According to a survey of practitioners, about 3 of every 4 UX Researchers have advanced degrees.

It’s easy to understand why. The experience has you drinking from the fire-hose of books and scholarly articles meant to give you a firm theoretical basis in your field of study. You’ll sit through courses with other bright students to discuss and debate the finer points of the readings. You’ll apply it all with hands-on research that contributes to the field. And you get paired with an advisor who oversees your progress and ensures you’re sticking to the right path.

But not everyone has the opportunity or the desire to go back to school. And even for those professionals who have an advanced degree, nearly half (45%) learned their UX skills outside of a formal setting.

One alternative is self-study. If you’re willing to expand your library and apply yourself, you can learn a lot outside the walls of a classroom. 

This article outlines SPUR (Self-Paced UX Research), a comprehensive self-study program. It’s structured like a university curriculum spanning four semesters (or two years). It includes theoretical knowledge on cognition and learning, practical skills in interview and test facilitation, statistical analysis, and effective stakeholder management. Within this structure, you’re encouraged to find an advisor and study group, and to apply your learning through a capstone project.

It’s no substitute for jobs that require certain degrees or certifications, but it will deepen your craft and make you a more well-rounded researcher.

First Semester: Foundations

Assuming you have no prior knowledge of user experience or the social sciences that form its backbone, this semester is designed to develop that foundation. The goal is to provide you with the essential principles and frameworks that will undergird the later semesters. As you progress, be sure to take notes on key concepts and practical takeaways to solidify your understanding.

SPUR 101: Survey of UX Research Methods

• The Design of Everyday Things by Don Norman

• Universal Methods of Design: 100 Ways to Research Complex Problems, Develop Innovative Ideas, and Design Effective Solutions by Bella Martin and Bruce Hanington

The first task for an absolute beginner is to begin to imbibe a UX worldview — a new way of thinking about how we interact with the tools and systems we use. Don Norman's Design of Everyday Things has helped neophytes do that for decades, covering topics like design thinking, mental models, feedback, and affordances.

Alongside this, Universal Methods of Design by Bella Martin and Bruce Hanington offers an array of tools and techniques that researchers can apply to questions. These resources together equip you with some of the fundamental skills and knowledge to get you going on your way.

SPUR 200: Cognition and Decision Making

• Thinking, Fast and Slow by Daniel Kahneman

• Nudge: The Final Edition: Improving Decisions About Money, Health, and the Environment by Richard Thaler and Cass Sunstein

The roots of UX research lie in the field of Human Factors, which integrates what we know about human cognition into systems design. So researchers need a working understanding of how people make decisions and process information. Thinking, Fast and Slow by Daniel Kahneman and Nudge by Richard Thaler and Cass Sunstein are seminal works by Nobel Prize-winning authors. These books provide a solid foundation in human rationality, cognitive biases, and the analytical and intuitive modes of thought. They also explore how these insights can be used ethically to enhance user interactions.

Both these books were included in a community-generated list of non-UX books for UX practitioners, which you might use to supplement this self-study program.

SPUR 201: Learning and Behavior

• Tiny Habits: The Small Changes That Change Everything by BJ Fogg

• Hooked: How to Build Habit-Forming Products by Nir Eyal

We may not always have a direct influence on design, but the ultimate end of our work is to influence user behavior. BJ Fogg's model breaks down the essential components required to initiate behavior change, offering valuable insights into the mechanics behind user actions. Nir Eyal's Hooked provides a guide to creating products that promote user habits.

While these techniques have certainly been misused for manipulative purposes, understanding them is also helpful for ethically guiding users towards positive outcomes and achieving their goals.

Key Activities: Find an Advisor

In graduate school, students benefit from the guidance of an academic advisor—an experienced faculty member who helps shape their research journey. With any self-study path, finding a mentor is equally important. Here’s how you can approach it:

• Within your company or team: Start by exploring potential mentors in your current workplace. If you’re a solo practitioner, you’ll need to broaden your search.

• Examine your network: Identify a handful candidates within your professional network. These could be individuals at various career stages, from slightly more experienced peers to more seasoned experts.

• Use online platforms: Platforms like ADPList and UX Coffee Hours connect you with professionals willing to offer mentorship at a low cost or even for free.

• Consider career coaching: Dedicated career coaching services can be a good investment. Most coaches offer free consultations to see if there’s an initial fit.

Establish regular meetings with your “advisor” and set clear agendas. These sessions are ideal for discussing the concepts you’re studying and seeking feedback on how you’re applying them.

Second Semester: Essential Skills

With a foundational understanding of what UX Researchers study, it's time to explore how they do it. This semester focuses on mastering two key qualitative methods and extends into quantitative research.

SPUR 210: Interview and Test Facilitation I

• Interviewing Users: How to Uncover Compelling Insights (2nd Edition) by Steve Portigal

• The Moderator’s Survival Guide: Handling Common, Tricky, and Sticky Situations in User Research by Donna Tedesco and Fiona Tranquada

Talking to users is fundamental to UX research. But that framing can oversimplify it, making it seem like any casual conversation.

Instead, user interviews demand careful consideration to obtain reliable data and maintain the integrity and dignity of participants. Steve Portigal’s Interviewing Users is a concise and excellent primer that introduces you to the basics. Donna Tedesco’s The Moderator’s Survival Guide goes deeper, addressing edge cases, challenges, and troubleshooting, and offers helpful frameworks to navigate complex situations.

SPUR 211: Interview and Test Facilitation II

• A Practical Guide to Measuring Usability: 72 Answers to the Most Common Questions about Quantifying the Usability of Websites and Software by Jeff Sauro

• Usability Testing Essentials: Ready, Set ...Test! (2nd Edition) by Carol Barnum

Usability tests, whether remote and in-person, are a researcher’s bread and butter. Jeff Sauro’s “little green book,” A Practical Guide, is a brief and effective introduction to setting up and executing usability studies.

Carol Barnum’s Usability Testing Essentials goes further, detailing every stage of a usability test, from planning to reporting and acting on the findings. Barnum also addresses how to navigate challenges like budget and time constraints, giving you the knowledge and confidence you need to conduct effective tests.

SPUR 250: Statistics for UX Research

• Quantifying the User Experience: Practical Statistics for User Research by Jeff Sauro and James R. Lewis

• Quantitative User Experience Research: Informing Product Decisions by Understanding Users at Scale by Chris Chapman and Kerry Rodden

For researchers with a qualitative background, numbers and statistics can be intimidating. Nevertheless, having at least a working knowledge of quantitative methods will make you a stronger and more influential researcher.

Sauro and Lewis’s classic, Quantifying the User Experience, provides essential guidance for choosing the right metrics and tests. If you wish to specialize further in quantitative methods, Quantitative User Experience Research by Kerry Rodden and Chris Chapman offers advanced insights, particularly useful for products with live analytics and large user bases.

Key Activities: Find a Study Group

In graduate school, students tend to form study groups with their cohorts. Similarly, in your self-study journey, it’s important to connect with others who share similar goals.

Finding a group of peers allows you to read and discuss books together, critically evaluate ideas, and support each other’s growth. Here are some ways to find or create a study group:

1. Local UX book clubs, UXPA chapters, or networking events: These gatherings are great places to meet fellow professionals at various stages in their careers who also want to learn and grow.

2. Online communities: Join groups on platforms like Slack or Reddit. Communities such as The Research Ops Community and UX Research and Strategy offer events and networking.

3. Form your own “mastermind” group: Band together in a cohort of self-taught students who want to follow a curriculum like this one. Set regular meetings to review material and support each other’s progress.

