conceptual analysis essay

Essay Papers Writing Online

Master the art of crafting a concept essay and perfect your writing skills.

Every great work of literature begins with a spark of inspiration, a kernel of an idea that germinates within the writer’s mind. It is this concept, this central theme, that serves as the foundation of the entire writing process, guiding the writer along the creative journey. In the realm of academic writing, the concept essay holds a special place, as it requires the writer to explore abstract ideas, dissect complex theories, and present their understanding of a particular concept.

Unlike traditional essays where arguments are made, and evidence is provided, concept essays delve into the intangible realm of ideas, taking the reader on a captivating exploration of abstract concepts. These essays challenge the writer to convey their understanding of a concept without relying on concrete evidence or facts. Instead, they rely on the writer’s ability to provide clear definitions, logical explanations, and compelling examples that elucidate the intricacies of the concept at hand.

Effectively crafting a concept essay requires skillful mastery of language and an astute understanding of how ideas interconnect. It is a delicate dance between the power of words and the depth of thought, where metaphors and analogies can breathe life into otherwise elusive notions. The successful concept essay requires more than merely stating definitions or describing the concept; it necessitates the writer’s ability to engage and captivate the reader, transporting them into the realm of ideas where the abstract becomes clear and tangible.

Mastering the Art of Crafting a Conceptual Essay: Indispensable Suggestions and Instructions

Embarking on the journey of composing a conceptual essay necessitates an astute understanding of the complexities involved. This particular form of written expression empowers individuals to delve deeply into abstract concepts, unravel their intricacies, and articulate their findings in a clear and coherent manner. To accomplish this task with finesse, it is imperative to familiarize oneself with indispensable suggestions and instructions that pave the way to success.

1. Explore Profusely:

• Investigate, scrutinize, and immerse yourself in the vast realm of ideas, allowing your mind to explore a myriad of perspectives.
• Delve into diverse disciplines and subjects, sourcing inspiration and insight from a wide array of sources such as literature, art, philosophy, science, and history.
• Be cognizant of the fact that the more extensive your exploration, the richer your conceptual essay will be.

2. Define Your Focus:

• Once you have gathered an abundant collection of ideas, narrow down your focus to a specific concept that captivates your interest.
• Choose a concept that is both intriguing and stimulating, as this will fuel your motivation throughout the writing process.
• Strive to select a concept that possesses a level of complexity, rendering it ripe for analysis and interpretation.

3. Establish a Clear Structure:

• Prior to commencing the writing process, create a well-structured outline that delineates the key sections and points you wish to convey in your essay.
• Ensure that your essay possesses a clear introduction, body paragraphs that expound upon your chosen concept, and a comprehensive conclusion that ties together your arguments.
• Organize your thoughts in a logical manner, employing effective transitions that allow your essay to flow seamlessly.

4. Support your Claims:

• Avoid presenting mere conjecture or personal opinions; instead, bolster your arguments with credible evidence and examples.
• Cite reputable sources, such as scholarly articles, books, or studies, to lend credibility and authority to your assertions.
• Engage critically with the works of other esteemed thinkers, analyzing their viewpoints and incorporating them into your own exploration of the concept.

5. Polish and Perfect:

• Once you have crafted the initial draft of your conceptual essay, allocate ample time for revision and refinement.
• Engage in meticulous proofreading to eliminate any errors in grammar, punctuation, or syntax that may detract from the overall impact of your work.
• Solicit feedback from trusted peers or mentors, incorporating their suggestions into your final version.

In conclusion, mastering the art of crafting a conceptual essay demands diligent exploration, focused attention, and a commitment to delivering a well-structured and thought-provoking piece of writing. By following these essential tips and guidelines, you can navigate the intricacies of this unique form of expression and develop an essay that both captivates and informs its readers.

Understanding the Purpose of a Concept Essay

Having a clear understanding of the purpose behind writing a concept essay is crucial for creating a successful piece of writing. Concept essays aim to explore and explain abstract ideas, theories, or concepts in a way that is accessible and engaging to readers.

Although concept essays may vary in subject matter, their main objective is to break down complex ideas and make them understandable to a wider audience. These essays often require deep analysis and critical thinking to present the chosen concept in a comprehensive and enlightening manner.

A concept essay goes beyond simply defining a concept but delves deeper into the underlying principles and implications. It requires the writer to provide insight, examples, and evidence to support their claims and demonstrate a thorough understanding of the concept being discussed.

Concept essays also provide an opportunity for writers to explore new and innovative ideas and present them in a thought-provoking way. They allow for personal interpretation and creativity, encouraging writers to examine a concept from different angles and offer unique perspectives.

Furthermore, concept essays can be used as a tool for education and learning, helping readers expand their knowledge and gain a deeper understanding of various concepts. By breaking down complex ideas into more digestible forms, these essays enable readers to grasp abstract concepts and apply them to real-world situations.

In conclusion, the purpose of a concept essay is to convey abstract ideas or concepts in a clear and engaging manner, utilizing critical thinking and analysis. By presenting complex ideas in a comprehensive way, concept essays facilitate understanding and encourage readers to explore and expand their knowledge in the chosen subject area.

Choosing a Strong and Specific Concept

When it comes to crafting a well-written piece of work, selecting a compelling and precise concept is crucial. The concept you choose will serve as the foundation for your essay, shaping the content, tone, and direction of your writing.

Before diving into the process of choosing a concept, it’s important to understand what exactly a concept is. In this context, a concept can be defined as a broad idea or theme that encapsulates a particular subject or topic. It is the main point or central idea that you want to convey to your readers through your essay.

An effective concept should be strong, meaning it should be able to capture the attention and interest of your readers. It should be something that has depth and substance, allowing for exploration and analysis. A strong concept will engage your audience and motivate them to continue reading.

In addition to being strong, your concept should also be specific. It should be focused and clearly defined, narrowing down your topic to a specific aspect or angle. A specific concept will help you maintain a clear direction in your writing and prevent your essay from becoming too broad or unfocused.

To choose a strong and specific concept, start by brainstorming ideas related to your topic. Think about the main themes or issues you want to address in your essay. Consider what aspects of the topic interest you the most and which ones you feel are worth exploring further.

Once you have a list of potential concepts, evaluate each one based on its strength and specificity. Ask yourself whether the concept captures your interest and whether it has the potential to captivate your audience. Consider whether it is specific enough to guide your writing and provide a clear focus for your essay.

By choosing a strong and specific concept, you will set yourself up for success in writing your concept essay. Remember to select a concept that is compelling, focused, and meaningful to you and your readers. With a well-chosen concept, you will be able to create a thought-provoking and engaging essay that effectively conveys your ideas.

Developing a Clear and Coherent Thesis Statement

When crafting an effective essay, one of the most important elements to consider is the development of a clear and coherent thesis statement. The thesis statement acts as the central theme or main argument of your essay, providing a roadmap for your readers to understand the purpose and direction of your writing.

A well-developed thesis statement not only states your main argument but also provides a clear focus for your essay. It helps you organize your thoughts and ensures that your essay remains cohesive and logical. A strong thesis statement sets the tone for your entire essay and guides the reader through your main ideas.

To develop a clear and coherent thesis statement, it is crucial to thoroughly understand the topic you are writing about. Conducting research and gathering relevant information will help you form a solid foundation for your thesis statement. Make sure to analyze different perspectives on the topic and consider any counterarguments that may arise.

Once you have a good understanding of the topic, you can begin brainstorming and drafting your thesis statement. Start by considering the main idea or argument you want to communicate to your readers. Your thesis statement should be concise and specific, clearly conveying your main point. Avoid vague or general statements that lack focus.

In addition to being clear and concise, your thesis statement should also be arguable. It should present a debatable claim that can be supported with evidence and logical reasoning. This allows you to engage your readers and encourages them to consider different perspectives on the topic.

After drafting your thesis statement, it is important to review and revise it as needed. Make sure it accurately reflects the content and direction of your essay. Consider seeking feedback from peers or instructors to ensure that your thesis statement is clear, coherent, and effectively conveys your main argument.

In conclusion, developing a clear and coherent thesis statement is essential for writing an effective essay. It sets the tone for your entire essay, provides a clear focus, and guides the reader through your main ideas. By thoroughly understanding the topic, brainstorming and drafting a concise and arguable thesis statement, and revising as needed, you can ensure that your essay is well-structured and persuasive.

Structuring Your Concept Essay Effectively

Creating a well-organized structure is vital when it comes to conveying your ideas effectively in a concept essay. By carefully structuring your essay, you can ensure that your audience understands your concept and its various aspects clearly. In this section, we will explore some essential guidelines for structuring your concept essay.

1. Introduction: Begin your essay with an engaging introduction that captures the reader’s attention. This section should provide a brief overview of the concept you will be discussing and its significance. You can use an anecdote, a rhetorical question, or a thought-provoking statement to make your introduction compelling.

2. Definition: After the introduction, it is crucial to provide a clear definition of the concept you will be exploring in your essay. Define the concept in your own words and highlight its key characteristics. You may also include any relevant background information or historical context to enhance the reader’s understanding.

3. Explanation: In this section, you will delve deeper into the concept and explain its various elements, components, or features. Use examples, analogies, or real-life situations to illustrate your points and make them more relatable to the reader. Break down complex ideas into simpler terms and highlight the connections between different aspects of the concept.

4. Analysis: Once you have provided a thorough explanation of the concept, it is time to analyze it critically. Discuss different perspectives or interpretations of the concept and evaluate their strengths and weaknesses. Consider any controversies or debates surrounding the concept and present a balanced view by weighing different arguments.

5. Examples and Case Studies: To further support your arguments and enhance the reader’s understanding, include relevant examples and case studies. These examples can be from real-life situations, historical events, or fictional scenarios. Analyze how the concept has been applied or manifested in these examples and discuss their implications.

6. Conclusion: Conclude your concept essay by summarizing your main points and restating the significance of the concept. Reflect on the insights gained from your analysis and offer any recommendations or suggestions for further exploration. End your essay on a thought-provoking note that leaves the reader with a lasting impression.

By structuring your concept essay effectively, you can ensure that your ideas are presented coherently and persuasively. Remember to use clear and concise language, provide logical transitions between sections, and support your arguments with evidence. With a well-structured essay, you can effectively communicate your understanding of the concept to your audience.

Using Concrete Examples to Illustrate Your Concept

One effective way to clarify and reinforce your concept in a concept essay is by using concrete examples. By providing specific and tangible instances, you can help your readers grasp the abstract and theoretical nature of your concept. Concrete examples bring your concept to life, making it easier for your audience to understand and relate to.

Instead of relying solely on abstract theories, you can support your concept with real-life scenarios, research studies, or personal anecdotes. These examples add depth and relevance to your essay, making it more engaging and meaningful.

When choosing examples to illustrate your concept, it is important to select ones that accurately represent the core elements of your concept. Look for examples that exhibit the underlying principles, attributes, or behaviors that are associated with your concept.

For instance, if your concept is “leadership,” you can provide examples of influential leaders from history or modern-day society. These examples can demonstrate the qualities that define effective leadership, such as integrity, communication skills, and the ability to inspire and motivate others.

Additionally, when presenting concrete examples, ensure that they are relevant and relatable to your target audience. Consider the background and interests of your readers and choose examples that they can easily comprehend and connect with. This will enhance the effectiveness of your essay and create a stronger impact.

In conclusion, using concrete examples is a powerful technique for illustrating your concept in a concept essay. By incorporating specific instances, you can bring clarity, relevance, and authenticity to your writing. This approach allows your readers to grasp your concept more easily and appreciate its practical application in real-life scenarios.

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Concept Essay Paper

Concept essay generator.

Every writer has his/her own way of presenting a topic or an idea to the readers. Some of them wants to stir imaginations and make you create characters and places of your own. Others want to provoke your emotions and indulge you into the story. While others want to simply demonstrate a subject.

Essay writing is considered a talent. It requires a creative mind to be able to present thoughts and emotions and put them into writing. And the most difficult part is how to make it appealing to the readers. Knowing how to start an essay is even more difficult because you have to find the right inspiration to write.

What is Concept Essay? A concept essay is a piece writing that is used to present an idea or a topic with the sole purpose of providing a clear definition and explanation. Their usual content are those topics that may have previously been presented but were not given with full emphasis. Others are controversial and timely issues that raises questions but are not given full answers. What is Concept Paper? A concept paper is a brief document written to provide an overview of a project, research, or idea. It outlines the main goals, objectives, and methods of the intended project, serving as a preliminary proposal. Concept papers are often used to seek approval or funding, presenting the project’s significance, potential impact, and feasibility in a concise manner. This document helps stakeholders, such as sponsors or academic committees, understand the essence of the proposed work and decide whether to support it further.

Concept Paper Writing Topics & Ideas

In conceptual writing, the central focus lies on the idea or concept driving the work, positioning it as the cornerstone of the narrative. This approach dictates that all planning and critical decisions are determined in advance, rendering the actual writing process secondary. Essentially, the concept acts as a blueprint, guiding the creation of the text in a manner that is almost mechanical. Through this method, the initial idea transforms into an engine that propels the development of the written piece, underscoring the precedence of thought over the act of writing itself. Below are the topics and ideas of concept writing

• The Evolution of Digital Privacy
• The Psychology Behind Social Media Addiction
• The Impact of Remote Work on Urban Development
• Sustainability in Fashion: A New Trend
• The Future of Artificial Intelligence in Healthcare
• Cultural Identity in a Globalized World
• The Ethics of Genetic Editing
• The Role of Cryptocurrency in Modern Finance
• Mental Health Awareness in the Workplace
• The Influence of Music on Cognitive Development
• Climate Change and Its Effects on Biodiversity
• The Philosophy of Minimalism and Its Life Benefits
• The Rise of E-Learning and Its Educational Impacts
• Urban Farming: Solutions for Food Security
• Virtual Reality: Transforming Entertainment and Education
• The Gig Economy and Its Impact on Traditional Employment
• Social Entrepreneurship: Business for Social Good
• The Intersection of Art and Technology
• Cybersecurity in the Age of Internet of Things
• The Role of Nutrition in Preventing Chronic Diseases

Concept Essay Paper Format

Introduction.

Hook : Start with an engaging sentence to capture the reader’s interest. Background Information : Provide a brief context for the concept you are going to explore. Thesis Statement : Clearly state the concept or idea you will discuss, outlining the main point or argument of your essay.

Body Paragraphs

Each paragraph should focus on a specific aspect of the concept or idea.

Topic Sentence : Introduce the main idea of the paragraph that supports your thesis. Explanation : Offer a detailed explanation of the idea, including definitions, descriptions, and relevant information. Examples and Evidence : Use specific examples, illustrations, or evidence to support your explanations and arguments. This could include statistics, quotes from experts, or real-life scenarios. Analysis : Analyze how the example or evidence supports your topic sentence and thesis, explaining its significance. Transition : Conclude the paragraph with a sentence that smoothly transitions to the next point or paragraph.

Summary of Main Points : Briefly recap the key arguments or explanations presented in your essay. Restatement of Thesis : Reiterate your thesis statement, highlighting how it has been supported through your discussion. Final Thoughts : Offer closing remarks that leave a lasting impression on the reader. This could include implications, future prospects, or a call to action related to the concept.

Concept Paper Example

Enhancing Digital Literacy in Rural Communities: A Pathway to Bridging the Digital Divide   The rapid advancement of digital technologies has significantly transformed the way we live, work, and communicate. However, this digital revolution has also led to a widening gap between urban and rural areas in terms of access to technology and digital skills. This concept paper proposes a comprehensive project aimed at enhancing digital literacy in rural communities as a fundamental step toward bridging the digital divide. By equipping rural populations with the necessary digital skills, the project seeks to empower individuals, improve educational outcomes, and unlock economic opportunities.   The purpose of this initiative is to develop and implement a scalable digital literacy program tailored to the needs of rural communities. This program will focus on basic computer skills, internet navigation, online safety, and the use of digital tools for education and entrepreneurship. The significance of this project lies in its potential to transform the lives of rural residents, providing them with the skills required to participate fully in the digital world.   Objectives of the project include: Assessing the current level of digital literacy in targeted rural areas. Developing a comprehensive digital literacy curriculum that addresses identified needs. Delivering digital literacy training to residents of rural communities through workshops and online modules. Establishing community-based digital hubs equipped with internet access and computing resources. Evaluating the impact of the program on participants’ digital skills, economic opportunities, and educational outcomes.   The methodology will encompass a needs assessment to identify specific digital literacy gaps, followed by the development of a curriculum that incorporates both theoretical knowledge and practical skills. Training will be delivered through a combination of in-person workshops and online modules, ensuring broad access. Pre- and post-program assessments will measure the effectiveness of the training.   Expected outcomes include improved digital literacy rates among rural populations, increased access to educational and economic opportunities, and enhanced participation in the digital economy. The project aims to establish a model for digital literacy training that can be replicated and scaled in other rural areas.   In conclusion, enhancing digital literacy in rural communities presents a critical opportunity to bridge the digital divide and foster equitable access to the benefits of the digital age. This concept paper outlines a clear and actionable plan to empower rural residents with the digital skills necessary for success in a rapidly evolving world.

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What Are the Steps to Writing a Concept Essay?

One of the things to consider in essay writing is to know how to start an essay. In addition to that, you have to come up with the steps on how to write an effective one.

• Choose a topic. An effective essay is one that presents a more relevant topic. You need to choose the right topic first before you start writing.
• Do your research. You have to back up your claims with factual information from reliable sources. Present at least three to four points for reference.
• Create your outline. The essay outline of your concept essay because readers will consider how your ideas are presented.

Key Elements of Concept Paper

A concept paper outlines a project or idea, presenting its purpose, significance, methodology, expected outcomes, and, if applicable, budget and timeline. It serves to introduce and justify the project, aiming to secure interest or support by succinctly detailing its goals and potential impact.

The key elements are:

How to Make/ Create Concept Paper

1. choose your topic wisely.

Select a topic that is both interesting to you and relevant to your audience or potential funders. It should address a specific problem, need, or question.

2. Conduct Preliminary Research

Gather information on your topic to ensure there’s enough background material to support your concept. This research will help refine your idea and identify gaps your project could fill.

3. Write the Introduction

Start with a strong introduction that captures the essence of your concept. Include a brief overview of the problem or issue your project intends to address, its significance, and why it is worth exploring or implementing.

4. State the Purpose or Objective

Clearly articulate the purpose or objectives of your project or research. What do you aim to achieve? Be specific about the outcomes you anticipate.

5. Provide Background Information

Offer a detailed background that gives context to your concept. This section should include any relevant research, current findings, and a justification for your project or study.

6. Describe the Project or Research Design

Outline how you plan to achieve your objectives. This includes your methodology, the steps you will take, and the resources you will need. For research projects, specify your research questions, hypothesis, and the methods for data collection and analysis.

7. Discuss the Significance

Explain the potential impact of your project or research. How will it contribute to the field, benefit a specific group, or solve a problem? This section is crucial for persuading readers of the value of your concept.

8. Outline the Budget (if applicable)

If your concept paper is for a project requiring funding, provide an estimate of the budget. Break down the costs involved, including materials, personnel, and any other resources.

9. Set a Timeline

Include a proposed timeline for your project or research. This demonstrates planning and feasibility and helps funders understand the project’s scope.

10. Conclude Your Paper

Summarize the key points of your concept paper, reinforcing the importance and feasibility of your project or research. End with a call to action or a statement of next steps.

Importance of Concept Essay

As we go along the path of discovering new and better ideas that could feed our minds with more useful information, we also need to pause and make sure that these concepts are well explained.

The main importance of a concept analytical essay is to provide a more vivid evaluation as well as explanation of the ideas that may seem ambiguous. We cannot just live in a world where we are fed with information that we are supposed to accept. Remember that we have the freedom to accept what is true and decline what is not. With a concept essay, we can dig deeper into things and find out its true essence.

When do you need a concept paper?

A concept paper is needed when initiating a project, seeking funding, or proposing an idea to stakeholders. It serves as a preliminary outline, presenting the project’s rationale, goals, and methodology in a concise format to gauge interest or secure support.

How is a concept paper different from a research paper?

A concept paper differs from a research paper in its purpose and scope. While a concept paper outlines a project idea, seeking approval or funding with a focus on potential impact and methodology, a research paper presents detailed findings from completed research, including analysis and results.

What is the purpose of a concept essay?

The purpose of a concept essay is to explore and clarify a specific idea or concept. It aims to deepen understanding and stimulate thought by examining the concept from various angles, using examples, definitions, and personal insights to articulate its significance and implications.

Text prompt

• Instructive
• Professional

Explore the concept essay of happiness: what does it mean and how is it achieved?

Discuss in a concept essay the idea of freedom in the modern world.

Conceptual Analysis: The Cornerstone of Philosophical Inquiry

Table of Contents

Have you ever wondered how philosophers manage to navigate through the dense forest of complex ideas to find clarity and understanding? The secret lies in a method known as conceptual analysis , a philosophical technique that dissects intricate concepts into their elemental parts. Think of it as a mental toolkit that helps us break down big ideas to understand and evaluate them better.

What is conceptual analysis?

At its core, conceptual analysis is the process of examining and clarifying what we mean when we use particular concepts. The aim is to uncover the underlying structure of a concept by asking what conditions must be met for it to apply. This might sound straightforward, but it’s often anything but. Philosophers like John Locke and Immanuel Kant have shown that the way we analyze concepts can have profound implications for how we understand the world and our place in it.

