design paper essay

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by Gordon Harvey

Students often do their best and hardest thinking, and feel the greatest sense of mastery and growth, in their writing. Courses and assignments should be planned with this in mind. Three principles are paramount:

1. Name what you want and imagine students doing it

However free students are to range and explore in a paper, the general kind of paper you’re inviting has common components, operations, and criteria of success, and you should make these explicit. Having satisfied yourself, as you should, that what you’re asking is doable, with dignity, by writers just learning the material, try to anticipate in your prompt or discussions of the assignment the following queries:

• What is the purpose of this? How am I going beyond what we have done, or applying it in a new area, or practicing a key academic skill or kind of work?
• To what audience should I imagine myself writing?
• What is the main task or tasks, in a nutshell? What does that key word (e.g., analyze, significance of, critique, explore, interesting, support) really mean in this context or this field?
• What will be most challenging in this and what qualities will most distinguish a good paper? Where should I put my energy? (Lists of possible questions for students to answer in a paper are often not sufficiently prioritized to be helpful.)
• What misconceptions might I have about what I’m to do? (How is this like or unlike other papers I may have written?) Are there too-easy approaches I might take or likely pitfalls? An ambitious goal or standard that I might think I’m expected to meet but am not?
• What form will evidence take in my paper (e.g., block quotations? paraphrase? graphs or charts?) How should I cite it? Should I use/cite material from lecture or section?
• Are there some broad options for structure, emphasis, or approach that I’ll likely be choosing among?
• How should I get started on this? What would be a helpful (or unhelpful) way to take notes, gather data, discover a question or idea? Should I do research? 

2. Take time in class to prepare students to succeed at the paper

Resist the impulse to think of class meetings as time for “content” and of writing as work done outside class. Your students won’t have mastered the art of paper writing (if such a mastery is possible) and won’t know the particular disciplinary expectations or moves relevant to the material at hand. Take time in class to show them: 

• discuss the assignment in class when you give it, so students can see that you take it seriously, so they can ask questions about it, so they can have it in mind during subsequent class discussions;
• introduce the analytic vocabulary of your assignment into class discussions, and take opportunities to note relevant moves made in discussion or good paper topics that arise;
• have students practice key tasks in class discussions, or in informal writing they do in before or after discussions;
• show examples of writing that illustrates components and criteria of the assignment and that inspires (class readings can sometimes serve as illustrations of a writing principle; so can short excerpts of writing—e.g., a sampling of introductions; and so can bad writing—e.g., a list of problematic thesis statements);
• the topics of originality and plagiarism (what the temptations might be, how to avoid risks) should at some point be addressed directly. 

3. Build in process

Ideas develop over time, in a process of posing and revising and getting feedback and revising some more. Assignments should allow for this process in the following ways:

• smaller assignments should prepare for larger ones later;
• students should do some thinking and writing before they write a draft and get a response to it (even if only a response to a proposal or thesis statement sent by email, or described in class);
• for larger papers, students should write and get response (using the skills vocabulary of the assignment) to a draft—at least an “oral draft” (condensed for delivery to the class);
• if possible, meet with students individually about their writing: nothing inspires them more than feeling that you care about their work and development;
• let students reflect on their own writing, in brief cover letters attached to drafts and revisions (these may also ask students to perform certain checks on what they have written, before submitting);
• have clear and firm policies about late work that nonetheless allow for exception if students talk to you in advance.

• Pedagogy Workshops
• Responding to Student Writing
• Commenting Efficiently
• Vocabulary for Discussing Student Writing
• Guides to Teaching Writing
• HarvardWrites Instructor Toolkit
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Sample Papers

This page contains sample papers formatted in seventh edition APA Style. The sample papers show the format that authors should use to submit a manuscript for publication in a professional journal and that students should use to submit a paper to an instructor for a course assignment. You can download the Word files to use as templates and edit them as needed for the purposes of your own papers.