By forming a study group, you can enrich your learning experience and stay motivated along the way.

Third Semester: Advanced Skills

Having developed a solid grasp of the theory and technical skills essential for UX research, it's time to focus on the soft and/or auxiliary skills that make you effective. This semester will help you refine your communication, influence your colleagues, and manage your time efficiently, ensuring your work has a greater impact.

SPUR 300: Stakeholder Management

• Influence, New and Expanded: The Psychology of Persuasion by Robert B. Cialdini

• Getting to Yes: Negotiating Agreement Without Giving In by Roger Fisher, William Ury and Bruce Patton

One of the most critical skills often lacking among UX Researchers is effective collaboration with stakeholders. It’s not magic, but rather the timeless skill of working with others toward a common goal.

Influence by Robert Cialdini offers practical tips grounded in social psychology and real-world examples, from con artists to salespeople. Getting to Yes by Fisher, Ury, and Patton, based on Harvard-backed research, provides insights into effective negotiation across various situations. Together, these provide tools to engage and influence stakeholders successfully.

SPUR 310: Business Communication I

• The Elements of Style, by William Strunk, Jr. and E.B. White

• The Sense of Style: The Thinking Person's Guide to Writing in the 21st Century by Steven Pinker

These two books cover fundamental principles of effective communication. Strunk and White’s classic, The Elements of Style, emphasizes clarity, brevity, and precision, offering pithy axioms to enhance any piece of writing. Steven Pinker’s The Sense of Style leverages cognitive linguistics and style recommendations to help you find and refine your natural voice, with particularly insightful discussions like avoiding ‘The Curse of Knowledge’ (wherein one forgets what it was like to not know something).

SPUR 320: Productivity

• Getting Things Done: The Art of Stress-Free Productivity by David Allen

• Deep Work: Rules for Focused Success in a Distracted World by Cal Newport

To be an effective researcher, you need both time and a system to deliver quality work efficiently. Getting Things Done by David Allen provides a method for organizing and prioritizing all your tasks, breaking them down, and assigning them appropriately. Cal Newport’s Deep Work guides knowledge workers on how to eliminate distractions, make room for productive rest and breaks, and carve out time for focused work on important goals.

Key Activities: Plan your Capstone

Most graduate programs culminate in a thesis, dissertation, or capstone project to demonstrate a comprehensive understanding of the material and practical application of learned skills.

If you don't have a portfolio of work experience, use this capstone project as an opportunity to practice the methods and ideas you've encountered. Your advisor's guidance will be crucial in ensuring your project is realistic and polished enough to share with prospective employers.

If you’ve already undertaken a few self-dirercted projects, consider other ways to gain hands-on experience before your first professional role. For instance, volunteering with non-profits or joining apprenticeship groups can provide valuable practical experience.

Fourth Semester: Business Impact

The final semester integrates everything you’ve learned and prepares you for the later stages of your career. This phase offers a framework for long-term career planning, including considerations about moving into management. It also deepens your understanding of how UX aligns with business objectives, ensuring you can effectively contribute to your organization and its products.

SPUR 311: Business Communication II

• Made to Stick: Why Some Ideas Survive and Others Die by Chip Heath and Dan Heath

• The Pyramid Principle: Logic in Writing and Thinking by Barbara Minto

The first semester on communication focused on clarity; this one focuses on impact. Made to Stick by Chip Heath and Dan Heath explores how to make ideas simple, concrete, and emotionally engaging. Barbara Minto’s The Pyramid Principle provides a method for logically structuring your thoughts.

For further exploration, refer to past articles on using rhetorical pillars in research readouts and a three-step framework for crafting your reports. These resources will help you communicate your insights more powerfully and persuasively.

SPUR 330: Career Development I

• The Making of a Manager: What to Do When Everyone Looks to You by Julie Zhou

• The Long Game: How to Be a Long-Term Thinker in a Short-Term World by Dorie Clark

As you advance in your career, you’ll approach a number of forks in the road. Should you pursue a managerial role or remain an individual contributor?

Dorie Clark’s The Long Game provides strategic guidance on achieving your long-term career goals, with her other books offering additional tools to help you along the way. For those considering the leadership path, Julie Zhou’s The Making of a Manager offers valuable insights into transitioning to people management. Even if you decide to remain an individual contributor, this book will enhance your understanding of managerial responsibilities, promote more productive relationships with your manager, and show ways to offer effective support.

SPUR 350: Organizational Finance

• Cost-Justifying Usability: An Update for the Internet Age by Randolph Bias and Deborah J. Mayhew

• The HBR Guide to Finance Basics for Managers

Many researchers enter the field driven by ideals of improving user experiences and well-being. But balancing these aspirations with business realities—dollars and cents—keeps us gainfully employed. 

Bias and Mayhew’s classic book, Cost-Justifying Usability, helps bridge this gap, guiding researchers to align their work with business goals, secure more resources for their teams, and maintain their organization's value. The HBR Guide to Finance Basics for Managers provides a solid understanding of financial fundamentals, enabling you to articulate the value of UX initiatives through financial data, making it easier to secure budgets and resources.

Key Activities: Complete your Capstone

As the program concludes, it's time to wrap up your capstone project. Complete your data collection, analyze your findings, and create a research deliverable to present to your stakeholders.

If you’re seeking your first role, compile this work into a compelling UX research case study. This will showcase practical application of your knowledge, making you a stronger candidate in the job market.

Putting it all together

Many of the books included in this curriculum are part of real college and graduate courses — but they have been written for a mass audience, making them accessible and easier to understand. As of publication, the total cost for all these books is just over $600 (USD), a worthy investment of your professional development budget should your organization provide one. (You can also save money by purchasing second-hand copies or borrowing from a library.)

Having finished this self-paced program, it’s time to apply to jobs and build upon your learning with real world experience. It’s possible the experience will inspire you to consider pursuing a formal graduate degree. Regardless of your path, the key is to continue learning.

From the archives

User research is a creative profession.

We’re not artists, by any means. But our success as researchers hinges on the ability to apply innovative thinking to solve complex problems — often multiple times each day. Overlooking the creative aspects of our role can limit our usefulness: you don’t think to improve skills you don’t think you need.

This article explores strategies for generating and enhancing ideas within the context of everyday UX research tasks. From formulating research questions to navigating logistical challenges and delivering design recommendations, you’ll learn how to approach problems from multiple angles.

We’ll offer a framework for creating, refining, modifying, and maturing ideas, providing concrete examples and actionable tactics along the way. These strategies in hand, you’ll be better prepared to tackle research challenges and impress stakeholders with your ability to think outside the box.

Strategies for idea capture

The best ideas often happen outside of structured brainstorming sessions.

Cultivating environments that foster spontaneous thought is key. For example, the solitude of showers or outdoor physical activities like walking or running can engage the default mode network associated with divergent thinking by reducing distractions and providing sensory calmness.

You might find, for example, that ideas emerge spontaneously after setting aside work on a research report for the day. Capturing these might enrich the suggestions for continued research in your reports and deepen stakeholder engagement.

Develop a system and habit for capturing these ideas whenever they occur. Options include carrying a simple notebook, using a digital note-taking app, or a more sophisticated system like Obsidian. The goal is to create consistency and reduce friction with a daily routine.

Group and individual brainstorming

One technique for generating many ideas is to set a target of writing a list — for example, ten (or even twenty) ideas for a given prompt. The key is to push past your initial, obvious thoughts and reach deeper, even if some feel imperfect or ridiculous. Often, the process of documenting "bad" ideas can paradoxically lead to uncovering valuable ones.

