thesis statement of fear

Home — Essay Samples — Life — Fear — The Causes And Effects Of Fear

The Causes and Effects of Fear

• Categories: Fear Feeling

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Published: Feb 8, 2022

Words: 569 | Page: 1 | 3 min read

Works Cited:

• Chen, K. (2004). Chinese silk: A cultural history. Hong Kong: Hong Kong University Press.
• Chinese Silk Association. (2020). Chinese Silk Association. Retrieved from https://www.silk.org.cn/index.html
• Kuhn, D. (2009). The age of Confucian rule: The Song transformation of China. Cambridge, MA: Harvard University Press.
• Liu, X. (2012). The silk road in world history. Oxford: Oxford University Press.
• Mazumdar, S. (2015). Silk textiles of India. London: Bloomsbury Academic.
• Ministry of Culture and Tourism of the People's Republic of China. (2020). Silk Road. Retrieved from http://en.silkroad.org.cn/
• Park, J., & Han, D. (2019). Historical review on the characteristics and manufacturing process of silk. Journal of Textile and Apparel, Technology and Management, 11(3), 1-22.
• Tong, X. (2016). A history of silk: The history of China's legendary silk cloth, from its creation to the 21st century. London: Academic Press.
• Wei, L., & Lai, W. (2013). Analysis of the production and export of silk in ancient China. The Journal of International Trade & Economic Development, 22(4), 488-503.
• Yang, Y. (2014). Exploring the silk road. New York: China Institute Gallery.

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Fear Theses Samples For Students

21 samples of this type

Over the course of studying in college, you will surely have to write a bunch of Theses on Fear. Lucky you if putting words together and organizing them into relevant text comes easy to you; if it's not the case, you can save the day by finding an already written Fear Thesis example and using it as a model to follow.

This is when you will certainly find WowEssays' free samples database extremely helpful as it embodies numerous skillfully written works on most various Fear Theses topics. Ideally, you should be able to find a piece that meets your criteria and use it as a template to build your own Thesis. Alternatively, our expert essay writers can deliver you a unique Fear Thesis model written from scratch according to your personal instructions.

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Acknowledgments.

I am especially grateful for the support and guidance of my dissertation advisor, Dr. A. P., provided all through the progression and development on this research study.

My thanks also go to the men and women in blue of Singapore Police Force whom had provided their valuable time to participate in the survey and their willingness to share. I also would like to thank DSP (NS) Azrin Abdul Rahim and SSI Selamat Bustaman for their valuable guidance and insights towards this study.

Singapore Police Force Empowerment Thesis Example

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One of the risks that illegal migrants face when they illegally cross the border is that they risk being apprehended by the country they are migrating to. The border law enforcement agencies of the country they are migrating to will arrest the immigrants and probably depot them.

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Thesis Statements

What this handout is about.

This handout describes what a thesis statement is, how thesis statements work in your writing, and how you can craft or refine one for your draft.

Introduction

Writing in college often takes the form of persuasion—convincing others that you have an interesting, logical point of view on the subject you are studying. Persuasion is a skill you practice regularly in your daily life. You persuade your roommate to clean up, your parents to let you borrow the car, your friend to vote for your favorite candidate or policy. In college, course assignments often ask you to make a persuasive case in writing. You are asked to convince your reader of your point of view. This form of persuasion, often called academic argument, follows a predictable pattern in writing. After a brief introduction of your topic, you state your point of view on the topic directly and often in one sentence. This sentence is the thesis statement, and it serves as a summary of the argument you’ll make in the rest of your paper.

What is a thesis statement?

A thesis statement:

• tells the reader how you will interpret the significance of the subject matter under discussion.
• is a road map for the paper; in other words, it tells the reader what to expect from the rest of the paper.
• directly answers the question asked of you. A thesis is an interpretation of a question or subject, not the subject itself. The subject, or topic, of an essay might be World War II or Moby Dick; a thesis must then offer a way to understand the war or the novel.
• makes a claim that others might dispute.
• is usually a single sentence near the beginning of your paper (most often, at the end of the first paragraph) that presents your argument to the reader. The rest of the paper, the body of the essay, gathers and organizes evidence that will persuade the reader of the logic of your interpretation.

If your assignment asks you to take a position or develop a claim about a subject, you may need to convey that position or claim in a thesis statement near the beginning of your draft. The assignment may not explicitly state that you need a thesis statement because your instructor may assume you will include one. When in doubt, ask your instructor if the assignment requires a thesis statement. When an assignment asks you to analyze, to interpret, to compare and contrast, to demonstrate cause and effect, or to take a stand on an issue, it is likely that you are being asked to develop a thesis and to support it persuasively. (Check out our handout on understanding assignments for more information.)

How do I create a thesis?

A thesis is the result of a lengthy thinking process. Formulating a thesis is not the first thing you do after reading an essay assignment. Before you develop an argument on any topic, you have to collect and organize evidence, look for possible relationships between known facts (such as surprising contrasts or similarities), and think about the significance of these relationships. Once you do this thinking, you will probably have a “working thesis” that presents a basic or main idea and an argument that you think you can support with evidence. Both the argument and your thesis are likely to need adjustment along the way.

Writers use all kinds of techniques to stimulate their thinking and to help them clarify relationships or comprehend the broader significance of a topic and arrive at a thesis statement. For more ideas on how to get started, see our handout on brainstorming .

How do I know if my thesis is strong?

If there’s time, run it by your instructor or make an appointment at the Writing Center to get some feedback. Even if you do not have time to get advice elsewhere, you can do some thesis evaluation of your own. When reviewing your first draft and its working thesis, ask yourself the following :

• Do I answer the question? Re-reading the question prompt after constructing a working thesis can help you fix an argument that misses the focus of the question. If the prompt isn’t phrased as a question, try to rephrase it. For example, “Discuss the effect of X on Y” can be rephrased as “What is the effect of X on Y?”
• Have I taken a position that others might challenge or oppose? If your thesis simply states facts that no one would, or even could, disagree with, it’s possible that you are simply providing a summary, rather than making an argument.
• Is my thesis statement specific enough? Thesis statements that are too vague often do not have a strong argument. If your thesis contains words like “good” or “successful,” see if you could be more specific: why is something “good”; what specifically makes something “successful”?
• Does my thesis pass the “So what?” test? If a reader’s first response is likely to  be “So what?” then you need to clarify, to forge a relationship, or to connect to a larger issue.
• Does my essay support my thesis specifically and without wandering? If your thesis and the body of your essay do not seem to go together, one of them has to change. It’s okay to change your working thesis to reflect things you have figured out in the course of writing your paper. Remember, always reassess and revise your writing as necessary.
• Does my thesis pass the “how and why?” test? If a reader’s first response is “how?” or “why?” your thesis may be too open-ended and lack guidance for the reader. See what you can add to give the reader a better take on your position right from the beginning.

Suppose you are taking a course on contemporary communication, and the instructor hands out the following essay assignment: “Discuss the impact of social media on public awareness.” Looking back at your notes, you might start with this working thesis:

Social media impacts public awareness in both positive and negative ways.

You can use the questions above to help you revise this general statement into a stronger thesis.

• Do I answer the question? You can analyze this if you rephrase “discuss the impact” as “what is the impact?” This way, you can see that you’ve answered the question only very generally with the vague “positive and negative ways.”
• Have I taken a position that others might challenge or oppose? Not likely. Only people who maintain that social media has a solely positive or solely negative impact could disagree.
• Is my thesis statement specific enough? No. What are the positive effects? What are the negative effects?
• Does my thesis pass the “how and why?” test? No. Why are they positive? How are they positive? What are their causes? Why are they negative? How are they negative? What are their causes?
• Does my thesis pass the “So what?” test? No. Why should anyone care about the positive and/or negative impact of social media?

After thinking about your answers to these questions, you decide to focus on the one impact you feel strongly about and have strong evidence for:

Because not every voice on social media is reliable, people have become much more critical consumers of information, and thus, more informed voters.

This version is a much stronger thesis! It answers the question, takes a specific position that others can challenge, and it gives a sense of why it matters.

Let’s try another. Suppose your literature professor hands out the following assignment in a class on the American novel: Write an analysis of some aspect of Mark Twain’s novel Huckleberry Finn. “This will be easy,” you think. “I loved Huckleberry Finn!” You grab a pad of paper and write:

Mark Twain’s Huckleberry Finn is a great American novel.

You begin to analyze your thesis:

• Do I answer the question? No. The prompt asks you to analyze some aspect of the novel. Your working thesis is a statement of general appreciation for the entire novel.

Think about aspects of the novel that are important to its structure or meaning—for example, the role of storytelling, the contrasting scenes between the shore and the river, or the relationships between adults and children. Now you write:

In Huckleberry Finn, Mark Twain develops a contrast between life on the river and life on the shore.

• Do I answer the question? Yes!
• Have I taken a position that others might challenge or oppose? Not really. This contrast is well-known and accepted.
• Is my thesis statement specific enough? It’s getting there–you have highlighted an important aspect of the novel for investigation. However, it’s still not clear what your analysis will reveal.
• Does my thesis pass the “how and why?” test? Not yet. Compare scenes from the book and see what you discover. Free write, make lists, jot down Huck’s actions and reactions and anything else that seems interesting.
• Does my thesis pass the “So what?” test? What’s the point of this contrast? What does it signify?”

After examining the evidence and considering your own insights, you write:

Through its contrasting river and shore scenes, Twain’s Huckleberry Finn suggests that to find the true expression of American democratic ideals, one must leave “civilized” society and go back to nature.

This final thesis statement presents an interpretation of a literary work based on an analysis of its content. Of course, for the essay itself to be successful, you must now present evidence from the novel that will convince the reader of your interpretation.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Anson, Chris M., and Robert A. Schwegler. 2010. The Longman Handbook for Writers and Readers , 6th ed. New York: Longman.

Lunsford, Andrea A. 2015. The St. Martin’s Handbook , 8th ed. Boston: Bedford/St Martin’s.

Ramage, John D., John C. Bean, and June Johnson. 2018. The Allyn & Bacon Guide to Writing , 8th ed. New York: Pearson.

Ruszkiewicz, John J., Christy Friend, Daniel Seward, and Maxine Hairston. 2010. The Scott, Foresman Handbook for Writers , 9th ed. Boston: Pearson Education.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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• Knowledge Base
• How to Write a Thesis Statement | 4 Steps & Examples

How to Write a Thesis Statement | 4 Steps & Examples

Published on January 11, 2019 by Shona McCombes . Revised on August 15, 2023 by Eoghan Ryan.

A thesis statement is a sentence that sums up the central point of your paper or essay . It usually comes near the end of your introduction .

Your thesis will look a bit different depending on the type of essay you’re writing. But the thesis statement should always clearly state the main idea you want to get across. Everything else in your essay should relate back to this idea.

You can write your thesis statement by following four simple steps:

• Start with a question
• Write your initial answer
• Develop your answer
• Refine your thesis statement

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Table of contents

What is a thesis statement, placement of the thesis statement, step 1: start with a question, step 2: write your initial answer, step 3: develop your answer, step 4: refine your thesis statement, types of thesis statements, other interesting articles, frequently asked questions about thesis statements.

A thesis statement summarizes the central points of your essay. It is a signpost telling the reader what the essay will argue and why.

The best thesis statements are:

• Concise: A good thesis statement is short and sweet—don’t use more words than necessary. State your point clearly and directly in one or two sentences.
• Contentious: Your thesis shouldn’t be a simple statement of fact that everyone already knows. A good thesis statement is a claim that requires further evidence or analysis to back it up.
• Coherent: Everything mentioned in your thesis statement must be supported and explained in the rest of your paper.

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The thesis statement generally appears at the end of your essay introduction or research paper introduction .

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts and among young people more generally is hotly debated. For many who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its many benefits for education: the internet facilitates easier access to information, exposure to different perspectives, and a flexible learning environment for both students and teachers.

You should come up with an initial thesis, sometimes called a working thesis , early in the writing process . As soon as you’ve decided on your essay topic , you need to work out what you want to say about it—a clear thesis will give your essay direction and structure.

You might already have a question in your assignment, but if not, try to come up with your own. What would you like to find out or decide about your topic?

For example, you might ask:

After some initial research, you can formulate a tentative answer to this question. At this stage it can be simple, and it should guide the research process and writing process .

Now you need to consider why this is your answer and how you will convince your reader to agree with you. As you read more about your topic and begin writing, your answer should get more detailed.

In your essay about the internet and education, the thesis states your position and sketches out the key arguments you’ll use to support it.

The negatives of internet use are outweighed by its many benefits for education because it facilitates easier access to information.