Most guidelines in the Publication Manual apply to both professional manuscripts and student papers. However, there are specific guidelines for professional papers versus student papers, including professional and student title page formats. All authors should check with the person or entity to whom they are submitting their paper (e.g., publisher or instructor) for guidelines that are different from or in addition to those specified by APA Style.

Sample papers from the Publication Manual

The following two sample papers were published in annotated form in the Publication Manual and are reproduced here as PDFs for your ease of use. The annotations draw attention to content and formatting and provide the relevant sections of the Publication Manual (7th ed.) to consult for more information.

• Student sample paper with annotations (PDF, 5MB)
• Professional sample paper with annotations (PDF, 2.7MB)

We also offer these sample papers in Microsoft Word (.docx) format with the annotations as comments to the text.

• Student sample paper with annotations as comments (DOCX, 42KB)
• Professional sample paper with annotations as comments (DOCX, 103KB)

Finally, we offer these sample papers in Microsoft Word (.docx) format without the annotations.

• Student sample paper without annotations (DOCX, 36KB)
• Professional sample paper without annotations (DOCX, 96KB)

Sample professional paper templates by paper type

These sample papers demonstrate APA Style formatting standards for different professional paper types. Professional papers can contain many different elements depending on the nature of the work. Authors seeking publication should refer to the journal’s instructions for authors or manuscript submission guidelines for specific requirements and/or sections to include.

• Literature review professional paper template (DOCX, 47KB)
• Mixed methods professional paper template (DOCX, 68KB)
• Qualitative professional paper template (DOCX, 72KB)
• Quantitative professional paper template (DOCX, 77KB)
• Review professional paper template (DOCX, 112KB)

Sample papers are covered in the seventh edition APA Style manuals in the Publication Manual Chapter 2 and the Concise Guide Chapter 1

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• Heading Levels Template: Student Paper (PDF, 257KB)
• Heading Levels Template: Professional Paper (PDF, 213KB)

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Sample student paper templates by paper type

These sample papers demonstrate APA Style formatting standards for different student paper types. Students may write the same types of papers as professional authors (e.g., quantitative studies, literature reviews) or other types of papers for course assignments (e.g., reaction or response papers, discussion posts), dissertations, and theses.

APA does not set formal requirements for the nature or contents of an APA Style student paper. Students should follow the guidelines and requirements of their instructor, department, and/or institution when writing papers. For instance, an abstract and keywords are not required for APA Style student papers, although an instructor may request them in student papers that are longer or more complex. Specific questions about a paper being written for a course assignment should be directed to the instructor or institution assigning the paper.

• Discussion post student paper template (DOCX, 31KB)
• Literature review student paper template (DOCX, 37KB)
• Quantitative study student paper template (DOCX, 53KB)

Sample papers in real life

Although published articles differ in format from manuscripts submitted for publication or student papers (e.g., different line spacing, font, margins, and column format), articles published in APA journals provide excellent demonstrations of APA Style in action.

APA journals began publishing papers in seventh edition APA Style in 2020. Professional authors should check the author submission guidelines for the journal to which they want to submit their paper for any journal-specific style requirements.

Credits for sample professional paper templates

Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020, Journal of Experimental Psychology: General , 149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.

Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020, Psychology of Popular Media , 10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.

Mixed methods professional paper template: Adapted from “‘I Am a Change Agent’: A Mixed Methods Analysis of Students’ Social Justice Value Orientation in an Undergraduate Community Psychology Course,” by D. X. Henderson, A. T. Majors, and M. Wright, 2019,  Scholarship of Teaching and Learning in Psychology , 7 (1), 68–80. ( https://doi.org/10.1037/stl0000171 ). Copyright 2019 by the American Psychological Association.

Literature review professional paper template: Adapted from “Rethinking Emotions in the Context of Infants’ Prosocial Behavior: The Role of Interest and Positive Emotions,” by S. I. Hammond and J. K. Drummond, 2019, Developmental Psychology , 55 (9), pp. 1882–1888 ( https://doi.org/10.1037/dev0000685 ). Copyright 2019 by the American Psychological Association.

