tesla T4 GPU on Google Colaboratory. To elaborate on the computational expense, we calculated the computation times and costs for the above experiment under a condition where 1155 training samples were used. Consequently, training the AES model with 1155 samples, including evaluating the RMSE for the development set of 289 essays in each epoch, took approximately 10 min in total. Moreover, it required about 10 s to predict the abilities of 361 examinees from their essays using the trained model. The computational units consumed on Google Colaboratory for both training and inference amounted to 0.44, which corresponds to approximately $0.044. These costs and the time required are significantly smaller than what is required for human scoring.

Analysis of rater characteristics

The MCMC statistics and model fitting section demonstrated that the GMFM provides a better fit to the actual data compared with the MFRM and MFRM with RSS. To explain this, Table  13 shows the rater parameters estimated by the GMFM using the entire dataset. Additionally, Fig.  14 illustrates the item response curves (IRCs) for raters 3, 16, 31, and 34, where the horizontal axis represents the ability \(\theta _j\) , and the vertical axis depicts the response probability for each category.

The table and figure reveal that the raters exhibit diverse and unique characteristics in terms of severity, consistency, and range restriction. For instance, Rater 3 demonstrates nearly average values for all parameters, indicating standard rating characteristics. In contrast, Rater 16 exhibits a pronounced extreme response tendency, as evidenced by higher \(d_{r2}\) and lower \(d_{r5}\) values. Additionally, Rater 31 is characterized by a low severity score, generally preferring higher scores (four and five). Rater 34 exhibits a low consistency value \(\alpha _r\) , which results in minimal variation in response probabilities among categories. This indicates that the rater is likely to assign different ratings to essays of similar quality.

As detailed in the Item Response Theory section, the GMFM can capture these variations in rater severity, consistency, and range restriction simultaneously, while the MFRM and MFRM with RSS can consider only its subsets. We can infer that this capability, along with the large variety of rater characteristics, contributed to the superior model fit of the GMFM compared with the other models.

It is important to note that, the proposed method is also useful for facilitating linking for MFRM and MFRM with RSS, even though the model fits for them were relatively worse, as well as for the GMFM, which we mentioned earlier and is shown in Appendix B .

Effect of using cloud workers as raters

As we detailed in the Actual data section, we used scores given by untrained non-expert cloud workers instead of expert raters. A concern with using raters from cloud workers without adequate training is the potential for greater variability in rating characteristics compared with expert raters. This variability is evidenced by the diverse correlations between the raters’ scores and their ground truth, reported in the Actual data section, and the large variety of rater parameters discussed above. These observations suggest the importance of the following two strategies for ensuring reliable essay scoring when employing crowd workers as raters.

Assigning a larger number of raters to each essay than would typically be used with expert raters.

Estimating the standardized essay scores while accounting for differences in rater characteristics, potentially through the use of IRT models that incorporate rater parameters, which we used in this study.

In this study, we propose a novel IRT-based linking method for essay-writing tests that uses AES technology to enable parameter linking based on IRT models with rater parameters across multiple groups in which neither examinees nor raters are shared. Specifically, we use a deep neural AES method capable of predicting IRT-based examinee abilities based on their essays. The core concept of our approach involves developing an AES model to predict examinee abilities using data from a reference group. This AES model is then applied to predict the abilities of examinees in the focal group. These predictions are used to estimate the linking coefficients required for linear linking. Experimental results with real data demonstrate that our method successfully accomplishes test linking with accuracy comparable to that of linear linking using few common examinees.

In our experiments, we compared the linking performance of the proposed method with linear linking based on the mean and sigma method using only five or ten common examinees. However, such a small number of common examinees is generally insufficient for accurate linear linking and thus leads to unstable estimation of linking coefficients, as discussed in the “Analysis of the linking method based on common examinees” section. Although this study concluded that our method could perform linking with accuracy comparable to that of linear linking using few common examinees, further detailed evaluations of our method involving comparisons with various conventional linking methods using different numbers of common examinees and raters will be the target of future work.

Additionally, our experimental results suggest that although the AES model may not provide sufficient predictive accuracy for individual examinee abilities, it does tend to yield reasonable mean and SD values for the ability distribution of focal groups. This lends credence to our assumption stated in the Proposed method section that AES models incorporating IRT can offer valuable insights into differences in ability distribution across various groups, thereby validating their utility for test linking. This result also supports the use of the mean and sigma method for linking. While concurrent calibration, another common linking method, requires highly accurate individual AES-predicted abilities to serve as anchor values, linear linking through the mean and sigma method necessitates only the mean and SD of the ability distribution. Given that the AES model can provide accurate estimates for these statistics, successful linking can be achieved, as shown in our experiments.

A limitation of this study is that our method is designed for test situations where a single essay writing item is administered to multiple groups, each comprising different examinees and raters. Consequently, the method is not directly applicable for linking multiple tests that offer different items. Developing an extension of our approach to accommodate such test situations is one direction for future research. Another involves evaluating the effectiveness of our method using other datasets. To the best of our knowledge, there are no open datasets that include examinee essays along with scores from multiple assigned raters. Therefore, we plan to develop additional datasets and to conduct further evaluations. Further investigation of the impact of the AES model’s accuracy on linking performance is also warranted.