In your essay about braille, the thesis statement summarizes the key historical development that you’ll explain.

The invention of braille in the 19th century transformed the lives of blind people, allowing them to participate more actively in public life.

A strong thesis statement should tell the reader:

• Why you hold this position
• What they’ll learn from your essay
• The key points of your argument or narrative

The final thesis statement doesn’t just state your position, but summarizes your overall argument or the entire topic you’re going to explain. To strengthen a weak thesis statement, it can help to consider the broader context of your topic.

These examples are more specific and show that you’ll explore your topic in depth.

Your thesis statement should match the goals of your essay, which vary depending on the type of essay you’re writing:

• In an argumentative essay , your thesis statement should take a strong position. Your aim in the essay is to convince your reader of this thesis based on evidence and logical reasoning.
• In an expository essay , you’ll aim to explain the facts of a topic or process. Your thesis statement doesn’t have to include a strong opinion in this case, but it should clearly state the central point you want to make, and mention the key elements you’ll explain.

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
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• False cause fallacy
• Sunk cost fallacy

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A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

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Tips and Examples for Writing Thesis Statements

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This resource provides tips for creating a thesis statement and examples of different types of thesis statements.

Tips for Writing Your Thesis Statement

1. Determine what kind of paper you are writing:

• An analytical paper breaks down an issue or an idea into its component parts, evaluates the issue or idea, and presents this breakdown and evaluation to the audience.
• An expository (explanatory) paper explains something to the audience.
• An argumentative paper makes a claim about a topic and justifies this claim with specific evidence. The claim could be an opinion, a policy proposal, an evaluation, a cause-and-effect statement, or an interpretation. The goal of the argumentative paper is to convince the audience that the claim is true based on the evidence provided.

If you are writing a text that does not fall under these three categories (e.g., a narrative), a thesis statement somewhere in the first paragraph could still be helpful to your reader.

2. Your thesis statement should be specific—it should cover only what you will discuss in your paper and should be supported with specific evidence.

3. The thesis statement usually appears at the end of the first paragraph of a paper.

4. Your topic may change as you write, so you may need to revise your thesis statement to reflect exactly what you have discussed in the paper.

Thesis Statement Examples

Example of an analytical thesis statement:

The paper that follows should:

• Explain the analysis of the college admission process
• Explain the challenge facing admissions counselors

Example of an expository (explanatory) thesis statement:

• Explain how students spend their time studying, attending class, and socializing with peers

Example of an argumentative thesis statement:

• Present an argument and give evidence to support the claim that students should pursue community projects before entering college

• Thesis SupportAll
• Academic writing
• Getting started tips
• How to choose a thesis topic

Fear of failure

• Perfectionism
• Procrastination
• Relationship with your supervisor
• Stress Management
• Time Management
• Writer’s block
• Thesis bookshelf

What is fear of failure?

Fear of failure (atychiphobia) is a paralysing feeling which you experience in a situation where performance really counts or when there is great pressure to do well.

We all have this fear occasionally, but sometimes we can be so concerned with this emotion that it brings about the very failure that was feared. This fear can be so strong that it leads to putting off risks, putting off tasks, not using talents and to undermining success. For some of us the fear of failing becomes so overwhelming that it can prevent us from getting on with and enjoying our lives.

What causes fear of failure? (or, why high achievers fail…)

Every strength can become a weakness; every talent contains an opposite. The academic environment sets high standards and wants you to exploit all your capacities. In this environment you may sometimes fear you will not succeed; when that emotion becomes too big it can undermine your goal.

There are many triggers for this feeling; common are perfectionism (too high expectations from you or your environment), clinging on too long to old habits (which were successful in the past) or simply because you believe that failure was unthinkable (the unexpected and sudden notion paralyzes you). Focusing on the bad things that could happen fuels the fear of failure. For example, if you worry about doing poorly you might have thoughts like, “What if I forget to use theory x”, or “what if my writing is not critical enough”? Too many thoughts like these leave no mental space for thinking positively about your thesis and create a vicious negative circle.

The more you focus on the bad things that could happen, the stronger the feeling of anxiety becomes. This makes you feel worse and, because your head is full of distracting thoughts and fears, it will influence your productivity in the very end.

What are the symptoms?

When you are in a stressful situation your heart beats faster and you breathe quicker, it is your body’s primitive response to a perceived attack, the fight-or-flight-reaction.

This is the physical response, other symptoms that show you are suffering from this fear:

• In case you feel reluctant to venture into new aspects of your thesis (example: instead of writing you keep looking for more articles)
• Refusal to get involved in difficult projects (example: you do all kind of tasks but postpone working on the thesis)
• Self-sabotage (example: excessive delay of plans, because you have rescheduled meetings with your supervisor you find it hard to establish contact again)
• Lack of self-confidence and very low self-esteem, you harbour negative feelings towards your own self like “I will not be able to do this”, etc.

How to manage the fear of failure?

We learn many positive things from making mistakes. Mistakes are not the equivalent of ‘failing’. Not immediately succeeding in what we want, can be seen as good in two ways: it teaches us valuable lessons (how to change things, so that we don’t repeat the same again) and it also makes us stronger by adding to our inner resources.

You can take several action steps to manage your fail of failure by navigating the toggles below.

Accept Your Fear

This is the most important thing you can do for yourself. Failure in life is inevitable. It’s not possible for you to be amazing at something immediately. It’s not possible for you to achieve everything you want to achieve immediately. It takes time. It takes work.

Since fear of failure immobilizes you, in order to overcome it, you need to take action. Do something! Action gives you the ability to change the circumstances that hold you back.

Ask any successful person and they’ll tell you they didn’t succeed after their first attempt. If you give up, failure is inevitable. But if you keep on trying, you’ll eventually get there. Whenever you feel yourself letting your fear of failure get the best of you, just ask yourself, “What would I attempt if I knew I couldn’t fail?”

Treat Failure as a Learning Opportunity

Being successful is all about learning what doesn’t work for you. Once you know what doesn’t work, you can improve on the circumstance and eventually find what does work.

Keep Believing

Believe in yourself! Know that you can do this. Know that you will do this.

Use relaxation techniques

Engage in deep breathing for 2-5 minutes. Close your eyes and concentrate on the air going in and out of your lungs. Take long, deep breaths, fill your lungs and abdomen, hold your breath, and then exhale slowly. The belly goes up and down, it goes up when inhaling and goes down when exhaling. Tense and relax different muscle groups. For example, if your shoulders are tense pull them back and hold them for a few seconds, then relax. This will help you to be aware of the relaxation of muscles and help you to relax more. Engage in guided imagery for a few minutes. Pick a scene that you find peaceful, beautiful, and natural. Think about what you see, what you hear, what you feel and what you smell while in this scene.

General tips

• When we feel afraid that we will fail at something, there are a few things we could do to gain a different perspective.
• Remember how you managed to get through the last ‘failure’ you experienced. Expect that you will sometimes fail.
• When you do fail, embrace it. Turn the failure into a positive by figuring out where you went wrong, then applying what you learned to your next endeavour.
• Consider whether you could lower your standards and still feel ‘OK’ about your result?
• Think about exactly why you are afraid to fail. Is it more about failing yourself, or failing those around you – parents? friends? supervisor?
• When you are fazed by the hugeness of a task, break it down into manageable pieces.

Get support

Seek help early! Sometimes simply talking through a problem can help you find a solution. Pick people whom you know will be sympathetic, will listen and encourage you. Student psychologists can offer short-term counselling and are specialised in fear of failure linked to student issues.

Available workshops at Maastricht University

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Appealing to fear: A Meta-Analysis of Fear Appeal Effectiveness and Theories

Melanie b. tannenbaum.

University of Illinois at Urbana-Champaign

Justin Hepler

University of Nevada, Reno

Rick S. Zimmerman

University of Missouri – St. Louis

Lindsey Saul

Virginia Commonwealth University

Samantha Jacobs

Kristina wilson, dolores albarracin.

Fear appeals are a polarizing issue, with proponents confident in their efficacy and opponents confident that they backfire. We present the results of a comprehensive meta-analysis investigating fear appeals’ effectiveness for influencing attitudes, intentions, and behaviors. We tested predictions from a large number of theories, the majority of which have never been tested meta-analytically until now. Studies were included if they contained a treatment group exposed to a fear appeal, a valid comparison group, a manipulation of depicted fear, a measure of attitudes, intentions, or behaviors concerning the targeted risk or recommended solution, and adequate statistics to calculate effect sizes. The meta-analysis included 127 papers (9% unpublished) yielding 248 independent samples (N Total = 27,372) collected from diverse populations. Results showed a positive effect of fear appeals on attitudes, intentions, and behaviors, with the average effect on a composite index being random-effects d ¯ = 0.29. Moderation analyses based on prominent fear appeal theories showed that the effectiveness of fear appeals increased when the message included efficacy statements, depicted high susceptibility and severity, recommended one-time only (vs. repeated) behaviors, and targeted audiences that included a larger percentage of female message recipients. Overall, we conclude that (a) fear appeals are effective at positively influencing attitude, intentions, and behaviors, (b) there are very few circumstances under which they are not effective, and (c) there are no identified circumstances under which they backfire and lead to undesirable outcomes.

Fear appeals are persuasive messages that attempt to arouse fear by emphasizing the potential danger and harm that will befall individuals if they do not adopt the messages’ recommendations ( Dillard, 1996 ; Maddux & Rogers, 1983 ). Although these messages are often used in political, public health, and advertising campaigns in the hopes of reducing risky attitudes, intentions, or behaviors, their use is often a polarizing issue. Whereas some practitioners are confident in the power of fear appeals to persuade audiences (e.g., CDC, 2014 ; Xu et al., 2015 ), others are adamant that such messages are counterproductive (e.g., Drug Free Action Alliance, 2013 ; Ruiter et al., 2014 ). The fear appeal literature reflects this disagreement, and empirical studies, literature reviews, and meta-analyses conducted over the past six decades have offered a diverse array of perspectives on the topic. Although some meta-analytic examinations have found positive effects of fear appeals on some outcomes ( Witte & Allen, 2000 ), others have found null effects ( de Hoog et al., 2007 ) or even negative effects ( Peters et al., 2012 ). In the current paper, we present the results of a comprehensive meta-analysis of fear appeal research with two goals in mind. Our first goal was to compile the largest available meta-analytic database of fear appeal research and estimate average effects. Our second goal was to test a variety of theoretical predictions, many of which have never been examined meta-analytically, and to organize them within a framework that takes into account characteristics of a fear appeal’s message, recommended behavior, and audience.

Existing theories about fear appeals have focused on either the content of the message , the nature of the behavior recommended by the communication, or the characteristics of the audience receiving the message. However, all three of these aspects (message, behavior, and audience) are important and were considered in the framework that guided this review. This integrative framework gave our meta-analysis a broader scope beyond past analyses of fear appeals. Specifically, each prior meta-analysis has only tested theories relevant to the message portion of our framework, and thus was only able to address a limited set of questions pertaining to fear appeal effectiveness (for a description of prior meta-analyses, see Table 1 ) ( Boster & Mongeau, 1984 ; de Hoog et al., 2007 ; Earl & Albarracin, 2007 ; Floyd et al., 2000 ; Milne et al., 2000 ; Peters et al., 2012 ; Sutton, 1982 ; Witte & Allen, 2000 ). By adopting this more holistic view of fear appeals, we connected existing models that are generally treated as separate and examined novel hypotheses about fear appeal effectiveness that have previously gone untested. Further, the current meta-analysis used a substantially larger meta-analytic database than prior analyses, thus providing us with more precision to test relevant hypotheses.

Theories and hypotheses tested.

Note: LM = Linear Model. CM = Curvilinear Model. ES = Efficacy Statements. SM = Stage Model. RSAT = Robertson’s Single Action Theory. PT = Prospect Theory. TMT = Terror Management Theory. RFT = Regulatory Focus Theory. TM = Transtheoretical Model. SEE = Self-esteem enhancing. SEH = Self-esteem hindering.

The Content of Fear Appeals

Six prominent theories make predictions about the impact of message characteristics on fear appeal effectiveness 1 : The linear model of fear appeals (e.g., Witte & Allen, 2000 ), the curvilinear model of fear appeals (e.g., Hovland et al., 1953 ), the health belief model ( Rosenstock, 1966 ; Becker, 1974 ; Becker et al., 1977 ; Becker et al., 1978 ; Rosenstock, 1974 ), the parallel process model ( Leventhal, 1970 ), the extended parallel process model ( Witte, 1992 ; Witte, 1998 ), and the stage model ( de Hoog et al., 2007 ). These theories concern the level of depicted fear within messages, the use (or omission) of efficacy statements within messages, and the level of depicted susceptibility and/or severity within messages.