The process of conceptual analysis

Conceptual analysis typically involves several key steps:

• Identifying the concept: This is the starting point where we define which concept we want to analyze.
• Clarifying the concept: Here, we try to articulate what we mean when we use the concept in question.
• Breaking down the concept: We dissect the concept into its fundamental components or attributes.
• Assessing the concept: Finally, we evaluate the concept’s coherence and utility in philosophical discourse.

Locke and Kant: Analytic vs. Synthetic propositions

Two titans of philosophy, Locke and Kant, approached conceptual analysis in distinct ways, particularly when dealing with analytic and synthetic propositions .

Locke’s take on analytic propositions

Locke’s perspective was that analytic propositions are those where the predicate concept is contained within the subject concept. A classic example is the statement “All bachelors are unmarried men.” The concept of being unmarried is part of what we mean by ‘bachelor,’ so the proposition is true by virtue of the meanings of the words involved.

Kant’s revolution with synthetic propositions

Kant, however, introduced the idea of synthetic propositions, which are statements where the predicate concept is not contained within the subject but is connected to it. An example of a synthetic proposition is “All bachelors are unhappy.” Unhappiness is not a defining characteristic of a bachelor, so we must look beyond definitions and into the world to determine the truth of the proposition.

Applying conceptual analysis: Real-world examples

Conceptual analysis isn’t just for armchair philosophers; it has practical applications in many areas including law, ethics , and artificial intelligence .

In law: Interpreting statutes and precedents

When lawyers argue over the interpretation of a law, they are often engaging in conceptual analysis. They must dissect legal concepts to understand precisely what a statute means and how it should be applied to a specific case.

In ethics: Understanding moral concepts

What do we mean when we say something is ‘good’ or ‘just’? Ethicists use conceptual analysis to unpack these terms, which can help us make clearer ethical decisions.

In artificial intelligence: Programming understanding

For AI to “understand” human commands, it must have a framework for analyzing concepts. This often involves creating algorithms that mimic the way humans use conceptual analysis to parse language and ideas.

Challenges and criticisms of conceptual analysis

While conceptual analysis is a valuable tool, it’s not without its challenges and critics. Some argue that our concepts are too fluid and context-dependent for such analysis to be truly effective. Others suggest that focusing too much on language can distract us from engaging with the real substance of philosophical problems.

Conceptual change and evolution

One of the main challenges is that concepts evolve over time. Consider how the concept of ‘privacy’ has changed with the advent of the internet and social media. This dynamic nature of concepts can make any analysis potentially outdated.

Philosophical skepticism about language

Critics like Ludwig Wittgenstein have questioned whether language can ever truly capture the essence of reality, suggesting that conceptual analysis may be fundamentally limited.

Conceptual analysis remains a cornerstone of philosophical inquiry, despite its challenges. By understanding the nuances of this method, we can better appreciate the work of philosophers and apply their insights to our own lives. By dissecting complex ideas into simpler components, we develop a clearer understanding of the intricate tapestry of concepts that form our worldviews.

What do you think? Is there a concept you’ve struggled to understand that might benefit from this kind of analysis? How might conceptual analysis change with the evolution of language and society?

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Research Methodology

1 Introduction to Research in General

• Research in General
• Research Circle
• Tools of Research
• Methods: Quantitative or Qualitative
• The Product: Research Report or Papers

2 Original Unity of Philosophy and Science

• Myth Philosophy and Science: Original Unity
• The Myth: A Spiritual Metaphor
• Myth Philosophy and Science
• The Greek Quest for Unity
• The Ionian School
• Towards a Grand Unification Theory or Theory of Everything
• Einstein’s Perennial Quest for Unity

3 Evolution of the Distinct Methods of Science

• Definition of Scientific Method
• The Evolution of Scientific Methods
• Theory-Dependence of Observation
• Scope of Science and Scientific Methods
• Prevalent Mistakes in Applying the Scientific Method

4 Relation of Scientific and Philosophical Methods

• Definitions of Scientific and Philosophical method
• Philosophical method
• Scientific method
• The relation
• The Importance of Philosophical and scientific methods

5 Dialectical Method

• Introduction and a Brief Survey of the Method
• Types of Dialectics
• Dialectics in Classical Philosophy
• Dialectics in Modern Philosophy
• Critique of Dialectical Method

6 Rational Method

• Understanding Rationalism
• Rational Method of Investigation
• Descartes’ Rational Method
• Leibniz’ Aim of Philosophy
• Spinoza’ Aim of Philosophy

7 Empirical Method

• Common Features of Philosophical Method
• Empirical Method
• Exposition of Empiricism
• Locke’s Empirical Method
• Berkeley’s Empirical Method
• David Hume’s Empirical Method

8 Critical Method

• Basic Features of Critical Theory
• On Instrumental Reason
• Conception of Society
• Human History as Dialectic of Enlightenment
• Substantive Reason
• Habermasian Critical Theory
• Habermas’ Theory of Society
• Habermas’ Critique of Scientism
• Theory of Communicative Action
• Discourse Ethics of Habermas

9 Phenomenological Method (Western and Indian)

• Phenomenology in Philosophy
• Phenomenology as a Method
• Phenomenological Analysis of Knowledge
• Phenomenological Reduction
• Husserl’s Triad: Ego Cogito Cogitata
• Intentionality
• Understanding ‘Consciousness’
• Phenomenological Method in Indian Tradition
• Phenomenological Method in Religion

10 Analytical Method (Western and Indian)

• Analysis in History of Philosophy
• Conceptual Analysis
• Analysis as a Method
• Analysis in Logical Atomism and Logical Positivism
• Analytic Method in Ethics
• Language Analysis
• Quine’s Analytical Method
• Analysis in Indian Traditions

11 Hermeneutical Method (Western and Indian)

• The Power (Sakti) to Convey Meaning
• Three Meanings
• Pre-understanding
• The Semantic Autonomy of the Text
• Towards a Fusion of Horizons
• The Hermeneutical Circle
• The True Scandal of the Text
• Literary Forms

12 Deconstructive Method

• The Seminal Idea of Deconstruction in Heidegger
• Deconstruction in Derrida
• Structuralism and Post-structuralism
• Sign Signifier and Signified
• Writing and Trace
• Deconstruction as a Strategic Reading
• The Logic of Supplement
• No Outside-text

13 Method of Bibliography

• Preparing to Write
• Writing a Paper
• The Main Divisions of a Paper
• Writing Bibliography in Turabian and APA
• Sample Bibliography

14 Method of Footnotes

• Citations and Notes
• General Hints for Footnotes
• Writing Footnotes
• Examples of Footnote or Endnote
• Example of a Research Article

15 Method of Notes Taking

• Methods of Note-taking
• Note Book Style
• Note taking in a Computer
• Types of Note-taking
• Notes from Field Research
• Errors to be Avoided

16 Method of Thesis Proposal and Presentation

• Preliminary Section
• Presenting the Problem of the Thesis
• Design of the Study
• Main Body of the Thesis
• Conclusion Summary and Recommendations
• Reference Material

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• Belitung Nurs J
• v.9(5); 2023
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Beyond the classics: A comprehensive look at concept analysis methods in nursing education and research

Joko gunawan.

1 Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

2 Belitung Raya Foundation, Manggar, East Belitung, Bangka Belitung, Indonesia

Yupin Aungsuroch

Colleen marzilli.

3 University of Maine, School of Nursing, Orono, ME USA

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This editorial presents eight concept analysis methods for use in nursing research and education. In addition to the two classical methods of Walker and Avant’s and Rodgers’ concept analysis approaches that are typically utilized in nursing education and briefly discussed within this editorial, six additional methods are also presented including Schwartz-Barcott and Kim’s Hybrid model, Chinn and Kramer’s approach, Simultaneous Concept Analysis, Pragmatic Utility, Principle-Based Concept Analysis, and Semantic Concept Analysis. By familiarizing nursing educators, researchers, and students with these methods, educators can enhance their critical thinking and understanding of complex nursing concepts, preparing them for enhanced, multi-faceted contributions to nursing science.

Introduction

Concepts serve as abstract mental constructs, mental images of phenomena, units of meaning, or building blocks of theory, intended to summarize specific aspects or elements of the human experience (Chinn & Kramer, 1995 ; Penrod & Hupcey, 2005 ; Smith & Mörelius, 2021 ). However, for theory to be grounded in, and arise from real-world nursing practice, it is essential to bring clarity to the concepts under examination, known as concept analysis (Smith & Mörelius, 2021 ).

The primary aim of a concept analysis is to carefully study, clarify, develop, and critically assess a particular concept (Smith & Mörelius, 2021 ), all to attain a more profound and detailed understanding of the concept. While various methodologies for concept analysis are discussed in the nursing scientific literature, the most prominent approaches used among nursing students include the classical methods of Walker and Avant’s technique (Walker & Avant, 2014 ) and Rodger’s evolutionary approach (Rodgers, 1989 ).

This heavy reliance on these two methodologies raises an important question: are nursing students aware of the spectrum of available concept analysis methodologies rather than just the two common approaches? To our knowledge, teaching focuses mainly on Walker and Avant’s concept analysis and Rodgers’ evolutionary method in doctoral education influencing the analytical patterns of educators, researchers, and students. As a result, many students might not be familiar with other strategies for concept analysis. With this context in mind, this editorial article aims to provide a concise overview of various approaches that can be employed to conduct a thorough concept analysis.

Types of Concept Analysis Methods

1. walker and avant’s concept analysis.

Walker and Avant’s model presents a step-by-step method for analyzing a concept and creating a clear definition of the concept in question (Walker & Avant, 2014 ). It has eight stages based on Wilson’s techniques (Wilson, 1973 ). It starts by choosing a concept related to research goals and outlining the purpose of the analysis. Various uses of the concept in nursing are studied to understand its significance. Identifying defining attributes is crucial, serving as the concept’s core and distinguishing it from related ideas (Walker & Avant, 2014 ). While some researchers spot attributes through repeated terms, others use content analysis, thematic analysis, keyword clustering, or summative content analysis.

To make the concept more transparent, a model case is constructed as a “real-life” example, illustrating all main attributes. Additional cases, including borderline, related, and contrary examples, further explain the concept’s variations, refining its boundaries, and addressing differences from the model case (Walker & Avant, 2014 ). Antecedents and consequences are pinpointed. Empirical referents, or measurable indicators, ensure the concept’s practical applicability and verifiability (Walker & Avant, 2014 ).

It is noted that the method’s strengths lie in its systematic and organized approach, facilitating replication by other researchers. It is also tailored for nursing concepts, ensuring its relevance and practicality. However, the method may potentially oversimplify complex concepts, limit philosophical foundation, overlook contextual considerations and qualitative insights, and overclaim the operational definition of the concept (Weaver & Mitcham, 2008 ). Thus, researchers should consider complementing the method with other approaches to understand the concept under study better.

2. Schwartz-Barcott and Kim’s Hybrid Model

This model aims to refine concepts for theory development, builds upon Wilson’s method, and provides a learning platform for graduate students. As the term “hybrid” suggests, this model connects theoretical analysis and practical observation. It is built upon insights from three knowledge domains: the philosophy of science, the sociology of theory development, and participant observation, or field research. The method comprises three phases: Theoretical, Field Work, and Analytical (Schwartz-Barcott, 2000 ).

The Theoretical Phase establishes a foundation by selecting a loose concept definition, starting a literature review, and outlining essential elements. The Field Work Phase validates and refines through empirical observations and using standard qualitative research steps focusing on definition and measurement. The minimum data collection time is 2.5 to 3 months. The Analytical Phase involves comparing findings, and it also includes addressing the concept’s nursing relevance, justification, support in literature, theory, and data (Schwartz-Barcott, 2000 ).

The Schwartz-Barcott and Kim’s hybrid model provides a comprehensive and structured approach to concept analysis, combining theoretical and empirical aspects. Like any qualitative research approach, this hybrid model has limitations, such as potential bias and limited generalizability to broader groups or settings.

3. Chinn and Kramer’s Method

Chinn and Kramer [Jacobs] introduced their concept analysis methodology in 1983, crediting its origins to Wilson. The steps outlined by Chinn and Kramer in 1991 contrast with the method proposed by Walker and Avant by excluding “identifying antecedents and consequences” and “formulating criteria.” Instead, they formulate criteria after collecting and analyzing data, considering values and social context (Hupcey et al., 1996 ). They also include cases as “data sources” and incorporate various potential data sources for analysis, such as visual images, contemporary and traditional literature, musical expressions, poems, and insights from individuals interacting with the concept.

Chinn and Kramer’s method offers a less linear process that involves more interaction between steps. Their purpose of this technique is to better understand the concept by looking at the term used, what it represents, the linked emotions, principles, and perspectives. Chinn and Jacobs ( 1987 ) also describe the outcomes of a concept analysis as tentative, acknowledging that the concept’s definition and criteria for presence in a specific context may change as new evidence emerges. Chinn and Kramer’s method aligns more closely with Wilson’s approach than Walker and Avant’s interpretation. The method creates cases to find characteristics linked with the concept and distinguish criteria that genuinely belong to it from those that don't. They also explore the social situation and values related to the concept, similar to Wilson. Chinn and Kramer anticipate that criteria should be developed only after examining all these aspects. While Chinn and Kramer consider various factors in concept analysis, they may not stress the same level of intellectual rigor as Wilson (Hupcey et al., 1996 ). Chinn and Kramer’s method of concept analysis consists of choosing, establishing a purpose, investigating data, and developing validation criteria for the concept (Weaver & Mitcham, 2008 ).

4. Rodgers’ Evolutionary Concept Analysis

Rodgers' evolutionary concept analysis is an inductive approach that highlights how concepts evolve over time and are impacted by their context (Rodgers, 1989 ). This approach consistently examines a concept’s context, surrogate and related terms, antecedents, attributes, examples, and consequences. This approach does not offer definitive conclusions but serves as a guide for further research (Rodgers, 1989 ). In essence, Rodgers presents a cyclical model that accommodates the ever-changing nature of concepts.

Rodgers suggests six preliminary activities ( Table 1 ), which can occur simultaneously during the study. Unlike Walker and Avant, the research process is non-linear, rotational, and flexible (Ghadirian et al., 2014 ; Rodgers, 1989 ). The activities involve recognizing the concept of focus and its linked terms, choosing a suitable context, gathering data to determine the traits of the concept and its context, analyzing the collected information, pinpointing a prime example of the concept if applicable, and creating hypotheses and potential outcomes for advancing the concept's understanding. These stages represent the activities that should occur during the study rather than a continuous process. Rogers’ approach emphasizes detailed analysis and focuses on gathering and analyzing raw data, particularly within a profession’s specific social and cultural context (Ghadirian et al., 2014 ; Rodgers, 1989 ).

Summary of the steps/phases/principles of each concept analysis method

Despite its strengths, such as the inductive approach, flexibility and adaptability, and the utilization of comprehensive data sources, the findings derived from Rodger’s concept analysis might not always be readily generalizable to other contexts or populations, as the focus on specific social and cultural contexts restricts the broader applicability of the results. Furthermore, the iterative and flexible nature of the analysis may hinder the study’s reproducibility, making it challenging for other researchers to replicate the exact process and achieve identical results.

5. Simultaneous Concept Analysis

The Simultaneous Concept Analysis method consists of nine executive steps proposed by Haase et al. ( 2000 ), firmly rooted in Rodgers’ evolutionary perspective. The foundational principle of the Simultaneous Concept Analysis model lies in the recognition that numerous concepts have intricate interconnections, rendering isolated analysis impractical. However, these concepts can be effectively comprehended through comparative assessment, as their shared characteristics often warrant examination of closely related counterparts (Tavares et al., 2022 ).

The main goal of the Simultaneous Concept Analysis is not to establish a definitive and ultimate concept definition but to lay the groundwork for future exploration in the field of nursing. The analysis involves carefully examining each article to discover the attributes, antecedents, and outcomes associated with individual concepts (Haase et al., 2000 ; Tavares et al., 2022 ). This first analysis helps make a validity matrix. Thorough analysis ensures the method is systematic, verifiable, and replicable. Attributes, antecedents, and outcomes are gathered from relevant literature and subjected to comprehensive comparison within and across disciplines, thereby setting the stage for constructing a comparative validity matrix. Subsequently, the next step involves independently deriving the concept’s critical attributes, theoretical definitions, antecedents, and consequences (Tavares et al., 2022 ). The Simultaneous Concept Analysis comprises a series of nine stages (see Table 1 ).

6. Pragmatic Utility

Janice M. Morse initially developed the pragmatic utility concept analysis method as an alternative to Wilsonian and Rodgers’ methods. The pragmatic utility method examines the concept maturation level by scrutinizing its internal composition, utility, representational attributes, and interconnections with other concepts (Morse, 2000 ). Contrary to a linear progression, the pragmatic utility embodies a non-linear and iterative approach (Weaver & Mitcham, 2008 ). This method serves various purposes, including refining or elucidating concepts and examining the alignment between a concept’s definition and its operationalization (Zumstein & Riese, 2020 ).

The pragmatic utility aims to develop “partially mature” concepts using literature as data. Instead of synthesis, this meta-analytic approach examines how other researchers use the lay concept in their work. It uncovers definitions, attributes, and uses through systematic analysis, asking analytical questions about their conceptualizations and synthesizing data (Morse, 2016 ). This reveals implied/explicit assumptions, inferred meaning, and components. It identifies the lay concept’s commonalities, differences, perspectives, and operationalization degrees (Morse, 2016 ).

The pragmatic utility is not a literature summary or critique, nor a research synthesis or meta-analysis. It compares more than perspectives; it goes beyond creating new models and insights (Morse, 2016 ). Pragmatic utility stands apart from common literature summaries. It is also noted that this method emphasizes the ‘critical appraisal’ technique (Weaver & Mitcham, 2008 ), comparing attributes from different authors and revealing underlying assumptions and practical applications. Also, Morse et al. ( 1996 ) set a guideline, including the database’s extensiveness, analysis depth, argument logic, abstractness level, validity, and knowledge contribution, to assess rigor. Procedures of pragmatic utility include 1) clarifying the inquiry purpose, 2) pinpointing a partially mature lay concept, 3) determining concept maturity, 4) formulating key analytic questions, and 5) Synthesizing outcomes (Morse, 2016 ).

7. Principle-Based Concept Analysis

Penrod and Hupcey ( 2005 ) developed the Principle-Based Concept Analysis approach based on Morse et al. ( 1996 ) to define concepts based on principles exclusively within scientific use, disregarding creative interpretations found in art or fiction. The intentional and strategic extraction of data forms the foundation of this method. The approach acknowledges the dynamic and evolving nature of concept advancement over time, offering a robust framework for theoretically defining and understanding a concept’s state within the scientific community.

The analysis revolves around four broad principles: epistemological, pragmatic, linguistic, and logical (Penrod & Hupcey, 2005 ). The concept’s alignment with these principles determines its level of maturity and advancement. The epistemological principle is the study of how knowledge plays a role in revealing the scientific knowledge underpinning the concept (Waldon, 2018 ). The epistemological analysis focuses on the concept’s distinctiveness within the discipline’s knowledge base, indicating maturity through differentiation and clear positioning (Penrod & Hupcey, 2005 ). The pragmatic principle assesses a concept’s utility within the discipline and its operationalization, particularly in nursing (Penrod & Hupcey, 2005 ). The principle evaluates whether the concept’s applicability is supported by the literature and recognized by the discipline, profession, and society. Mature concepts are manifested in clinical practice (Penrod & Hupcey, 2005 ). The linguistic principle examines language and human speech, assessing a concept’s contextual flexibility and consistent meaning (Penrod & Hupcey, 2005 ; Waldon, 2018 ). The analysis includes various contexts, ensuring the concept’s relevance across different settings (Penrod & Hupcey, 2005 ). The logical principle involves a concept’s compatibility with related concepts and the clarity of its boundaries. Clearly defined conceptual boundaries prevent ambiguity when the concept is positioned alongside others in a theoretical framework (Penrod & Hupcey, 2005 ).

The outcome of Principle-Based Concept Analysis involves a comprehensive synthesis of the concept using scientific literature, along with identifying gaps and inconsistencies to drive concept development. Subsequently, the results are integrated into a theoretical definition, enhancing the concept’s understanding. In addition, Smith and Mörelius ( 2021 ) also combine Principle-Based Concept Analysis with a phased approach to enhance method clarity.

8. Semantic Concept Analysis

The semantic concept analysis, initially formulated by Koort ( 1975 ) and subsequently refined by Eriksson ( 2010 ), constitutes a prevalent approach in Nordic nursing science research aimed at enhancing comprehension of concepts or phenomena requiring clarification (Almerud Österberg et al., 2023 ). This method transcends a mere combination of words or letters, and it instead intimately intertwines with human existence and lived encounters (Almerud Österberg et al., 2023 ; Eriksson, 2010 ). This method includes an analysis of etymological, semantic, and discrimination (Honkavuo et al., 2018 ). Etymological analysis involves exploring a concept’s origin, transformation, and evolution using etymological dictionaries (Koort, 1975 ). Historical meanings may not persist in current language usage (Honkavuo et al., 2018 ; Koort, 1975 ). The semantic analysis uses dictionaries and synonyms to find linguistic consensus. It is about interpreting linguistic expressions, symbols, words, and terms, and if researchers agree on synonyms, the analysis concludes. If not, a discrimination analysis comes next, exploring closely related concepts to distinguish the concept in question. These related concepts form clusters based on qualitative differences in meanings and degrees of synonymy (Honkavuo et al., 2018 ; Koort, 1975 ).