Review professional paper template: Adapted from “Joining the Conversation: Teaching Students to Think and Communicate Like Scholars,” by E. L. Parks, 2022, Scholarship of Teaching and Learning in Psychology , 8 (1), pp. 70–78 ( https://doi.org/10.1037/stl0000193 ). Copyright 2020 by the American Psychological Association.

Credits for sample student paper templates

These papers came from real students who gave their permission to have them edited and posted by APA.

Free Cover Page templates

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After writing the whole report, dissertation, or paper, which is the hardest part, you should now create a cover page that suits the rest of the project. Part of the grade for your work depends on the first impression of the teacher who corrects it.

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Free editable templates for title pages

As you can see, it's simple to create cover pages for schoolwork and it won’t take much time. We recommend using the same colors on the cover as the ones you used for your essay titles to create a cohesive design. It’s also crucial to add the name and logo of the institution for which you are doing the essay. A visually attractive project is likely to be graded very well, so taking care of the small details will make your work look professional.

On Edit.org, you can also reuse all your designs and adapt them to different projects. Thanks to the users' internal memory, you can access and edit old templates anytime and anywhere.

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 16 September 2024, from https://www.scribbr.co.uk/research-methods/research-design/

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Graphic Essays and Comics

Overview   |   Recommended Software   |   Student-Made Examples   |   Other Examples   |   Instructional Video

A graphic essay (sometimes called a visual essay) uses a combination of text and images to explore a specific topic. Graphic essays can look like comics, graphic novels, magazines, collages, artist books, textbooks, or even websites. Graphic essays often first take the form of written essays and then have graphic elements added to enrich the reader experience. Unlike infographics, which also combine text and images, graphic essays are often more text-based and usually have a narrative arc or specific reading order.

Comics are a genre used to express ideas through images combined with text or other visual information. Comics can take the form of a single panel or a series of juxtaposed panels of images, sometimes called a strip. Text is conveyed via captions below the panel(s), or speech bubbles and onomatopoeias within the panel(s), to indicate dialogue, narration, sound effects, or other information. Graphic novels are often considered to be a longer form of comics, typically in book form.

A web-based graphic essay can take the form of a blog or a single page website, such as a Microsoft Sway page or an interactive Prezi. For Microsoft Sway and Prezi graphic essays, see the examples below. If you are creating a blog we recommend visiting the Web-Based Projects page .

Graphic Essay Design Tip: Graphic essays can take many forms, so we recommend being creative within the scope of your project! Get some help from DesignLab to brainstorm options and talk through the various tools available!

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Recommended Software

There are many different software programs that can be used to create graphic essays. Below is a list of the software that we recommend for making a graphic essay. We organized the software by category and put the software from top to bottom from best to worst. We recommend using a software you know well or learning the software well enough to establish an easy workflow, so you can spend less time troubleshooting and spend more time on your project. Check out our Software Support page for links to tutorials for all of these programs.

General Graphic Essay Software

Web-Based Graphic Essay Software

Comic-Specific Graphic Essay Software

Student-Made Examples

Print style graphic essay.

Becoming a Witness by Jessica Posnock

Creative Graphic Essay

Virtual Communication by Max Hautala   *Award Winning*

Curb Magazine (2012) by Journalism 417

Web-Based (Magazine) Graphic Essay

Curb Magazine (Current) by Journalism 417

Web-Based (Sway) Graphic Essay

Language Influences Culture, Thoughts, and Identity by Kristen Luckow   *Award Winning*

Dyslexia by Maria Swanke *Award Winning*

Other Examples

Web-based (blog) graphic essay.

Switch It Up: Graphic Essay by Amanda Zieba

Graphic Novel

Graphic Novels in the Classroom by Gene Yang

Instructional Video

How to write the perfect design dissertation

Tutors and students from top design colleges share their advice.