Amount of depicted fear

Perhaps the most central aspect of a fear appeal message is the amount of fear it is intended to arouse in message recipients. We will refer to this as depicted fear to emphasize that it reflects a property of the message’s content, rather than the subjective state of fear that message recipients experience. 2 Two competing theories make predictions about amount of depicted fear, which we will refer to as the linear model (e.g., Witte & Allen, 2000 ) and the curvilinear model ( Hovland et al., 1953 ; Janis, 1967 ; Janis & Feshbach, 1953 ; McGuire, 1968 ; McGuire, 1969 ). Both theoretical perspectives conceptualize depicted fear as a source of motivation, such that exposure to depicted fear increases motivation to adopt the message’s recommendations ( Hovland et al., 1953 ; Witte & Allen, 2000 ). Further, both models predict that low levels of depicted fear will be relatively less motivating and thus less effective than moderate levels of fear. However, the linear model predicts that depicted fear has a positive and monotonic influences on attitudes, intentions, and behaviors, such that high depicted fear is more effective than moderate depicted fear (e.g., Witte & Allen, 2000 ). In contrast, the curvilinear model predicts that high depicted fear elicits defensive avoidance, a reaction in which message recipients disengage from the message, avoid further exposure to the message, and/or derogate the message because it is too frightening ( Higbee, 1969 ; Hovland et al., 1953 ; Janis, 1967 ; 1968 ; Janis & Feshbach, 1953 ; Janis & Leventhal, 1968 ; McGuire, 1968 ; 1969 ; Millman, 1968 ). Consequently, the curvilinear theory predicts that high levels of depicted fear should be less effective than moderate levels of depicted fear.

The linear and curvilinear models have been tested in prior meta-analyses, and the linear model has consistently been supported by existing data, whereas the curvilinear model has not (e.g., Witte & Allen, 2000 ). One drawback to prior investigations of the linear and curvilinear models is that the analyses included comparisons from studies that used two levels of depicted fear, even though it is difficult to equate levels of depicted fear across different studies – what may qualify as moderate depicted fear in one study may qualify as low depicted fear in a different study. Thus, an appropriate test of the linear and curvilinear models requires depicted fear to be manipulated with at least three levels within the same study to ensure that moderate depicted fear is operationalized as an intermediate level between extremes. We therefore tested the linear and curvilinear models in the current meta-analysis by comparing the effects of high versus moderate depicted fear, using only studies that manipulated depicted fear across several levels. The linear model predicts that high depicted fear will be more effective than moderate depicted fear, whereas the curvilinear model predicts that high depicted fear will be less effective than moderate depicted fear.

Efficacy statements

According to the health belief model (HBM; Rosenstock, 1966 ; Becker, 1974 ; Becker et al., 1977 ; Becker et al., 1978 ; Rosenstock, 1974 ), the stage model (e.g., de Hoog et al., 2007 ), the parallel process model (PPM; Leventhal, 1970 ), and the extended parallel process model (EPPM; Witte, 1992 ; Witte, 1998 ), fear appeals “work only when accompanied by… efficacy messages” ( Witte & Allen, 2000 , p.606). An efficacy message is a statement that assures message recipients that they are capable of performing the fear appeal’s recommended actions (self-efficacy) and/or that performing the recommended actions will result in desirable consequences (response-efficacy). The HBM, stage model, PPM, and EPPM suggest that when message recipients are presented with a threat (i.e., depicted fear), resulting feelings of vulnerability lead them to evaluate whether or not adopting the message’s recommendations will protect them from the threat-related negative consequences. If recipients decide that adopting the recommended action(s) will protect them, the fear appeal should be more effective. As efficacy statements provide this assurance, fear appeal messages that include statements about self- or response-efficacy should be more effective than fear appeal messages that include neither ( de Hoog et al., 2007 ; Witte & Allen, 2000 ).

There are two forms of the efficacy statement hypothesis. The strong hypothesis is that fear appeals without efficacy statements will produce negative effects (i.e., will backfire). The weak hypothesis is that fear appeals without efficacy statements will produce weaker (i.e., less positive or null) effects relative to fear appeals with efficacy statements. Three meta-analyses have tested whether the inclusion of efficacy statements in fear appeals leads to increased effectiveness, and all found support for the weak hypothesis ( de Hoog et al., 2007 ; Mongeau, 1998 ; Witte & Allen, 2000 ). However, those studies were conducted using less comprehensive meta-analytic databases, and thus the current synthesis can provide a more thorough assessment of the strong and weak hypotheses.

Depicted susceptibility and severity

According to the stage model ( de Hoog et al., 2007 ), the effectiveness of fear appeals should depend on their levels of depicted susceptibility and severity. A message high in depicted susceptibility emphasizes the message recipient’s personal risk for negative consequences (e.g., “One of fourteen women is destined to develop breast cancer during her life. So every woman may get breast cancer. You also run that risk!”; Siero et al., 1984 ), whereas a message low in depicted susceptibility does not personalize risk (e.g., “One of fourteen women is destined to develop breast cancer during her life.”; Siero et al., 1984 ). A message high in depicted severity describes the negative consequences of not taking action (e.g., “Breast cancer is a serious disease of which many women die, contrary to, for example, cancer of the uterus, where 90% to 95% recover.”; Siero et al., 1984 ), whereas a message low in depicted severity portrays manageable consequences (e.g., “If breast cancer is detected at an early stage it can be cured in a number of cases, contrary to, for example, lung cancer where 90% die of it.”; Siero et al., 1984 ). According to this model, high depicted severity (but not susceptibility) should improve attitudes, whereas high depicted susceptibility (but not severity) should improve intentions and behaviors. Consequently, only the combination of high-depicted susceptibility and severity should improve attitudes, intentions, and behaviors. A previous meta-analysis found mixed results concerning these predictions ( de Hoog et al., 2007 ). Specifically, messages with high depicted severity positively influenced attitudes, intentions, and behaviors, whereas messages with high depicted susceptibility positively influenced intentions and behaviors but not attitudes. We tested these hypotheses on our more comprehensive database.

Three prominent theories make predictions about the impact of the recommended behaviors on fear appeal effectiveness: Robertson’s single action theory ( Robertson, 1975 ; Rothman, Martino, Bedell, Detweiler, & Salovey, 1999 ), prospect theory ( Rothman et al., 1999 ; Rothman & Salovey, 1997 ; Tversky & Kahneman, 1981 ) and terror management theory ( Goldenberg & Arndt, 2008 ; Pyszczynski, Greenberg, & Solomon, 1999 ; Shehryar & Hunt, 2005 ; Solomon, Greenberg, & Pyszczynski, 1991 ). These theories concern whether the recommended behavior is a one-time or recurring activity, involves detection or prevention/promotion, occurs immediately or after a delay, can enhance self-esteem, and is intended to replace a self-esteem enhancing behavior.

One-time versus repeated behaviors

According to Robertson (1975 ; also see Rothman et al., 1999 ), persuasive messages should be more successful when they recommend one-time behaviors (e.g., getting vaccinated) compared to behaviors that must be repeated over an extended period of time (e.g., exercising). As it takes less effort to do something once than many times, people are likely to be more compliant when a single behavior is recommended. Using this principle, we compared the effectiveness of fear appeals recommending one-time versus repeated behaviors.

Detection versus prevention/promotion behaviors

According to prospect theory, negative outcomes can be categorized as incurring a loss or foregoing a gain, and losses tend to be more psychologically impactful than foregone gains of objectively equal magnitude ( Tversky & Kahneman, 1981 ). Several researchers have extended the logic of prospect theory to fear appeals, hypothesizing that fear appeals should be more effective when recommending detection behaviors relative to prevention/promotion behaviors ( Rothman, Martino, Bedell, Detweiler, & Salovey, 1999 ; Rothman & Salovey, 1997 ). Detection behaviors are enacted to obtain information about potential risk factors or existing health issues (e.g., being screened for cancer), and thus engaging in a detection behavior increases risk for incurring a loss (e.g., acquiring the unwanted and undesirable information that one has cancer). In contrast, prevention/promotion behaviors are enacted to obtain desirable outcomes (e.g., exercising to lose weight or avoid weight gain), and thus engaging in prevention/promotion behaviors does not increase risk for incurring a loss (e.g., exercising will only bring one closer to the desired outcome of losing weight or avoiding weight gain, so there is no potential for loss by engaging in exercise). Fear appeals are loss-framed messages because they emphasize negative consequences, and loss-framed information makes people more willing than usual to take risks ( Meyerowitz & Chaiken, 1987 ; van’t Riet et al., 2014 ). Therefore, although fear appeals should be effective for both detection and prevention/promotion behaviors, they should be particularly effective for detection behaviors because the loss-framed nature of the message should make people more willing than usual to take on the risk of the detection behavior ( Meyerowitz & Chaiken, 1987 ; Rothman, Martino, Bedell, Detweiler, & Salovey, 1999 ; Rothman & Salovey, 1997 ; van’t Riet et al., 2014 ).

Mentioning death, self-esteem relevance, and time delays

Many fear appeals explicitly mention death (89 of the 248 studies in our meta-analysis), and terror management theory (TMT) makes three predictions about this factor. According to TMT, when people are reminded of their mortality by being exposed to the concept of death, they often become motivated to buffer their self-esteem to reduce mortality related anxiety ( Goldenberg & Arndt, 2008 ; Pyszczynski et al., 1999 ; Shehryar & Hunt, 2005 ; Solomon et al., 1991 ). Some fear appeals recommend behaviors that can enhance self-esteem (e.g., dieting, which can improve body image; Goldenberg & Arndt, 2008 ), whereas others attempt to persuade people to stop engaging in behaviors that enhance self-esteem (e.g., tanning, which can also improve body image; Janssen et al., 2013 ). When fear appeals mention death, message recipients should increase commitment to behaviors that enhance self-esteem, regardless of whether the fear appeals encourage or discourage those behaviors. Consequently, fear appeals recommending self-esteem enhancing behaviors (e.g., dieting) should be more effective when they mention death than when they do not. In contrast, fear appeals recommending the cessation of behaviors that enhance self-esteem (e.g., tanning abstinence) should be less effective when they mention death than when they do not.

TMT also posits that reminders of death activate two types of defensive responses: Short-term proximal defenses and long-term distal defenses. Proximal defenses involve refuting information to avoid considering one’s death, whereas distal defenses involve buffering one’s self-esteem and pursuing long-term goals (e.g., a healthy lifestyle; Goldenberg & Arndt, 2008 ). Consequently, fear appeals that mention death should be more effective if there is a delay between fear appeal exposure and occurrence of the outcome, rather than if outcomes occur immediately after exposure when proximal defenses are still active (e.g., Greenberg et al., 1990; Shehryar & Hunt, 2005 ). 3

Two prominent theories make predictions about the impact of the audience on fear appeal effectiveness: Regulatory fit theory ( Higgins, Pierro, & Kruglanski, 2008 ; Kurman & Hui, 2011 ; Lockwood, Marshall, & Sadler, 2005 ) and the transtheoretical model ( Prochaska & DiClemente, 1983 ; Prochaska et al., 1992 ; Prochaska & Velicer, 1997 ). These predictions concern whether the message’s audience is primarily female (versus male), from a collectivist culture (versus an individualistic culture), and already attempting to change risk behaviors (versus not).

Gender and culture

According to regulatory fit theory, people can be promotion or prevention focused, placing greater value on either the pursuit of positive outcomes or on the avoidance of negative outcomes, respectively ( Higgins et al., 2008 ; Kurman & Hui, 2011 ; Lockwood et al., 2005 ). Message frames that match the promotion versus prevention tendencies of the audience are more persuasive ( Cesario, Higgins, & Scholar, 2008 ), and fear appeals are definitionally prevention-framed messages because they emphasize what one should do to avoid negative outcomes. Consequently, prevention-focused populations should be more persuaded by fear appeals relative to promotion-focused populations. Cultural research in the area of regulatory focus has found that women tend to be more prevention focused than men, and members of collectivist groups tend to be more prevention focused than members of individualist ones ( Kurman & Hui, 2011 ; Lockwood, Marshall, & Sadler, 2005 ). Therefore, fear appeals should be particularly effective for female (versus male) and collectivist (versus individualist) audiences.