The eight concept analysis methods discussed above provide various ways to systematically examine and understand complex concepts across different fields, especially in nursing. The first three methods—Walker and Avant’s concept analysis, Schwartz-Barcott and Kim’s hybrid model, and the Chinn and Kramer’s method—have evolved and expanded from Wilson’s foundational approach. Each brings unique contributions, adaptations, and modifications to the concept analysis process.

Rodgers’ cyclical model considers the dynamic nature of concepts, encouraging an iterative analysis and emphasizing the importance of considering the context and cultural factors. The Simultaneous Concept Analysis method incorporates principles from Rodgers’ approach and focuses on how concepts are interconnected. It also highlights the comparison of related concepts, offering a more complete perspective.

Like Rodger’s approach, the pragmatic utility concept analysis method uses a non-linear and iterative approach. It emphasizes the practical usefulness of concepts and how they align with guiding principles. It also strongly emphasizes critical appraisal, resulting in a rigorous evaluation. In addition, the Principle-Based Concept Analysis approach extends Morse’s approach aiming to align concepts with epistemological, pragmatic, linguistic, and logical principles. Lastly, the Semantic Concept Analysis method deeply explores a concept’s linguistic origins, synonyms, and distinctive features. It provides a comprehensive understanding of concepts within their linguistic context.

It is noteworthy that these methods are not mutually exclusive. This editorial aims to spur educators, researchers and students to adopt, adapt, and combine elements from different ways to create a customized approach that suits their research needs as well as to further discuss the concept analysis development. Ultimately, the chosen concept analysis method should align with the research objectives and be accountable conceptually, critically, and philosophically. Additionally, it should contribute to a comprehensive understanding of the concept under study.

Acknowledgment

Declaration of conflicting interest.

The authors declare that they have no conflict of interest in this study.

Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand.

Authors’ Contributions

All authors contributed equally in this article.

Authors’ Biographies

Joko Gunawan, S.Kep.Ners, PhD is a Managing Editor of Belitung Nursing Journal and a Research Fellow at the Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand.

Yupin Aungsuroch, PhD, RN is an Associate Professor at the Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand. She is also an Editor-in-Chief of Belitung Nursing Journal.

Colleen Marzilli, PhD, DNP, MBA, RN-BC, CCM, PHNA-BC, CNE, NEA-BC, FNAP is a Professor of Nursing at the University of Maine, School of Nursing, Orono, ME USA. She is also an Editorial Advisory Board Member of Belitung Nursing Journal.

Data Availability

Declaration of use of ai in scientific writing.

Nothing to declare.

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NURS 700: Advanced Nursing Science

Concept analysis assignment.

• Scholarly Journal Articles
• Article Database Tutorials
• Books & Films

For your Concept Analysis assignment, you need to find specific types of scholarly sources. These can include books or peer-reviewed articles from scholarly journals. Remember, you can use a reference book such as an encyclopedia, thesaurus or dictionary, but you are limited to only one of each.

Please see the D2L page for your class for assignment instructions and the Walker and Avant book chapter. It is very important that you read both carefully.

Selection of appropriate search terms is very appropriate. We recommend connecting the term for your concept with any of these search terms using the Boolean operator AND. Also, truncate words using the * symbol to search for variant word endings. Here are some helpful search strategies to try:

• compassion AND "concept analysis"
• compassion AND philosoph*
• compassion AND theor*
• compassion AND defin*
• compassion AND concept*
• compassion AND (religious OR religion* OR theolog*)
• compassion AND anthropolog*
• compassion AND theor* AND psycholog*

It is often useful to use the advanced search screens in the article databases. Please also see the videos below for more search tips.

For a complete list of article databases available through the library, click on Search for Articles - Library Databases on the homepage of the library website ( www.metrostate.edu/library) or go to the A-Z Listing of Guides .

• Sample Concept Analysis Paper

Click on the PDF icon below to download a sample concept analysis paper written by a student for a previous term (shared with the consent of the student).

Please note the comments from the instructor.

Sample Database Searches

Here are some videos of sample searches in some commonly used databases. Note: You will need headphones or speakers in order to hear the audio.

Academic Search Premier

Tutorial: Peer Review in Five Minutes

The Peer Review In Five Minutes video was created by North Carolina State University Libraries , and is licensed for free, noncommercial use under the Creative Commons Attribution - Noncommercial-Share Alike 3.0 United States License .

Boolean Operators Tutorial

Learn to use the Boolean operators AND, OR and NOT to retrieve more items on your topic and to search more precisely.

Searching Effectively Using AND, OR, NOT

Tutorial produced by the Colorado State University Libraries. Used with permission.

Reference Desk:  (651) 793-1614 General information:  (651) 793-1616 Email:  [email protected]

• Next: Scholarly Journal Articles >>
• Last Updated: Apr 24, 2024 4:02 PM
• URL: https://libguides.metrostate.edu/nurs700

A Guide to Conceptual Analysis Research

Understand how to conduct a more accurate and detailed conceptual analysis research to convert it into a more concrete concept.

There are countless researches, theories, studies and concepts on countless subjects. This is the result of years of collecting data and turning it into informative text. 

There are times when it is necessary to investigate existing theories and concepts in order to gain a better understanding or to bring out more nuances. When it comes to concepts, the best way to approach them is through a conceptual analysis research , which will provide and clarify all possible information, coherent or not, that surrounds this specific concept. 

This article will walk you through how to conduct a more accurate and detailed conceptual analysis research to convert an abstract and ambiguous idea into a more concrete concept.

What is conceptual analysis, and how does it work?

Conceptual Analysis Research can be defined as the examination of a concept into simpler elements to promote clarification while having a consistent understanding; analysis can include distinguishing, analyzing, and representing the various aspects to which the concept refers.

The ultimate goal in general is to improve the conceptual clarity and coherence of careful clarifications and definitions of meaning. Or, on occasion, to expose practical inconsistency.

Preparing a conceptual analysis entails conducting a literature review, identifying the key characteristics or attributes of the concept, identifying its antecedents and consequences, and possibly applying them to a model case.  

How to write a conceptual analysis?

Choose a Concept to Study

First and foremost, determine the concept that will guide the research. Examine specific and interesting areas, then select a field of study for the research, which should be based on a theoretical framework.

And remember, the conceptual analysis is supposed to bring clarity and coherence; the subject chosen must allow this. Avoid subjects that do not allow further clarification, do not have enough data, or are not ambiguous enough to warrant in-depth analysis.

Conduct a Literature Review

An initial review of the literature on the chosen concept can provide countless insights into the concept and hence the researcher can find out whatever is known, not known, or confusing about a concept.

Conduct a review of the literature on your chosen concept from a wide range of disciplines. Instead of focusing solely on the chosen field of study, search for nuances in other disciplines and potential collaborations.

Let’s say the chosen field is medicine, for example, look for psychological, sociological, interpersonal, and any other possible aspects of it. While conducting the literature review, begin by identifying surrogate terms, relevant uses, and inconsistencies in the concept.

Select an Appropriate Sample to Collect Data

There will be a large amount of data to understand and use after conducting a thorough literature review. This step is inestimable since it is so important to be critical during this stage of the process to select the best and more assertive data to analyze and include in the analysis.

It might be useful to inquire whether the authors are “describing the concept the same,” “similarly using the concept,” or “were any inconsistencies available in literature?”. 

Identify Characteristics

Determine the key characteristics or features of your concept. The characteristics of a concept are what makes it true. Assume the concept is a pregnancy, and the fetus’s heartbeat is what makes the pregnancy real, therefore this is the main characteristic of the concept.

Making good use of the concept’s characteristics will result in the operationalization of the concept, which will lead to the selection of a measurement tool.

Assess the Concept Antecedents and Consequences

By definition, antecedents are what initiated the concept, what led to it. While consequences are the results and outcomes, what follows the concept.

This is an important step in dissecting the concept and understanding all of its nuances. This step gives the researchers an idea of how tangible the concept is.

Identify Concepts Related to the Concept of Interest

After defining the concept’s characteristics, antecedents and consequences, it is time to determine whether any related concepts in the literature require clarification.

Again, a critical review of the literature will reveal to the researcher what relevant research has been conducted, any conceptual ambiguity, and the implications for future research.

Construct Cases for Analysis

When doing conceptual analysis research , enrich your analysis by adding cases into the research, which will provide the research with a more concrete understanding of the concept and will aid in the clarification of the research’s direction.

Include a model case, a contrary case, a related case, and a borderline case in the analysis. A model case has all of the concept’s key characteristics, all or most of the defining criteria, and at least one of the antecedents and consequences.

A contrary case possesses none of the defining characteristics, while a related case possesses a similar defining characteristic, and a borderline case may be a metaphorical application of the concept.

Understanding the uses of conceptual analysis in research

Before deciding to do a conceptual analysis research, it is critical to consider both the advantages and disadvantages of conducting it. 

Advantages of Conceptual Analysis

• Refining and validating a possibly confusing concept.
• Provides valuable historical and cultural insights over time.
• Clarify any ambiguous concepts that could be used as synonyms
• Possibly developing instruments for better measurement of the concept’s data.

Disadvantages of Conceptual Analysis

• Conceptual analysis can not create a new concept, only validate existing ones. 
• It’s a difficult process that requires a long time and persistence to investigate and validate a concept.
• Dealing with such a large amount of data can be uncertain and overwhelming for the researcher.
• More prone to error, especially when a relational analysis is used to achieve a higher level of interpretation.

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Concepts are the building blocks of thoughts. Consequently, they are crucial to such psychological processes as categorization, inference, memory, learning, and decision-making. This much is relatively uncontroversial. But the nature of concepts—the kind of things concepts are—and the constraints that govern a theory of concepts have been the subject of much debate (Margolis & Laurence 1999, Margolis & Laurence 2015). This is due, at least in part, to the fact that disputes about concepts often reflect deeply opposing approaches to the study of the mind, to language, and even to philosophy itself. In this entry, we provide an overview of theories of concepts, and outline some of the disputes that have shaped debates surrounding the nature of concepts. The entry is organized around five significant issues that are focal points for many theories of concepts. Not every theory of concepts takes a stand on each of the five, but viewed collectively these issues show why the theory of concepts has been such a rich and lively topic in recent years. The five issues are: (1) the ontology of concepts, (2) the structure of concepts, (3) empiricism and nativism about concepts, (4) concepts and natural language, and (5) concepts and conceptual analysis.

• 1.1 Concepts as mental representations

1.2 Concepts as abilities

1.3 concepts as abstract objects, 1.4 is the issue merely terminological, 2.1 the classical theory, 2.2 the prototype theory, 2.3 the theory theory, 2.4 conceptual atomism, 2.5 pluralism and eliminativism, 3.1 renewed interest in the empiricism/nativism dispute, 3.2 empiricism about concepts, 3.3 nativism about concepts, 4.1 can there be concepts without language, 4.2 the priority between language and concepts, 4.3 linguistic determinism and linguistic relativity, 5.1 attractions of conceptual analysis, 5.2 objections to conceptual analysis, other internet resources, related entries, 1. the ontology of concepts.

We begin with the issue of the ontological status of a concept. The three main options are to identify concepts with mental representations, with abilities, and with abstract objects such as Fregean senses.

1.1. Concepts as mental representations

The first of these views maintains that concepts are psychological entities, taking as its starting point the representational theory of the mind (RTM). According to RTM, thinking occurs in an internal system of representation. Beliefs and desires and other propositional attitudes enter into mental processes as internal symbols. For example, Sue might believe that Dave is taller than Cathy, and also believe that Cathy is taller than Ben, and together these may cause Sue to believe that Dave is taller than Ben. Her beliefs would be constituted by mental representations that are about Dave, Cathy and Ben and their relative heights. What makes these beliefs, as opposed to desires or other psychological states, is that the symbols have the characteristic causal-functional role of beliefs. (RTM is usually presented as taking beliefs and other propositional attitudes to be relations between an agent and a mental representation (e.g., Fodor 1987). But given that the relation in question is a matter of having a representation with a particular type of functional role, it is simpler to say that occurrent beliefs just are mental representations with this functional role.)

Many advocates of RTM take the mental representations involved in beliefs and other propositional attitudes to have internal structure. Accordingly, the representations that figure in Sue’s beliefs would be composed of more basic representations. For theorists who adopt the mental representation view of concepts, concepts are identified with these more basic representations.

Early advocates of RTM (e.g., Locke (1690/1975) and Hume (1739/1978)) called these more basic representations ideas , and often took them to be mental images. But modern versions of RTM assume that much thought is not grounded in mental images. The classic contemporary treatment maintains, instead, that the internal system of representation has a language-like syntax and a compositional semantics. According to this view, much of thought is grounded in word-like mental representations . This view is often referred to as the language of thought hypothesis (Fodor 1975). However, the analogy with language isn’t perfect; obviously, the internal symbol system must lack many of the properties associated with a natural language. Nonetheless, like a natural language, the internal system’s formulae are taken to have subject/predicate form and to include logical devices, such as quantifiers and variables. In addition, the content of a complex symbol is supposed to be a function of its syntactic structure and the contents of its constituents. Returning to Sue’s beliefs, the supposition is that they are composed of such symbols as DAVE, CATHY and TALLER and that her beliefs represent what they do in virtue of the contents of these symbols and how they are arranged .

The mental representation view of concepts is the default position in cognitive science (Carey 2009, Pinker 2007) and enjoys widespread support in the philosophy of mind, particularly among philosophers who view their work as being aligned with research in cognitive science (e.g., Carruthers 2006, Millikan 2000, Fodor 2003, Margolis & Laurence 2007). [ 1 ] Supporters of this view argue for it on explanatory grounds. They maintain that concepts and structured mental representations play a crucial role in accounting for the productivity of thought (i.e., the fact that human beings can entertain an unbounded number of thoughts), in explaining how mental processes can be both rational and implemented in the brain, and in accommodating the need for structure-sensitive mental processes (Fodor 1987; see also the entry language of thought hypothesis ).

Critics of this view argue that it is possible to have propositional attitudes without having explicitly entertained the relevant mental representations. Daniel Dennett (1977), for example, argues that most people believe zebras don’t wear overcoats in the wild —and a million other similar facts—even though they have never stopped to consider such matters. Dennett also notes that computing systems can lack representations corresponding to the explanations we cite in characterizing and predicting their behavior. For example, it may make perfect sense to say of a chess-playing computer that it thinks that it is good to get one’s queen out early, even though we know from how the computer is programmed that it has no representation with that very content (see Dennett 1978, 1987 for these and related criticisms and Fodor 1987 and Manfredi 1993 for responses).

Other critics claim that RTM is too closely associated with commonsense psychology, which they argue should be abandoned as a stagnant and degenerate research program (Churchland 1981; see entry eliminative materialism ), or that developments in computational modeling (esp. connectionism and dynamic systems theory) offer alternatives, particularly to the language of thought version of RTM (e.g., see Clark 1993, Van Gelder 1995, Elman et al. 1996, McClelland et al. 2010; see Fodor & Pylyshyn 1988, Marcus 2001, and Gallistel & King 2009 for critical discussion of theories that don’t employ combinatorial structure.)

According to the abilities view, it’s wrong to maintain that concepts are mental particulars—concepts are neither mental images nor word-like entities in a language of thought. Rather, concepts are abilities that are peculiar to cognitive agents (e.g., Dummett 1993, Bennett & Hacker 2008, Kenny 2010). The concept CAT, for example, might amount to the ability to discriminate cats from non-cats and to draw certain inferences about cats.

While the abilities view is maintained by a diverse group of philosophers, the most prominent reason for adopting the view is a deep skepticism about the existence and utility of mental representations, skepticism that traces back Ludwig Wittgenstein (1953/1958). One of the most influential arguments along these lines claims that mental representations are explanatorily idle because they reintroduce the very sorts of problems they are supposed to explain. For example, Michael Dummett cautions against trying to explain knowledge of a first language on the model of knowledge of a second language. In the case of a second language, it is reasonable to suppose that understanding the language involves translating its words and sentences into words and sentences of one’s first language. But according to Dummett, one can’t go on to translate words and sentences of one’s first language into a prior mental language. “[T]here is really no sense to speaking of a concept’s coming into someone’s mind. All we can think of is some image coming to mind which we take as in some way representing the concept, and this gets us no further forward, since we still have to ask in what his associating that concept with that image consists” (Dummett 1993, p. 98). In other words, the mental representation itself is just another item whose significance bears explaining. Either we are involved in a vicious regress, having to invoke yet another layer of representation (and so on indefinitely) or we might as well stop with the external language and explain its significance directly. (For critical discussion of this type of regress argument, see Fodor 1975, Crane 2015, Laurence & Margolis 1997).

Not surprisingly, critics of the abilities view argue in the other direction. They note difficulties that the abilities view inherits by its rejection of mental representations. One is that the view is ill-equipped to explain the productivity of thought; another is that it can say little about mental processes. And if proponents of the abilities view remain neutral about the existence of mental representations, they open themselves to the criticism that explication of these abilities is best given in terms of underlying mental representations and processes (see Fodor 1968 and Chomsky 1980 for general discussion of the anti-intellectualist tradition in the philosophy of mind).

An alternative conception of concepts takes concepts to be abstract objects of one type or another. The idea behind this view is that concepts are the meanings (or “contents”) of words and phrases as opposed to mental objects or mental states. This type of view has most prominently been associated with the view that concepts are Fregean senses (e.g., Peacocke 1992, Zalta 2001), so it is this version of the view that concepts are abstract objects that we will focus on here. For proponents of this view, concepts, as meanings, mediate between thought and language, on the one hand, and referents, on the other. An expression without a referent (“Pegasus”) needn’t lack a meaning, since it still has a sense. Similarly, the same referent can be associated with different expressions (e.g., “Eric Blair” and “George Orwell”) because they convey different senses. Senses are more discriminating than referents. Each sense has a unique perspective on its referent—a mode of presentation which represents the referent in a particular way. Differences in cognitive content trace back to differences in modes of presentation. It’s for this reason that the thought that George Orwell is Eric Blair lacks the triviality of the thought that George Orwell is George Orwell even though George Orwell and Eric Blair are the same person. Philosophers who take concepts to be senses particularly emphasize this feature of senses. Christopher Peacocke, for example, locates the subject matter of a theory of concepts as follows: “Concepts C and D are distinct if and only if there are two complete propositional contents that differ at most in that one contains C substituted in one or more places for D , and one of which is potentially informative while the other is not” (Peacocke 1992, p. 2). In other words, C and D embody differing modes of presentation. (See the entry Frege for discussion of the sense/reference distinction and for more on the explanatory functions associated with senses. To avoid terminological confusion, we should note that Frege himself did not use the term "concept" for senses, but rather for the referents of predicates. Similarly, it is worth noting that Frege uses the term "thought" to stand for propositions, so for Frege thoughts are not psychological states at all but rather the meanings of psychological states.)

The view that concepts are Fregean senses, like the abilities view, is generally held by philosophers who are opposed to identifying concepts with mental representations. Peacocke himself doesn’t go so far as to argue that mental representations are explanatorily idle, but he does think that mental representations are too fine-grained for philosophical purposes. “It is possible for one and the same concept to receive different mental representations in different individuals” (Peacocke 1992, p. 3). He is also concerned that identifying concepts with mental representations rules out the possibility of there being concepts that human beings have never entertained, or couldn’t ever entertain.

If we accept that a thinker’s possession of a concept must be realized by some subpersonal state involving a mental representation, why not say simply that the concept is the mental representation? Just this proposal is made by Margolis and Laurence (1999, 77). Mental representations that are concepts could even be typed by the corresponding possession condition of the sort I favour. This seems to me an entirely legitimate notion of a kind of mental representation; but it is not quite the notion of a concept. It can, for instance, be true that there are concepts human beings may never acquire, because of their intellectual limitations, or because the sun will expand to eradicate human life before humans reach a stage at which they can acquire these concepts. ‘There are concepts that will never be acquired’ cannot mean or imply ‘There are mental representations which are not mental representations in anyone’s mind’. If concepts are individuated by their possession conditions, on the other hand, there is no problem about the existence of concepts that will never be acquired. They are simply concepts whose possession conditions will never be satisfied by any thinkers. (Peacocke, 2005, p. 169).

Advocates of the mental representation view would respond to these arguments by invoking the type/token distinction with respect to mental representations. Just as two speakers can each produce distinct instances (that is, tokens)of the same type of word (e.g., the word “chair”), different thinkers’ minds can produce distinct instances (that is, tokens) of the same type of mental representation (e.g., the mental representation CHAIR). How does this help with the present objection? According to the mental representation view, to say that there are concepts that haven’t been acquired by anyone is just to say that, for some types of mental representations, there has not yet been a token in anyone’s mind; to say that some concepts will never be acquired by anyone is just to say that, for some types of mental representations, there never will be a token in anyone’s mind (Margolis & Laurence 2007).