For instance, imagine you were tasked with assessing trust and hesitations in a study on emerging tools like AI, where prior research may be sparse. A solo brainstorming session aimed at generating at least ten initial concepts could serve as a fruitful starting point.

In a group brainstorm, start by having each participant independently create a list of ideas. Afterwards, look for common themes and unique ideas by sharing together in a non-judgmental, open forum.

If you’re working alone or on a strapped team, you don't always have the luxury of collaborators to bounce ideas off of. LLMs like GPT-4, Claude, or Gemini can help. Craft a clear prompt describing the challenge, and consider providing your initial list of ideas as an example. The output won’t usually match a human partner, but it can offer fresh perspectives and push you towards new lines of inquiry.

For example, after detailing your findings, you might prompt an LLM to anticipate potential questions or hesitations from stakeholders. Incorporating these insights can preemptively address potential issues in your final report, creating a stronger final product.

Selecting top contenders

After broadening your pool of ideas, the next step is to refine and focus on the most viable options. 

First, articulate (or revisit) your goals. What are you trying to accomplish with this exercise? Defining objectives helps to discriminate between ideas. Consider feasibility, alignment with goals or KPIs, less tangible effects like emotional impact, and any other relevant factors.

For example, when working with complex data visualizations, you’ll want to select ideas that clearly communicate the intended message to your audience. Or if you’re looking for ways to recruit a hard-to-reach demographic, some of the key concerns might be budget, timing, and viability.

With goals in mind, decide how deeply you’ll analyze the options or whether a more intuitive gut reaction is best. As a rule of thumb, allocate time proportional to each idea's level of refinement: a rough concept deserves less scrutiny than a detailed proposal. But make sure originators have a chance to properly explain ideas that may have been imperfectly worded.

Decision-making can be democratic, where each participant has an equal vote, or it can be centralized, with a designated "decider" who makes the final call after considering group input. This choice depends on the need to avoid groupthink or when specific stakeholder approval is critical. Define these sessions with clear agendas and time limits, even setting a timer for activities like dot-voting to keep things moving.

As you refine your process over time, these strategies will enable you to transform broad concepts into actionable solutions more efficiently.

Improving ideas with modifiers

When developing ideas for projects with significant implications or long-term objectives, such as annual team goals or high-stakes research initiatives, applying creative modifiers can unearth the full potential of your most promising ideas. Here are some strategies to expand and refine these ideas:

• Scope variations: Explore different scales of your idea. For instance, how would the project look as a minimum viable product? Alternatively, consider its form if the scope were doubled. Think about both pared-down versions and more ambitious, comprehensive plans.

• Inversion: Flip your idea on its head for a new perspective. You might, for example, consider how one could theoretically sabotage your project. This exercise can reveal valuable insights into how to strengthen the approach.

• Interdisciplinary lenses: Integrate viewpoints from diverse fields. How would a florist, physician, or even a medieval warlord tackle this challenge? This could involve collaborating with team members from various backgrounds or using LLMs to simulate alternative perspectives. 

• Find the core: Identify the essential attributes that make your idea appealing or problematic. Develop new concepts that amplify these strengths or address the weaknesses.

• Use the Creative Matrix: Particularly useful when the problem space involves multiple intersecting dimensions. Separate the facets along two axes, then systematically map out ideas at each intersection point on the resulting grid.

• Oblique Strategies: Try experimental musician Brian Eno’s method of using a (physical or digital) deck of cards with creative prompts to disrupt conventional thought. This can be a fun, serendipitous way to uncover new angles that might not occur in a more structured brainstorm.

• Vanishing Options: It often seems you can keep squeezing toothpaste from a tube long after it’s “empty.” Similarly, once you feel you've exhausted all possibilities, ask what you could do if all current options were off the table, an approach popularized by Chip and Dan Heath.

These modifiers are intended to stretch and challenge your initial concepts. This step isn’t strictly necessary in all cases, but the results are often worth it for important situations with a long shelf-life. 

Taking ideas to maturity

As ideas start to encounter reality, they get scrutinized from multiple angles. Like polishing a gemstone, this process wears away rough edges, revealing the concept's inner essence and luster.

This can be as simple as the passage of time, a phenomenon known to cognitive psychologists as the incubation effect. After breaks with seemingly irrelevant stimuli like task-switching or rest, problem-solving improves — particularly for "insight" problems where solutions emerge suddenly through unexpected associations and connections.

For instance, if you're deadlocked with stakeholders on the project’s direction, pausing the discussion can not only diminish tensions but allow new ideas to surface. It can also improve most communications. I've never written anything that wouldn't have benefited from sleeping on it and returning with fresh eyes.

Another method is to iteratively discuss and refine your ideas in the context of everyday conversation. For example, when trying to name a new persona group, you might casually introduce potential names to colleagues to gauge their reactions. This feedback helps refine your choices.

Here's another area where LLMs can serve as inexpensive sparring partners. For instance, you might seek summary feedback on a research report from the LLM. Be specific about the type of critique you want — for instance, request perspectives from different stakeholder roles like product managers, designers, or executives. This can help identify the more obvious blind spots and strengthen your presentations.

The bottom line

Seldom acknowledged, many aspects of user research require creativity — that is, innovative thinking to solve complex problems.

This article outlined a framework for generating, selecting, and refining ideas that support these dimensions of our work:

• Cultivating: Place yourself in environments where the mind is likely to wander, and make a habit of capturing new ideas as they arise.

• Broadening: Use solo and group brainstorming sessions to rapidly expand your set of ideas in response to a prompt.

• Selecting: Define goals, criteria, and an approach for evaluating ideas, setting ground rules for group settings.

• Modifying: Scale ideas up and down, invert them, and filter them through lenses to generate modified variations of the best ideas.

• Maturing: Let the passage of time and feedback from others smooth the rough edges from finalists.

Depending how critical or long-lasting the solution is likely to be, you can use all or part of this framework. It’s an intense process, but ideas that endure this evolutionary gauntlet become brilliant.

Mastering these brainstorming techniques can help you overcome logistical hurdles, develop insightful research questions, articulate compelling design recommendations, and consistently deliver innovative, thoroughly-vetted solutions. Regularly stretching these creative muscles will make you an indispensable member of any research team. 

From the archives

Most UX Researchers I know avoid meetings like the plague. Especially when the goal or agenda is unclear, we hate to waste our time on anything that takes us away from what we do best — studying our users and bringing insights to our teams.

But once you’ve pruned the unnecessary events from your calendar, there will still be meetings where your attention or contribution is useful. These are unavoidable, but they can be improved. And one way to do that is to structure them around templates.

Having a predefined structure brings a goal and agenda to the meeting by default. It also saves preparation time and ultimately makes things predictable for all the attendees. Even for meetings that you don’t lead, you can templatize how you prepare.

In this article, we’ll offer templates for some of the most common meetings UX Researchers have, covering everything from team updates to career development.

Meetings with your team

The daily stand-up is a staple on digital product teams, but can easily drift into unfocused territory. Maintain sharp concentration with a simple yet effective template:

• Focus areas: Share your main goals for the day and any major project updates.

• Blockers: If you're facing an obstacle, don't hesitate to ask for assistance. This is where the collective expertise of your team shines.

• Personal share (optional, but encouraged): Especially in remote work cultures, foster team connection by briefly sharing something personal, (e.g., an exciting project, a fun weekend plan, etc.).