Studying design is about crafting a great design portfolio that will wow potential employers, right? Well, yes. But don't discount the importance of astute creative thinking, and expressing yourself eloquently through the written word. In short, your design dissertation matters.

"I don't believe that design students should be focused entirely on portfolio work," argues Myrna MacLeod , programme leader for Graphic Design at Edinburgh Napier University. "They should also be able to demonstrate an interest in the contexts that underpin their work, and the histories and connections that have informed our practice."

• 5 top tips for graduate designers

"Think of a dissertation as an opportunity, not a burden," urges Craig Burston , Graphic and Media Design course leader at London College of Communication (LCC). "It gives us visually-minded people an opportunity to demonstrate that we too can construct arguments and distil complex notions." 

As Burston points out, this is not just an academic exercise: the power of persuasion is often key to success as a commercial designer. "Clients seek clarity, and project concepts or proposals need to be put into context," he says.

Read on to discover some top tips from leading tutors and their students for nailing your design dissertation…

01. Treat it like a design brief

"A great dissertation should be a designed artefact, and portfolio-worthy in its own right," says Burston. And like a design brief, it should be about solving a problem: "Make sure it has clearly stated aims, strong focus, and doesn't lack opinion or rhetoric," he adds.

• Best laptops for graphic design

"The value of a designed dissertation as a portfolio piece is that it's a holistic view of the individual," agrees Sarah James , senior lecturer in Visual Communication at Arts University Bournemouth (AUB). 

"It shows, type, editorial, research and aesthetic skill, as well as the personal interests and convictions of the individual."

James identifies AUB student Maarit Koobas , who investigated responsive type in both her dissertation and final project, as a particularly strong example of this. "Her design version was one of the most authentic, restrained and elegantly expressive I have ever received," she enthuses.

Koobas conducted a huge amount of initial research into both the contexts in which responsive type can be seen – such as advertising, product design, science and material cultures – and the theories behind its analysis, including semiotics, philosophy and politics. "Creating and analysing ideas, before they end up in your portfolio, is what design is all about," argues Koobas.

• 5 must-read books for design students

02. Write about your passion

"To develop essay questions, AUB students are asked to consider what they love, hate or are puzzled by in their practice – essentially, what moves them," says James. 

"A poor dissertation is inauthentically chosen for ease as opposed to interest," she adds. "It rambles and blusters, using complex language to mask insufficient research." 

"You can tell a mile off when the writer isn't interested," agrees Burston. "How can you expect the reader to care about it if you don't? Write about something that reflects your interests, focus and direction. I've read fascinating dissertations on topics as diverse as patterns in nature, and Brutalist car parks. Make me interested in what interests you."

For Edinburgh Napier graduate Fiona Winchester , this topic turned out to be typography in graphic novels. "I love reading them, but I think people still don't take them seriously as an art form, which is a shame," she says. For her dissertation, she conducted qualitative interviews using modified pages with and without imagery (shown above). 

Her advice is simple: "Narrow down your idea to be as precise as possible. The smaller your question, the easier it is to research and try to answer it."

If you're struggling to get the ball rolling on the actual writing process, Winchester advocates starting with whichever bit you have ideas for. "If you're stuck, it's so much easier to write in whatever order it comes to you, and then edit it into a dissertation, than to try write straight through from beginning to end," she insists.

03. Don't be afraid to talk to people

"I always think my students get the most out of the new streams of knowledge they find from talking to people," says McLeod. "It breaks down barriers and allows them to find answers to problems. Hopefully they will adopt that approach when designing for people also."

In some cases, this can involve interviewing your design heroes. "Students are very surprised when they send a question to Stefan Sagmeister , Milton Glaser or Michael Wolff and they reply with the most precious nugget of knowledge," smiles McLeod. 

But remember: it's your dissertation, so don't get lazy and expect your interview subject to do all the heavy lifting.