Critics of the sense-based view have questioned the utility of appealing to such abstract objects (Quine 1960). One difficulty stems from the fact that senses, as abstract entities, stand outside of the causal realm. The question then is how we can access these objects. Advocates of the Fregean sense view describe our access to senses by means of the metaphor of “grasping”—we are said to grasp the sense of an expression. But grasping here is just a metaphor for a cognitive relation that needs to be explicated. Moreover, though senses are hypothesized as providing different modes of presentation for referents, it is not clear why senses themselves do not generate the mode of presentation problem (Fodor 1998). Since they are external to our minds, just as referents typically are, it isn’t clear why we can’t stand in different epistemic relations towards them just as we can to referents. In the same way that we can have different modes of presentation for a number (the only even prime, the sum of one and one, Tim’s favorite number, etc.), we ought to be able to have different modes of presentation for a given sense.

Stepping back from the details of these three views, there is no reason, in principle, why the different views of concepts couldn’t be combined in various ways. For instance, one might maintain that concepts are mental representations that are typed in terms of the Fregean senses they express (see Margolis & Laurence 2007 for discussion).

One might also question whether the dispute about ontology is a substantive dispute. Perhaps there is only a terminological issue about which things ought to be granted the label “concepts”. If so, why not just call mental representations “concepts 1 ”, the relevant abilities “concepts 2 ”, senses “concepts 3 ”, and leave it at that?

However, the participants in the dispute don’t generally view it as a terminological one. Perhaps this is because they associate their own theories of concepts with large-scale commitments about the way that philosophers should approach the study of mind and language. Undoubtedly, from Dummett’s perspective, philosophers who embrace the mental representation view also embrace RTM, and RTM, as he sees it, is fundamentally misguided. Likewise, from Fodor’s perspective, RTM is critical to the study of the mind, so an approach like Dummett’s, which disallows RTM, places inappropriate a priori constraints on the study of the mind. Given that the disagreement about concepts is so closely tied to such clearly substantive explanatory disagreements, the debate about what concepts are would itself seem to be a substantive disagreement about what sorts of entities are best suited to playing the central explanatory roles associated with concepts.

Of course, it is possible to introduce new theoretical terms (“concepts 1 ”, “concepts 2 ”, and “concepts 3 ”) for the different theoretical posits made by the different approaches we have been considering (the ability view, the mental representation view, and the abstract object view), and then reconstruct the debate regarding these different approaches as a merely terminological disagreement about which of these terms we should use. But notice that it is possible to do the same thing even for clear-cut substantive debates. For example, we could introduce new terms for moral goodness as understood by deontologists (“good 1 ”) and as understood by consequentialists (“good 2 ”), and then reconstruct the debate about what moral goodness consists in as a merely terminological disagreement about which term we should use, “good 1 ” or “good 2 ”. Or we could introduce “human 1 ” for humans as understood by standard evolutionary accounts (according to which humans have nonhuman primate ancestors) and “human 2 ” for humans understood by creationists (according to which they don't), and reconstruct the debate between these different approaches as a merely terminological issue about which term we should use, “human 1 ” or “human 2 ”. The danger, of course, is that this would drain the very idea of a merely terminological debate of its utility and obscure very real differences between different sorts of disagreements (e.g., stipulative vs. substantive) that would all end up counting as “merely terminological”.

In the remainder of this entry we will frame questions from the perspective of the mental representation view, but readers who prefer a different view can reframe the issues in their preferred terms. For example, the questions “Can there be concepts without language?” is typically understood on the mental representation view as asking whether a pre-linguistic or non-linguistic agent can entertain mental representations of a particular kind. If one adopts the view that concepts are abstract objects, the corresponding question might be whether a pre-linguistic or non-linguistic agent can stand in the concept possession relation to concepts understood as abstract objects of a certain type. And if one adopts the view that concepts are abilities, the corresponding question might be whether a pre-linguistic or non-linguistic agent can have the abilities that are constitutive of concept possession.

2. The structure of concepts

Just as thoughts are composed of concepts, many concepts are themselves complex entities that are composed of other concepts or more basic representational components. In this section, we look at different proposals about the structure of what are often called lexical concepts . Roughly speaking, these are concepts that tend to be associated with individual words in natural language—concepts like BIRD and WALK as opposed to manifestly complex concepts like NOCTURNAL ANIMALS THAT LIVE IN TREES or TO WALK AIMLESSLY ON A NEWLY PAVED ROAD. With BIRD and WALK and similar concepts, it isn’t obvious what type of internal structure (if any) they have, and this has led to a great deal of controversy (Margolis & Laurence 1999, Murphy 2002). Accounts of the structure of lexical concepts have focused on concepts for categories of objects, actions, and events rather than concepts for individuals. Our discussion will share this focus (see, e.g., Blok et al. 2005 and Levine 2010 for discussion of concepts of individuals).

In one way or another, all theories regarding the structure of concepts are developments of, or reactions to, the classical theory of concepts . According to the classical theory, a lexical concept C has definitional structure in that it is composed of simpler concepts that express necessary and sufficient conditions for falling under C. The stock example is the concept BACHELOR, which is traditionally said to have the constituents UNMARRIED and MAN. If the example is taken at face value, the idea is that something falls under BACHELOR if it is an unmarried man and only if it is an unmarried man. According to the classical theory, lexical concepts generally will exhibit this same sort of definitional structure. This includes such philosophically interesting concepts as TRUTH, GOODNESS, FREEDOM, and JUSTICE.

Before turning to other theories of conceptual structure, it’s worth pausing to see what’s so appealing about classical or definitional structure. Much of its appeal comes from the way it offers unified treatments of concept acquisition, categorization, and reference determination. In each case, the crucial work is being done by the very same components. Concept acquisition can be understood as a process in which new complex concepts are created by assembling their definitional constituents. Categorization can be understood as a psychological process in which a complex concept is matched to a target item by checking to see if each and every one of its definitional constituents applies to the target. And reference determination, we’ve already seen, is a matter of whether the definitional constituents do apply to the target.

These considerations alone would be enough to show why the classical theory has been held in such high regard. But the classical theory receives further motivation through its connection with a philosophical method that goes back to antiquity and that continues to exert its influence over contemporary thought. This is the method of conceptual analysis . Paradigmatic conceptual analyses offer definitions of concepts that are to be tested against potential counterexamples that are identified via thought experiments. Conceptual analysis is supposed to be a distinctively a priori activity that many take to be the essence of philosophy. To the extent that paradigmatic conceptual analyses are available and successful, this will convey support for the classical theory. Conversely, if the definitions aren’t there to be discovered, this would seem to put in jeopardy a venerable view of what philosophy is and how philosophical investigations ought to proceed (see section 5 below).

The classical theory has come under considerable pressure in the last forty years or so, not just in philosophy but in psychology and other fields as well. For psychologists, the main problem has been that the classical theory has difficulty explaining a robust set of empirical findings. At the center of this work is the discovery that certain categories are taken to be more representative or typical and that typicality scores correlate with a wide variety of psychological data (for reviews, see Smith & Medin 1981, Murphy 2002). For instance, apples are judged to be more typical than plums with respect to the category of fruit, and correspondingly apples are judged to have more features in common with fruit. There are many other findings of this kind. One other is that more typical items are categorized more efficiently. For example, subjects are quicker to judge that apples are a kind of fruit than to judge that plums are. The problem isn’t that the classical theory is inconsistent with results like these but that it does nothing to explain them.

In philosophy, the classical theory has been subjected to a number of criticisms but perhaps the most fundamental is that attempts to specify definitions for concepts have a poor track record. Quite simply, there are too few examples of successful definitional analyses, and certainly none that are uncontroversial (Wittgenstein 1953/1958, Fodor 1981). The huge literature on the analysis of knowledge is representative of the state of things. Since Edmund Gettier (1963) first challenged the traditional definition of KNOWLEDGE (as JUSTIFIED TRUE BELIEF), there has been widespread agreement among philosophers that the traditional definition is incorrect or at least incomplete (see the entry the analysis of knowledge )). But no one can seem to agree on what the correct definition is. Despite the enormous amount of effort that has gone into the matter, and the dozens of papers written on the issue, we are still lacking a satisfactory and complete definition. It could be that the problem is that definitions are hard to come by. But another possibility—one that many philosophers are now taking seriously—is that our concepts lack definitional structure.

What other type of structure could they have? A non-classical alternative that emerged in the second half of the twentieth century is the prototype theory (e.g., Hampton 2006). According to this theory, a lexical concept C doesn’t have definitional structure but has probabilistic structure in that something falls under C just in case it satisfies a sufficient number of properties encoded by C ’s constituents. The prototype theory has its philosophical roots in Wittgenstein’s (1953/1958) famous remark that the things covered by a term often share a family resemblance, and it has its psychological roots in Eleanor Rosch’s groundbreaking experimental treatment of much the same idea (Rosch & Mervis 1975, Rosch 1978). The prototype theory is especially at home in dealing with the typicality effects that were left unexplained by the classical theory. One standard strategy is to maintain that, on the prototype theory, categorization is to be understood as a similarity comparison process, where similarity is computed as a function of the number of constituents that two concepts hold in common. On this model, the reason apples are judged to be more typical than plums is that the concept APPLE shares more of its constituents with FRUIT. Likewise, this is why apples are judged to be a kind of fruit faster than plums are.

The prototype theory does well in accounting for a variety of psychological phenomena and it helps to explain why definitions may be so hard to produce. But the prototype theory has its own problems and limitations. One is that its treatment of categorization works best for quick and unreflective judgments. Yet when it comes to more reflective judgments, people go beyond the outcome of a similarity comparison. If asked whether a dog that is surgically altered to look like a raccoon is a dog or a raccoon, the answer for most of us, and even for children, is that it is remains a dog (see Keil 1989, Gelman 2003 for discussion). Another criticism that has been raised against taking concepts to have prototype structure concerns compositionality. When a patently complex concept has a prototype structure, it often has emergent properties, ones that don’t derive from the prototypes of its constituents (e.g., PET FISH encodes properties such as brightly colored, which have no basis in the prototype structure for either PET or FISH). Further, many patently complex concepts don’t even have a prototype structure (e.g., CHAIRS THAT WERE PURCHASED ON A WEDNESDAY) (Fodor & Lepore 1996, Fodor 1998; for responses to the arguments from compositionality, see Prinz 2002, Robbins 2002, Hampton & Jönsson 2012, and Del Pinal 2016).

One general solution that addresses all of these problems is to hold that a prototype constitutes just part of the structure of a concept. In addition, concepts have conceptual cores , which specify the information relevant to more considered judgments and which underwrite compositional processes. Of course, this just raises the question of what sort of structure conceptual cores have. One suggestion is that conceptual cores have classical structure (Osherson & Smith 1981, Landau 1982). This won’t do, however, since it just raises once again most of the problems associated with the classical theory (Laurence & Margolis 1999).

Another and currently more popular suggestion is that cores are best understood in terms of the theory theory of concepts . This is the view that concepts stand in relation to one another in the same way as the terms of a scientific theory and that categorization is a process that strongly resembles scientific theorizing (see, e.g., Carey 1985, 2009, Gopnik & Meltzoff 1997, Keil 1989). It’s generally assumed, as well, that the terms of a scientific theory are interdefined so that a theoretical term’s content is determined by its unique role in the theory in which it occurs.

The theory theory is especially well-suited to explaining the sorts of reflective categorization judgments that proved to be difficult for the prototype theory. For example, theory theorists maintain that children override perceptual similarity in assessing the situation where the dog is made to look like a raccoon, claiming that even children are in possession of a rudimentary biological theory. This theory tells them that being a dog isn’t just a matter of looking like a dog. More important is having the appropriate hidden properties of dogs—the dog essence (see Atran & Medin 2008 on folkbiology). Another advantage of the theory theory is that it is supposed to help to explain important aspects of conceptual development. Conceptual change in childhood is said to follow the same pattern as theory change in science.

One problem that has been raised against the theory theory is that it has difficulty in allowing for different people to possess the same concepts (or even for the same person to have the same concept over time). The reason is that the theory theory is holistic . A concept’s content is determined by its role in a theory, not by its being composed of just a handful of constituents. Since beliefs that enter people’s mental theories are likely to be different from one another (and are likely to change), there may be no principled basis for comparison (Fodor & Lepore 1992). Another problem with the theory theory concerns the analogy to theory change in science. The analogy suggests that children undergo radical conceptual reorganization in the course of cognitive development, but many of the central case studies have proved to be controversial on empirical grounds, with evidence that the relevant concepts are implicated in core knowledge systems that are enriched in development but not fundamentally altered (see Spelke & Kinzler 2007 on core knowledge). However, there are certain specific examples where radical conceptual reorganization is plausible, for instance, when children eventually develop a theory of matter that allows them to differentiate weight from density, and air from nothing (Carey 2009).

A radical alternative to all of the theories we’ve mentioned so far is conceptual atomism , the view that lexical concepts have no semantic structure (Fodor 1998, Millikan 2000). According to conceptual atomism, the content of a concept isn’t determined by its relation to other concepts but by its relation to the world.

Conceptual atomism follows in the anti-descriptivist tradition that traces back to Saul Kripke, Hilary Putnam, and others working in the philosophy of language (see Kripke 1972/80, Putnam 1975, Devitt 1981). Kripke, for example, argues that proper names function like mere tags in that they have no descriptive content (Kripke 1972/80). On a description theory one might suppose that “Gödel” means something like the discoverer of the incompleteness of arithmetic . But Kripke points out we could discover that Schmitt really discovered the incompleteness of arithmetic and that Gödel could have killed Schmitt and passed the work off as his own. The point is that if the description theory were correct, we would be referring to Schmitt when we say “Gödel”. But intuitively that’s not the case at all. In the imagined scenario, the sentence “Gödel discovered the incompleteness of arithmetic” is saying something false about Gödel, not something trivially true about the discoverer of the incompleteness of arithmetic, whoever that might be (though see Machery et al. 2004 on whether this intuition is universal and Genone 2012 for discussion of the role of intuitions in theories of reference). Kripke’s alternative account of names is that they achieve their reference by standing in a causal relation to their referents. Conceptual atomism employs a similar strategy while extending the model to all sorts of concepts, not just ones for proper names.

At present, the nature of conceptual structure remains unsettled. Perhaps part of the problem is that more attention needs to be given to the question of what explanatory work conceptual structure is supposed to do and the possibility that there are different types of structure associated with different explanatory functions. We’ve seen that conceptual structure is invoked to explain, among other things, typicality effects, reflective categorization, cognitive development, reference determination, and compositionality. But there is no reason to assume that a single type of structure can explain all of these things. As a result, there is no reason why philosophers shouldn’t maintain that concepts have different types of structure. For example, notice that atomism is largely motivated by anti-descriptivism. In effect, the atomist maintains that considerable psychological variability is consistent with concepts entering into the same mind-world causal relations, and that it’s the latter that determines a concept’s reference. But just because the mechanisms of reference determination permit considerable psychological variability doesn’t mean that there aren’t, in fact, significant patterns for psychologists to uncover. On the contrary, the evidence for typicality effects is impressive by any measure. For this reason, it isn’t unreasonable to claim that concepts do have prototype structure even if that structure has nothing to do with the determination of a concept’s referent. Similar considerations suggest that concepts may have theory-structure and perhaps other types of structure as well (see Laurence & Margolis 1999 on different types of conceptual structure).

One way of responding to the plurality of conceptual structures is to suppose that concepts have multiple types of structure. This is the central idea behind conceptual pluralism . According to one version of conceptual pluralism, suggested by Laurence & Margolis (1999), a given concept will have a variety of different types of structure associated with it as components of the concept in question. For example, concepts may have atomic cores that are linked to prototypes, internalized theories, and so on. On this approach, the different types of structure that are components of a given concept play different explanatory roles. Reference determination and compositionality have more to do with the atomic cores themselves and how they are causally related to things outside of the mind, while rapid categorization and certain inferences depend on prototype structure, and more considered inferences and reasoning depend upon theory structure. Many variants on this general proposal are possible, but the basic idea is that, while concepts have a plurality of different types of structure with different explanatory roles, this differing structure remains unified through the links to an atomic representation that provides a concept’s reference. One challenge for this type of account is to delineate which of the cognitive resources that are associated with a concept should be counted as part of its structure and which should not. As a general framework, the account is neutral regarding this question, but as the framework is filled in, clarification will be needed regarding the status of potential types of structure.

A different form of pluralism about conceptual structure doesn’t employ atomic cores but simply says that the prototype, theory, etc. are all themselves concepts (Weiskopf 2009). Rather than holding that a single concept (e.g., the concept CAT) has multiple types of structure as components, as in the first form of pluralism, this form takes each type of structure to be a concept on its own, resulting in a plurality of concepts (CAT 1 , CAT 2 , CAT 3 , etc). On this view, it is wrong to suppose that there is such a thing as the concept CAT. Instead, there are many cat-concepts, each with a different type of structure, where each is involved in just a subset of the high-level psychological processes associated with cats. CAT 1 , for example, might explain some instances of categorization and some inferences, while CAT 2 , CAT 3 , etc. explain others. What’s more, on this form of pluralism, people might also differ with respect to which kinds of cat-concepts they possess. And even if two people have a cat-concept with the same general type of structure (e.g., prototype structure), the concepts might still be rather different (treating prototypical cats as having rather different sorts of properties). In response to this second form of pluralism, some philosophers have argued that CAT 1 , CAT 2 , etc. may be better understood as different senses of a single CAT concept, on analogy with the different senses of a polysemous linguistic expression (e.g. the physical object sense and the content sense of “book”)(Vicente & Manrique 2016). Regardless of whether one accepts this point about polysemy, an important challenge facing this second (“multiple concepts”) version of pluralism is to explain why all of the different cat-concepts count as cat-concepts—that is, to explain what unifies the plurality of cat-concepts. A natural answer to this challenge is that what unifies them is that they all refer to the same category, the category of cats. But it is not so clear that they can all refer to the same category given the differences between the different cat-concepts and the way that they function in cognition. For example, a standard prototype structure would capture prototypical cats and exclude the highly unusual, atypical cats that a theory structure would cover, and consequently the two concepts would refer to distinct (though related) categories.

In all of its forms, pluralism about conceptual structure recognizes that concepts have diverse functions and that a corresponding variety of types of representations are needed to fulfill these functions. These same considerations have led some theorists to advocate concept eliminativism —the view that there are no concepts (Machery 2009). The reasoning behind concept eliminativism is that concept should be understood to be a natural kind if concepts exist at all, and that natural kinds ought to have significant commonalities that can be discovered using empirical methods, including commonalities that go well beyond the criteria that are initially used to characterize them. But according to concept eliminativists, there are no such commonalities that hold among the types of representations that pluralists embrace. Perhaps we need prototypes and theories and other types of representations for distinct higher-level cognitive processes, but they are too diverse to warrant the claim that they constitute a single kind. On this view, then, we should simply abandon the theoretical construct of a concept and refer only to more fine-grained types of representations, such as prototypes and theories. Opponents of concept eliminativism have responded to the eliminativist’s challenge in a number of ways. Some have argued that Machery’s criteria for elimination are simply too strong and that concept , understood as a higher-level kind or perhaps a functional kind, has great utility in psychological models of cognitive processes (e.g., Hampton 2010, Lalumera 2010, Strohminger & Moore 2010). Others have argued that Machery’s criteria for something’s being a natural kind are too restrictive and that his view would have the consequence of ruling out clear cases of legitimate higher-level kinds in science generally (e.g., Gonnerman & Weinberg 2010, Margolis & Laurence 2010). And others have argued that even if we grant Machery’s stringent criteria for being a natural kind, elimination wouldn’t follow, as concepts are natural kinds according to his criteria (Samuels & Ferreira 2010, Weiskopf 2010). (For further critical discussion of eliminativism, see the peer commentary that appears with Machery 2010 and the author’s response.)

3. Empiricism and nativism about concepts

One of the oldest questions about concepts concerns whether there are any innate concepts and, if so, how much of the conceptual system is innate. Empiricists maintain that there are few if any innate concepts and that most cognitive capacities are acquired on the basis of a few relatively simple general-purpose cognitive mechanisms. Nativists, on the other hand, maintain that there may be many innate concepts and that the mind has a great deal of innate differentiation into complex domain-specific subsystems.

In recent years, the debate over innate concepts has been reinvigorated as advances in cognitive science have provided philosophers with new tools for revisiting and refining the traditional dispute (see, e.g., Elman et al. 1996, Carruthers, Laurence, & Stich 2005, 2006, 2007, Johnson 2010). Philosophers have greatly benefited from empirical studies in such diverse fields as developmental psychology, evolutionary psychology, cognitive anthropology, neuroscience, linguistics, and ethology. Part of the philosophical interest of this work is that, while the scientists themselves take sides on the empiricist-nativist dispute, their theories and data are often open to interpretation.

As an example, one of the earliest lines of investigation that appeared to support traditional nativist conceptions of the mind was the study of language (Pinker 1994). Noam Chomsky and his followers argued that language acquisition succeeds even though children are only exposed to severely limited evidence about the structure of their language (Chomsky 1967, 1975, 1988; see also Laurence & Margolis 2001). Given the way that the final state (e.g., knowledge of English) outstrips the data that are available to children, we can only postulate that the human mind brings to language acquisition a complex set of language-specific dispositions. For Chomsky, these dispositions are grounded in a set of innate principles that constrain all possible human natural languages, viz., universal grammar (see Baker 2001 and Roberts 2017 on universal grammar).