Early versus late stages of change

According to the transtheoretical model, people engaging in risky behaviors can be classified as belonging to an early stage (the model’s precontemplation, contemplation, and preparation stages) or a late stage (the model’s action and maintenance stages) in the change process ( Prochaska & DiClemente, 1983 ; Prochaska et al., 1992 ; Prochaska & Velicer, 1997 ). According to the early-effectiveness hypothesis, fear appeals should be more effective for individuals in the early (vs. late) stages because the former require motivational appeals to understand that a threat exists and to increase commitment to adopting desirable behaviors and/or abandoning undesirable behaviors. In contrast, late stage individuals are already committed to behavior change and do not require such motivational appeals ( DiClemente et al., 1991 ; Nabi et al., 2008 ; Prochaska & DiClemente, 1983 ; Prochaska et al., 1992 ). The late-effectiveness hypothesis competes with the early one, and predicts that success at behavior change is associated with increases in self- and response efficacy ( Cho & Salmon, 2000 ). As a result, exposure to a fear appeal should lead individuals who have already enacted change to process the fear appeal in the context of their high response efficacy ( Cho & Salmon, 2006 ). Consequently, the late-effectiveness hypothesis predicts that fear appeals should be more effective for late stage relative to early stage individuals. 4 To test the early-effectiveness and late-effectiveness hypotheses, we classified each study’s sample as belonging to one of the transtheoretical model’s first three stages or last two stages. We then compared the effectiveness of fear appeals for individuals in the early versus late stages.

We compiled the largest meta-analytic database of fear appeals to date to examine the effectiveness of fear appeals for changing attitudes, intentions, and behaviors, and also to test moderator predictions made by a variety of influential fear appeal theories. Each of these theories tends to focus on one of three things – the content of the message , the type of behavior recommended by the communication, or the characteristics of the audience receiving the message (see Table 1 for a full list of theories and related hypotheses). Of the 16 fear appeal hypotheses discussed, only seven have been tested in prior meta-analyses, and all of them fall under the message aspect of our framework ( Table 1 ). Thus, the present research represents the first meta-analytic test for nine of the 16 hypotheses and the first meta-analytic test for any hypotheses related to the behavior and audience aspects of our framework.

Review and Inclusion Criteria

To locate studies, we conducted a search of the PsycInfo and Medline databases using the keywords (risk or fear or shock or severity or susceptibility) AND (persuasion or appeal or argument or tactic or campaign or communication or intervention). To supplement these database searches, we examined the reference lists of previous fear appeal meta-analyses, review articles, and chapters. We also contacted researchers to request unpublished data and sent requests to the e-mail lists of the Society of Behavioral Medicine , the Society for Personality and Social Psychology , the European Health Psychology Society , and the American Academy of Health Behavior . Our search extended through February 2015 and yielded 430 potentially eligible articles, which were subsequently screened for inclusion in the current meta-analysis based on several inclusion criteria. For inclusion in this meta-analysis, studies had to meet the following eligibility criteria:

• Studies were included if they contained an experimental research design in which a treatment group was exposed to a message designed to induce fear (i.e., a fear appeal).
• Studies were included if they contained a comparison group. The comparison group could have been a group that was not exposed to any message, a group that was exposed to a message that was not designed to induce fear, or a message that was designed to induce less fear than the treatment group’s message. When a study included more than one potential comparison groups, we opted to compare the highest depicted fear condition with the lowest depicted fear condition, prioritizing them in the following order: No message comparison group, neutral message comparison group, and low depicted fear comparison group. Thus, for a study containing a low depicted fear group and a neutral message group, we used the neutral message group as the comparison group. Overall, all results should be interpreted as the effect of exposure to messages designed to depict relatively high levels of fear compared to conditions designed to depict relatively lower levels of fear (including no fear). 5
• Studies were included if they experimentally manipulated depicted fear across groups. Studies were excluded if they used correlational research designs or provided all groups with the same level of depicted fear.
• Studies were included if they measured one or more of the following variables as an outcome in both the treatment and comparison groups: Attitudes, intentions, or behaviors.
• Studies were excluded if they did not contain appropriate statistics (e.g., F ratios, means and standard deviations, frequencies, or exact p values) for calculating an effect size representing the difference of outcomes for treatment versus comparison groups. If a study was otherwise eligible but did not contain appropriate statistics (e.g., it provided path coefficients from a structural equation analysis but did not supply means and standard deviations for treatment and comparison groups), we attempted to contact the study’s authors to retrieve usable data such as means and standard deviations. We contacted authors of 39 papers for this purpose: Three provided us with the requested data, six responded but could not provide us with the relevant data, and the rest did not respond to multiple contact requests.

Of the 430 reports considered for inclusion in this meta-analysis, 127 met our inclusion criteria (9% unpublished), providing 248 statistically independent samples with a total N of 27,372 participants in the treatment and comparison groups combined. Samples ranged in age from 9-87 years ( M = 22.77 years, SD = 9.24 years) and were on average 66% female ( SD = 33%). An average of 81% of each sample had completed high school ( SD = 37%). Further, samples were on average 71% White or European-American ( SD = 34%), 14% Asian or Asian-American ( SD = 31%), 8% Black or African-American ( SD = 18%), and 5% Hispanic/Latino(a) ( SD = 14%).

Coding of Outcomes (Effect Size Calculation)

We calculated a single effect size per sample that compared attitudes, intentions, and behaviors for the treatment group relative to the comparison group. First, for each sample we recorded all measures of attitudes, intentions, and behaviors. For each outcome, we calculated the standardized mean difference between treatment and comparison groups correcting for sample size bias ( Johnson & Eagly, 2014 , p. 686). Effect sizes ( d ) were calculated based on provided F -ratios, t -tests, odds ratios, or means and standard deviations. To produce d for any odds ratios, we divided the log of the odds ratio by 1.81 ( Haddock, Rindskopf, & Shadish, 1998 ; Hasselblad & Hedges, 1995 ).

Note that outcomes could have concerned the negative behavior/issue targeted by the fear appeal (e.g., attitudes toward smoking) or the fear appeal’s recommendations (e.g., attitudes toward smoking cessation). Effect sizes were calculated such that higher positive values indicate the treatment group scored higher in the message’s direction. For example, if a study used anti-smoking messages, a positive d would indicate that the treatment group (relative to the comparison group) had more negative attitudes toward smoking or more positive attitudes toward smoking cessation. Thus, a positive effect size indicates the fear appeal worked, whereas a negative effect size indicates the fear appeal backfired.

The majority of samples ( k = 170) included only one type of dependent measure (attitudes, intentions, or behaviors), but some samples included two types ( k = 61) or all three ( k = 17). Therefore, after calculating d for each outcome in a sample, we averaged all d values together to form a single effect size per sample that represents positive change in the direction advocated by the fear appeal. Further, if a sample included two or more measures of the same outcome type (e.g., attitudes toward smoking and attitudes toward smoking cessation), each was included in the average and weighted equally (the number of samples with multiple attitude, intention, and behavior measures was respectively k = 18, k = 24, and k = 12). This approach is justified on several grounds. First, for studies that included all three types of outcomes (attitudes, intentions, and behaviors), Cronbach’s alpha for the composite measure was .87, indicating that the three types of measures are highly internally consistent. Further, prior research has demonstrated that composite measures combining attitudes, intentions, and behaviors are a valid outcome of interest when investigating the relative persuasiveness of messages ( O’Keefe, 2013 ). We therefore combined all attitude, intention, and behavior measures within each sample to form a single effect size per sample, which is how the results will be presented in the present manuscript. However, we also conducted all analyses separately for attitude, intention, and behavior measures; these results are presented in Appendix A and are consistent with the results based on the combined measure. Several hypotheses made specific predictions about attitudes, intentions, or behaviors, and for those hypotheses (see Table 1 ), we present the relevant outcomes of interest in the body of the manuscript.

Of note, attitudes were most commonly measured with semantic differential scales (e.g., positive/negative, beneficial/harmful, wise/foolish, etc.; Roskos-Ewoldsen, Yu, & Rhodes, 2004 ; Nabi et al., 2008 ) and Likert style scales (e.g., agreement with statements such as, “I don’t like speeding”; Cauberghe et al, 2009 , p. 280). Intentions were frequently measured with Likert style scales (e.g., agreement with statements such as, “In the immediate future, I plan to find someone who will teach me to do an accurate breast self-examination”; Roskos-Ewoldsen et al., 2004 , p. 58) and questions with dichotomous response options (e.g., “In the future, I intend to stop spending time outside strictly for the purpose of getting a tan,” with responses Yes and No ; McMath & Prentice-Dunn, 2005 , p.629). Finally, behaviors were often measured dichotomously with self-report questions (e.g., “As a direct result of this message, did you seek help?” with responses Yes and No ; Smalec & Klingle, 2000 , p. 45) or behavioral observation data (e.g., information obtained from medical records; Ordoñana et al., 2009 ).

Coding of Potential Moderators

To test each hypothesis from the message, behavior, and audience portions of our framework, we coded several relevant variables (moderator codes for each paper included in the meta-analysis are displayed in Table 2 ). The first author trained two independent coders, who then coded all study characteristics relevant to each report. Intercoder reliability was calculated on 20% of the overall database using Cohen’s kappa (κ) for categorical variables and Pearson’s r for continuous variables. Agreement for all variables was good: Categorical variables had average κ = .93 (SD = .06, minimum = .80), and continuous variables had average r = .92 (SD = .12, minimum = .73). Disagreements were resolved by discussion and further examination of the studies.

Effect sizes, sample sizes, and moderator codes for each sample in the meta-analysis.

Note: d = Standardized mean effect size. N = Sample size for treatment plus comparison. AIB = Whether d was based on attitude (A), intention (I), and/or behavior (B) outcomes. EFF = Whether an efficacy statement was included (Y) or not (N). Sev = Whether the treatment message was manipulated to be higher in depicted severity than the comparison message (Y) or not (N). Sus = Whether the treatment message was manipulated to be higher in depicted susceptibility than the comparison message (Y) or not (N). OR = Whether the recommended behavior was one-time (O) or repeated (R). DPP = Whether the recommended behavior was detection (D) or prevention/promotion (PP). DP = Whether the word death was present in the message (Y) or not (N). SE = Whether the recommended behavior was self-esteem enhancing (SEE) or self-esteem hindering (SEH). Delay = Whether the outcome followed exposure to the message by less than 24 hours (S), 1–14 days (M), or more than 14 days (L). %F = Percent of sample that was female (0–100%). IC = Whether the sample was from an individualist (I) or collectivist (C) culture. SOC = Whether the sample was in the early (E) or late (L) stages of change. Dash (–) indicates the variable was not relevant for the study.

Moderators related to message content

To test hypotheses concerning the message content, we coded messages’ amount of depicted fear, inclusion (or absence) of efficacy statements, and levels of depicted susceptibility and severity.

To test the linear and curvilinear hypotheses, we coded whether studies included a moderate depicted fear group. To qualify, studies had to contain at least three experimental groups that were exposed to different levels of depicted fear. Thus, a study containing a high depicted fear group, a moderate depicted fear group, and a low depicted fear group would be included, whereas a study containing a high depicted fear group, a low depicted fear group, and a neutral control group would not. As noted above, an appropriate test of the linear and curvilinear hypotheses requires a comparison between high and moderate depicted fear; thus, the moderate group must represent a level of depicted fear between high and low (rather than between high and none). In the entire database ( k = 248), 21 samples included more than two experimental groups exposed to varying levels of depicted fear. To test the linear and curvilinear hypotheses, we calculated effect sizes ( d ) comparing outcomes for the highest versus middle depicted fear groups (the calculation of these effect sizes followed the same procedure detailed above for the calculation of treatment versus comparison effect sizes). The moderate depicted fear groups (total N = 1,626) were not included in other analyses (the studies and corresponding effect sizes included in this analysis can be found in Table 3 )

Effect sizes and sample sizes for each sample included in the linear versus curvilinear test.

Note: d = Standardized mean effect size. N H = Sample size for the high depicted fear group. N M = Sample size for the medium depicted fear group. Combined outcomes = Average of all attitude, intention, and behavior measures. Dash (–) indicates the variable was not relevant for the study. The attitude, intention, and behavior measures are analyzed separately in Appendix A .

For each article, we dichotomously coded whether or not an efficacy message was embedded in the fear appeal. The efficacy message could have focused on self-efficacy (e.g., emphasizing that people have a built-in urge for physical activity and this basic human physical need will make it easy to begin a regular exercise program; Wurtele & Maddux, 1987 ), response-efficacy (e.g., emphasizing that exercise leads to higher levels of high-density lipoprotein and thus prevents heart attacks; Wurtele & Maddux, 1987 ), or both (e.g., highlighting that condoms substantially reduce the risk of HIV transmission if used correctly and are easy to use consistently; Witte & Morrison, 1995 ).