In other cases, it could be as simple as asking friends or family to help proofread. "It is quite daunting writing such a large body of text," admits Kaori Toh , a recent graduate from Central Saint Martins, whose dissertation explored the politics of design and technology.

"I often felt I'd get lost in all that text and research," she confesses. "Therefore, I would often send my drafts to a couple of friends to have them look through, and keep my writing cohesive."

04. Reflect on your design practice 

Most of all, dissertations are an opportunity to reflect on, and develop, your creative process as a designer. "Ultimately, it's your job to make your work relevant and credible, and the dissertation helps you learn how to do this," adds Burston. 

Of course, writing doesn't always come easily to visually minded people – and Burston highlights the fact that dyslexia is not uncommon amongst designers. 

"You're not on your own – in our profession, quite the opposite in fact – so do seek academic support, and just enjoy thinking and writing about 'stuff' that informs your practice," is his advice.

One of Burston's stand-out students from this year, Tom Baber , welcomed support from the university to help with his dyslexia. Baber's dissertation focused on type design, and particularly the extent to which the longwinded design process is worth the effort, compared to using an existing typeface.

"I saw it as an opportunity to approach other type designers and see what they thought. Turns out I'm not the first to ask the question," he smiles. "Writing my dissertation helped me change from a 'maker' mentality to a 'designer' mentality, and be more critical of my ideas."

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Nick has worked with world-class agencies including Wolff Olins, Taxi Studio and Vault49 on brand storytelling, tone of voice and verbal strategy for global brands such as Virgin, TikTok, and Bite Back 2030. Nick launched the Brand Impact Awards in 2013 while editor of Computer Arts, and remains chair of judges. He's written for Creative Bloq on design and branding matters since the site's launch.

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Visual Rhetoric in Advertising

How it works

Advertising is everywhere these days. It shapes how we think, what we buy, and even our views on politics. A big part of its power comes from visual rhetoric, which is just a fancy way of saying using pictures, colors, and designs to send messages and convince us of things. Unlike words, which make us think logically, visuals hit us faster and often without us even noticing. This essay digs into how ads use visuals to change how we see things and make us take action.

• 1 The Power of Pictures
• 2 Symbols and Hidden Messages
• 3 How Things Are Arranged
• 4 Wrapping It Up

The Power of Pictures

Pictures in ads aren’t just for looks; they’re super powerful tools for convincing us. A picture can show big ideas, stir up feelings, and stick in our minds better than just words. Like the famous “Got Milk?” ads with celebrities and their milk mustaches. The simple picture, plus seeing someone famous, says “health” and “vitality” without needing a lot of words. That milk mustache becomes a symbol of being healthy, making the message unforgettable.

Colors in pictures also mess with our heads. Colors have meanings; red can make us feel excited or urgent, blue can calm us down and make us trust, and green usually means nature and health. Advertisers know this and use colors to push our buttons. A fast-food ad might use bright reds and yellows to make you hungry and think of fast service. On the flip side, a fancy brand might use soft colors and simple designs to seem classy and exclusive.

Symbols and Hidden Messages

Ads don’t just look nice; they pack deeper meanings through symbols and semiotics (the study of signs and symbols). These symbols work on different levels, tapping into our cultural values and norms. Think of car ads with wild landscapes. The car isn’t just for getting around; it stands for freedom, adventure, and being your own person. These ideas are part of our culture, making the ad hit home harder.

Looking at an ad through semiotics shows how one picture can mean many things. There are the plain meanings and the hidden ones. Take a perfume ad with a single flower. It shows the scent but also hints at purity, elegance, and natural beauty. The flower is a sign that wraps up all these nice ideas, subtly shaping how we feel about the product.

How Things Are Arranged

How stuff is arranged in an ad matters a lot. Good composition, or layout, grabs our attention and gets the message across. Advertisers use tricks like balance, contrast, and focal points to make the layout look good and easy to follow. One trick is the rule of thirds, which splits the image into nine parts, putting key stuff along the lines or where they meet. This makes the picture look balanced and guides our eyes to the important bits.