Not surprisingly, many philosophers have questioned Chomsky’s position. The ensuing debate has helped to sharpen the crucial arguments and the extent to which nativist models should continue to command their central place in linguistic theory. (On the empiricist side, see Cowie 1999, Prinz 2002, and Sampson 2005; on the nativist side, see Laurence & Margolis 2001 and Pietroski & Crain 2012; see also the entry innateness and language and the entry philosophy of linguistics ). For instance, one of Fiona Cowie’s criticisms of Chomsky’s poverty of the stimulus argument is that any induction establishes a conclusion that outstrips the available data; hence, going beyond the data in the case of language acquisition doesn’t argue for innate language-specific dispositions—or else there would have to be a specific innate disposition for every induction we make (for an earlier version of this argument, see Putnam 1967, Goodman 1969). Both Laurence & Margolis (2001) and Crain & Pietroski (2001) respond by teasing out the various ways in which the problem of language acquisition goes beyond general problems about induction.

Traditionally, empiricists have argued that all concepts derive from sensations. Concepts were understood to be formed from copies of sensory representations and assembled in accordance with a set of general-purpose learning rules, e.g., Hume’s principles of association (Hume 1739/1978). On this view, the content of any concept must be analyzable in terms of its perceptual basis. Any purported concept that fails this test embodies a confusion. Thus David Hume ends his Enquiry with the famous remark:

When we run over libraries, persuaded of these principles, what havoc must we make? If we take in our hand any volume; of divinity or school metaphysics, for instance; let us ask, Does it contain any abstract reasoning concerning quantity or number? No. Does it contain any experimental reasoning concerning matter of fact and existence? No. Commit it then to the flames: For it can contain nothing but sophistry and illusion. (1748/1975, p. 165)

A similar doctrine was maintained by the logical positivists in the early Twentieth Century, though the positivists couched the view in linguistic terms (Ayer 1959). Their principle of verification required for a sentence or statement to be meaningful that it have empirical consequences, and, on some formulations of the principle, that the meaning of a sentence is the empirical procedure for confirming it (see the entry Vienna Circle ). Sentences that have no empirical consequences were deemed to be meaningless. Since a good deal of philosophy purports to express propositions that transcend all possible experience, the positivists were happy to say that these philosophical doctrines are entirely devoid of content and are composed of sentences that aren’t merely false but are literally gibberish.

Despite the unpopularity of verificationism (though see Dummett 1993, Wright 1989, and Dennett 1991), a growing number of philosophers are attracted to modified forms of empiricism, forms that primarily emphasize psychological relations between the conceptual system and perceptual and motor states, not semantic relations. An example is Lawrence Shapiro’s defense of the claim that the type of body that an organism has profoundly affects its cognitive operations as well as the way that the organism is likely to conceptualize the world (Shapiro 2004). Shapiro’s claim is directed against philosophical theories that willfully ignore contingent facts about human bodies as if a human mind could inhere in wildly different body types. Drawing on a number of empirical research programs, Shapiro cites examples that appear to support what he calls the embodied mind thesis , viz., that “minds profoundly reflect the bodies in which they are contained” (Shapiro 2004, p. 167).

Jesse Prinz (2002) also defends a modified form of empiricism. Prinz claims that “all (human) concepts are copies or combinations of copies of perceptual representations” (Prinz 2002, p. 108). Though the reference to copies is a nod to Hume, Prinz certainly doesn’t buy into Hume’s verificationism. In fact, Prinz adopts a causal theory of content of the kind that is usually associated with atomistic theories of concepts (e.g., Fodor 1998); thus Prinz’s theory of intentional content doesn’t require a concept to inherit the specifically perceptual content of its constituents. Nonetheless, Prinz thinks that every concept derives from perceptual representations. Perhaps the best way to understand the claim is that the mental representations that are activated when someone thinks about something—no matter what the thought—are representations that originate in neural circuits with perceptual or motor functions and that the mental process is affected by that origin. Suppose, for example, that one is thinking about a hammer. Then she is either activating representations that inhere in visual circuits, or representations involved in circuits that control hand shape, etc., and her thought is affected in some way by the primary function of these circuits. Following Lawrence Barsalou (1999; see also Barsalou et al. 2003), Prinz characterizes concept possession as a kind of simulation “tantamount to entering a perceptual state of the kind one would be in if one were to experience the thing it represents” (Prinz 2002, p. 150).

One challenge to this view of cognition is its implication for abstract concepts. It’s one thing to say that the concept HAMMER involves the activation of circuits related to hand shape; it’s quite another to identify representations tied to a particular sensory modality underlying such concepts as TRUTH, DEMOCRACY, ENTROPY, and NINETEEN (Adams & Campbell 1999, Brewer 1999). Logical concepts are also a challenge. Prinz suggests as a perceptual basis for the concept of disjunction that it is based on feelings of hesitation. However, his more considered view seems to be that logical concepts are best understood as operations, not representations. The resulting theory is one in which thoughts lack logical form. The trouble is that this makes it difficult to see how to distinguish logically equivalent thoughts. A related problem is that, since composition for Prinz does not yield structurally complex representations, there seems to be nothing to distinguish the type of contents associated with judgements (propositional contents) from those associated lists or even single concepts (for related discussion Fodor 2003). Finally, there are difficulties regarding how to interpret behavioral and neurological evidence that is supposed to support Prinz and Barsalou’s case against amodal representations. For example, Machery (2007) points out that proponents of amodal representations typically suppose that imagery is useful in solving certain types of problems. So to argue against amodal representations, it is not enough to show that sensory representations show up in a task in which experimental subjects are not explicitly told to visualize a solution, since the use of sensory-based imagery in a task is perfectly compatible with there also being amodal representations in play. (For further critical discussion of the form of empiricism that is opposed to amodal representations, see Weiskopf 2007, Dove 2009, and Mahon 2015).

Perhaps the most influential discussion of concepts in relation to the nativism/empiricism debate is Jerry Fodor’s (1975, 1981) argument for the claim that virtually all lexical concepts are innate. Fodor (1975) argued that there are theoretical problems with all models of concept learning in that all such models treat concept learning as hypothesis testing. The problem is that the correct hypothesis invariably employs the very concept to be learned and hence the concept has to be available to a learner prior to the learning taking place. In his (1981), Fodor developed this argument by allowing that complex concepts (and only complex concepts) can be learned in that they can be assembled from their constituents during the learning process. He went on to argue that lexical concepts lack semantic structure and consequently that virtually all lexical concepts must be innate—a position known as radical concept nativism . Fodor’s arguments have had a great deal of influence on debates about nativism and concept learning, especially amongst cognitive scientists. Few if any have endorsed Fodor’s radical conclusions, but many have shaped their views of cognitive development at least in part in response to Fodor’s arguments (Jackendoff 1989, Levin & Pinker 1991, Spelke & Tsivkin 2001, Carey 2009). And Fodor has convinced many that primitive concepts are in principle unlearnable (see, e.g., Pinker 2007). Fodor’s arguments for this conclusion, however, can be challenged in a number of ways. The most direct way to challenge it is to construct an account of what it is to learn a primitive concept and to show that it is immune to Fodor’s challenges (Margolis 1998, Laurence & Margolis 2002, Carey 2009).

Fodor himself changed his view about these issues when he revisited them in Fodor (2008). He now maintains that while considerations about the need for hypothesis testing show that no concepts can be learned, not even complex concepts, this does not require concepts to be innate (Fodor 2008). Instead, Fodor suggests that they are largely acquired via processes that are merely biological in that they don’t admit of a psychological-level description. Though such a “biological” account of concept acquisition does offer an alternative to the innate/learned dichotomy, there are reasons for supposing that many concepts are learned all the same (Margolis & Laurence 2011). These include the fact that a person’s conceptual system is highly sensitive to the surrounding culture. For example, the concept PURGATORY comes from cultural products such as books, stories, and sermons. But clearly these can only succeed in conveying the concept when mediated by the right sort of psychological processes. Acquiring such concepts is a cognitive-level achievement, not a merely biological one.

Although much of the philosophical discussion of concept nativism has focused on Fodor’s radical concept nativism, it is important to note that other less radical approaches to the origins of concepts exist and that these other approaches embrace a fair amount of learning—they just hold that much of this learning is structured by innate special-purpose acquisition systems (for example, Gallistel 1990, Keil 1989, Spelke & Newport 1998, Baillargeon 2008, Carey 2009, Margolis & Laurence 2013, Tooby & Cosmides 2016).

One further issue concerning innate concepts that is in dispute is whether the very idea of innateness makes sense. A common point among those who are skeptical of the notion is the observation that all traits are dependent upon interactions between genes and the environment and that there is no way to fully untangle the two (Elman et al. 1996, Griffiths 2002; see also Clark 1998 and Marcus 2004, and the entry on the distinction between innate and acquired characteristics ). Nonetheless, there are clear differences between models of the mind with empiricist leanings and models of the mind with nativist leanings, and the notion of innateness may be thought to earn its usefulness by marking these differences (Margolis & Laurence (2013). For discussion of different proposals of what innateness is see Ariew (1999), Cowie (1999), Samuels (2002), Mallon & Weinberg (2006), Mameli (2008) and Khalidi (2015).

4. Concepts and natural language

We turn now to the issue of how concepts and thoughts relate to language.

Some philosophers maintain that possession of natural language is necessary for having any concepts (Brandom 1994, Davidson 1975, Dummett 1993) and that the tight connection between the two can be established on a priori grounds. In a well known passage, Donald Davidson summarizes his position as follows:

We have the idea of belief only from the role of belief in the interpretation of language, for as a private attitude it is not intelligible except as an adjustment to the public norm provided by language. It follows that a creature must be a member of a speech community if it is to have the concept of belief. And given the dependence of other attitudes on belief, we can say more generally that only a creature that can interpret speech can have the concept of a thought.

Can a creature have a belief if it does not have the concept of belief? It seems to me it cannot, and for this reason. Someone cannot have a belief unless he understands the possibility of being mistaken, and this requires grasping the contrast between truth and error—true belief and false belief. But this contrast, I have argued, can emerge only in the context of interpretation, which alone forces us to the idea of an objective, public truth. (Davidson 1975, p. 170).

The argument links having beliefs and concepts with having the concept of belief. Since Davidson thinks that non-linguistic creatures can’t have the concept of belief, they can’t have other concepts as well. Why the concept of belief is needed to have other concepts is somewhat obscure in Davidson’s writings (Carruthers 1992). And whether language is necessary for this particular concept is not obvious. In fact, there is an ongoing research program in cognitive science that addresses this very issue. A variety of non-linguistic tasks have been given to animals and infants to determine the extent to which they are able to attribute mental states to others (see Andrews & Beck 2018 for work on animals and Baillargeon et al. 2015 for work on infants). These and related studies provide strong evidence that at least some aspects of theory of mind are nonlinguistic.

Davidson offers a pair of supplementary arguments that may elucidate why he is hesitant to turn the issue over to the cognitive scientists. He gives the example of a man engaging in a non-linguistic task where the man indicates his answer by making a choice, for example, selecting an apple over a pear. Davidson comments that until the man actually says what he has in mind, there will always be a question about the conceptualization guiding his choice. “Repeated tests may make some readings of his actions more plausible than others, but the problem will remain how to determine when he judges two objects of choice to be identical” (1975, p. 163). The second argument points to the difficulties of settling upon a specification of what a non-linguistic creature is thinking. “The dog, we say, knows that its master is home. But does it know that Mr. Smith (who is the master) is home? We have no real idea how to settle, or make sense of, these questions” (1975, p, 163). It’s not clear how seriously Davidson himself takes these arguments. Many philosophers have been unconvinced. Notice that both arguments turn on an underdetermination claim—e.g., that the interpretation of the man’s action is underdetermined by the non-linguistic evidence. But much the same thing is true even if we add what the man says (or to be more precise, if we add what the man utters). The linguistic evidence doesn’t guarantee a correct interpretation any more than the non-linguistic evidence does.

Davidson appears to be employing a very high standard for attributing concepts to animals. In effect, he is asking for proof that our attributions are correct. In contrast, most philosophers who are happy to attribute concepts to animals do so because of a wealth of data that are best explained by appealing to an internal system of representation (e.g., Bermudez 2003, Carruthers 2006, Camp 2009; for overviews within cognitive science, see Gallistel 1990, Bekoff, Allen, & Burghardt 2002, and Shettleworth 2010). For example, many species of birds cache food for later retrieval. Their very survival depends upon their ability to successfully recover, in some cases, more than 10,000 different caches in a single season. Researchers studying one species of caching birds have shown that not only do the birds represent the location of the food, but they integrate this information with information about the quality of the food, its perishability, and whether their caching was observed by other birds. Evidence here comes from demonstrations of selective retrieval and recaching of food items under experimentally controlled conditions. Birds will retrieve more perishable items first. When highly valued food items become highly perishable, they shift strategies to retrieve a higher percentage of less perishable food items. And birds that have themselves stolen food from other birds will selectively recache stored food when they are observed caching it (see Clayton, Bussey, & Dickinson 2003, Emery, Dally, & Clayton 2004). Experimental data of this kind provide evidence for particular concepts in birds (of food types, locations, and so on) as well as surprisingly sophisticated cognitive operations that make use of them.

There is a great deal of controversy among philosophers about the implications of this type of research. Proponents of RTM are, of course, entirely happy with the idea that the scientific theories of what birds are doing can be taken at face value. Other philosophers maintain that if the scientific theories say that birds are computing an algorithm for determining a caching strategy, then this can only be read as a façon de parler. Still others will grant that animals have representations but go on to claim that these representation are of a lesser status, not to be confused with concepts (Brandom 1994, 2000, McDowell 1994).

This raises an interesting question about whether there is a motivated and principled difference between concepts in humans and mere representations in animals (Laurence & Margolis 2012). Philosophers who maintain that there is such a difference often cite the role of concepts in reasoning. For example, Robert Brandom claims that representations in animals do little more than act as reliable mechanisms of discrimination. These representations are supposed to be like thermometers, responding to specific environmental features yet without entering into appropriate inferential processes. However, it’s not clear what counts as an appropriate inferential process, and certainly there is room for differing opinions on this point. Moreover, whatever reasoning amounts to, comparative psychology is replete with examples that suggest that animals are capable of far more than reliable detection. Animals may not be as smart as humans, but that doesn’t mean they are as dumb as thermometers (see Hurley & Nudds 2006 and Carruthers 2006 on reasoning in animals).

Even if it’s agreed that it is possible to have concepts in the absence of language, there is a dispute about how the two are related. Some maintain that concepts are prior to and independent of natural language, and that natural language is just a means for conveying thought (Fodor 1975, Pinker 1994). Others maintain that at least some types of thinking (and hence some concepts) occur in the internal system of representation constituting our natural language competence (Carruthers 1996, 2002, Spelke 2003) or that natural language augments our concepts in significant ways (Lupyan 2012).

The arguments for deciding between these two positions involve a mixture of theoretical and empirical considerations. Proponents of the first view have claimed that language is ambiguous in ways that thought presumably is not. For example, the natural language sentence everyone loves someone could be interpreted to mean that for each person, there is some person that they love, or to mean that everyone loves one and the same person (Pinker 1994). Proponents of the first view have also argued that since language itself has to be learned, thought is prior to language (Fodor 1975; Pinker 1994). A third and similar consideration is that people seem to be able formulate novel concepts which are given a linguistic label later; the concept comes first, the linguistic label second (Pinker 1994).

Proponents of the alternative view—that some thinking occurs in language—have pointed to the phenomenology of thought. It certainly seems as if we are thinking in language when we “hear” ourselves silently talking to ourselves (Carruthers 1996; see also Langland-Hassan & Vicente 2018 on the nature and significance of inner speech). Another type of consideration that proponents of this view highlight is the finding that success on certain tasks (e.g., spatial reorientation that relies on combining landmark information with geometrical information) is selectively impaired when the linguistic system is engaged but not when comparable attention is given to non-linguistic distractors. The suggestion is that solving these tasks requires thinking in one’s natural language and that some of the crucial concepts must be couched linguistically (Hermer-Vazquez, Spelke, & Katsnelson 2001; Shusterman & Spelke 2005; Carruthers 2002; see also Gleitman & Papafragou 2012 for a critical overview of a range of tasks where online processing has been thought to rely on linguistic representation).

Finally, one further issue that bears mentioning is the status of various claims regarding linguistic determinism and linguistic relativity. Linguistic determinism is the doctrine that the language a person speaks both causes her to conceptualize the world in certain ways and limits what she can think about by imposing boundaries on her conceptual system; as a result, people who speak very different languages are likely to conceptualize the world in correspondingly different ways. Linguistic relativity is the weaker doctrine that the language one speaks influences how one thinks.

Linguistic determinism is historically associated with the writings of Benjamin Lee Whorf (Whorf 1956). Whorf was especially interested in the languages of the indigenous people of America. He famously argued that the Hopi both speak and think about time in ways that are incongruent with European languages and thought. Rather than viewing time as a continuum that flows evenly throughout the universe and that can be broken up into countable events occurring in the past, present, and future, the Hopi are supposed to focus on change as a process. Their conceptual system is also supposed to differ from ours in that it embodies a distinction between things that are or have been accessible to perception versus things that are not, where the latter category includes things in the future as well as mythical and mental constructs.

The claim that the Hopi lack our concept of time has not stood up to scrutiny. Whorf used clumsy translations of Hopi speech that concealed the extent to which they talk about time (references to yesterday, tomorrow, days of the week, lunar phases, etc.). More interestingly, Whorf provided no direct evidence of how the Hopi think. Instead, he used the circular reasoning that they don’t think about time as we do because they don’t talk about time as we do. In fact, the Hopi use numerous familiar devices for time keeping, such as calendar strings and sun dials, and their sensitivity to time is evident a wide variety of cultural practices (Pinker 1994).

Linguistic determinism isn’t an especially promising doctrine and has few adherents these days, but linguistic relativity is the subject of a spirited debate (see Gumperz & Levinson 1996, Bowerman & Levinson 2001, and Gentner & Goldin-Meadow 2003). Some recent examples of particular interest include whether language influences how we conceptualize spatial frames of reference (in non-linguistic spatial reasoning) (e.g., Li & Gleitman 2002, Levinson et al. 2002, Levinson 2003, Li et al. 2011), spatial relations (in non-linguistic reasoning) (e.g., Choi & Bowerman 1991, Hespos & Spelke 2004), sex (the impact of grammatical gender) (e.g., Boroditsky, Schmidt, & Phillips 2003) and number (e.g., Gordon 2004, Pica et al. 2004, Laurence & Margolis 2007).

5. Concepts and conceptual analysis

Some of the deepest divides in contemporary philosophy concern the limits of empirical inquiry, the status of conceptual analysis, and the nature of philosophy itself (see, e.g., Chalmers 1996, Jackson 1998, DePaul & Ramsey 1998, Block & Stalnaker 1999, Williamson 2007, Stich 2012, Machery 2017, Strevens 2018). And concepts are right at the center of these disputes. For many, philosophy is essentially the a priori analysis of concepts, which can and should be done without leaving the proverbial armchair. We’ve already seen that in the paradigm case, an analysis embodies a definition; it specifies a set of conditions that are individually necessary and jointly sufficient for the application of the concept. For proponents of traditional conceptual analysis, the analysis of a concept is successful to the extent that the proposed definition matches people’s intuitions about particular cases, including hypothetical cases that figure in crucial thought experiments.

Conceptual analysis has been attractive to philosophers for a number of reasons. One is that it makes sense of a good deal of philosophical practice—what George Bealer (1998) calls the standard justificatory procedure . Thought experiments and the elicitation of intuitions are characteristic features of philosophical practice. If this practice is justified, then there has to be an understanding of what philosophy is that explains why. Conceptual analysis is supposed to provide just what’s needed here. Intuitions can be said to be of value to philosophy precisely because they help us to get clearer about our concepts, especially concepts of intrinsic philosophical interest (JUSTICE, KNOWLEDGE, etc.).

A related attraction is that conceptual analysis explains how philosophy could be an a priori discipline, as many suppose it is. If philosophy is primarily about concepts and concepts can be investigated from the armchair, then the a priori character of philosophy is secured (Jackson 1998).

A third attraction of conceptual analysis is that conceptual analysis has been argued to be a necessary precursor for answering questions about ontological reduction, that is, the sort of reduction that takes place when it’s argued that genes are DNA segments, that sensations are brain states, and so on (Chalmers 1996, Jackson 1998). According to one way of filling this view out, one has to begin with an a priori analysis of the higher-level concept, particularly an analysis that makes explicit its causal relations. One can then appeal to empirical findings regarding the things that actually have those causal relations. For example, neuroscience may reveal that such-and-such brain state has the causal relations that analysis reveals to be constitutive of our concept of pain. In the course of doing this, neuroscience is supposed to be showing us what pain is (Lewis 1966, Armstrong 1968). But neuroscience is only in a position to do this against the background of the philosophical work that goes into articulating the concept. (For detailed treatments of this view of reduction, see Chalmers 1996, 2010 and Jackson 1998—though it should be noted that Chalmers argues that PAIN, and other concepts of conscious mental states, cannot be analyzed solely in terms of their causal relations and concludes from this that consciousness itself is irreducible.) This work has generated a great deal of debate (e.g., Block & Stalnaker 1999, Levine 2001, Laurence & Margolis 2003, Yablo 2008, Papineau 2002).