For each article, we coded whether depicted severity was manipulated to be higher in the treatment group relative to the comparison group (e.g., the treatment group received a message emphasizing the drastic consequences of not wearing bicycle helmets; Rodriguez, 1995 ) and whether depicted susceptibility was manipulated to be higher in the treatment group relative to the comparison group (e.g., the treatment group received a message focusing on how coffee consumption will likely lead the message recipient to develop fibromyalgia; Lieberman & Chaiken, 1992 ).

Moderators related to behavior characteristics

To test hypotheses concerning the targeted behavior, we coded whether the fear appeals recommended behaviors that were one-time or recurring and whether the behavior was a detection or prevention/promotion behavior. We also coded whether death was mentioned when discussing the behavior, whether the behavior was measured immediately versus after a delay, and whether the recommended behaviors was self-esteem enhancing or self-esteem hindering.

We coded whether the recommended behaviors concerned one-time-only instances (e.g., signing up for a stress management training; Das et al., 2003 ) or would need to be enacted over an extended period of time (e.g., regularly using child safety devices when traveling by car; Chang et al., 1989 ).

Detection versus prevention/promotion

For each article, we coded if the recommended behavior was a detection behavior (e.g., getting tested for syphilis; Fukada 1975 ) or a prevention/promotion behavior (e.g., attending a training to prevent repetitive stress injury; Pengchit, 2010 ). We initially attempted to code prevention and promotion behaviors separately. However, due to the nature of these constructs, it was often difficult to discern how participants would construe a behavior (e.g., did participants conceptualize exercising as promoting a healthy BMI or preventing obesity?). As the relevant hypothesis solely concerned fear appeals being more effective when recommending detection (vs. prevention/promotion) behaviors, promotion and prevention behaviors were collapsed into a single code.

We created a dichotomous code for whether or not the message explicitly used the word death . Messages dealing with behaviors or issues that could clearly lead to death were still coded as non-death if the word death was not explicitly mentioned within the message itself (e.g., messages about smoking or HIV/AIDS that did not explicitly mention death as one of the potential consequences; Insko et al., 1965 ; McMath & Prentice-Dunn, 2005 ; Raleigh, 2002 ; Witte & Allen, 2000 ). This decision allowed for a more stringent test of TMT hypotheses, and provided an even distribution of death versus non-death conditions, which avoids the potential confound of death messages always being about more severe topics than non-death messages.

Self-esteem relevance

We coded whether the recommended behavior was self-esteem hindering or self-esteem enhancing. Self-esteem hindering behaviors were intended to replace existing behaviors that allowed message recipients to derive self-esteem. Samples were coded as containing a self-esteem hindering behavior if the researchers specifically measured self-esteem for the existing behavior being targeted by the fear appeal and described the sample as high (e.g., high driving-related self-esteem; Taubman Ben-Ari et al., 2000 ), if the sample was designated as committed to the existing behavior (e.g., smokers that were highly committed to smoking; Priolo & Milhabet, 2008 ), or if the existing behavior is one that people typically engage in to improve self-esteem and/or physical attractiveness (e.g., tanning or bulimia; Janssen et al., 2013 ; Smalec & Klingle, 2000 ).

In contrast, self-esteem enhancing behaviors have the potential to provide individuals with self-esteem. Samples were coded as containing a self-esteem enhancing behavior if the recommended behavior is commonly associated with the pursuit of improved self-esteem and/or physical attractiveness (e.g., fear appeals recommending a healthy diet to decrease BMI; Goldenberg & Arndt, 2008 ). Samples were also coded as self-esteem enhancing when fear appeals targeted behaviors that the audience had clearly already made the choice to forego (e.g., antismoking ads directed at non-smokers; Insko et al., 1965 ) because message recipients should generally be able to derive self-esteem by continuing to avoid engaging in the discouraged behavior (e.g., non-smokers who are told that smoking is bad and smoking abstinence is good should feel as though their decision to abstain from smoking reflects positively on them). Thus, studies were coded as self-esteem enhancing if the recommended behavior could improve self-esteem via the pursuit of physical attractiveness (e.g., exercise; Wurtele & Maddux, 1987 ), if the addressed behavior was not relevant for the sample (e.g., anti-smoking ads for non-smokers; Insko et al., 1965 ; Smart & Fejer, 1974 ), if the sample was designated as not committed to the behavior in question (e.g., smokers that were not committed to smoking; Priolo & Milhabet, 2008 ), or if the researchers specifically measured self-esteem related to the existing behavior being targeted by the fear appeal and described the sample as low (e.g., low driving-related self-esteem; Taubman Ben-Ari et al., 2000 ).

We coded the amount of time between the fear appeal and the measurement of the outcome variable using three discrete categories: (a) The measure occurred the same day as the fear appeal exposure (e.g., Taubman Ben-Ari et al., 2000 ; Cho & Salmon, 2006 ; Nabi et al., 2008 ; Smart & Fejer, 1974 ; Stainback & Rogers, 1983 ); (b) the measure occurred one to fourteen days after fear appeal exposure (e.g., Berkowitz, 1998 ; Kirscht et al., 1978 ; Muthusamy et al., 2009 ); and (c) the measure occurred more than fourteen days after fear appeal exposure (e.g., Bagley & Low, 1992 ; Smith & Stutts, 2003 ; Witte & Morrison, 1995 ). We used categories because delayed outcomes often occurred within a specified range – e.g., participants returned to the lab during the following two weeks, but the exact number of days was not specified.

Moderators related to the audience

To test hypotheses concerning the audience portion of our framework, we coded the gender composition of the sample, whether the sample was from a collectivist or individualist country, and the transtheoretical model stage of change that was applicable to the sample.

Gender composition

We coded the percent of the sample that was female.

Collectivism and individualism

We dichotomously coded whether each study’s sample came from a primarily collectivist culture (e.g., East Asian cultures like South Korea, Japan, and Taiwan; Chu, 1966 ; Fukada, 1973 ; 1975 ; 1988 ; Kim et al., 2009 ) or a primarily individualist culture (e.g., Western cultures like Australia, Canada, and the United States; Beck, 1984 ; Brouwers & Sorrentino, 1993 ; Dahl et al., 2003 ; Hill & Gardner, 1980 ; Jones & Owen, 2006 ; LaTour & Tanner, 2003 ; Lewis et al., 2010 ; Smart & Fejer, 1974 ).

Stage of change

We coded the transtheoretical model’s stage of change that was most applicable to the audience. As most studies did not specifically measure this variable, we designed a conservative coding scheme to ensure we could include the maximum number of reports in this analysis while avoiding misclassifications. The early-effectiveness and late-effectiveness hypotheses both make predictions that compare individuals in the first three stages of the model (precontemplation, contemplation, and preparation) versus the last two stages of the model (action and maintenance). Thus, we created a dichotomous code indicating whether the sample was in the early or late stages of the model.

Samples were considered precontemplation if there was a clear indication that it was a sample merely at risk for a given behavior (e.g., participants who were designated as noncompliant with safe sex recommendations; Raleigh, 2002 ), or participants were being persuaded about a fictitious or not well-known disease/risk for which they had clearly not been engaging in protective action beforehand (e.g., hypoglycemia; de Hoog et al., 2008 ). We excluded samples in which the participants may have been in the precontemplation stage but for which there were no pretest measures available (e.g., if the sample was given a message about drinking and driving but there were no baseline measures available to indicate whether or not the sample had engaged in drunk driving in the past; Shehryar & Hunt, 2005 ). Samples were considered contemplation or preparation if there was a clear indication that they were already preparing to engage in the recommended action (e.g., a sample of women under 50 years old who had not yet received mammograms, but the majority of whom stated they intended to receive mammograms after age 50; Jones & Owen, 2006 ). Samples were classified into the action/maintenance category if participants had explicitly been engaging in the recommended behavior (e.g., a message promoted breast self-exams and 80% of the sample indicated they already performed breast self-exams regularly; Siero, Kok, & Pruyn, 1984 ) or if they were recruited from a population that would definitionally be in this stage (e.g., patients receiving treatment in alcohol rehabilitation clinics; Brown, 1979 ).

All analyses were conducted in R using the meta-analytic software package metafor, version 1.9.4 ( Viechtbauer, 2010 ). We conducted all analyses using random- and fixed-effects analyses. As both types of analyses produced comparable results, we present the random-effects analyses.

Distribution of Effect Sizes

We first analyzed the distribution of effect sizes in our sample to determine whether there were biases in study retrieval and inclusion. Figure 1 displays a forest plot for our meta-analytic database, and Figure 2 displays the corresponding funnel plot. In a forest plot, each study is represented by a horizontal line that indicates the confidence interval for the study’s effect size. By examining a forest plot, it is possible to assess the precision of effect size estimates from each study. Further, forest plots can also be used to assess the distribution of effect sizes across studies. As can be seen in the forest plot, the precision of effect size estimates varies across studies, with most studies displaying moderate precision. Further, the distribution of effect sizes appears to be roughly continuous and normal, which indicates a lack of inclusion bias. If no retrieval or inclusion bias is present in a meta-analytic database, the distribution of effect sizes in the funnel plot should be centered on and symmetric around the mean effect size, with smaller variability toward the top of the figure. If retrieval or inclusion biases are present, then the distribution should be asymmetric around the mean effect size. As can be seen in the figure, the distribution appears quite symmetric with smaller variability toward the top of the plot. We conducted a formal test of funnel plot asymmetry known as Begg and Mazumdar’s rank correlation test, which is a non-parametric correlation of the effect sizes with their corresponding standard errors ( Begg & Mazumdar, 1994 ). If this correlation is significantly different from zero, there is evidence of inclusion bias. The rank correlation was r = −.02, p = .67. Thus, there is no evidence of retrieval or inclusion bias.

Forest plot of the effect sizes.

Note: This forest plot includes point estimates and confidence intervals for all studies in the manuscript. The solid vertical line represents the combined effect size ( d = .29).

Funnel plot of effect sizes.

Note: Effect size ( d ) is plotted on the x-axis and standard error on the y-axis. The solid vertical line represents the combined effect size ( d = .29). The dotted line represents the x-intercept (x = 0) for a reference line. The white region represents the inside of the 95% pseudo confidence interval, whereas the shaded region represents the outside (i.e., the area of statistical significance).

Another way of testing for biases is to use the normal quantile plot method ( Wang & Bushman, 1999 ). In a normal quantile plot, the observed values of a variable are plotted against the expected values given normality. If the sample of effect sizes is from a normal distribution, data points cluster around the diagonal; if the sample of effect sizes is biased by publication practices or eligibility criteria, data points deviate from the diagonal ( Wang & Bushman, 1999 ). As can be seen from Figure 3 , the effect sizes followed a straight line and generally fell within the 95% confidence interval of the normality line, and thus there is no evidence of retrieval or inclusion bias.

Normal quantile plot.

Note: The dashed lines represents a 95% confidence band. The line on the diagonal indicates normality.

Average Effect Size and Between-Effects Variability

The average weighted effect size comparing outcomes for treatment to comparison groups was d = 0.29 with a 95% CI of [0.22, 0.35]. Therefore, fear appeals have a significant and positive effect on outcomes. That is, relative to participants in comparison groups, participants in treatment groups (i.e., those exposed to relatively high levels of depicted fear) had attitudes, intentions, and behaviors that were more in line with the position advocated by the fear appeal. There was also significant heterogeneity among effect sizes Q (247) = 1,287, I 2 = 85.11, p < .0001.

For studies that included a manipulation check of subjectively experienced fear, we coded this variable and calculated d for treatment versus comparison groups using the same methods employed for primary outcomes. We included all measures that asked respondents to report their current levels of fear (e.g., Cauberghe, De Pelsmacker, Janssens & Dens, 2009 ; Cho & Salmon, 2006 ; Nabi et al., 2008 ). Based on the 71 samples that included such manipulation checks, fear appeals were generally successful at inducing experienced fear, such that treatment groups reported more fear than comparison groups, d = 1.00 (95% CI: [0.83, 1.18]), Q(70) = 697, I 2 = 90.67, p < .0001. Importantly, this result should be taken as an estimate of how much fear was induced by the particular messages used in this sample, rather than an estimate of how much fear is induced by fear appeal messages in general.

Moderator Tests

To test our hypotheses of interest (see Table 1 ), we primarily conducted moderator analyses by calculating weighted effect sizes and corresponding 95% CIs for each level of our moderator variables (i.e., we meta-analyzed samples within each moderator level separately to produce an overall effect size estimate for that level). If the CIs for two moderator levels are not overlapping, then those levels of the moderator are significantly different from each other. In contrast, if the CIs are overlapping, then those levels of the moderator are not different from each other. We also conducted moderated meta-regressions to analyze all moderator variables; the results were the same as the 95% CI analyses and are thus not presented here. Table 5 displays average weighted effect sizes and corresponding 95% CIs for all levels of our moderator variables.