Another thing is visual flow, or how our eyes move around the picture. Advertisers use lines, like roads or arrows, or where a model is looking to guide our gaze. This makes sure we notice the main message or product. For example, in a beauty ad, the model might look at the product, nudging us to look at it too.

Wrapping It Up

Visual rhetoric in ads is a complex and ever-changing game that uses pictures, symbols, and layouts to win us over. Knowing how it works helps advertisers craft messages that hit us on many levels, making us buy stuff and shaping our culture. As viewers, being aware of these tricks helps us see through the ads and understand how they try to shape our views and choices. Studying visual rhetoric shows us not just how we get persuaded but also how big a deal visual culture is in our daily lives.

Cite this page

Visual Rhetoric in Advertising. (2024, Sep 17). Retrieved from https://papersowl.com/examples/visual-rhetoric-in-advertising/

"Visual Rhetoric in Advertising." PapersOwl.com , 17 Sep 2024, https://papersowl.com/examples/visual-rhetoric-in-advertising/

PapersOwl.com. (2024). Visual Rhetoric in Advertising . [Online]. Available at: https://papersowl.com/examples/visual-rhetoric-in-advertising/ [Accessed: 17 Sep. 2024]

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"Visual Rhetoric in Advertising," PapersOwl.com , 17-Sep-2024. [Online]. Available: https://papersowl.com/examples/visual-rhetoric-in-advertising/. [Accessed: 17-Sep-2024]

PapersOwl.com. (2024). Visual Rhetoric in Advertising . [Online]. Available at: https://papersowl.com/examples/visual-rhetoric-in-advertising/ [Accessed: 17-Sep-2024]

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Call for Chapters: Harnessing Advanced Technologies for Data-Driven Urban Design

Call for chapters, introduction, target audience, recommended topics.

- Advanced Technologies in Urban Design

• Integration of big data in urban planning
• Applications of artificial intelligence in urban design
• Development and use of predictive models for urban planning
• Implementation of IoT in city infrastructures
• Urban simulation models and digital twins
• Use of AR, VR, and Mixed Reality for urban design, analysis, and simulation

- Biosensors, Emotions, and Analysis of Urban Spaces

• Use of biosensors for analyzing urban spaces
• Monitoring citizens' emotions and behavior through biosensors
• Use of biosensors for environmental monitoring and public health

- Sustainability, Green Space Management, and Energy Efficiency

• Technological strategies for sustainable and resilient cities
• Applications of artificial intelligence in urban green space management
• Energy management and efficiency through advanced technologies
• Data analysis for waste management, water resources, and electricity

- Intelligent Mobility and Transportation Systems

• Data-driven intelligent mobility systems
• Predictive models for traffic and urban mobility management

- Urban Security, Crime, and Monitoring

• Urban security and real-time monitoring
• Data analysis and predictive models for crime prevention
• Advanced technologies for improving public safety
• Use of big data and AI to identify criminal patterns
• Implementation of intelligent surveillance systems in urban areas

- Civic Participation and Governance

• Digital platforms for civic participation
• Impact of emerging technologies on urban governance

- Ethics and Privacy in Data-Driven Urban Design

• Ethical and privacy considerations in the use of advanced technologies for data-driven urban design

- Case Studies and Real Implementations

• Case studies on real implementations of advanced technologies in urban design
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• Case studies on the use of biosensors in urban analysis and planning
• Case studies on the application of predictive models in urban contexts
• Case studies on the use of advanced technologies for crime prevention
• Case studies on the use of AI in urban green space management

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Researchers and practitioners are invited to submit on or before September 15, 2024 , a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by September 29, 2024 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by December 8, 2024 , and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Harnessing Advanced Technologies for Data-Driven Urban Design. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery ® online submission manager.

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com . This publication is anticipated to be released in 2025.