A fourth attraction is that conceptual analysis may offer normative guidance (Goldman 1986). For instance, epistemologists face the question of whether our inferential practices are justified and, if so, what justifies them. One standard answer is that they can be justified if they conform to our intuitions about what counts as a justified inference (Goldman 1986). In other words, an analysis of our concept of justification is supposed to be all that is needed in order to establish that a set of inference rules is justified. So if it ever turned out that different groups of people employed qualitatively different sets of inferential principles, we could establish the epistemically preferable one by showing that it does a better job of conforming to our concept of justification.

Many philosophers who are opposed to conceptual analysis identify their approach as being naturalistic (e.g., Devitt 1996, Kornblith 2002, Papineau 2013; see also the entry naturalism ). A common theme of this work is that philosophy is supposed to be continuous with science and that philosophical theories are to be defended on largely explanatory grounds, not on the basis of a priori arguments that appeal to intuition. Accordingly, perceived difficulties with conceptual analysis provide arguments for naturalism.

One such argument centers around the failures of the classical theory of concepts. Earlier, in Section 2 , we noted that paradigmatic conceptual analyses require concepts to have classical structure, an assumption that is increasingly difficult to maintain. For this reason, a number of philosophers have expressed skepticism about the viability of conceptual analysis as a philosophical method (e.g., Ramsey 1998, Stich 1992). Others, however, have called into question the connection between conceptual analysis and definitions (Chalmers & Jackson 2001).

Another objection to conceptual analysis is that the intuitions that philosophers routinely rely upon may not be shared. Anyone who teaches philosophy certainly knows that half the time students have the “wrong intuitions”. But who are we to say that they are wrong? And given that people disagree about their intuitions, these can hardly be treated as objective data (Cummins 1998).

Things become even more interesting if we branch out to other cultures. In a preliminary study of East Asian vs. Western intuitions, Jonathan Weinberg, Shaun Nichols, & Stephen Stich (2001) found that East Asians often have the “wrong intuitions” regarding variations on classic philosophical thought experiments, including Gettier-type thought experiments (though see Machery et al 2017 for evidence that some of these intuitions may in fact be universal). At the very least, this work suggests that philosophers should be cautious about moving from their own intuitions to claims about the proper analysis of a concept.

What’s more, the possibility of cultural diversity raises a troubling question for philosophers who want to establish normative claims on the basis of analyses of concepts, such as the concept of justification. Suppose, for example, that East Asian culture offers a different concept of justification than the one that is embedded in Western commonsense thought (assuming for sake of argument that there is a single concept of justification in each culture). In addition, suppose that East Asians employ different inferential practices than our own and that their practices do a fair job of conforming to their concept of justification and that ours do a fair job of conforming to our own. On what basis, then, are we to compare and evaluate these differing practices? Does it really make sense to say that ours are superior on the ground that they conform better to our concept of justification? Wouldn’t this just be a form of epistemic prejudice? After all, the question arises whether, given the two concepts of justification, ours is the one that ought to be used for performing normative epistemic evaluations (Stich 1990; for further discussion see Williamson 2005, Sosa 2009, Stich 2009, Weinberg, Nichols, & Stich 2001, Weinberg et al. 2010).

The critique of traditional philosophical analysis has also generated proposals for various types of normative, revisionary projects. Instead of asking which cases are or are not covered by a concept, they ask how a concept should be modified or what new concept should be adopted in its place, given the practical context in which the concept is used—for example, given the goal of promoting social equality (Haslanger 2012, Cappelen 2018).

Much is at stake in the debate between conceptual analysts and naturalists, and it is likely to be a central topic in the theory of concepts for the foreseeable future.

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• How to write a rhetorical analysis | Key concepts & examples

How to Write a Rhetorical Analysis | Key Concepts & Examples

Published on August 28, 2020 by Jack Caulfield . Revised on July 23, 2023.

A rhetorical analysis is a type of essay  that looks at a text in terms of rhetoric. This means it is less concerned with what the author is saying than with how they say it: their goals, techniques, and appeals to the audience.

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Table of contents

Key concepts in rhetoric, analyzing the text, introducing your rhetorical analysis, the body: doing the analysis, concluding a rhetorical analysis, other interesting articles, frequently asked questions about rhetorical analysis.

Rhetoric, the art of effective speaking and writing, is a subject that trains you to look at texts, arguments and speeches in terms of how they are designed to persuade the audience. This section introduces a few of the key concepts of this field.

Appeals: Logos, ethos, pathos

Appeals are how the author convinces their audience. Three central appeals are discussed in rhetoric, established by the philosopher Aristotle and sometimes called the rhetorical triangle: logos, ethos, and pathos.

Logos , or the logical appeal, refers to the use of reasoned argument to persuade. This is the dominant approach in academic writing , where arguments are built up using reasoning and evidence.

Ethos , or the ethical appeal, involves the author presenting themselves as an authority on their subject. For example, someone making a moral argument might highlight their own morally admirable behavior; someone speaking about a technical subject might present themselves as an expert by mentioning their qualifications.

Pathos , or the pathetic appeal, evokes the audience’s emotions. This might involve speaking in a passionate way, employing vivid imagery, or trying to provoke anger, sympathy, or any other emotional response in the audience.

These three appeals are all treated as integral parts of rhetoric, and a given author may combine all three of them to convince their audience.

Text and context

In rhetoric, a text is not necessarily a piece of writing (though it may be this). A text is whatever piece of communication you are analyzing. This could be, for example, a speech, an advertisement, or a satirical image.

In these cases, your analysis would focus on more than just language—you might look at visual or sonic elements of the text too.

The context is everything surrounding the text: Who is the author (or speaker, designer, etc.)? Who is their (intended or actual) audience? When and where was the text produced, and for what purpose?

Looking at the context can help to inform your rhetorical analysis. For example, Martin Luther King, Jr.’s “I Have a Dream” speech has universal power, but the context of the civil rights movement is an important part of understanding why.

Claims, supports, and warrants

A piece of rhetoric is always making some sort of argument, whether it’s a very clearly defined and logical one (e.g. in a philosophy essay) or one that the reader has to infer (e.g. in a satirical article). These arguments are built up with claims, supports, and warrants.

A claim is the fact or idea the author wants to convince the reader of. An argument might center on a single claim, or be built up out of many. Claims are usually explicitly stated, but they may also just be implied in some kinds of text.

The author uses supports to back up each claim they make. These might range from hard evidence to emotional appeals—anything that is used to convince the reader to accept a claim.

The warrant is the logic or assumption that connects a support with a claim. Outside of quite formal argumentation, the warrant is often unstated—the author assumes their audience will understand the connection without it. But that doesn’t mean you can’t still explore the implicit warrant in these cases.

For example, look at the following statement:

We can see a claim and a support here, but the warrant is implicit. Here, the warrant is the assumption that more likeable candidates would have inspired greater turnout. We might be more or less convinced by the argument depending on whether we think this is a fair assumption.

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Rhetorical analysis isn’t a matter of choosing concepts in advance and applying them to a text. Instead, it starts with looking at the text in detail and asking the appropriate questions about how it works:

• What is the author’s purpose?
• Do they focus closely on their key claims, or do they discuss various topics?
• What tone do they take—angry or sympathetic? Personal or authoritative? Formal or informal?
• Who seems to be the intended audience? Is this audience likely to be successfully reached and convinced?
• What kinds of evidence are presented?

By asking these questions, you’ll discover the various rhetorical devices the text uses. Don’t feel that you have to cram in every rhetorical term you know—focus on those that are most important to the text.

The following sections show how to write the different parts of a rhetorical analysis.

Like all essays, a rhetorical analysis begins with an introduction . The introduction tells readers what text you’ll be discussing, provides relevant background information, and presents your thesis statement .

Hover over different parts of the example below to see how an introduction works.

Martin Luther King, Jr.’s “I Have a Dream” speech is widely regarded as one of the most important pieces of oratory in American history. Delivered in 1963 to thousands of civil rights activists outside the Lincoln Memorial in Washington, D.C., the speech has come to symbolize the spirit of the civil rights movement and even to function as a major part of the American national myth. This rhetorical analysis argues that King’s assumption of the prophetic voice, amplified by the historic size of his audience, creates a powerful sense of ethos that has retained its inspirational power over the years.

The body of your rhetorical analysis is where you’ll tackle the text directly. It’s often divided into three paragraphs, although it may be more in a longer essay.

Each paragraph should focus on a different element of the text, and they should all contribute to your overall argument for your thesis statement.

Hover over the example to explore how a typical body paragraph is constructed.

King’s speech is infused with prophetic language throughout. Even before the famous “dream” part of the speech, King’s language consistently strikes a prophetic tone. He refers to the Lincoln Memorial as a “hallowed spot” and speaks of rising “from the dark and desolate valley of segregation” to “make justice a reality for all of God’s children.” The assumption of this prophetic voice constitutes the text’s strongest ethical appeal; after linking himself with political figures like Lincoln and the Founding Fathers, King’s ethos adopts a distinctly religious tone, recalling Biblical prophets and preachers of change from across history. This adds significant force to his words; standing before an audience of hundreds of thousands, he states not just what the future should be, but what it will be: “The whirlwinds of revolt will continue to shake the foundations of our nation until the bright day of justice emerges.” This warning is almost apocalyptic in tone, though it concludes with the positive image of the “bright day of justice.” The power of King’s rhetoric thus stems not only from the pathos of his vision of a brighter future, but from the ethos of the prophetic voice he adopts in expressing this vision.

The conclusion of a rhetorical analysis wraps up the essay by restating the main argument and showing how it has been developed by your analysis. It may also try to link the text, and your analysis of it, with broader concerns.

Explore the example below to get a sense of the conclusion.

It is clear from this analysis that the effectiveness of King’s rhetoric stems less from the pathetic appeal of his utopian “dream” than it does from the ethos he carefully constructs to give force to his statements. By framing contemporary upheavals as part of a prophecy whose fulfillment will result in the better future he imagines, King ensures not only the effectiveness of his words in the moment but their continuing resonance today. Even if we have not yet achieved King’s dream, we cannot deny the role his words played in setting us on the path toward it.

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!

• Ad hominem fallacy
• Post hoc fallacy
• Appeal to authority fallacy
• False cause fallacy
• Sunk cost fallacy

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The goal of a rhetorical analysis is to explain the effect a piece of writing or oratory has on its audience, how successful it is, and the devices and appeals it uses to achieve its goals.

Unlike a standard argumentative essay , it’s less about taking a position on the arguments presented, and more about exploring how they are constructed.

The term “text” in a rhetorical analysis essay refers to whatever object you’re analyzing. It’s frequently a piece of writing or a speech, but it doesn’t have to be. For example, you could also treat an advertisement or political cartoon as a text.

Logos appeals to the audience’s reason, building up logical arguments . Ethos appeals to the speaker’s status or authority, making the audience more likely to trust them. Pathos appeals to the emotions, trying to make the audience feel angry or sympathetic, for example.

Collectively, these three appeals are sometimes called the rhetorical triangle . They are central to rhetorical analysis , though a piece of rhetoric might not necessarily use all of them.

In rhetorical analysis , a claim is something the author wants the audience to believe. A support is the evidence or appeal they use to convince the reader to believe the claim. A warrant is the (often implicit) assumption that links the support with the claim.

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Using Content Analysis

This guide provides an introduction to content analysis, a research methodology that examines words or phrases within a wide range of texts.

• Introduction to Content Analysis : Read about the history and uses of content analysis.
• Conceptual Analysis : Read an overview of conceptual analysis and its associated methodology.
• Relational Analysis : Read an overview of relational analysis and its associated methodology.
• Commentary : Read about issues of reliability and validity with regard to content analysis as well as the advantages and disadvantages of using content analysis as a research methodology.
• Examples : View examples of real and hypothetical studies that use content analysis.
• Annotated Bibliography : Complete list of resources used in this guide and beyond.

An Introduction to Content Analysis

Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Researchers quantify and analyze the presence, meanings and relationships of such words and concepts, then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of which these are a part. Texts can be defined broadly as books, book chapters, essays, interviews, discussions, newspaper headlines and articles, historical documents, speeches, conversations, advertising, theater, informal conversation, or really any occurrence of communicative language. Texts in a single study may also represent a variety of different types of occurrences, such as Palmquist's 1990 study of two composition classes, in which he analyzed student and teacher interviews, writing journals, classroom discussions and lectures, and out-of-class interaction sheets. To conduct a content analysis on any such text, the text is coded, or broken down, into manageable categories on a variety of levels--word, word sense, phrase, sentence, or theme--and then examined using one of content analysis' basic methods: conceptual analysis or relational analysis.

A Brief History of Content Analysis

Historically, content analysis was a time consuming process. Analysis was done manually, or slow mainframe computers were used to analyze punch cards containing data punched in by human coders. Single studies could employ thousands of these cards. Human error and time constraints made this method impractical for large texts. However, despite its impracticality, content analysis was already an often utilized research method by the 1940's. Although initially limited to studies that examined texts for the frequency of the occurrence of identified terms (word counts), by the mid-1950's researchers were already starting to consider the need for more sophisticated methods of analysis, focusing on concepts rather than simply words, and on semantic relationships rather than just presence (de Sola Pool 1959). While both traditions still continue today, content analysis now is also utilized to explore mental models, and their linguistic, affective, cognitive, social, cultural and historical significance.

Uses of Content Analysis

Perhaps due to the fact that it can be applied to examine any piece of writing or occurrence of recorded communication, content analysis is currently used in a dizzying array of fields, ranging from marketing and media studies, to literature and rhetoric, ethnography and cultural studies, gender and age issues, sociology and political science, psychology and cognitive science, and many other fields of inquiry. Additionally, content analysis reflects a close relationship with socio- and psycholinguistics, and is playing an integral role in the development of artificial intelligence. The following list (adapted from Berelson, 1952) offers more possibilities for the uses of content analysis:

• Reveal international differences in communication content
• Detect the existence of propaganda
• Identify the intentions, focus or communication trends of an individual, group or institution
• Describe attitudinal and behavioral responses to communications
• Determine psychological or emotional state of persons or groups

Types of Content Analysis

In this guide, we discuss two general categories of content analysis: conceptual analysis and relational analysis. Conceptual analysis can be thought of as establishing the existence and frequency of concepts most often represented by words of phrases in a text. For instance, say you have a hunch that your favorite poet often writes about hunger. With conceptual analysis you can determine how many times words such as hunger, hungry, famished, or starving appear in a volume of poems. In contrast, relational analysis goes one step further by examining the relationships among concepts in a text. Returning to the hunger example, with relational analysis, you could identify what other words or phrases hunger or famished appear next to and then determine what different meanings emerge as a result of these groupings.

Conceptual Analysis

Traditionally, content analysis has most often been thought of in terms of conceptual analysis. In conceptual analysis, a concept is chosen for examination, and the analysis involves quantifying and tallying its presence. Also known as thematic analysis [although this term is somewhat problematic, given its varied definitions in current literature--see Palmquist, Carley, & Dale (1997) vis-a-vis Smith (1992)], the focus here is on looking at the occurrence of selected terms within a text or texts, although the terms may be implicit as well as explicit. While explicit terms obviously are easy to identify, coding for implicit terms and deciding their level of implication is complicated by the need to base judgments on a somewhat subjective system. To attempt to limit the subjectivity, then (as well as to limit problems of reliability and validity ), coding such implicit terms usually involves the use of either a specialized dictionary or contextual translation rules. And sometimes, both tools are used--a trend reflected in recent versions of the Harvard and Lasswell dictionaries.

Methods of Conceptual Analysis

Conceptual analysis begins with identifying research questions and choosing a sample or samples. Once chosen, the text must be coded into manageable content categories. The process of coding is basically one of selective reduction . By reducing the text to categories consisting of a word, set of words or phrases, the researcher can focus on, and code for, specific words or patterns that are indicative of the research question.

An example of a conceptual analysis would be to examine several Clinton speeches on health care, made during the 1992 presidential campaign, and code them for the existence of certain words. In looking at these speeches, the research question might involve examining the number of positive words used to describe Clinton's proposed plan, and the number of negative words used to describe the current status of health care in America. The researcher would be interested only in quantifying these words, not in examining how they are related, which is a function of relational analysis. In conceptual analysis, the researcher simply wants to examine presence with respect to his/her research question, i.e. is there a stronger presence of positive or negative words used with respect to proposed or current health care plans, respectively.

Once the research question has been established, the researcher must make his/her coding choices with respect to the eight category coding steps indicated by Carley (1992).

Steps for Conducting Conceptual Analysis

The following discussion of steps that can be followed to code a text or set of texts during conceptual analysis use campaign speeches made by Bill Clinton during the 1992 presidential campaign as an example. To read about each step, click on the items in the list below:

• Decide the level of analysis.

First, the researcher must decide upon the level of analysis . With the health care speeches, to continue the example, the researcher must decide whether to code for a single word, such as "inexpensive," or for sets of words or phrases, such as "coverage for everyone."

• Decide how many concepts to code for.

The researcher must now decide how many different concepts to code for. This involves developing a pre-defined or interactive set of concepts and categories. The researcher must decide whether or not to code for every single positive or negative word that appears, or only certain ones that the researcher determines are most relevant to health care. Then, with this pre-defined number set, the researcher has to determine how much flexibility he/she allows him/herself when coding. The question of whether the researcher codes only from this pre-defined set, or allows him/herself to add relevant categories not included in the set as he/she finds them in the text, must be answered. Determining a certain number and set of concepts allows a researcher to examine a text for very specific things, keeping him/her on task. But introducing a level of coding flexibility allows new, important material to be incorporated into the coding process that could have significant bearings on one's results.

• Decide whether to code for existence or frequency of a concept.

After a certain number and set of concepts are chosen for coding , the researcher must answer a key question: is he/she going to code for existence or frequency ? This is important, because it changes the coding process. When coding for existence, "inexpensive" would only be counted once, no matter how many times it appeared. This would be a very basic coding process and would give the researcher a very limited perspective of the text. However, the number of times "inexpensive" appears in a text might be more indicative of importance. Knowing that "inexpensive" appeared 50 times, for example, compared to 15 appearances of "coverage for everyone," might lead a researcher to interpret that Clinton is trying to sell his health care plan based more on economic benefits, not comprehensive coverage. Knowing that "inexpensive" appeared, but not that it appeared 50 times, would not allow the researcher to make this interpretation, regardless of whether it is valid or not.

• Decide on how you will distinguish among concepts.

The researcher must next decide on the , i.e. whether concepts are to be coded exactly as they appear, or if they can be recorded as the same even when they appear in different forms. For example, "expensive" might also appear as "expensiveness." The research needs to determine if the two words mean radically different things to him/her, or if they are similar enough that they can be coded as being the same thing, i.e. "expensive words." In line with this, is the need to determine the level of implication one is going to allow. This entails more than subtle differences in tense or spelling, as with "expensive" and "expensiveness." Determining the level of implication would allow the researcher to code not only for the word "expensive," but also for words that imply "expensive." This could perhaps include technical words, jargon, or political euphemism, such as "economically challenging," that the researcher decides does not merit a separate category, but is better represented under the category "expensive," due to its implicit meaning of "expensive."

• Develop rules for coding your texts.

After taking the generalization of concepts into consideration, a researcher will want to create translation rules that will allow him/her to streamline and organize the coding process so that he/she is coding for exactly what he/she wants to code for. Developing a set of rules helps the researcher insure that he/she is coding things consistently throughout the text, in the same way every time. If a researcher coded "economically challenging" as a separate category from "expensive" in one paragraph, then coded it under the umbrella of "expensive" when it occurred in the next paragraph, his/her data would be invalid. The interpretations drawn from that data will subsequently be invalid as well. Translation rules protect against this and give the coding process a crucial level of consistency and coherence.

• Decide what to do with "irrelevant" information.

The next choice a researcher must make involves irrelevant information . The researcher must decide whether irrelevant information should be ignored (as Weber, 1990, suggests), or used to reexamine and/or alter the coding scheme. In the case of this example, words like "and" and "the," as they appear by themselves, would be ignored. They add nothing to the quantification of words like "inexpensive" and "expensive" and can be disregarded without impacting the outcome of the coding.

• Code the texts.

Once these choices about irrelevant information are made, the next step is to code the text. This is done either by hand, i.e. reading through the text and manually writing down concept occurrences, or through the use of various computer programs. Coding with a computer is one of contemporary conceptual analysis' greatest assets. By inputting one's categories, content analysis programs can easily automate the coding process and examine huge amounts of data, and a wider range of texts, quickly and efficiently. But automation is very dependent on the researcher's preparation and category construction. When coding is done manually, a researcher can recognize errors far more easily. A computer is only a tool and can only code based on the information it is given. This problem is most apparent when coding for implicit information, where category preparation is essential for accurate coding.

• Analyze your results.

Once the coding is done, the researcher examines the data and attempts to draw whatever conclusions and generalizations are possible. Of course, before these can be drawn, the researcher must decide what to do with the information in the text that is not coded. One's options include either deleting or skipping over unwanted material, or viewing all information as relevant and important and using it to reexamine, reassess and perhaps even alter one's coding scheme. Furthermore, given that the conceptual analyst is dealing only with quantitative data, the levels of interpretation and generalizability are very limited. The researcher can only extrapolate as far as the data will allow. But it is possible to see trends, for example, that are indicative of much larger ideas. Using the example from step three, if the concept "inexpensive" appears 50 times, compared to 15 appearances of "coverage for everyone," then the researcher can pretty safely extrapolate that there does appear to be a greater emphasis on the economics of the health care plan, as opposed to its universal coverage for all Americans. It must be kept in mind that conceptual analysis, while extremely useful and effective for providing this type of information when done right, is limited by its focus and the quantitative nature of its examination. To more fully explore the relationships that exist between these concepts, one must turn to relational analysis.