Moderator analysis results for categorical moderators.

Note: SE = Self-esteem. DP = Death present in message. DA = Death absent in message. PP = Promotion/prevention. SEE = Self-esteem enhancing recommended behaviors. SEH = Self-esteem hindering recommended behaviors. d = Standardized mean effect size estimated meta-analytically for the indicated moderator level. 95% CI = The 95% confidence interval for d . k = The number of studies for each moderator level.

Study Characteristics

For descriptive purposes, we recorded the following for each sample: (a) Study source (journal article, unpublished dissertation or thesis, or conference paper); (b) institution of the paper’s first author (university/college, research center); (c) the sampled population (general population, college students, high school students, children, other); (d) whether participants were run individually or in groups; (e) the study setting (laboratory, field); (f) the specificity of the message – whether the message targeted a single specific outcome (e.g., signing up for a training to prevent stress-related illness; Das et al., 2003 ), multiple specific outcomes (e.g., consuming calcium and performing weight-bearing exercises to prevent osteoporosis; Klohn & Rogers, 1991 ), or multiple non-specific outcomes (e.g., general recommendations to improve diet and increase exercise without mentioning specific dietary concerns or specific forms of exercise; Kirscht & Haefner, 1973 ); (g) whether the study measured subjective fear; (h) the type of media used to present the message (text information, pictures/videos); (i) whether the message targeted a health relevant outcome; and (j) the domain of the study’s targeted issue (dental hygiene, driving safety, HIV/STDs, drinking/drugs, smoking, cancer prevention, disease prevention, general safety, environment/society, other). As can be seen in Table 4 , none of these methodological factors moderated fear appeal effectiveness – within each factor, 95% CIs for each factor level overlap for all levels of all factors. In addition to these factors, we also recorded publication year, average age of participants, and sample size. Separate meta-regressions for each of these three variables revealed that none were related to fear appeal effectiveness: b = −0.0029 (SE = 0.0022, p = .18, 95% CI: [−0.0072, 0.0013]), b = −0.0046 (SE = 0.0039, p = .23, 95% CI: [−0.0122, 0.0030]), and b = 0.0000 (SE = 0.0002, p = .91, 95% CI: [−0.0003, 0.0003]), respectively for publication year, average age of sample, and sample size.

Moderator analysis results for methodological variables.

Note: d = Standardized mean effect size estimated meta-analytically for the indicated moderator level. 95% CI = The 95% confidence interval for d . k = The number of studies for each moderator level.

Tests of message content hypotheses

Message content: depicted fear.

To test the linear and curvilinear hypotheses, we calculated an average weighted effect size comparing groups that were exposed to moderate depicted fear versus high depicted fear. The linear hypothesis predicts that this effect size should be positive and significant, whereas the curvilinear hypothesis predicts that this effect size should be negative and significant. The combined effect size was d = −0.05 with a 95% CI of [−0.34, 0.24] and Q(20) = 154 (I 2 = 92.89, p < .0001). Therefore, outcomes did not differ for groups exposed to moderate versus high depicted fear. Instead of supporting either the linear or curvilinear hypothesis, this result suggests that depicted fear may have a maximum effective value, beyond which there is no impact of depicting additional fear. This finding may have implications for practitioners using fear appeals - i.e., once a message depicts moderate fear, there is no value in depicting additional fear, but depicting additional fear will not lead to negative effects.

One caveat is that this analysis was only based on 21 samples. However, to our knowledge, this is the largest and most valid test of the linear and curvilinear hypotheses to date. Specifically, to ensure that the test concerned high depicted fear versus moderate depicted fear, we only included studies with at least three levels of depicted fear. Given that we obtained an overall positive effect of depicted fear when comparing treatment and comparison groups, the results here can be interpreted as supporting a modified version of the linear hypothesis. Specifically, depicted fear has significant positive effects, but depicted fear cannot be effectively manipulated indefinitely and results in diminishing returns beyond a certain point (rather than negative effects causing the message to backfire, as suggested by the curvilinear hypothesis). However, given the limited sample size, this conclusion should be confirmed in future research.

Message content: Efficacy statements

The strong and weak efficacy hypotheses both predict that inclusion of efficacy statements in a fear appeal will lead to increased effectiveness. The results support this hypothesis: Fear appeals were more effective when they included efficacy statements (95% CI: [0.31, 0.55]) than when they did not (95% CI: [0.13, 0.29]). However, the strong hypothesis predicts that fear appeals without efficacy messages will backfire and produce negative effects, whereas the weak hypothesis predicts that fear appeals without efficacy statements will simply produce less positive or null effects. The results clearly support the weak efficacy hypothesis and disconfirm the strong efficacy hypothesis. Thus, fear appeals are effective with or without efficacy statements, but the inclusion of efficacy statements is associated with increased effectiveness. These results confirm the conclusions of prior meta-analyses concerning the use of efficacy statements ( de Hoog et al., 2007 ; Peters et al., 2012 ; Witte & Allen, 2000 ).

Message content: Depicted susceptibility and severity

The first hypothesis concerning depicted susceptibility and severity states that fear appeals high in depicted severity (but not depicted susceptibility) will positively influence attitudes but will not influence intentions or behaviors. The 95% CIs indicated that fear appeals that were only high in depicted severity had positive effects for attitudes (95% CI: [0.06, 0.37]) and intentions (95% CI: [0.20, 0.39]) but not behaviors (95% CI: [−0.08, 0.42]) (see Appendix A for the results of all analyses done separately for attitudes, intentions, and behavior). Although this hypothesis was not supported, our results partially replicated a previous meta-analytic finding in which high depicted severity influenced all three outcome measures ( de Hoog et al., 2007 ). The second hypothesis is that fear appeals high in depicted susceptibility (but not severity) will positively influence intentions and behaviors but will not influence attitudes. The 95% CIs indicated that fear appeals that were only high in depicted susceptibility had positive effects for intentions (95% CI: [0.15, 0.59]) and behaviors (95% CI: [0.01, 0.88]) but not attitudes (95% CI: [−0.51, 1.47]). Therefore, this hypothesis was supported. The third hypothesis is that fear appeals with high depicted severity and high depicted susceptibility will positively influence attitudes, intentions, and behaviors. The 95% CIs confirmed this prediction and indicated that fear appeals high on both moderators had positive effects for attitudes (95% CI: [0.05, 0.38]), intentions (95% CI: [0.23, 0.47]), and behaviors (95% CI: [0.24, 0.63]). Further, the 95% CI for the focal outcome in our meta-analysis (the average of attitude, intention, and behavior outcomes) also supported this result: [0.28, 0.50]. Thus, when testing all three hypotheses, fear appeals generally had positive effects on attitudes, intentions, and behaviors when they were high in depicted severity and/or susceptibility.

Tests of the recommended behavior hypotheses

Recommended behavior: one-time versus repeated behaviors.

According to Robertson’s (1975) single action theory, fear appeals that attempt to persuade people about one-time behaviors (e.g., getting vaccinated) should be more effective than fear appeals that attempt to persuade people about repeated behaviors (e.g., exercising multiple times per week every week). The results supported this hypothesis, such that fear appeals recommending one-time behaviors (95% CI: [0.30, 0.56]) were more effective than fear appeals recommending repeated behaviors (95% CI: [0.14, 0.29]). However, it is worth noting that fear appeals were effective for both types of recommended behaviors, and they were simply more effective for one-time behaviors.

Recommended behavior: Detection versus prevention/promotion behaviors

Based on hypotheses derived from prospect theory, several researchers have hypothesized that fear appeals should be more effective when recommending detection behaviors relative to prevention/promotion behaviors. The results did not support this hypothesis, as fear appeals recommending detection behaviors (95% CI: [0.21, 0.49]) and prevention/promotion behaviors (95% CI: [0.20, 0.38]) were equally effective.

Recommended behavior: Death and self-esteem

Based on predictions from TMT, fear appeals that mention death (versus not) should be more effective when the recommended behavior is self-esteem enhancing but less effective when the recommended behavior is self-esteem hindering. The results did not support these predictions, as fear appeals were equally effective when they mentioned death and recommended a self-esteem hindering behavior (95% CI: [−0.41, 0.18]), did not mention death and recommended a self-esteem hindering behavior (95% CI: [0.00, 0.96]), mentioned death and recommended a self-esteem enhancing behavior (95% CI: [0.11, 0.67]), or did not mention death and recommended a self-esteem enhancing behavior (95% CI: [−0.04, 0.47]). Thus, neither self-esteem hypotheses derived from TMT was supported.

Recommended behavior: Death and delay

A separate prediction derived from TMT is that fear appeals that mention death will be more effective if the recommended behavior is measured after a delay rather than immediately. These predictions were not supported. When fear appeals mentioned death, they were equally effective for outcomes that occurred the same day (95% CI: [0.04, 0.27]), between one and fourteen days after fear appeal exposure (95% CI: [0.21, 1.37]), or more than fourteen days later (95% CI: [0.19, 0.51]). Similarly, when fear appeals did not mention death, they were equally effective for outcomes that occurred the same day (95% CI: [0.25, 0.44]), between one and fourteen days after fear appeal exposure (95% CI: [−0.29, 0.33]), or more than fourteen days later (95% CI: [0.03, 0.88]). Therefore, the death and delay hypothesis was not supported.

Tests of the audience hypotheses

Audience: gender.

Based on predictions derived from regulatory fit theory, fear appeals should be more effective for women than men. We tested this hypothesis via meta-regression, using percent of the sample that was female as a predictor of effect size. This analysis produced a small but significant effect, b = 0.0031 ( SE = 0.0012, 95% CI for the slope: [0.0007, 0.0055]), p < .0001. Therefore, for every 10% increase in the percent of the sample that is female, fear appeal effectiveness increases by approximately d = 0.03. Thus, the hypothesis was supported: Fear appeals are more effective for audiences with a larger percentage of female message recipients than male message recipients.

Audience: Collectivism versus individualism

Based on predictions derived from regulatory fit theory, fear appeals should be more effective for collectivist samples than individualist samples. The results did not support this hypothesis. Fear appeals were equally effective in studies conducted in collectivist countries (95% CI: [0.27, 0.66]) and individualist countries (95% CI: [0.19, 0.33]).

Audience: Stages of change

Based on the early-effectiveness hypothesis, fear appeals should be more effective for samples that occupy the first three stages of the stages of change model relative to the last two stages. In contrast, the late-effectiveness hypothesis predicts the opposite. Neither hypothesis was supported by the data because audiences in the early stages (95% CI: [0.21, 0.40]) and late stages (95% CI: [0.14, 0.54]) were equally impacted by fear appeals.

General Discussion

Fear appeals are effective. The present meta-analysis found that fear appeals were successful at influencing attitudes, intentions, and behaviors across nearly all conditions that were analyzed. Even when a moderator was unrelated to fear appeal effectiveness, fear appeals were still more effective than comparison treatments. Further, there was not one level of any moderator that we tested for which fear appeals backfired to produce worse outcomes relative to the comparison groups. These results are striking given the wide range of theories that attempt to specify conditions under which fear appeals should be ineffective or counter-productive (e.g., the curvilinear model, the strong efficacy hypothesis, the stage model) and given the numerous practitioners who make bold claims stating that fear appeals are futile or even dangerous (e.g., Drug Free Action Alliance, 2013 ; Kok et al., 2014 ; Ruiter et al., 2014 ). Rather, fear appeals consistently work, and through our meta-analysis we were able to identify various factors that can enhance their effectiveness to make them work even better. We believe that these results make important contributions to theory, practice, and policy.

A Message-Behavior-Audience Framework of Fear Appeals

We structured our review around a framework that considers three important aspects of any fear appeal communication: The message’s content, the recommended behavior, and the audience. This model is meant to be an organizing thread to help connect existing theories and research, and to identify areas in need of future research. Specifically, we believe this framework is useful for several reasons. First, each aspect (message, behavior, and audience) has the potential to vary independently of the others and may impact the communication’s effectiveness in ways scholars must consider. Second, this structure connects and organizes seemingly unrelated theories and hypotheses concerning fear appeals, including the linear model, the stage model, and hypotheses derived from prospect theory. Specifically, we found that fear appeals were more effective when the message depicted relatively high amounts of fear, included an efficacy message, and stressed susceptibility and severity related to the concerns being addressed (i.e., factors concerning the message). We also found that fear appeals were more effective when they recommended one-time only behaviors (i.e., a factor concerning the recommended behavior) and when audiences included a higher percentage of women (i.e., a factor concerning the audience).