Important Dates

September 15, 2024 : Proposal Submission Deadline September 29, 2024 : Notification of Acceptance December 8, 2024 : Full Chapter Submission January 19, 2025 : Review Results Returned February 16, 2025 : Final Acceptance Notification February 23, 2025 : Final Chapter Submission

Marco Seccaroni Politecnico di Milano, Italy & Massachusetts Institute of Technology, USA [email protected]

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Climate Capitalists

Firms' perceived cost of green capital has decreased since the rise of sustainable investing. Green and brown firms perceived their cost of capital to be the same before 2016, but after the post-2016 surge in sustainable investing, green firms perceived their cost of capital to be on average 1 percentage point lower. This difference has widened as sustainable investing has intensified. Within some of the largest energy and utility firms, managers have started applying a lower cost of capital to greener divisions. The changes in the perceived cost of green capital incentivize cross-firm and within-firm reallocation of capital toward greener investments.

We are grateful to Marcin Kacperczyk, Lasse Pedersen, Monika Piazessi, and Martin Schneider for helpful comments. This research was supported by the Asness Junior Faculty Fellowship, the Becker Friedman Institute, the Fishman Faculty Research Fund, the Fama-Miller Center, the Lamfalussy Research Fellowship, and the William S. Fishman Fund. Gormsen has been a paid consultant to the World Bank in connection with projects on the cost of capital and firm investment in developing nations. We thank Rahul Chauhan, Jason Jia, Sonali Mishra, Esfandiar Rouhani, Felix Trikos, and Raymon Yue for outstanding research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Liverpool reportedly interested in signing Lille midfielder Angel Gomes - Paper Talk

Plus: Crystal Palace and Ipswich are both tracking Ben Chilwell and could make a move for the defender in January; Reece James has suffered a delay to his long-anticipated return for Chelsea; popular Man City kitman Brandon Ashton announces departure after 15 years of service at the club

Tuesday 17 September 2024 07:56, UK

The top stories and transfer rumours from Tuesday's newspapers...

LIVERPOOL ECHO

Liverpool are reportedly interested in signing Lille midfielder Angel Gomes, with the Reds scouts in attendance for the 24-year-old's impressive first England start against Finland this month.

Crystal Palace and Ipswich are both tracking Ben Chilwell and could make a move for the defender in January.

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Referees' chiefs have lost the latest round of their courtroom battle with the taxman and it could mean paid holiday for officials.

Reece James has suffered a delay to his long-anticipated return for Chelsea.

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DAILY TELEGRAPH

The Premier League is facing frustration from clubs over the cloak of secrecy that surrounds sport's "trial of the century" into the 115 alleged rules breaches by Manchester City.

England head coach Steve Borthwick is on the verge of appointing a new defence coach, with Oyonnax head coach Joe El-Abd and Paul Gustard, the Stade Français defence coach, on the final shortlist to replace Felix Jones.

Kevin Pietersen has withdrawn his application to join the exclusive Sunningdale Golf Club after members threatened a veto.

US businessman John Textor's swoop for Everton is set to enter a crucial stage as he aims to fast-track the sale of his Crystal Palace stake.

Hearts are in talks with Brighton's billionaire owner Tony Bloom about a £10m investment plan which could revolutionise player recruitment at the Tynecastle club.

Former Juventus midfielder Adrien Rabiot is on the verge of a return to Ligue 1 with Marseille.

A cult hero at Manchester City has announced their departure after 15 years of service to the club. Brandon Ashton, City's kitman and one of the most popular figures within the dressing room, took to social media to share the news.

THE GUARDIAN

Erik ten Hag has stated that despite Antony being Manchester United's second most expensive player it is the "law" that he has to fight against the academy products Amad Diallo and Alejandro Garnacho to regain his place.

DAILY RECORD

Luke McCowan insists Celtic are not going to be just tourists in the Champions League.

Tom from Southampton is ONE MILLION POUNDS richer after correctly predicting six scorelines!

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

You might have to write up a research design as a standalone assignment, or it might be part of a larger   research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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