Relational Analysis

Relational analysis, like conceptual analysis, begins with the act of identifying concepts present in a given text or set of texts. However, relational analysis seeks to go beyond presence by exploring the relationships between the concepts identified. Relational analysis has also been termed semantic analysis (Palmquist, Carley, & Dale, 1997). In other words, the focus of relational analysis is to look for semantic, or meaningful, relationships. Individual concepts, in and of themselves, are viewed as having no inherent meaning. Rather, meaning is a product of the relationships among concepts in a text. Carley (1992) asserts that concepts are "ideational kernels;" these kernels can be thought of as symbols which acquire meaning through their connections to other symbols.

Theoretical Influences on Relational Analysis

The kind of analysis that researchers employ will vary significantly according to their theoretical approach. Key theoretical approaches that inform content analysis include linguistics and cognitive science.

Linguistic approaches to content analysis focus analysis of texts on the level of a linguistic unit, typically single clause units. One example of this type of research is Gottschalk (1975), who developed an automated procedure which analyzes each clause in a text and assigns it a numerical score based on several emotional/psychological scales. Another technique is to code a text grammatically into clauses and parts of speech to establish a matrix representation (Carley, 1990).

Approaches that derive from cognitive science include the creation of decision maps and mental models. Decision maps attempt to represent the relationship(s) between ideas, beliefs, attitudes, and information available to an author when making a decision within a text. These relationships can be represented as logical, inferential, causal, sequential, and mathematical relationships. Typically, two of these links are compared in a single study, and are analyzed as networks. For example, Heise (1987) used logical and sequential links to examine symbolic interaction. This methodology is thought of as a more generalized cognitive mapping technique, rather than the more specific mental models approach.

Mental models are groups or networks of interrelated concepts that are thought to reflect conscious or subconscious perceptions of reality. According to cognitive scientists, internal mental structures are created as people draw inferences and gather information about the world. Mental models are a more specific approach to mapping because beyond extraction and comparison because they can be numerically and graphically analyzed. Such models rely heavily on the use of computers to help analyze and construct mapping representations. Typically, studies based on this approach follow five general steps:

• Identifing concepts
• Defining relationship types
• Coding the text on the basis of 1 and 2
• Coding the statements
• Graphically displaying and numerically analyzing the resulting maps

To create the model, a researcher converts a text into a map of concepts and relations; the map is then analyzed on the level of concepts and statements, where a statement consists of two concepts and their relationship. Carley (1990) asserts that this makes possible the comparison of a wide variety of maps, representing multiple sources, implicit and explicit information, as well as socially shared cognitions.

Relational Analysis: Overview of Methods

As with other sorts of inquiry, initial choices with regard to what is being studied and/or coded for often determine the possibilities of that particular study. For relational analysis, it is important to first decide which concept type(s) will be explored in the analysis. Studies have been conducted with as few as one and as many as 500 concept categories. Obviously, too many categories may obscure your results and too few can lead to unreliable and potentially invalid conclusions. Therefore, it is important to allow the context and necessities of your research to guide your coding procedures.

The steps to relational analysis that we consider in this guide suggest some of the possible avenues available to a researcher doing content analysis. We provide an example to make the process easier to grasp. However, the choices made within the context of the example are but only a few of many possibilities. The diversity of techniques available suggests that there is quite a bit of enthusiasm for this mode of research. Once a procedure is rigorously tested, it can be applied and compared across populations over time. The process of relational analysis has achieved a high degree of computer automation but still is, like most forms of research, time consuming. Perhaps the strongest claim that can be made is that it maintains a high degree of statistical rigor without losing the richness of detail apparent in even more qualitative methods.

Three Subcategories of Relational Analysis

Affect extraction: This approach provides an emotional evaluation of concepts explicit in a text. It is problematic because emotion may vary across time and populations. Nevertheless, when extended it can be a potent means of exploring the emotional/psychological state of the speaker and/or writer. Gottschalk (1995) provides an example of this type of analysis. By assigning concepts identified a numeric value on corresponding emotional/psychological scales that can then be statistically examined, Gottschalk claims that the emotional/psychological state of the speaker or writer can be ascertained via their verbal behavior.

Proximity analysis: This approach, on the other hand, is concerned with the co-occurrence of explicit concepts in the text. In this procedure, the text is defined as a string of words. A given length of words, called a window , is determined. The window is then scanned across a text to check for the co-occurrence of concepts. The result is the creation of a concept determined by the concept matrix . In other words, a matrix, or a group of interrelated, co-occurring concepts, might suggest a certain overall meaning. The technique is problematic because the window records only explicit concepts and treats meaning as proximal co-occurrence. Other techniques such as clustering, grouping, and scaling are also useful in proximity analysis.

Cognitive mapping: This approach is one that allows for further analysis of the results from the two previous approaches. It attempts to take the above processes one step further by representing these relationships visually for comparison. Whereas affective and proximal analysis function primarily within the preserved order of the text, cognitive mapping attempts to create a model of the overall meaning of the text. This can be represented as a graphic map that represents the relationships between concepts.

In this manner, cognitive mapping lends itself to the comparison of semantic connections across texts. This is known as map analysis which allows for comparisons to explore "how meanings and definitions shift across people and time" (Palmquist, Carley, & Dale, 1997). Maps can depict a variety of different mental models (such as that of the text, the writer/speaker, or the social group/period), according to the focus of the researcher. This variety is indicative of the theoretical assumptions that support mapping: mental models are representations of interrelated concepts that reflect conscious or subconscious perceptions of reality; language is the key to understanding these models; and these models can be represented as networks (Carley, 1990). Given these assumptions, it's not surprising to see how closely this technique reflects the cognitive concerns of socio-and psycholinguistics, and lends itself to the development of artificial intelligence models.

Steps for Conducting Relational Analysis

The following discussion of the steps (or, perhaps more accurately, strategies) that can be followed to code a text or set of texts during relational analysis. These explanations are accompanied by examples of relational analysis possibilities for statements made by Bill Clinton during the 1998 hearings.

• Identify the Question.

The question is important because it indicates where you are headed and why. Without a focused question, the concept types and options open to interpretation are limitless and therefore the analysis difficult to complete. Possibilities for the Hairy Hearings of 1998 might be:

What did Bill Clinton say in the speech? OR What concrete information did he present to the public?

• Choose a sample or samples for analysis.

Once the question has been identified, the researcher must select sections of text/speech from the hearings in which Bill Clinton may have not told the entire truth or is obviously holding back information. For relational content analysis, the primary consideration is how much information to preserve for analysis. One must be careful not to limit the results by doing so, but the researcher must also take special care not to take on so much that the coding process becomes too heavy and extensive to supply worthwhile results.

• Determine the type of analysis.

Once the sample has been chosen for analysis, it is necessary to determine what type or types of relationships you would like to examine. There are different subcategories of relational analysis that can be used to examine the relationships in texts.

In this example, we will use proximity analysis because it is concerned with the co-occurrence of explicit concepts in the text. In this instance, we are not particularly interested in affect extraction because we are trying to get to the hard facts of what exactly was said rather than determining the emotional considerations of speaker and receivers surrounding the speech which may be unrecoverable.

Once the subcategory of analysis is chosen, the selected text must be reviewed to determine the level of analysis. The researcher must decide whether to code for a single word, such as "perhaps," or for sets of words or phrases like "I may have forgotten."

• Reduce the text to categories and code for words or patterns.

At the simplest level, a researcher can code merely for existence. This is not to say that simplicity of procedure leads to simplistic results. Many studies have successfully employed this strategy. For example, Palmquist (1990) did not attempt to establish the relationships among concept terms in the classrooms he studied; his study did, however, look at the change in the presence of concepts over the course of the semester, comparing a map analysis from the beginning of the semester to one constructed at the end. On the other hand, the requirement of one's specific research question may necessitate deeper levels of coding to preserve greater detail for analysis.

In relation to our extended example, the researcher might code for how often Bill Clinton used words that were ambiguous, held double meanings, or left an opening for change or "re-evaluation." The researcher might also choose to code for what words he used that have such an ambiguous nature in relation to the importance of the information directly related to those words.

• Explore the relationships between concepts (Strength, Sign & Direction).

Once words are coded, the text can be analyzed for the relationships among the concepts set forth. There are three concepts which play a central role in exploring the relations among concepts in content analysis.

• Strength of Relationship: Refers to the degree to which two or more concepts are related. These relationships are easiest to analyze, compare, and graph when all relationships between concepts are considered to be equal. However, assigning strength to relationships retains a greater degree of the detail found in the original text. Identifying strength of a relationship is key when determining whether or not words like unless, perhaps, or maybe are related to a particular section of text, phrase, or idea.
• Sign of a Relationship: Refers to whether or not the concepts are positively or negatively related. To illustrate, the concept "bear" is negatively related to the concept "stock market" in the same sense as the concept "bull" is positively related. Thus "it's a bear market" could be coded to show a negative relationship between "bear" and "market". Another approach to coding for strength entails the creation of separate categories for binary oppositions. The above example emphasizes "bull" as the negation of "bear," but could be coded as being two separate categories, one positive and one negative. There has been little research to determine the benefits and liabilities of these differing strategies. Use of Sign coding for relationships in regard to the hearings my be to find out whether or not the words under observation or in question were used adversely or in favor of the concepts (this is tricky, but important to establishing meaning).
• Direction of the Relationship: Refers to the type of relationship categories exhibit. Coding for this sort of information can be useful in establishing, for example, the impact of new information in a decision making process. Various types of directional relationships include, "X implies Y," "X occurs before Y" and "if X then Y," or quite simply the decision whether concept X is the "prime mover" of Y or vice versa. In the case of the 1998 hearings, the researcher might note that, "maybe implies doubt," "perhaps occurs before statements of clarification," and "if possibly exists, then there is room for Clinton to change his stance." In some cases, concepts can be said to be bi-directional, or having equal influence. This is equivalent to ignoring directionality. Both approaches are useful, but differ in focus. Coding all categories as bi-directional is most useful for exploratory studies where pre-coding may influence results, and is also most easily automated, or computer coded.
• Code the relationships.

One of the main differences between conceptual analysis and relational analysis is that the statements or relationships between concepts are coded. At this point, to continue our extended example, it is important to take special care with assigning value to the relationships in an effort to determine whether the ambiguous words in Bill Clinton's speech are just fillers, or hold information about the statements he is making.

• Perform Statisical Analyses.

This step involves conducting statistical analyses of the data you've coded during your relational analysis. This may involve exploring for differences or looking for relationships among the variables you've identified in your study.

• Map out the Representations.

In addition to statistical analysis, relational analysis often leads to viewing the representations of the concepts and their associations in a text (or across texts) in a graphical -- or map -- form. Relational analysis is also informed by a variety of different theoretical approaches: linguistic content analysis, decision mapping, and mental models.

The authors of this guide have created the following commentaries on content analysis.

Issues of Reliability & Validity

The issues of reliability and validity are concurrent with those addressed in other research methods. The reliability of a content analysis study refers to its stability , or the tendency for coders to consistently re-code the same data in the same way over a period of time; reproducibility , or the tendency for a group of coders to classify categories membership in the same way; and accuracy , or the extent to which the classification of a text corresponds to a standard or norm statistically. Gottschalk (1995) points out that the issue of reliability may be further complicated by the inescapably human nature of researchers. For this reason, he suggests that coding errors can only be minimized, and not eliminated (he shoots for 80% as an acceptable margin for reliability).

On the other hand, the validity of a content analysis study refers to the correspondence of the categories to the conclusions , and the generalizability of results to a theory.

The validity of categories in implicit concept analysis, in particular, is achieved by utilizing multiple classifiers to arrive at an agreed upon definition of the category. For example, a content analysis study might measure the occurrence of the concept category "communist" in presidential inaugural speeches. Using multiple classifiers, the concept category can be broadened to include synonyms such as "red," "Soviet threat," "pinkos," "godless infidels" and "Marxist sympathizers." "Communist" is held to be the explicit variable, while "red," etc. are the implicit variables.

The overarching problem of concept analysis research is the challenge-able nature of conclusions reached by its inferential procedures. The question lies in what level of implication is allowable, i.e. do the conclusions follow from the data or are they explainable due to some other phenomenon? For occurrence-specific studies, for example, can the second occurrence of a word carry equal weight as the ninety-ninth? Reasonable conclusions can be drawn from substantive amounts of quantitative data, but the question of proof may still remain unanswered.

This problem is again best illustrated when one uses computer programs to conduct word counts. The problem of distinguishing between synonyms and homonyms can completely throw off one's results, invalidating any conclusions one infers from the results. The word "mine," for example, variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. One may obtain an accurate count of that word's occurrence and frequency, but not have an accurate accounting of the meaning inherent in each particular usage. For example, one may find 50 occurrences of the word "mine." But, if one is only looking specifically for "mine" as an explosive device, and 17 of the occurrences are actually personal pronouns, the resulting 50 is an inaccurate result. Any conclusions drawn as a result of that number would render that conclusion invalid.

The generalizability of one's conclusions, then, is very dependent on how one determines concept categories, as well as on how reliable those categories are. It is imperative that one defines categories that accurately measure the idea and/or items one is seeking to measure. Akin to this is the construction of rules. Developing rules that allow one, and others, to categorize and code the same data in the same way over a period of time, referred to as stability , is essential to the success of a conceptual analysis. Reproducibility , not only of specific categories, but of general methods applied to establishing all sets of categories, makes a study, and its subsequent conclusions and results, more sound. A study which does this, i.e. in which the classification of a text corresponds to a standard or norm, is said to have accuracy .

Advantages of Content Analysis

Content analysis offers several advantages to researchers who consider using it. In particular, content analysis:

• looks directly at communication via texts or transcripts, and hence gets at the central aspect of social interaction
• can allow for both quantitative and qualitative operations
• can provides valuable historical/cultural insights over time through analysis of texts
• allows a closeness to text which can alternate between specific categories and relationships and also statistically analyzes the coded form of the text
• can be used to interpret texts for purposes such as the development of expert systems (since knowledge and rules can both be coded in terms of explicit statements about the relationships among concepts)
• is an unobtrusive means of analyzing interactions
• provides insight into complex models of human thought and language use

Disadvantages of Content Analysis

Content analysis suffers from several disadvantages, both theoretical and procedural. In particular, content analysis:

• can be extremely time consuming
• is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation
• is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study
• is inherently reductive, particularly when dealing with complex texts
• tends too often to simply consist of word counts
• often disregards the context that produced the text, as well as the state of things after the text is produced
• can be difficult to automate or computerize

The Palmquist, Carley and Dale study, a summary of "Applications of Computer-Aided Text Analysis: Analyzing Literary and Non-Literary Texts" (1997) is an example of two studies that have been conducted using both conceptual and relational analysis. The Problematic Text for Content Analysis shows the differences in results obtained by a conceptual and a relational approach to a study.

Related Information: Example of a Problematic Text for Content Analysis

In this example, both students observed a scientist and were asked to write about the experience.

Student A: I found that scientists engage in research in order to make discoveries and generate new ideas. Such research by scientists is hard work and often involves collaboration with other scientists which leads to discoveries which make the scientists famous. Such collaboration may be informal, such as when they share new ideas over lunch, or formal, such as when they are co-authors of a paper.

Student B: It was hard work to research famous scientists engaged in collaboration and I made many informal discoveries. My research showed that scientists engaged in collaboration with other scientists are co-authors of at least one paper containing their new ideas. Some scientists make formal discoveries and have new ideas.

Content analysis coding for explicit concepts may not reveal any significant differences. For example, the existence of "I, scientist, research, hard work, collaboration, discoveries, new ideas, etc..." are explicit in both texts, occur the same number of times, and have the same emphasis. Relational analysis or cognitive mapping, however, reveals that while all concepts in the text are shared, only five concepts are common to both. Analyzing these statements reveals that Student A reports on what "I" found out about "scientists," and elaborated the notion of "scientists" doing "research." Student B focuses on what "I's" research was and sees scientists as "making discoveries" without emphasis on research.

Related Information: The Palmquist, Carley and Dale Study

Consider these two questions: How has the depiction of robots changed over more than a century's worth of writing? And, do students and writing instructors share the same terms for describing the writing process? Although these questions seem totally unrelated, they do share a commonality: in the Palmquist, Carley & Dale study, their answers rely on computer-aided text analysis to demonstrate how different texts can be analyzed.

Literary texts

One half of the study explored the depiction of robots in 27 science fiction texts written between 1818 and 1988. After texts were divided into three historically defined groups, readers look for how the depiction of robots has changed over time. To do this, researchers had to create concept lists and relationship types, create maps using a computer software (see Fig. 1), modify those maps and then ultimately analyze them. The final product of the analysis revealed that over time authors were less likely to depict robots as metallic humanoids.

Non-literary texts

The second half of the study used student journals and interviews, teacher interviews, texts books, and classroom observations as the non-literary texts from which concepts and words were taken. The purpose behind the study was to determine if, in fact, over time teacher and students would begin to share a similar vocabulary about the writing process. Again, researchers used computer software to assist in the process. This time, computers helped researchers generated a concept list based on frequently occurring words and phrases from all texts. Maps were also created and analyzed in this study (see Fig. 2).

Annotated Bibliography

Resources On How To Conduct Content Analysis

Beard, J., & Yaprak, A. (1989). Language implications for advertising in international markets: A model for message content and message execution. A paper presented at the 8th International Conference on Language Communication for World Business and the Professions. Ann Arbor, MI.

This report discusses the development and testing of a content analysis model for assessing advertising themes and messages aimed primarily at U.S. markets which seeks to overcome barriers in the cultural environment of international markets. Texts were categorized under 3 headings: rational, emotional, and moral. The goal here was to teach students to appreciate differences in language and culture.

Berelson, B. (1971). Content analysis in communication research . New York: Hafner Publishing Company.

While this book provides an extensive outline of the uses of content analysis, it is far more concerned with conveying a critical approach to current literature on the subject. In this respect, it assumes a bit of prior knowledge, but is still accessible through the use of concrete examples.

Budd, R. W., Thorp, R.K., & Donohew, L. (1967). Content analysis of communications . New York: Macmillan Company.

Although published in 1967, the decision of the authors to focus on recent trends in content analysis keeps their insights relevant even to modern audiences. The book focuses on specific uses and methods of content analysis with an emphasis on its potential for researching human behavior. It is also geared toward the beginning researcher and breaks down the process of designing a content analysis study into 6 steps that are outlined in successive chapters. A useful annotated bibliography is included.

Carley, K. (1992). Coding choices for textual analysis: A comparison of content analysis and map analysis. Unpublished Working Paper.

Comparison of the coding choices necessary to conceptual analysis and relational analysis, especially focusing on cognitive maps. Discusses concept coding rules needed for sufficient reliability and validity in a Content Analysis study. In addition, several pitfalls common to texts are discussed.

Carley, K. (1990). Content analysis. In R.E. Asher (Ed.), The Encyclopedia of Language and Linguistics. Edinburgh: Pergamon Press.

Quick, yet detailed, overview of the different methodological kinds of Content Analysis. Carley breaks down her paper into five sections, including: Conceptual Analysis, Procedural Analysis, Relational Analysis, Emotional Analysis and Discussion. Also included is an excellent and comprehensive Content Analysis reference list.

Carley, K. (1989). Computer analysis of qualitative data . Pittsburgh, PA: Carnegie Mellon University.

Presents graphic, illustrated representations of computer based approaches to content analysis.

Carley, K. (1992). MECA . Pittsburgh, PA: Carnegie Mellon University.

A resource guide explaining the fifteen routines that compose the Map Extraction Comparison and Analysis (MECA) software program. Lists the source file, input and out files, and the purpose for each routine.

Carney, T. F. (1972). Content analysis: A technique for systematic inference from communications . Winnipeg, Canada: University of Manitoba Press.

This book introduces and explains in detail the concept and practice of content analysis. Carney defines it; traces its history; discusses how content analysis works and its strengths and weaknesses; and explains through examples and illustrations how one goes about doing a content analysis.

de Sola Pool, I. (1959). Trends in content analysis . Urbana, Ill: University of Illinois Press.

The 1959 collection of papers begins by differentiating quantitative and qualitative approaches to content analysis, and then details facets of its uses in a wide variety of disciplines: from linguistics and folklore to biography and history. Includes a discussion on the selection of relevant methods and representational models.

Duncan, D. F. (1989). Content analysis in health educaton research: An introduction to purposes and methods. Heatlth Education, 20 (7).

This article proposes using content analysis as a research technique in health education. A review of literature relating to applications of this technique and a procedure for content analysis are presented.

Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc.

This book primarily focuses on the Gottschalk-Gleser method of content analysis, and its application as a method of measuring psychological dimensions of children and adults via the content and form analysis of their verbal behavior, using the grammatical clause as the basic unit of communication for carrying semantic messages generated by speakers or writers.

Krippendorf, K. (1980). Content analysis: An introduction to its methodology Beverly Hills, CA: Sage Publications.

This is one of the most widely quoted resources in many of the current studies of Content Analysis. Recommended as another good, basic resource, as Krippendorf presents the major issues of Content Analysis in much the same way as Weber (1975).