Our framework also highlights that prior research has strongly focused on one particular aspect of fear appeals somewhat to the exclusion of the other aspects. Specifically, the bulk of prior research on fear appeals has investigated questions about the message’s content – indeed, of the prior meta-analyses on fear appeals, all of them addressed questions related to the message’s content while overlooking questions related to the recommended behavior and audience. However, this bias is clearly not due to a lack of interesting or potentially important effects concerning the behavior or audience, as significant effects emerged pertaining to each. Thus, we hope that our framework will help generate interest in research directed toward these previously under-studied aspects of fear appeal effectiveness.

Limitations

Four specific limitations are worth mentioning. First, as discussed in the introduction, the present results concern fear appeals rather than fear. That is, our meta-analysis did not compare people who were subjectively afraid to people who were subjectively unafraid, but rather we compared groups that were exposed to more or less fear inducing content. Consequently, all comparisons between the treatment and comparison groups must be interpreted as effects of exposure to depicted levels of fear rather than effects of fear per se. However, this feature is not unique to our analyses, and prior meta-analyses of fear appeals are subject to the same considerations (e.g., Boster & Mongeau, 1984 ; de Hoog et al., 2007 ; Peters et al., 2012 ; Sutton, 1984; Witte & Allen, 2000 ). As researchers and practitioners alike are typically concerned with how to design effective communications, knowledge of the effectiveness of fear appeals is quite useful.

Relatedly, although we were able to determine that the treatment groups generally experienced more subjective fear than the comparison groups by analyzing fear-related manipulation check questions, the majority of samples included no assessment of subjective fear ( k = 177, which is 71% of samples in our database). This is a serious limitation of the existing literature for three reasons. First, if fear appeals are presumed to have an effect on outcomes by instilling fear in message recipients, it is important to verify that these messages actually evoke fear, and that it is the evoked fear that mediates the relation between message presentation and response. Indeed, many fear appeals may evoke emotions in addition to fear (e.g., disgust, anger), and these other emotions may partially (or in some cases fully) mediate the effects of fear appeals. Second, the lack of subjective fear measures makes it difficult (if not impossible) to equate fear appeal intensity across studies. What one research team refers to as low fear may represent what another research teams refers to as moderate fear or a control condition. However, the inclusion of subjective measures of fear in response to fear appeals would enable researchers to equate fear appeal intensity across studies and more precisely investigate effects via well-calibrated levels of fear. Finally, the lack of subjective fear measures makes it difficult for researchers interested in the effects of fear (rather than fear appeals) to investigate relevant hypotheses meta-analytically. All three of these issues can be easily resolved by including measures of subjective fear in future studies on fear appeals, and we therefore urge researchers to do so.

Third, our meta-analysis exclusively included experimental studies. As experiments often allow for increased internal validity at the cost of decreased external validity, it will be important for future research to investigate whether the present results generalize to naturalistic settings. For example, do fear appeals produce the same effects when used in real-world public health campaigns as they do when used in highly controlled experimental studies? Although we expect the results will generalize to such settings, future research will be necessary to definitively test this question.

The final limitation of note concerns the coding of variables in the current meta-analysis. Specifically, to test hypotheses related to TMT, studies were coded as either containing the word death or not. However, some studies did not include full texts for fear appeal messages, and thus it is possible that some messages did contain the word death but were nonetheless coded as not containing this word (however, studies were only coded as containing the word death if a portion of the message’s text was available that showed this word). Overall, it is likely that such miscodings would attenuate potential differences across conditions.

Future Directions

Experimental manipulations and mechanisms.

The present meta-analysis only included experimental studies that compared treatment and comparison groups, and thus internal validity is good when considering the effects of relatively high versus low depicted fear. However, meta-analyses are a correlational research design, and thus many of the moderator analyses we conducted should be interpreted with this in mind. For example, does using fear appeals to target one-time behaviors versus recurring behaviors actually cause the fear appeals to be more effective, or are fear appeals that target one-time behaviors systematically different from fear appeals that target recurring behaviors along some other dimension that results in the observed difference? Future experimental work will be necessary to address such questions, and we therefore encourage researchers to experimentally test our moderator findings concerning variables that were not manipulated in the primary studies.

It is also important for future research to uncover the mechanisms behind the moderation effects we identified. For example, why are fear appeals more effective for one-time behaviors? A number of the hypotheses that we substantiated are relatively agnostic concerning mechanisms, and this is a serious gap in the current fear appeal literature. To truly understand fear appeal effectiveness, it is necessary to know why they work. This knowledge could then be used to design more effective fear appeals, and it could potentially be used for other types of communications as well. Although some of the theories investigated do discuss mechanism to some degree (e.g., EPPM; Witte, 1992 ), our updated review of the literature is consistent with conclusions from prior reviews that these mechanisms are often under-studied and are in need of additional research (e.g., Popova, 2012 ).

Relatedly, future research could benefit from developing methods to manipulate perceptions of certain variables that were found to be significant moderators. For example, fear appeals were more effective for one-time behaviors, but this knowledge is currently of little use to researchers or practitioners who address recurring behaviors. However, this knowledge could become useful if methods were developed to successfully re-frame recurring behaviors as one-time behaviors. Such methods would also allow for experimental tests of the relevant dimensions and mechanisms (e.g., test whether fear appeals can be made more effective for a particular behavior if the behavior is framed as one-time rather than recurring).

Linear effect of fear

Another important question to address in future research concerns the linear and curvilinear hypotheses tested in the present study. Strictly speaking, we did not find support for either model. High levels of depicted fear did not lead to different outcomes than moderate depicted fear, suggesting that high and moderate depictions of fear produce similar results. However, the reason for this is unclear – were the high fear messages unsuccessful at evoking more subjective fear than the moderate messages, or is there simply a point beyond which additional fear (depicted or subjective) confers no benefit? To explore these possibilities, future studies should examine a large range of depicted fear along with measures of subjectively experienced fear.

Integration of findings

Finally, we believe that an additional benefit of our framework is its ability to guide researchers in generating future research questions. As mentioned, organizing the existing literature under this framework highlights the relative dearth of research addressing the behavior and audience aspects of the model relative to the message aspect. A number of interesting questions have yet to be explored in these areas. For example, are fear appeals more effective if they address behaviors concerning the self or close others (e.g., one’s children, romantic partners), public or private behaviors (e.g., exercising at a gym versus alone), or socially desirable or undesirable behaviors? Further, are fear appeals differentially effective for target populations that differ in age, education, social class, or personality? Such questions have received relatively little attention, but they have the potential to inform fear appeal theory and practice.

Additionally, what kinds of interactions exist when crossing aspects of message, behavior, and audience? We investigated two such questions in the present study with the hypotheses related to terror management theory – i.e., message content (presence versus absence of the word death) crossed with the recommended behavior (self-esteem enhancing versus hindering behaviors, immediate versus delayed outcomes). Although neither of these hypotheses was supported, the potential to test these types of interactions prompts the question of which variables may interact, particularly variables from separate aspects of the model. For example, might fear appeal effectiveness be moderated by interactions of culture (a factor of the audience) with the kind of behavior addressed by the fear appeal? Cross-cultural differences have rarely been explored in the effectiveness of fear appeals, and it is possible that cultural sensitivity to a behavior/issue may moderate the effectiveness of fear appeals addressing that behavior/issue. For example, East Asian countries have extremely low HIV prevalence rates and thus may be less susceptible to fear appeals on that topic relative to other topics. Whether this is true and whether it interacts with related findings is an empirical question that could be fruitfully explored in future research.

Importantly, aspects other than message content, behavior, and audience may moderate the effectiveness of fear appeal communications. However, based on our review of the literature, there simply appeared to be too little research on other aspects to include them in the current framework. Three potential aspects worth noting are the source of the communication, the subjective experience of the message recipient, and the channel used to transmit the message. First, based on a well-established body of literature in persuasion demonstrating that aspects of a message’s source can influence the persuasiveness of the message ( Briñol & Petty, 2009 ; Kumkale et al., 2010 ; Pornpitakpan, 2004 ; Wilson & Sherrell, 1993 ), the source of a fear appeal communication should be an important moderator for fear appeal effectiveness. For example, fear appeals from benevolent groups (e.g., a respected government institution, a close personal friend) may be more effective than fear appeals from self-interested groups (e.g., corporations or other for-profit entities). However, most empirical studies did not detail source information in a manner that allowed us to test such hypotheses. Further, many fear appeals are delivered in the form of public service announcements, and thus there is relatively little variation across existing studies on this dimension. Second, drawing on our previous distinction between fear appeals and fear, the subjective experience of the message recipient should be an important aspect of fear appeal communications. Although most empirical studies simply do not measure participants’ subjective states, such measures could be very informative to test a variety of interesting questions. For example, is fear the only emotion evoked by fear appeals? If not, what other negative emotions are evoked (e.g., disgust, shame, guilt, anger), and are they partially responsible for the effectiveness of fear appeals? Similarly, perhaps the effects of fear appeals are simply driven by induced negative affect or high arousal, and the specific experience of fear is superfluous? Future research using measures of subjective experience are needed to address these questions. The paucity of existing research addressing source characteristics and subjective experience led us to not include these as aspects of the current review framework, but they would be welcome additions in the future. Third, consistent with the focus of the persuasion literature on source, message, audience, and channel of communication as key components to understand in the persuasion process ( Shannon & Weaver, 1949 ), are certain channels of communication more likely to be effective in delivering fear appeals? For example, are graphic videos more likely to be effective than audio fear appeals without video? How do social media channels (generally more linked to liked peers) differ from mass media in effectiveness of delivered fear appeals?

To conclude, fear appeals are effective, and our synthesis organized and identified factors that make them even more effective. Specifically, fear appeals are particularly effective when the communication depicts relatively high amounts of fear, includes an efficacy message, stresses severity and susceptibility, recommends one-time only behaviors, and targets audiences that include a larger percentage of female message recipients. We formed these conclusions by meta-analytically testing a wide variety of influential fear appeal theories using the largest and most comprehensive fear appeals database to date. We believe our analysis has provided a thorough overview of the state of the literature and also generated a variety of important and exciting future directions.

Acknowledgments

This paper was facilitated by grants R01 MH094241, R01 NR08325, and R56 AI114501.

In the body of the manuscript, we presented random-effects analyses for a combined measure averaging across attitudes, intentions, and behaviors. Here, we present the analyses done separately for each type of measure.

First, the overall average effect size comparing treatment to comparison groups separately for attitudes, intentions, and behaviors was respectively d = 0.23 (95% CI: [0.11, 0.34]), d = 0.31 (95% CI: [0.24, 0.38]), and d = 0.27 (95% CI: [0.13, 0.42]). The heterogeneity statistics for each measure were respectively Q(109) = 614 (I 2 = 86.52, p < .0001), Q(162) = 615 (I 2 = 75.48, p < .0001), and Q(69) = 733 (I 2 = 92.37, p < .0001).

To examine the linear and curvilinear hypotheses for each outcome, we computed the average weighted effect size comparing outcomes for high fear versus moderate fear groups. For attitudes, intentions, and behaviors, the results were respectively d = 0.05 (95% CI: [−0.27, 0.36]), d = −0.09 (95% CI: [−0.29, 0.11]), and d = −0.04 (95% CI: [−0.63, 0.56]). The heterogeneity statistics for each measure were respectively Q(7) = 19 (I 2 = 66.10, p = .009), Q(8) = 19 (I 2 = 65.95, p = .01), and Q(9) = 118 (I 2 = 96.12, p < .0001).

To examine the gender hypothesis, we regressed outcomes onto the percent of the sample that was female. The results for attitudes, intentions, and behaviors were respectively b = 0.0019 ( SE = 0.0022, 95% CI for the slope: [−0.0024, 0.0061], p = .38, k = 72), b = 0.0043 ( SE = 0.0013, 95% CI for the slope: [0.0016, 0.0069], p = .002, k = 119), and b = 0.0037 ( SE = 0.0028, 95% CI for the slope: [−0.0018, 0.0091], p = .19, k = 49).

The results for all categorical moderator analyses are presented in Table A.1 .

Random-effects moderator analyses done separately for attitudes, intentions, and behaviors.

Note: SE = Self-esteem. DP = Death present in message. DA = Death absent in message. PP = Promotion/prevention. SEE = Self-esteem enhancing recommended behaviors. SEH = Self-esteem hindering recommended behaviors. d = Standardized mean effect size estimated meta-analytically for the indicated moderator level. 95% CI = The 95% confidence interval for d . k = The number of studies for each moderator level. Dash (−) indicates there were no observations at a particular moderator level.