For efficiency’s sake, consider the following tips:

• Keep it brief: Just because it occupies a 30-minute block on the calendar doesn’t mean it needs to take up the whole time. If done efficiently, even a 15-minute stand-up can be valuable.

• Make it predictable: Have a designated meeting leader, such as the team manager, and establish a consistent member update sequence (e.g., alphabetical order).

Commonly misunderstood, one-on-one meetings with a supervisor are less a venue for detailed status updates, but more an opportunity to look at the big picture. Think about these three core areas:

• Organization and team strategy: Gather any insights from your manager about the team or company's direction. Also ask, "Is there anything I can specifically do to support your goals?" This shows initiative and sometimes creates opportunites for advancement.

• Targeted feedback: Present a recent challenge you faced (with specific examples) and solicit constructive feedback from your manager, helping you to continuously improve.

• Career trajectory: Openly discuss your aspirations. Understanding the path to promotion and the necessary skills will guide your development.

Meetings about your research

Stakeholder interviews are often useful for understanding a project’s true purpose and the underlying assumptions that drive it. Here’s what to focus on:

• Do your homework: If possible, gather background information about your stakeholders (personality, past projects, etc.). This will allow you to tailor your questions and head off potential concerns.

• Context and perspectives: What’s the background of this project? Why is it happening now? What decisions will this research influence? Pay close attention to any discrepancies in perspectives among stakeholders.

• The core problem: What specific issue are you aiming to solve with this project? This is an opportunity to uncover their problems and understand their assumptions.

From there, a successful study kickoff meeting gathers all the logistical information and other specifics you need to get going. In a previous article, I’ve modeled language for how to elicit this information, but here’s a checklist to make sure nothing is overlooked:

• Methodology: What research methods will be employed?

• Participants: Who are your target users? How many will you recruit, and how will you find them?

• Stimuli: Will you use a live site/app or a prototype? Address all access logistics (logins, test accounts, etc.).

• Timeline: Are there any date changes? Account for holidays, potential delays, etc.

But an often forgotten element here is the importance of demonstrating your competence, communicating clearly, and assuring stakeholders that the project is being left in good hands.

• Communication: Some people live on Slack, while others won’t advance the project without a meeting on the calendar. Establish your preferred communication channels and meeting frequency.

• Prior materials: Be ready to discuss any relevant work you’ve done in a similar domain, or with the same methodology. If applicable, request access to existing research questions, scripts, or other documents.

Meetings about your career

Informational interviews help expand your professional network and can offer valuable insights for career development. Here's how to make them count:

• Define your goals: Vague questions like “What’s your top piece of advice?” and “What should most people ask you but don’t?” can be irritating. Instead, ask yourself what your current challenges are and where you envision yourself professionally, then prepare a handful of questions relevant to that.

• Do some preparation: Research your interviewee's background (e.g., by using their LinkedIn or personal website) and ask about specific experiences relevant to your goals (e.g., "How did you transition from [X] to [Y] role?").

• Be a resource, not just a taker: Informational interviews are a two-way street. Can you share insights from your own experience, offer to provide feedback on their work, or refer them to potentially useful resources or events?

As the recent volatility in the tech sector has painfully shown, it's wise to keep your options open and take requests for job interviews when they come in. Here's a proactive approach to early stages of the of the interview cycle, such as the phone screen or technical and behavioral interviews:

• Prepare the highlight reel: Have concise summaries ready for each role, describing key accomplishments and areas of expertise. Have a story or two ready to go when time allows to add depth and humanity.

• Anticipate FAQs: Practice your answers to frequent questions like "What are your career aspirations?" and "Why are you interested in this role?" Use compiled lists of interview questions for UX Researchers for further inspiration.

• Your assessment: Prepare insightful questions about the company's culture, team structure, and expectations to evaluate if the role aligns with your goals.

As you advance to later stages of the interview cycle, the agendas will largely be set for you. Still, remember the importance of making sure it’s the right opportunity for you. Prepare questions to gauge if a role aligns with your aspirations:

• Define your ideal role: What are your priorities? (e.g., team size, company culture, mentorship opportunities, UX maturity). Ask questions to assess if the company matches your vision.

• Team dynamics: Learn about the working style and processes of the team through questions about communication expectations, performance reviews, learning and development, etc.

• Typical day: Gain a realistic understanding of day-to-day responsibilities through questions like, "Could you describe a typical workweek?"

The bottom line

A good meeting has a clear goal and structure.

Rather than reinventing the wheel for each meeting or defaulting to whatever was done before, consider a templatized approach. This method ensures that time spent away from research is well-spent and contributes to the larger goals. 

In this article, we showed examples of templates in three common meeting types: team meetings (1:1s and standups), research meetings (stakeholder interviews and project kickoffs), and career meetings (informational and job interviews). However, this approach could be applied to any routine meetings that might be held within your team or in the context of your projects.

It can be challenging to try and impose a structure on a meeting that you don’t lead. In such situations, communicate the potential benefits of using a meeting template (e.g. “We’ll reach our goals more quickly and efficiently!”). You’ll often find that people are receptive to ideas to improve an otherwise ineffective meeting.

By strategically employing meeting templates, UX Researchers can optimize their meeting processes, reclaim valuable time, and ultimately elevate the quality of their research.

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If you’re a Mac person and you’ve ever needed to use Windows, you probably had a bad time. That's not a swipe at Microsoft: the same holds in reverse. But why?

Many habits don’t transfer from one environment to the other. On Windows, closing an application window quits the application, whereas macOS separates these functions. Between the two, the buttons to minimize a window are on opposite corners. In short, you need different cues and behaviors to accomplish even simple tasks.

Yet there are many other experiences where prior habits and knowledge will actually give you a boost. As UX professionals, we need a systematic way to understand how those past experiences can influence interactions with new products.

This article examines skill transfer, how it manifests as positive, neutral, or negative, and how it affects learning and performance. We’ll talk about how product development teams can promote the most effective kind of transfer, and discuss ways UX Researchers can measure transfer to help toward that goal.

What is transfer?

If you’ve worked in UX for any length of time, you’ve heard the term "intuitive" used to describe our ideal outcome. But what exactly does it mean for an interface to be intuitive?

My favorite definition comes from Jef Raskin, a human-computer interaction (HCI) expert who led the early development of the Apple Macintosh computer. According to Raskin, an intuitive interface leverages users' pre-existing skills and knowledge, allowing them to transfer what they already know from other contexts to effectively interact with the new interface. What this means is that our initial exposure to new products is never a true blank slate. We bring to it all our prior knowledge and experiences gained from interacting with other products, both similar and dissimilar. 

We can operationalize this definition using a concept from educational psychology: transfer of learning. It describes how prior knowledge or experience in one setting affects learning and performance in another. There are three main types.

Positive transfer occurs when prior knowledge or experience helps an individual understand and perform better in a new context. This is common when the contexts are related or similar. Examples:

• Many successful NFL quarterbacks, such as Colin Kaepernick, Dan Marino, and Troy Aikman, were also talented baseball pitchers. Both roles require the ability to throw a ball quickly and accurately.

• Learning a Romance language like French after having studied Latin, as French directly descends from Latin and shares many linguistic roots.

• Google Sheets and Microsoft Excel have similar interfaces and functionality. If proficient in one, learning the other is likely to be relatively quick.

• JASP, a free and open-source R-based statistical package, mimics the appearance of the more expensive SPSS. For those who may have used SPSS in graduate school but can no longer afford it, picking up JASP will come quickly.