Availability of data and materials

The data and materials from our experiments are available at https://github.com/AI-Behaviormetrics/LinkingIRTbyAES.git . This includes all experimental results and a sample dataset.

Code availability

The source code for our linking method, developed in R and Python, is available in the same GitHub repository.

The original paper referred to this model as the generalized MFRM. However, in this paper, we refer to it as GMFM because it does not strictly belong to the family of Rasch models.

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This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 19H05663, 21H00898, and 23K17585.

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All results presented from our experiments for all models, including MFRM, MFRM with RSS, and GMFM, as well as the results for each repetition, are available for download at https://github.com/AI-Behaviormetrics/LinkingIRTbyAES.git . This repository also includes programs for performing our linking method, along with a sample dataset. These programs were developed using R and Python, along with RStan and PyTorch. Please refer to the README file for information on program usage and data format details.

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Appendix A: Data splitting procedures

In this appendix, we explain the detailed procedures used to construct the reference group and the focal group while aiming to ensure distinct distributions of examinee abilities and rater severities, as outlined in experimental Procedure 2 in the Experimental procedures section.

Let \(\mu ^{\text {all}}_\theta \) and \(\sigma ^{\text {all}}_\theta \) be the mean and SD of the examinees’ abilities estimated from the entire dataset in Procedure 1 of the Experimental procedures section. Similarly, let \(\mu ^{\text {all}}_\beta \) and \(\sigma ^{\text {all}}_\beta \) be the mean and SD of the rater severity parameter estimated from the entire dataset. Using these values, we set target mean and SD values of abilities and severities for both the reference and focal groups. Specifically, let \(\acute{\mu }^{\text {ref}}_{\theta }\) and \(\acute{\sigma }^{\text {ref}}_{\theta }\) denote the target mean and SD for the abilities of examinees in the reference group, and \(\acute{\mu }^{\text {ref}}_{\beta }\) and \(\acute{\sigma }^{\text {ref}}_{\beta }\) be those for the rater severities in the reference group. Similarly, let \(\acute{\mu }^{\text {foc}}_{\theta }\) , \(\acute{\sigma }^{\text {foc}}_{\theta }\) , \(\acute{\mu }^{\text {foc}}_{\beta }\) , and \(\acute{\sigma }^{\text {foc}}_{\beta }\) represent the target mean and SD for the examinee abilities and rater severities in the focal group. Each of the eight conditions in Table 1 uses these target values, as summarized in Table  14 .

Given these target means and SDs, we constructed the reference and focal groups for each condition through the following procedure.

Prepare the entire set of examinees and raters along with their ability and severity estimates. Specifically, let \(\hat{\varvec{\theta }}\) and \(\hat{\varvec{\beta }}\) be the collections of ability and severity estimates, respectively.

Randomly sample a value from the normal distribution \(N(\acute{\mu }^{\text {ref}}_\theta , \acute{\sigma }^{\text {ref}}_\theta )\) , and choose an examinee with \(\hat{\theta }_j \in \hat{\varvec{\theta }}\) nearest to the sampled value. Add the examinee to the reference group, and remove it from the remaining pool of examinee candidates \(\hat{\varvec{\theta }}\) .

Similarly, randomly sample a value from \(N(\acute{\mu }^{\text {ref}}_\beta ,\acute{\sigma }^{\text {ref}}_\beta )\) , and choose a rater with \(\hat{\beta }_j \in \hat{\varvec{\beta }}\) nearest to the sampled value. Then, add the rater to the reference group, and remove it from the remaining pool of rater candidates \(\hat{\varvec{\beta }}\) .

Repeat Steps 2 and 3 for the focal group, using \(N(\acute{\mu }^{\text {foc}}_\theta , \) \(\acute{\sigma }^{\text {foc}}_\theta )\) and \(N(\acute{\mu }^{\text {foc}}_\beta ,\acute{\sigma }^{\text {foc}}_\beta )\) as the sampling distributions.

Continue to repeat Steps 2, 3, and 4 until the pools \(\hat{\varvec{\theta }}\) and \(\hat{\varvec{\beta }}\) are empty.

Given the examinees and raters in each group, create the data for the reference group \(\textbf{U}^{\text {ref}}\) and the focal group \(\textbf{U}^{\text {foc}}\) .

Remove examinees from each group, as well as their data, if they have received scores from only one rater, thereby ensuring that each examinee is graded by at least two raters.

Appendix B: Experimental results for MFRM and MFRM with RSS

The experiments discussed in the main text focus on the results obtained from GMFM, as this model demonstrated the best fit to the dataset. However, it is important to note that our linking method is not restricted to GMFM and can also be applied to other models, including MFRM and MFRM with RSS. Experiments involving these models were carried out in the manner described in the Experimental procedures section, and the results are shown in Tables  15 and 16 . These tables reveal trends similar to those observed for GMFM, validating the effectiveness of our linking method under the MFRM and MFRM with RSS as well.

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Uto, M., Aramaki, K. Linking essay-writing tests using many-facet models and neural automated essay scoring. Behav Res (2024). https://doi.org/10.3758/s13428-024-02485-2

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Accepted : 26 July 2024

Published : 20 August 2024

DOI : https://doi.org/10.3758/s13428-024-02485-2

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