Moeller, L. G. (1963). An introduction to content analysis--including annotated bibliography . Iowa City: University of Iowa Press.

A good reference for basic content analysis. Discusses the options of sampling, categories, direction, measurement, and the problems of reliability and validity in setting up a content analysis. Perhaps better as a historical text due to its age.

Smith, C. P. (Ed.). (1992). Motivation and personality: Handbook of thematic content analysis. New York: Cambridge University Press.

Billed by its authors as "the first book to be devoted primarily to content analysis systems for assessment of the characteristics of individuals, groups, or historical periods from their verbal materials." The text includes manuals for using various systems, theory, and research regarding the background of systems, as well as practice materials, making the book both a reference and a handbook.

Solomon, M. (1993). Content analysis: a potent tool in the searcher's arsenal. Database, 16 (2), 62-67.

Online databases can be used to analyze data, as well as to simply retrieve it. Online-media-source content analysis represents a potent but little-used tool for the business searcher. Content analysis benchmarks useful to advertisers include prominence, offspin, sponsor affiliation, verbatims, word play, positioning and notational visibility.

Weber, R. P. (1990). Basic content analysis, second edition . Newbury Park, CA: Sage Publications.

Good introduction to Content Analysis. The first chapter presents a quick overview of Content Analysis. The second chapter discusses content classification and interpretation, including sections on reliability, validity, and the creation of coding schemes and categories. Chapter three discusses techniques of Content Analysis, using a number of tables and graphs to illustrate the techniques. Chapter four examines issues in Content Analysis, such as measurement, indication, representation and interpretation.

Examples of Content Analysis

Adams, W., & Shriebman, F. (1978). Television network news: Issues in content research . Washington, DC: George Washington University Press.

A fairly comprehensive application of content analysis to the field of television news reporting. The books tripartite division discusses current trends and problems with news criticism from a content analysis perspective, four different content analysis studies of news media, and makes recommendations for future research in the area. Worth a look by anyone interested in mass communication research.

Auter, P. J., & Moore, R. L. (1993). Buying from a friend: a content analysis of two teleshopping programs. Journalism Quarterly, 70 (2), 425-437.

A preliminary study was conducted to content-analyze random samples of two teleshopping programs, using a measure of content interactivity and a locus of control message index.

Barker, S. P. (???) Fame: A content analysis study of the American film biography. Ohio State University. Thesis.

Barker examined thirty Oscar-nominated films dating from 1929 to 1979 using O.J. Harvey Belief System and the Kohlberg's Moral Stages to determine whether cinema heroes were positive role models for fame and success or morally ambiguous celebrities. Content analysis was successful in determining several trends relative to the frequency and portrayal of women in film, the generally high ethical character of the protagonists, and the dogmatic, close-minded nature of film antagonists.

Bernstein, J. M. & Lacy, S. (1992). Contextual coverage of government by local television news. Journalism Quarterly, 69 (2), 329-341.

This content analysis of 14 local television news operations in five markets looks at how local TV news shows contribute to the marketplace of ideas. Performance was measured as the allocation of stories to types of coverage that provide the context about events and issues confronting the public.

Blaikie, A. (1993). Images of age: a reflexive process. Applied Ergonomics, 24 (1), 51-58.

Content analysis of magazines provides a sharp instrument for reflecting the change in stereotypes of aging over past decades.

Craig, R. S. (1992). The effect of day part on gender portrayals in television commercials: a content analysis. Sex Roles: A Journal of Research, 26 (5-6), 197-213.

Gender portrayals in 2,209 network television commercials were content analyzed. To compare differences between three day parts, the sample was chosen from three time periods: daytime, evening prime time, and weekend afternoon sportscasts. The results indicate large and consistent differences in the way men and women are portrayed in these three day parts, with almost all comparisons reaching significance at the .05 level. Although ads in all day parts tended to portray men in stereotypical roles of authority and dominance, those on weekends tended to emphasize escape form home and family. The findings of earlier studies which did not consider day part differences may now have to be reevaluated.

Dillon, D. R. et al. (1992). Article content and authorship trends in The Reading Teacher, 1948-1991. The Reading Teacher, 45 (5), 362-368.

The authors explore changes in the focus of the journal over time.

Eberhardt, EA. (1991). The rhetorical analysis of three journal articles: The study of form, content, and ideology. Ft. Collins, CO: Colorado State University.

Eberhardt uses content analysis in this thesis paper to analyze three journal articles that reported on President Ronald Reagan's address in which he responded to the Tower Commission report concerning the IranContra Affair. The reports concentrated on three rhetorical elements: idea generation or content; linguistic style or choice of language; and the potential societal effect of both, which Eberhardt analyzes, along with the particular ideological orientation espoused by each magazine.

Ellis, B. G. & Dick, S. J. (1996). 'Who was 'Shadow'? The computer knows: applying grammar-program statistics in content analyses to solve mysteries about authorship. Journalism & Mass Communication Quarterly, 73 (4), 947-963.

This study's objective was to employ the statistics-documentation portion of a word-processing program's grammar-check feature as a final, definitive, and objective tool for content analyses - used in tandem with qualitative analyses - to determine authorship. Investigators concluded there was significant evidence from both modalities to support their theory that Henry Watterson, long-time editor of the Louisville Courier-Journal, probably was the South's famed Civil War correspondent "Shadow" and to rule out another prime suspect, John H. Linebaugh of the Memphis Daily Appeal. Until now, this Civil War mystery has never been conclusively solved, puzzling historians specializing in Confederate journalism.

Gottschalk, L. A., Stein, M. K. & Shapiro, D.H. (1997). The application of computerized content analysis in a psychiatric outpatient clinic. Journal of Clinical Psychology, 53 (5) , 427-442.

Twenty-five new psychiatric outpatients were clinically evaluated and were administered a brief psychological screening battery which included measurements of symptoms, personality, and cognitive function. Included in this assessment procedure were the Gottschalk-Gleser Content Analysis Scales on which scores were derived from five minute speech samples by means of an artificial intelligence-based computer program. The use of this computerized content analysis procedure for initial, rapid diagnostic neuropsychiatric appraisal is supported by this research.

Graham, J. L., Kamins, M. A., & Oetomo, D. S. (1993). Content analysis of German and Japanese advertising in print media from Indonesia, Spain, and the United States. Journal of Advertising , 22 (2), 5-16.

The authors analyze informational and emotional content in print advertisements in order to consider how home-country culture influences firms' marketing strategies and tactics in foreign markets. Research results provided evidence contrary to the original hypothesis that home-country culture would influence ads in each of the target countries.

Herzog, A. (1973). The B.S. Factor: The theory and technique of faking it in America . New York: Simon and Schuster.

Herzog takes a look at the rhetoric of American culture using content analysis to point out discrepancies between intention and reality in American society. The study reveals, albeit in a comedic tone, how double talk and "not quite lies" are pervasive in our culture.

Horton, N. S. (1986). Young adult literature and censorship: A content analysis of seventy-eight young adult books . Denton, TX: North Texas State University.

The purpose of Horton's content analysis was to analyze a representative seventy-eight current young adult books to determine the extent to which they contain items which are objectionable to would-be censors. Seventy-eight books were identified which fit the criteria of popularity and literary quality. Each book was analyzed for, and tallied for occurrence of, six categories, including profanity, sex, violence, parent conflict, drugs and condoned bad behavior.

Isaacs, J. S. (1984). A verbal content analysis of the early memories of psychiatric patients . Berkeley: California School of Professional Psychology.

Isaacs did a content analysis investigation on the relationship between words and phrases used in early memories and clinical diagnosis. His hypothesis was that in conveying their early memories schizophrenic patients tend to use an identifiable set of words and phrases more frequently than do nonpatients and that schizophrenic patients use these words and phrases more frequently than do patients with major affective disorders.

Jean Lee, S. K. & Hwee Hoon, T. (1993). Rhetorical vision of men and women managers in Singapore. Human Relations, 46 (4), 527-542.

A comparison of media portrayal of male and female managers' rhetorical vision in Singapore is made. Content analysis of newspaper articles used to make this comparison also reveals the inherent conflicts that women managers have to face. Purposive and multi-stage sampling of articles are utilized.

Kaur-Kasior, S. (1987). The treatment of culture in greeting cards: A content analysis . Bowling Green, OH: Bowling Green State University.

Using six historical periods dating from 1870 to 1987, this content analysis study attempted to determine what structural/cultural aspects of American society were reflected in greeting cards. The study determined that the size of cards increased over time, included more pages, and had animals and flowers as their most dominant symbols. In addition, white was the most common color used. Due to habituation and specialization, says the author, greeting cards have become institutionalized in American culture.

Koza, J. E. (1992). The missing males and other gender-related issues in music education: A critical analysis of evidence from the Music Supervisor's Journal, 1914-1924. Paper presented at the annual meeting of the American Educational Research Association. San Francisco.

The goal of this study was to identify all educational issues that would today be explicitly gender related and to analyze the explanations past music educators gave for the existence of gender-related problems. A content analysis of every gender-related reference was undertaken, finding that the current preoccupation with males in music education has a long history and that little has changed since the early part of this century.

Laccinole, M. D. (1982). Aging and married couples: A language content analysis of a conversational and expository speech task . Eugene, OR: University of Oregon.

Using content analysis, this paper investigated the relationship of age to the use of the grammatical categories, and described the differences in the usage of these grammatical categories in a conversation and expository speech task by fifty married couples. The subjects Laccinole used in his analysis were Caucasian, English speaking, middle class, ranged in ages from 20 to 83 years of age, were in good health and had no history of communication disorders.

Laffal, J. (1995). A concept analysis of Jonathan Swift's 'A Tale of a Tub' and 'Gulliver's Travels.' Computers and Humanities, 29 (5), 339-362.

In this study, comparisons of concept profiles of "Tub," "Gulliver," and Swift's own contemporary texts, as well as a composite text of 18th century writers, reveal that "Gulliver" is conceptually different from "Tub." The study also discovers that the concepts and words of these texts suggest two strands in Swift's thinking.

Lewis, S. M. (1991). Regulation from a deregulatory FCC: Avoiding discursive dissonance. Masters Thesis, Fort Collins, CO: Colorado State University.

This thesis uses content analysis to examine inconsistent statements made by the Federal Communications Commission (FCC) in its policy documents during the 1980s. Lewis analyzes positions set forth by the FCC in its policy statements and catalogues different strategies that can be used by speakers to be or to appear consistent, as well as strategies to avoid inconsistent speech or discursive dissonance.

Norton, T. L. (1987). The changing image of childhood: A content analysis of Caldecott Award books. Los Angeles: University of South Carolina.

Content analysis was conducted on 48 Caldecott Medal Recipient books dating from 1938 to 1985 to determine whether the reflect the idea that the social perception of childhood has altered since the early 1960's. The results revealed an increasing "loss of childhood innocence," as well as a general sentimentality for childhood pervasive in the texts. Suggests further study of children's literature to confirm the validity of such study.

O'Dell, J. W. & Weideman, D. (1993). Computer content analysis of the Schreber case. Journal of Clinical Psychology, 49 (1), 120-125.

An example of the application of content analysis as a means of recreating a mental model of the psychology of an individual.

Pratt, C. A. & Pratt, C. B. (1995). Comparative content analysis of food and nutrition advertisements in Ebony, Essence, and Ladies' Home Journal. Journal of Nutrition Education, 27 (1), 11-18.

This study used content analysis to measure the frequencies and forms of food, beverage, and nutrition advertisements and their associated health-promotional message in three U.S. consumer magazines during two 3-year periods: 1980-1982 and 1990-1992. The study showed statistically significant differences among the three magazines in both frequencies and types of major promotional messages in the advertisements. Differences between the advertisements in Ebony and Essence, the readerships of which were primarily African-American, and those found in Ladies Home Journal were noted, as were changes in the two time periods. Interesting tie in to ethnographic research studies?

Riffe, D., Lacy, S., & Drager, M. W. (1996). Sample size in content analysis of weekly news magazines. Journalism & Mass Communication Quarterly,73 (3), 635-645.

This study explores a variety of approaches to deciding sample size in analyzing magazine content. Having tested random samples of size six, eight, ten, twelve, fourteen, and sixteen issues, the authors show that a monthly stratified sample of twelve issues is the most efficient method for inferring to a year's issues.

Roberts, S. K. (1987). A content analysis of how male and female protagonists in Newbery Medal and Honor books overcome conflict: Incorporating a locus of control framework. Fayetteville, AR: University of Arkansas.

The purpose of this content analysis was to analyze Newbery Medal and Honor books in order to determine how male and female protagonists were assigned behavioral traits in overcoming conflict as it relates to an internal or external locus of control schema. Roberts used all, instead of just a sample, of the fictional Newbery Medal and Honor books which met his study's criteria. A total of 120 male and female protagonists were categorized, from Newbery books dating from 1922 to 1986.

Schneider, J. (1993). Square One TV content analysis: Final report . New York: Children's Television Workshop.

This report summarizes the mathematical and pedagogical content of the 230 programs in the Square One TV library after five seasons of production, relating that content to the goals of the series which were to make mathematics more accessible, meaningful, and interesting to the children viewers.

Smith, T. E., Sells, S. P., and Clevenger, T. Ethnographic content analysis of couple and therapist perceptions in a reflecting team setting. The Journal of Marital and Family Therapy, 20 (3), 267-286.

An ethnographic content analysis was used to examine couple and therapist perspectives about the use and value of reflecting team practice. Postsession ethnographic interviews from both couples and therapists were examined for the frequency of themes in seven categories that emerged from a previous ethnographic study of reflecting teams. Ethnographic content analysis is briefly contrasted with conventional modes of quantitative content analysis to illustrate its usefulness and rationale for discovering emergent patterns, themes, emphases, and process using both inductive and deductive methods of inquiry.

Stahl, N. A. (1987). Developing college vocabulary: A content analysis of instructional materials. Reading, Research and Instruction , 26 (3).

This study investigates the extent to which the content of 55 college vocabulary texts is consistent with current research and theory on vocabulary instruction. It recommends less reliance on memorization and more emphasis on deep understanding and independent vocabulary development.

Swetz, F. (1992). Fifteenth and sixteenth century arithmetic texts: What can we learn from them? Science and Education, 1 (4).

Surveys the format and content of 15th and 16th century arithmetic textbooks, discussing the types of problems that were most popular in these early texts and briefly analyses problem contents. Notes the residual educational influence of this era's arithmetical and instructional practices.

Walsh, K., et al. (1996). Management in the public sector: a content analysis of journals. Public Administration 74 (2), 315-325.

The popularity and implementaion of managerial ideas from 1980 to 1992 are examined through the content of five journals revolving on local government, health, education and social service. Contents were analyzed according to commercialism, user involvement, performance evaluation, staffing, strategy and involvement with other organizations. Overall, local government showed utmost involvement with commercialism while health and social care articles were most concerned with user involvement.

For Further Reading

Abernethy, A. M., & Franke, G. R. (1996).The information content of advertising: a meta-analysis. Journal of Advertising, Summer 25 (2) , 1-18.

Carley, K., & Palmquist, M. (1992). Extracting, representing and analyzing mental models. Social Forces , 70 (3), 601-636.

Fan, D. (1988). Predictions of public opinion from the mass media: Computer content analysis and mathematical modeling . New York, NY: Greenwood Press.

Franzosi, R. (1990). Computer-assisted coding of textual data: An application to semantic grammars. Sociological Methods and Research, 19 (2), 225-257.

McTavish, D.G., & Pirro, E. (1990) Contextual content analysis. Quality and Quantity , 24 , 245-265.

Palmquist, M. E. (1990). The lexicon of the classroom: language and learning in writing class rooms . Doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.

Palmquist, M. E., Carley, K.M., and Dale, T.A. (1997). Two applications of automated text analysis: Analyzing literary and non-literary texts. In C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Tanscripts. Hillsdale, NJ: Lawrence Erlbaum Associates.

Roberts, C.W. (1989). Other than counting words: A linguistic approach to content analysis. Social Forces, 68 , 147-177.

Issues in Content Analysis

Jolliffe, L. (1993). Yes! More content analysis! Newspaper Research Journal , 14 (3-4), 93-97.

The author responds to an editorial essay by Barbara Luebke which criticizes excessive use of content analysis in newspaper content studies. The author points out the positive applications of content analysis when it is theory-based and utilized as a means of suggesting how or why the content exists, or what its effects on public attitudes or behaviors may be.

Kang, N., Kara, A., Laskey, H. A., & Seaton, F. B. (1993). A SAS MACRO for calculating intercoder agreement in content analysis. Journal of Advertising, 22 (2), 17-28.

A key issue in content analysis is the level of agreement across the judgments which classify the objects or stimuli of interest. A review of articles published in the Journal of Advertising indicates that many authors are not fully utilizing recommended measures of intercoder agreement and thus may not be adequately establishing the reliability of their research. This paper presents a SAS MACRO which facilitates the computation of frequently recommended indices of intercoder agreement in content analysis.

Lacy, S. & Riffe, D. (1996). Sampling error and selecting intercoder reliability samples for nominal content categories. Journalism & Mass Communication Quarterly, 73 (4) , 693-704.

This study views intercoder reliability as a sampling problem. It develops a formula for generating sample sizes needed to have valid reliability estimates. It also suggests steps for reporting reliability. The resulting sample sizes will permit a known degree of confidence that the agreement in a sample of items is representative of the pattern that would occur if all content items were coded by all coders.

Riffe, D., Aust, C. F., & Lacy, S. R. (1993). The effectiveness of random, consecutive day and constructed week sampling in newspaper content analysis. Journalism Quarterly, 70 (1), 133-139.

This study compares 20 sets each of samples for four different sizes using simple random, constructed week and consecutive day samples of newspaper content. Comparisons of sample efficiency, based on the percentage of sample means in each set of 20 falling within one or two standard errors of the population mean, show the superiority of constructed week sampling.

Thomas, S. (1994). Artifactual study in the analysis of culture: A defense of content analysis in a postmodern age. Communication Research, 21 (6), 683-697.

Although both modern and postmodern scholars have criticized the method of content analysis with allegations of reductionism and other epistemological limitations, it is argued here that these criticisms are ill founded. In building and argument for the validity of content analysis, the general value of artifact or text study is first considered.

Zollars, C. (1994). The perils of periodical indexes: Some problems in constructing samples for content analysis and culture indicators research. Communication Research, 21 (6), 698-714.

The author examines problems in using periodical indexes to construct research samples via the use of content analysis and culture indicator research. Issues of historical and idiosyncratic changes in index subject category heading and subheadings make article headings potentially misleading indicators. Index subject categories are not necessarily invalid as a result; nevertheless, the author discusses the need to test for category longevity, coherence, and consistency over time, and suggests the use of oversampling, cross-references, and other techniques as a means of correcting and/or compensating for hidden inaccuracies in classification, and as a means of constructing purposive samples for analytic comparisons.

Busch, Carol, Paul S. De Maret, Teresa Flynn, Rachel Kellum, Sheri Le, Brad Meyers, Matt Saunders, Robert White, and Mike Palmquist. (2005). Content Analysis. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=61

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Introduction, defining and contextualizing genre, practical implications of navigating genre, broader impact on academic and non-academic writing.

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• Kindness in healthcare leadership and management: an evaluation and analysis of the concept
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• http://orcid.org/0000-0003-4246-7939 Rebecca Dyar 1 ,
• Karen Mattick 2 ,
• Andrew Griffiths 3
• 1 Anaesthetics and Intensive Care Medicine , Royal Devon University Healthcare NHS Foundation Trust , Exeter , UK
• 2 University of Exeter , Exeter , UK
• 3 Mid and South Essex Integrated Care Organisation , Chelmsford , UK
• Correspondence to Dr Rebecca Dyar, Anaesthetics and Intensive Care Medicine, Royal Devon University Healthcare NHS Foundation Trust, Exeter EX2 5DW, UK; rebecca.dyar{at}gmail.com

Background Healthcare leadership and management impacts every patient journey and every staff experience. Good leadership results in positive outcomes. Kindness is an understudied and underused leadership strategy. The research questions addressed in this study are the following: (1) Does kindness in healthcare leadership and management currently meet the criteria of a mature concept?; (2) Using concept analysis methodology, can we develop our understanding of kindness within this context?

Methods A systematic search of the peer-reviewed literature was conducted to inform a concept evaluation, followed by a concept analysis. Search terms consisted of ‘leader*’ or ‘manage*’ and ‘kindness’; databases searched comprised MEDLINE, HMIC, SPP, APA PsycInfo and CINAHL. Data extraction and thematic analysis of the data were performed manually according to concept analysis principles.

Results The 10 papers included from the search suggested that within healthcare leadership and management, kindness is an ‘emerging’ rather than a ‘mature’ concept. Concept analysis demonstrated a cluster of recurring attributes, allowing a theoretical definition to be put forth.

Conclusions Despite being a commonly used lay term, kindness in the context of healthcare leadership and management is not yet a mature concept. Work developing this concept is needed to validate the proposed theoretical definition. Observational studies and systematic review of the grey literature are recommended.

• patient experience
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All data relevant to the study are included in the article or uploaded as supplemental information.

https://doi.org/10.1136/leader-2023-000742

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Contributors RD (guarantor), KM and AG contributed to the design of the study. RD and KM implemented the methodology. RD wrote the manuscript with input from KM. All authors reviewed the results of the study and commented on the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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