Neutral (or zero) transfer describes when prior knowledge has little or no impact on learning in a new context, often due to a lack of similarity between the two contexts. Examples:

• As Michael Jordan famously learned, the explosive power, vertical jumping ability, speed and endurance required in basketball don’t translate cleanly to the unique demands of baseball.

• Learning Arabic won’t be any easier for Latin students, as the two languages share virtually no common linguistic roots or structures.

• Experience with Microsoft Excel or SPSS won’t give much of an advantage or disadvantage to those learning other specialized applications like Photoshop or Final Cut Pro, since they serve different purposes with different interfaces.

Negative transfer, then, is when prior knowledge or experience hinders learning and performance in a new context. This happens primarily when habits and behaviors rewarded in one context are counterproductive in the other.

We’ve already mentioned the differences between macOS and Windows as an illustrative case here, but a few more examples include:

• Learning to drive in the United States and then driving in the United Kingdom, where traffic flows on opposite sides of the road, can lead to negative transfer of driving habits.

• Learning to type on a Dvorak keyboard layout after being proficient with the standard QWERTY keyboard layout. Although the Dvorak layout positions the keys more efficiently, learning a new arrangement amounts to relearning to type.

• Encountering false cognates (words that appear similar but have different meanings) when learning a new language can cause negative transfer from one's native language.

In UX, we strive to promote positive transfer while minimizing negative transfer. By understanding the past experiences and mental models of users, we can tailor our designs to align with their expectations, making our products easier to learn and use.

Promoting positive transfer

Product teams can leverage two key strategies to promote positive transfer and enhance user experiences:

The first is designing for familiarity and consistency.

The goal is to intentionally incorporate design patterns, conventions, and interaction models that are likely to be familiar to a significant portion of the target user base. In principle, this is similar to one of Jakob Nielsen's usability heuristics, “Consistency and Standards.” This can be as simple as adopting recognizable icons for common actions like save, print, and undo/redo in productivity tools and office suites.

The second strategy is to provide effective training and support.

For complex products requiring training, focus on helping users build mental models that facilitate positive transfer. Explicitly highlight connections with familiar concepts and processes from other contexts. For example, if your team were building a video editing tool, you might relate key functions to similar tools users may have used before, like Microsoft Word's track changes for editing. 

Incorporate hands-on exercises that mirror real-world scenarios, to allow users to practice applying their developing skills in a controlled environment, such as having them edit a sample video project.

The goal of an “intuitive” interface becomes concrete and achievable when teams intentionally design for familiarity.

Measuring transfer in user research

One of the best ways UX Researchers can contribute to the goal of promoting positive transfer is by measuring it well. To do that, researchers have a few key considerations during study preparation, design, and analysis.

For a start, we want to recruit participants with relevant experiences.

During the screening process, researchers should ask about experience using similar products or tools. For example, if you’re studying the usability of a smart home device, it might be valuable to ask candidates about their experiences with other smart home devices, voice assistants, or chatbots. Depending on your goals, it could be useful to recruit participants with both low and high familiarity.

The example above highlights a few best practices when asking about relevant experience.

If you’re working in a complex space, it’s sometimes necessary to include definitions and explanations when a question includes unavoidable technical jargon. Further, we ask about a specific time frame, since participants may have extensive, but dated and no longer relevant experience. Instead of asking participants to rate their own proficiency, which might often be unreliable, it may be better to ask about frequency of use. And since inattentive responders can compromise your data quality, it’s typically helpful to include a distractor choice, like the fictitious CRM ZenConnect.

As you move into study design, a few choices will depend on your goals:

• Align the tasks: If measuring the effect of training or onboarding, ensure that the tasks participants perform align with the content covered in the training materials. For instance, if examining the helpfulness of an introductory onboarding flow, follow-up tasks should include scenarios similar to those covered.

• Measure both contexts: To get a stronger measure of transfer effects, assess participants' performance in both the original or baseline context and the new context within the same study. For example, in a redesign validation study, test performance on both the original or baseline design and the new design variation.

• Prefer behavioral measures: Relying solely on self-reported performance measures can lead to inaccurate conclusions. Instead, aim to collect objective performance data. In an unmoderated usability study, for example, participants could be asked to rate their confidence in successfully completing a task, while their actual success is verified through URL validation or knowledge-based validation questions.

During analysis, researchers have a few tools to quantify transfer effects.

Although correlations and statistically testing means through t-tests or ANOVAs can help, my recommendation is a regression. The resulting coefficient of determination or R² shows how much performance on the product we’re building is predicted or explained by performance from another context. Interpreting a single result in isolation can be challenging, but accumulating multiple coefficients of determination allows for a better understanding of the relative strength and consistency of the relationship.

By carefully considering these factors during study design, execution, and analysis, UX Researchers can generate valuable insights into the role of transfer in shaping user experiences with products and interfaces.

The bottom line

Transfer of learning describes the phenomenon where prior knowledge and experiences influence our ability to learn and perform in new contexts. There are three types: positive, where prior knowledge enhances performance; neutral, where prior knowledge has no significant impact; and negative, where prior knowledge hinders performance.

This article discussed a few key ways product teams and the UX Researchers who support them can promote positive transfer:

• Leverage familiar design patterns, metaphors, and interaction models aligned with users' prior experiences.

• Provide targeted training and support that explicitly connects new concepts to users' existing knowledge.

• Design research studies that accurately capture and measure transfer effects across different user segments.

• Analyze study data to quantify the impact of transfer and identify areas for optimization.

When stakeholders and colleagues request a more "intuitive" design, they often mean one that utilizes positive transfer. By operationalizing this definition and implementing the theory and strategies discussed here, teams can more reliably create intuitive interfaces.

ANOTHER THOUGHT…

The single-item usability scale to rule them all?

Is the best UX rating scale also the shortest?

If you've ever needed a way of measuring perceived usability, you've probably used the System Usability Scale (SUS) or newer, shorter instruments like the SUPR-Q or UMUX-LITE. But you may be less familiar with the Adjective Rating Scale, a single-item questionnaire developed by Bangor, Kortum, and Miller in 2009. It asks just one question: "Overall, I would rate the user-friendliness of this product as:" and then provides 7 adjective responses, from "Worst Imaginable" to "Best Imaginable."

Originally developed as an adjunct to the SUS to help practitioners interpret its results, there may be a good case for using it by itself. According to a recently published article in the journal Human Factors, it could do the job of much longer scales. The authors concluded that it "was as good as, and sometimes significantly better than, all other measures of perceived usability considered here at representing the difference in usability between a good-usability system and a poor-usability system."

If you're curious to read more, check out the full article (paywalled) on Human Factors. And let me know about your experiences using it.

At the start of their careers, many UX professionals have grand aspirations.

We want to make technology easier to use, help our organizations become customer-centered, and perhaps even make the world a better place. But the daily grind of UX work can often feel far removed from these lofty ideals, causing many to lose sight of their original vision.

In 1986, Richard Hamming, a renowned Bell Labs research scientist, delivered a seminal talk entitled “You and Your Research,” on why some scientists make significant contributions while others fade into obscurity. And though the methods and goals of user research may look very different, Hamming's wisdom is applicable to anyone striving to do outstanding work.

This article highlights five key principles from Hamming’s speech, offering practical advice for applying them in the context of UX research. By incorporating his ideas into our philosophy as practitioners, we can create lasting impacts for users, teams, organizations, and the UX field as a whole.

Pursue work of significance, instead of relying upon luck

“I will cite Pasteur who said, ‘Luck favors the prepared mind.’ And I think that says it the way I believe it. There is indeed an element of luck, and no, there isn’t. The prepared mind sooner or later finds something important and does it.”