1 We use the term effectiveness to indicate whether exposure to a fear appeal message resulted in more persuasion than a comparison condition. Thus, a fear appeal is considered effective if the effect size comparing treatment to control is significantly positive. Consequently, when testing moderation, fear appeals will be considered more effective for one level of a moderator versus another if the average effect size for the first level of the moderator is significantly larger than the average effect size for the second level of the moderator. In other words, when we compare fear appeal effectiveness for a moderator, we are comparing whether treatment led to more persuasion relative to control for one level of a moderator versus another level of that moderator.

2 Our framework addresses the relation between fear appeals and outcomes of interest (e.g., intentions) rather than the relation between fear and outcomes of interest. Although many fear appeal theories discuss fear, empirical studies typically test the impact of fear appeal messages on outcomes, and subsequently infer that message effects were mediated by experienced fear even though fear itself is rarely measured (for a discussion, see Popova, 2012 , p.466). Indeed, only 71 of the 248 studies in the current meta-analysis measured fear directly, and such measures were typically treated as manipulation checks rather than independent variables or mediators. We are therefore careful to discuss the influence of depicted message characteristics rather than subjectively experienced states (e.g., depicted fear versus experienced fear). This distinction applies to prior meta-analyses and primary studies as well, though the distinction is rarely made. We would like to thank an anonymous reviewer for encouraging us to frame our results in line with this distinction.

3 TMT theories also predict a higher order interaction between mentions of death, time delays, and self-esteem, such that the predicted effects of self-esteem discussed above become stronger after a delay ( Goldenberg & Arndt, 2008 ). Of the 12 conditions represented by this prediction (2 death × 3 delay × 2 self-esteem), four had zero observations in our meta-analysis. Thus, we are only able to test the simpler predictions concerning self-esteem and time delay in isolation.

4 Although many researchers investigate stage progression in the transtheoretical model (the process by which people move from one stage of the model to the next; Prochaska & DiClemente, 1983 ), this outcome is not directly relevant for our investigation because we are examining the effect of fear appeals on attitudes, intentions, and behaviors. It is possible that individuals would be classified as moving from one stage of the model to the next due to changes in attitudes, intentions, or behaviors, but such classification decisions are not the focus of the present study. The transtheoretical model also includes three dimensions other than the stages of change — the processes of change, self-efficacy, and decisional balance. Although we test predictions derived from the transtheoretical model more broadly, we limited our predictions to the areas that are relevant to fear appeal audiences (stages of change).

5 A number of papers did not provide the full text of the messages that were presented to each group, which made it impossible to determine if comparison groups labeled with the terms neutral message or control message were actually presented with neutral messages or with low depicted fear messages. Similarly, groups labeled with the term low depicted fear may have actually been presented with a neutral message but were nonetheless labeled as low fear because they were designed to induce relatively less fear than the experimental group. Thus, we could consistently compare relative levels of depicted fear across studies (more depicted fear vs. less depicted fear), but not absolute levels of fear (high depicted fear vs. low depicted fear vs. no depicted fear). Consequently, no message groups, neutral message groups, and low depicted fear groups were all considered appropriate comparison groups. Further, it was generally not possible to combine different potential comparison groups because information about standard deviations for the outcomes of each group was often lacking from reports, which made it unfeasible to calculate correct standard errors for combined comparison groups.

Contributor Information

Melanie B. Tannenbaum, University of Illinois at Urbana-Champaign.

Justin Hepler, University of Nevada, Reno.

Rick S. Zimmerman, University of Missouri – St. Louis.

Lindsey Saul, Virginia Commonwealth University.

Samantha Jacobs, Virginia Commonwealth University.

Kristina Wilson, University of Illinois at Urbana-Champaign.

Dolores Albarracin, University of Illinois at Urbana-Champaign.

References marked with an asterisk indicate studies included in the meta-analysis.

Aerophobia or Fear of Flying Research Paper

An experiment that could help investigate this phenomenon, the use of imaginal hypothesis against aerophobia, predictions, implications of the study.

Fear of flying, otherwise referred to as aerophobia is a condition that results in anxiety and fear, thus hindering chances of taking a flight as a means of travel. Aerophobia may be connected to numerous and unnecessary frightening issues. But sometimes it manifests independently. Sometimes aerophobia is coupled with claustrophobia or acrophobia; these imply panic that grips individuals in enclosed places and a fear of high altitude common in flights, respectively.

Aerophobia is estimated to take the toll on about 25 percent of the public, although a full-scale condition of phobia is considerably less recorded across the world (Allen, 2010). Although, cancellations of flights or delays are ordinary cases in the airline industry, such developments can aggravate the fear of flying.

Aerophobia is a serious condition that can be uncovered when a victim is closely engaged by a psychotherapist in a relaxed environment (Baker, 2010). Several sessions often yield positive results, because the victim will feel relaxation over time. An expert should use the first sessions primarily to look into the general facts of the condition of the victim.

This enables successful ventilation of any relevant facts concerning the issue. It is also imperative to clearly outline the best course of action that would enable a victim of aerophobia to shun the habit. This method of allowing a victim adequate time to slowly and individually recognize the condition and appreciate the essence of a recovery process is equally imperative.

The building of trust between a victim and a psychotherapist is paramount to the quest for better solution, as this has a bearing on the speed of treatment initiatives. But the lack of trust only serves to raise more anxiety in the victim. In general, anxiety is an impediment to such sessions as this condition erodes the respondent’s courage in taking part in the diagnosis and treatment sessions.

The use of imaginal approach enables aerophobia victims to draw the actual picture of the problem in their mind (Vanden, & De Raedt, 2008). Through imaginal approach, a psychotherapist would guide the respondent on all details of an air travel, until the plane touches down, while maintaining the sense of relaxation during the treatment session.

The expert may then pick a general conversation for sometime about the victim’s experience. Such conversations are normally received well by the victims. A keen observation of his or her face may reveal the positive response through some sort of a smile or approval through a nod.

A relaxed parting short may have far reaching effects. In view of this, toward the end of a recommended short session lasting about an hour, the victim should be taken through ways of ridding themselves of the fear of flying, though it is important to expose the gritty issues of facing the condition head-on, while closely monitoring the reaction of the victim of aerophobia, in order to achieve favorable outcomes (Allen, 2010).

The main aim of the careful explanation of the positive reasons of recovering from the condition is to enable the victim to have a feeling of absolute calmness as the session winds up and to contentedly recall the important points of eliminating the problem.

For instance, an effective session of counseling a victim on aerophobia should not end without the victim being asked to envisage engaging in a relaxed activity; such activity may involve strolling along a fine-looking water front and encouraged him to give an account of the attractiveness of the place. At this juncture, the victim may be asked to recall an activity that led him or her to adopt the attitude of fear flying. The victim may then be asked to ‘return’ the fear of flying to wherever it originated (Baker, 2010).

This development may trigger some worry in excessively nervous victims; however, if the victim becomes anxious the session should be paused to offer room for thought and possible unwinding. Positive outcomes would be registered if the victim grins, and says the fear of flying has ‘returned’ to its origin. It is important that a therapist and his or her victim review the significant issues of the discussion, before taking a drink to revive the lost energy.

Although, aerophobia is a condition which can be eliminated by effective therapy options, such friendly sessions rarely exist (Baker, 2010). Nevertheless, the successes of aerophobia sessions are pinned on correct timing and presentation of the issues that matter most to the elimination of the condition (Vanden, & De Raedt, 2008).

The slow release of uneasiness meant that the respondent needed psychological and body relaxation before and after the session. The diffusion of extreme anxiety, normally prepares aerophobia victims psychologically for the session. Within three months after the completion of the therapy session, a victim should have developed flying confidence, and taken at least one flawless flight.

The study implied that aerophobia varies from one person to another, but with proper intervention measures in place, the condition may just be temporary. Additionally, it is an established fact that victims of aerophobia are uncomfortable to continue experiencing the condition; therefore, most of them are willing to ‘return’ the condition where it originated and move on.

In view of this, aerophobia victims should make arrangements with their doctor to discuss how best to handle the situation. Professional therapists can then diagnose the fear of flying; establish whether the patient has any parallel disorders, and come up with an individualized plan for managing the condition.

Allen, J. (2010). Fear of flying. Therapy Today , 21(3), 47.

Baker, J. (2010). Fear of flying. Rural Society , 20(1), 21-34.

Vanden, B.A., & De Raedt, R. (2008). Cognitive vulnerability in fear of flying: the role of anxiety sensitivity. Depression & Anxiety , 25 (9), 768-773.

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Bibliography

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Essay on Exam Fear

Students are often asked to write an essay on Exam Fear in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Exam Fear

Understanding exam fear.

Exam fear is a common feeling among students. It is the anxiety or stress they experience before and during exams. This fear often stems from the pressure to perform well.

Causes of Exam Fear

The causes of exam fear can be high expectations from parents, competition with peers, or the fear of failing. It can also be due to lack of preparation.

Overcoming Exam Fear

To overcome exam fear, it’s essential to study regularly, take breaks, and have a positive mindset. Support from parents and teachers can also help in reducing this fear.

250 Words Essay on Exam Fear

Exam fear, often termed as ‘test anxiety’, is a common phenomenon among students. It is a psychological condition in which students experience severe distress and anxiety before or during exams.

The Root Causes

The primary cause of exam fear is the pressure of expectations, both self-imposed and from external sources like family and society. The fear of failure, lack of preparation, and the anticipation of a negative outcome further contribute to this anxiety.

Impact on Performance

Exam fear can significantly impact a student’s performance. It can lead to a decrease in concentration, memory, and understanding, thus affecting the overall academic performance. It can also result in physical symptoms like nausea, headache, and increased heart rate.

Overcoming exam fear requires a multifaceted approach. Adequate preparation and revision can build confidence. Time management is another key factor, as it reduces last-minute stress.

Mental Health and Mindfulness

Prioritizing mental health is crucial in managing exam fear. Techniques like meditation, deep breathing, and mindfulness can help combat anxiety. Regular exercise and a balanced diet also contribute to overall well-being and stress management.

Exam fear is a pervasive issue among students. However, with the right strategies and a focus on mental health, it can be mitigated. It’s important to remember that exams are just a part of the educational journey, not the entirety of it. The real success lies in learning and growing, not merely in scoring high grades.

500 Words Essay on Exam Fear

Introduction.

Exam fear, also known as test anxiety, is a psychological condition where individuals experience severe distress and anxiety during or before examinations. This fear often stems from pressure to perform well, fear of failure, and the anticipation of the unknown. It is a prevalent issue among students worldwide, irrespective of their academic level.

The Psychology of Exam Fear

Exam fear is deeply rooted in the psychology of the learner. It can be triggered by various factors such as high expectations from oneself or others, past experiences of failure, or lack of preparation. The fear is often amplified by the societal and educational systems that place significant emphasis on grades as the primary metric of success. This creates an environment where the stakes are high, and failure is seen as catastrophic, leading to heightened anxiety.

The Implications of Exam Fear

Exam fear can have profound implications on a student’s mental and physical health. It can lead to symptoms like insomnia, loss of appetite, and even depression. Moreover, it can significantly hamper the learning process. Instead of fostering a love for learning, the education system, when perceived as a source of stress, can make students associate knowledge acquisition with fear and anxiety. This negative association can deter students from engaging in the learning process, thus impeding their academic growth and intellectual development.

Strategies to Overcome Exam Fear

Overcoming exam fear requires a multifaceted approach. Firstly, it is important to foster a balanced perspective towards exams. While they are important, they are not the sole determinants of success. Secondly, effective preparation strategies, such as regular study, revision, and practice, can help build confidence and reduce anxiety. Thirdly, stress management techniques like meditation, deep breathing, and physical exercise can help regulate the physiological responses to stress.

The Role of Educational Institutions

Educational institutions play a crucial role in mitigating exam fear. They can do so by promoting a learning environment that values knowledge and understanding over rote memorization and high grades. Additionally, they can provide resources for mental health support and stress management. Teachers can also play a significant role by providing supportive feedback and fostering a classroom environment that encourages learning for the sake of understanding, rather than for the sake of exams.

Exam fear is a significant concern that can adversely affect a student’s academic performance and overall well-being. It is crucial to address this issue through a combination of individual efforts, supportive educational practices, and societal changes in the perception of success. By doing so, we can transform the education system into a platform for intellectual growth and development, rather than a source of fear and anxiety.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

• Essay on My Preparation for Examination
• Essay on My Exams
• Essay on Importance of Exams

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