What raises some individuals to the level of “greatness” in their field?

Were they simply in the right place at the right time? Was it pedigree or other circumstances? Hamming challenged this notion, asserting that breakthroughs aren't mere strokes of luck. Instead, he emphasized the importance of intentionally pursuing meaningful work. 

Early in my career, I worked for a UX consulting agency with strong quantitative positioning. But a more senior colleague on the team thought he’d be a more compelling candidate for his next role by ramping up his qualitative research skills. Though less frequent, he actively sought out those projects. The initiative paid off, ultimately securing a coveted role for him at a major social media platform. Rather than passively waiting for opportunity to knock, he vocalized his preferences and took steps to align his work with his goal.

I’ve also known graduate students who looked for smaller opportunities that could grow bigger. Rather than waiting for a job offer as a metaverse researcher, for example, they began cultivating expertise in virtual reality systems while still in school. Knowledge and experience built in academia isn’t always 100% transferable to industry roles, but it’s nevertheless a strong foundation to build upon. And it made them more competitive when they sought out internships.

Don't wait for luck to dictate your path. Look for opportunities where you are to advance towards your larger ambition. Small steps in the right direction add up over time.

Intentionally seek important problems

“I started asking, ‘What are the important problems of your field?’ And after a week or so, ‘What important problems are you working on?’ And after some more time I came in one day and said, ‘If what you are doing is not important, and if you don’t think it is going to lead to something important, why are you … working on it?’”

Hamming often posed these challenging questions to his colleagues in the cafeteria. This habit didn’t make him very popular, but it highlighted his belief that meaningful contributions come from tackling important problems. As he succinctly put it, "If you do not work on an important problem, it’s unlikely you’ll do important work."

Defining what constitutes an “important problem” in our context will depend to some extent on the individual. While advancing the field as a whole through public-facing work is one avenue, for many researchers, the focus will be on the team or organizational level. 

Depending on one's role and career stage, focusing on high-value projects for stakeholders, starting a team at a startup, or working on projects to improve the organization's UX maturity might constitute important problems.

There will always be urgent tasks demanding our attention, making it all the more important to take a step back. Even if immediate circumstances don’t allow for a dramatic shift, simply posing this question can gradually shift one's focus towards working on problems that matter.

Courageously follow independent thought

“One of the characteristics of successful scientists is having courage. Once you get your courage up and believe that you can do important problems, then you can. If you think you can’t, almost surely you are not going to.”

Hamming champions the courage to pursue independent thought, even in the face of conventional taboos or the fear of failure. By daring to ask "impossible" questions, researchers can unlock breakthroughs where others have faltered.

This ethos extends to the studies we take on as researchers. For example, we often think of the methodologies we use as though they were handed down from on high. Personally, I’ve found that the standard Kano functional/dysfunctional questions can be confusing to participants, and have seen improved results by tweaking and making variations. It often pays to adapt an approach to suit the needs of your project, whether or not it’s the accepted version.

Hamming emphasizes another characteristic shared by great scientists: the ability to tolerate ambiguity. Sometimes, the outcomes of a study may not align with existing theories or previous research. It is crucial to deeply reflect on these apparent contradictions, as they can have profound implications for our stakeholders.

Thinking independently and courageously means embracing the unknown, challenging conventional wisdom, and persisting in the face of complexity — key elements for achieving breakthroughs in research.

Make the most of the time, resources, and limitations you have

“Often the great scientists, by turning the problem around a bit, changed a defect to an asset. For example, many scientists when they found they couldn’t do a problem finally began to study why not. They then turned it around the other way and said, ‘But of course, this is what it is,’ and got an important result.”

We all have a tendency to excuse our failures with less-than-ideal circumstances. But as Hamming points out, great scientists have mastered the art of turning constraints into advantages.

Many teams today lack the capacity to satisfy every request for research. After prioritizing the work, what options do you have for meeting the demand? If used in a principled way, artificial intelligence can help offload or improve certain tasks, like extracting data or wordsmithing reports. Similarly, with the proper guardrails in place, teams might consider training non-researcher stakeholders who are willing and able to assist.

Lack the ideal tech stack? Get creative with what's available. Explore cost-effective tools, and see if you can combine the tools that your organization already uses to meet your goals.

This approach can be applied to any resource that you may lack. Once, when my team faced an impossible deadline, we used our two usability labs in an unconventional way. By staggering our participants’ scheduled interviews across the two, we minimized the impact of no-shows and sessions that went long or ended early. We were able to finish data collection on time and set an internal record for sessions in a day.

Also look for ways that colleagues can balance out your personal strengths and weaknesses. One of the best working relationships in my career was a product manager colleague who was able to act as our champion from the outside. 

By embracing limitations and finding strategic allies, researchers can both navigate challenges and achieve meaningful results.

Accept that selling your work is part of the job

“I have now come down to a topic which is very distasteful; it is not sufficient to do a job, you have to sell it. ‘Selling’ to a scientist is an awkward thing to do. It’s very ugly; you shouldn’t have to do it. The world is supposed to be waiting, and when you do something great, they should rush out and welcome it.”

Hamming reminds us that our organizations won't automatically recognize our achievements. To be effective, we have to push both our interests and our value.

Any salesperson worth their salt will take the time to ensure the conversation they’re having is with someone who can advance the deal. Likewise, researchers should be intentional about cultivating relationships with stakeholders who can advance the cause of UX research within the organization.

As you develop those relationships, identify how your work can contribute to your stakeholders’ outcomes, whether through meeting deadlines, shipping features, or achieving performance metrics. By aligning your work to their objectives, you establish yourself as an invaluable part of their success.

We should also lead with the advantages we provide — informing decisions, reducing risk, and creating empathy for the user — rather than our “features” (like a well-executed quantitative benchmark). Rather than focusing on methodology, emphasize the benefits your research offers in solving specific problems aligned with stakeholders' immediate and long-term goals.

Hamming makes a special point here on the importance of informal communication. There will always be the need to give a formal readout, but informal interactions can be more influential on decisions. Don't shy away from speaking up in meetings or offering input, even when your role there may seem ambiguous.

By “selling” the work you do, you help ensure it receives the recognition it deserves.

The bottom line

It can be easy to sit back and put your career on autopilot. That’s even more true during tough economic times. But a key theme running throughout Richard Hamming’s talk is approaching your work with deep intentionality:

• Don't rely on luck: Take advantage of present opportunities to build your skills and achieve your goals, whether it's mastering a new subspecialty or working in a specific industry.

• Define meaningful work: Identify what "working on an important problem" entails for you, whether through research or team development. Asking yourself this question can guide your efforts, even if you're not currently tackling major issues.

• Don't let fear hold you back: Challenge traditional approaches and explore creative solutions, such as digging deeper into conflicting data or embracing alternative methods.

• Seek creative solutions within constraints: Look for innovative approaches to meet goals, even with limited resources or staffing.

• Advocate for yourself and your work: Build relationships with supportive individuals and assertively communicate your value in decision-making meetings.

The full speech by Richard Hamming covers much more, like navigating bureaucratic hurdles and resisting the urge to conform, and includes personal experiences from his career as a research scientist. If you’ve found this summary intriguing, I encourage you to give the entirety of "You and Your Research" a close read.

In the end, receiving advice is easy, but striving for greatness as a UX Researcher remains a challenge. It falls upon each of us to harness our creativity and resolve in implementing Hamming's strategies to contribute to both our organizations and the broader field of UX research.

But as Hamming notes, “the value is in the struggle more than it is in the result. The struggle to make something of yourself seems to be worthwhile in itself.”

When you’re standing at an intersection looking to cross the street, how many times do you press the walk signal call button? Do you press it repeatedly, or is once enough?

Unless you’re in a position to know the truth of the matter — say, you helped to design and implement the signal call — your answer depends on your beliefs about how it works.

Multi-pressers operate on the assumption that presses accumulate, signaling stronger volume of demand. Thus, the signal is more likely to change or even to do so sooner. Single-pressers believe that the system either has a request to change the signal or it doesn’t, so another press during the same traffic cycle won’t add any benefit.

The walk signal, like many of the systems we use each day, doesn’t lay out how it works to any satisfying level of detail. In the absence of information, these emerging beliefs are called mental models.

Let’s take a closer look at what mental models are and ways you can measure them in your user research.

What are mental models?

As a child of the ‘90s playing video games on consoles like the Super Nintendo and Genesis, one common issue you’d encounter was glitchy screens when you turned it on. Many of us would take the cartridge out and blow on the contacts to clear the dust. Unfortunately, we later found out that this could add moisture and do more harm than good. As it turns out, all you needed to do was simply remove and reinsert the cartridge to realign the contacts.

This anecdote highlights two key qualities of mental models.

First, they’re beliefs about how something works, which may not always be factual or complete, and may even be entirely wrong. Accuracy isn't always necessary, particularly in complex B2B settings where users don't need full insight into back-end processes. What matters is that the mental model is accurate enough to empower users to achieve their goals, even if it's only analogous or informationally equivalent to reality.

Second, mental models change how we interpret system feedback, and thus affect our decision-making and later behavior. So understanding and aligning the system with users' mental models can have an outsized impact on the user experience.

While no two users have identical models, there are often similarities or themes observed within a user base. Going back to our example of the walk signal, we might broadly classify users into single-pressers or multi-pressers — but a closer inspection would show nuances, with individuals having varying levels of detail and formality in their mental models. Nevertheless, identifying common or representative models within a user base can help inform design changes.

Mental models are also adaptable and change based on experience. An influential human factors study led by Roger Schvaneveldt that examined fighter pilots revealed distinct mental model structures between novices and experts. Novices had more diverse models, while experts held more similar models.

This flexibility presents an upside: with training or intuitive system design, users' mental models can refine and become more accurate over time. For instance, the market success of the Nest thermostat can be partly attributed to its interface aligning closely with users' mental models, unlike traditional competitors. This underscores the potential impact of considering mental models in system design.

Approaches for measuring and analyzing them

There’s a wide range of methods for assessing users’ mental models, each with its own set of advantages.

The think-aloud protocol offers a glimpse into users' thought processes as they interact with a system. Participant utterances can reveal mismatches between their expectations and the system's outcomes, shedding some light on their underlying mental models. This approach, favored by Jakob Nielsen, involves a moderator periodically prompting users to verbalize their thoughts and expectations during a usability test. Across multiple participant sessions, recurring themes in user mental models often emerge.

Another method, promoted by Nikki Anderson-Stanier, is the retrospective interview. Here, users are asked to recall past experiences and describe their thought processes, pain points, expectations, and goals. This method is particularly useful for scenarios where stimuli are unavailable, or for multi-channel experiences that might be difficult to replicate in a usability test.

You can also pose a series of "what-if" questions to participants. Stephen Payne's research on ATMs used this approach, asking participants to predict outcomes based on various inputs. Through these questions, he discovered that participants varied widely in their beliefs about the information stored on debit cards.

Pairwise comparisons for relatedness ratings involve documenting all the possible inputs and outputs and having participants rate the closeness of each pair on a scale. If you were measuring mental models of Photoshop, for example, you might include concepts like “layer,” “blur,” “smudge,” and so on. As the list of concepts grows, you run the risk of fatiguing participants. For 5 items, they only need to make 10 comparisons — but for 15 items, that grows to 105! The final output is a matrix showing the relative strength of each pairing.

Card sorting offers a different perspective by grouping items based on similarity, making it suitable for scenarios where distinct categories are expected. For a complex system with many elements, this simplifies data collection by giving participants a more manageable task. 

Pathfinder networks may be useful for analysis and visualization. This approach mathematically prunes away weakly related ideas, highlighting the essential. The resulting figure makes it easy to compare the mental models of different user groups. This approach also offers similarity and reliability statistics if you want to understand change over time. But be aware that while analysis tools are freely available, they are no longer supported, posing potential challenges for researchers.

Which measurement approach is best?

How precisely and completely you need to understand users’ mental models will depend on your research objectives. For quick usability studies aimed at informing immediate design changes, lighter touch methods like think-aloud or retrospective interviews may suffice.

On the other hand, more advanced approaches are better suited for complex applications with many inputs and outputs, or where the risk of inaccurate models is high. Unlike think-alouds and retrospective interviews, more formal methods require some advance planning and setup. For example, you may uncover incidental insights about mental models while conducting think-alouds for a different research question — but you won’t get pairwise relatedness ratings without having designed the study for that purpose.

When measuring change over time, advanced methods like Pathfinder network analysis can provide statistical comparisons between different points. But using this tool requires some technical skill.

Also consider the desired output. While think-aloud may reveal enough aspects of user mental models to fill a slide in a report deck, more thorough methods that document the inputs and outputs can be visualized as more easily digestible flowcharts and other figures.

One way you might think of this progression from think-aloud to more advanced methods is that they reflect an increasingly complete assessment of mental models. While think-aloud may capture certain aspects, advanced methods take more inputs and outputs into account for a more comprehensive view.

The bottom line

Mental models are user theories about how systems work, based on beliefs rather than facts, that influence interactions and interpretations.

Assessment methods range from basic, like think-aloud and retrospective interviews, to advanced, such as relatedness ratings, card sorting, and Pathfinder networks. Basic methods offer quick insights, while advanced ones provide detailed assessments for longer, strategic initiatives. The choice depends on your research goals, the need for precision, and whether you have outputs like flow charts or visualizations in mind.

Understanding mental models is crucial for designing user-friendly experiences, aligning designs with users' expectations and enhancing system usability.

ANOTHER THOUGHT…

Defending user research with Crabtree’s Bludgeon

When explaining the unique value of user research compared to other insights-generating functions like data science or market research, we often emphasize our mixed-methods approach. You may even find yourself explaining lofty research concepts like triangulation, and using extended metaphors about geopositioning satellites.

At the crossroads of our value in uncovering meaningful patterns lies a fundamental aspect of human nature: the tendency to seek order in chaos. Consider for example the phenomenon of pareidolia, or the strong sense that a face can be perceived in some inanimate object like clouds or an electrical outlet. Even in random noise, we're prone to perceive underlying meaning.

In fairness, facial recognition is hardwired into our neural circuitry. But we’re almost equally disposed to find logic behind meaningless data or arguments. This principle is articulated in Crabtree’s Bludgeon, which asserts that human intellect can conjure a coherent explanation for any set of seemingly inconsistent observations. It serves as a reminder of our capacity to deceive ourselves, which we can mitigate through proper research.

So next time you’re defending the work we do as UX Researchers, consider invoking faces in clouds and Crabtree’s Bludgeon.

By acknowledging our inclination to find patterns where none exist, we underscore the necessity of rigorous, multi-method research to uncover genuine insights.

What non-UX books do you often recommend to other practitioners?

You’re probably familiar with some of the classics (e.g. The Design of Everyday Things, Don’t Make Me Think…). But even if a book isn’t written explicitly for us, it can have many direct lessons.

This was a question I recently posed on LinkedIn and X (fka Twitter). Both the quality and volume of responses were surprising.

Popular recommendations

Since the books aren’t specific to our discipline, you might expect that responses would have been all over the board, with no two responses looking alike. Although there was a lot of variety, a handful of books came up time and time again as perennial favorites.

All of these were recommended by at least two commenters, and several were recommended by three or more:

• Thinking, Fast and Slow, by Daniel Kahneman. Kahneman and his late colleague Amos Tversky’s Nobel prize-winning work on cognitive biases revolutionized the way economists thought about human decision-making. He distilled six decades of research into this accessible book.

• Nudge: The Final Edition, by Richard H. Thaler and Cass Sunstein: Anton Krotov, UX Research Lead at GoodHabitz, wrote: “Very good piece on socioeconomic behavior. Really expanded my view on how humans could behave and why it is so.” Indeed, this book was once required reading for a university course I taught on User Experience methods.

• Influence, New and Expanded: The Psychology of Persuasion, by Robert Cialdini. A professor of social psychology at Arizona State University wrote this compelling summary of the enduring principles of persuasion and how they manifest in salespeople, fundraisers, con artists, and other charmers. This offers a wide range of lessons for UX professionals, whether they're looking to get ahead in their careers or give their work more impact within their organizations.

• Crucial Conversations: Tools for Talking When Stakes are High, Third Edition, by Joseph Grenny, Kerry Patterson, Ron McMillan, Al Switzler, and Emily Gregory. A New York Times bestseller and business book classic, this book equips readers with tools for having productive dialogue when emotions are hot and stakes are high. 

• Humble Inquiry, Second Edition: The Gentle Art of Asking Instead of Telling, by Edgar H. Schein and Peter A. Schein. We often consider ourselves the student of our users in learning how we can make our products work for them. What if we applied that approach to the rest of our lives? This book treats questions as a tool to build relationships and collaborate with others.

• Never Split the Difference: Negotiating as if Your Life Depended on It, by Chris Voss and Tahl Raz: Unlike a hostage negotiation, there are few if any emergencies in UX — so why read a book on this skillset? Keith Ford, Principal UX Designer at Walmart, explained: “Designers are often in a position where they need to push back and influence without authority, this book teaches proven negotiation tactics used in life and death situations and how to apply them in a corporate environment.” 

• The Culture Map: Breaking Through the Invisible Boundaries of Global Business, by Erin Meyer. The way that we work, lead, and resolve conflicts as a team is conditioned by the culture surrounding us, which can create unseen obstacles in larger companies. As Cătălina Manea, Senior UX Designer at eBay, said, “Super helpful in any instance but especially when working with global distributed teams.”

• Invisible Women: Data Bias in a World Designed for Men, by Caroline Criado Perez. In an age of biased LLMs and GenAI tools trained on big data, many are trying to understand how the data and analytics that inform their tools can harm underrepresented users. Cecile Elliott, Senior UX Designer at Suited Connector, called it an “absolutely fascinating and infuriating read.”

• The Complete Short Stories, by Anton Chekhov: Notably one of the only fiction recommendations, but from a master who revolutionized the form of the short story. Rebecca Dodd, Senior User Researcher at the Department for Works and Pensions, elaborated: “Without the keen observations of his short stories … I would never have become a user researcher. His storytelling schema complements the more commonly cited, and also very useful schema from Aristotle’s Poetics, which I also recommend to anyone in a user-centred design profession.”

• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration, by Ed Catmull and Amy Wallace. A co-founder of the animation studio Pixar describes how their pioneering approach to storytelling and film production works on a fundamental level, with practical lessons for any professional.

• Same as Ever: A Guide to What Never Changes, by Morgan Housel. What lessons can history and psychology teach us about human nature? Housel distills them down in a way that you can apply to yourself, your users, and your organization.

Selected recommendations

Although they were recommended only once, these books stood out from among the others. In most cases, the subject matter was intriguing. In several others, it was an added benefit to get the perspective of an industry veteran or thought leader:

• Genius 101, by Dean Keith Simonton . This recommendation comes from usability pioneer and author of UX Tigers, Jakob Nielsen. “In this context, ‘genius’ doesn’t mean extreme outliers like Mozart or Oppenheimer (though they are discussed),” he said. “It’s about high-functioning, high-IQ individuals, and disproportionately many UX people fall into this category and will benefit from understanding things like the lifecycle of eminent intellectual performance.”

• Understanding Comics,  by Scott McCloud. “There are conventions that have strong associations which are almost inescapable,” explained Rob Solomon, Lead UX Engineer at CivicDX. “I read that for the first time in undergrad, and it gave me the lens to look at both existing and emerging technology platforms and consider the big picture surrounding the experience (including what the “reader” is bringing to the table).” Jakob Nieslen added: “One big point in the book is how simplification by removing specifics can make communication more universal. Like the standard smiley 🙂 which doesn’t look like any human (doesn’t even have a nose) and therefore represents all of us.”

• Bullshit Jobs: A Theory, by David Graeber. Many of us in UX enter the field with high ideals about our potential to affect the world for good. It’s tempting to make our work central to our identities. This book challenges those ideas. Maria Panagiotidi, Head of UX Research at Oyster and author of UX Psychology, said, “A bit political and maybe controversial but helpful to provide some context to modern jobs in the middle of a UX job market crisis.”

• Incognito: The Secret Lives of the Brain, by David Eagleman. Another entry on the list of books popularizing the lessons of cognitive science, Maria Panagiotidi described it as a “quick intro to subconscious brain and its importance in our everyday lives.”

• Disrupted: My Misadventure in the Start-Up Bubble, by Dan Lyons. We sometimes forget from the inside how strange working in tech can be to those on the outside. Disrupted tells the personal story of a magazine writer who joined HubSpot as a marketing fellow.  Independent UX Consultant  David Hamill wrote: “It teaches you about some of the toxic and cult-like behaviours you’ll encounter in tech companies. Also it’s really funny.” 

• The Halo Effect: ... and the Eight Other Business Delusions That Deceive Managers, by Phil Rosenzweig. Corporate leaders ostensibly reach their positions on a track record of good judgment, yet examples of biased and backwards decision-making aren’t hard to find. Author and Consultant Tom Kerwin  said it shows “just how massively we misattribute causes and effects based on retrospective coherence.” 

• Stealing the Corner Office: The Winning Career Strategies They'll Never Teach You in Business School, by Brendan Reid. Tom Kerwin explained, “career progression doesn’t really work the official way it’s talked about.” This book offers an unconventional playbook for climbing the corporate ladder. 

• The Art of Possibility: Transforming Professional and Personal Life, by Rosamund Stone Zander and Benjamin Zander. “A psychologist/therapist and an orchestra conductor write how to reframe one’s thinking to be less self-limiting. It’s a great read for problem solvers and people leaders,” wrote Jeff Scott, Principal UX Researcher at Charter Communications.

• The Timeless Way of Building, by Christopher Alexander. A philosophical book about architecture that contrasts modern approaches to the more organic traditional way that towns, villages, and the places within them have grown — with lessons that apply to building software and products. Principal Product Designer Leo Vroegindewey, Product Strategist at The Triangle Offense, described it as “very heavy, but rewarding.” (He also recommended Alexander’s book, Notes on the Synthesis of Form.)

• Wabi-Sabi for Artists, Designers, Poets & Philosophers, by Leonard Koren. If you struggle with perfectionism at work, you may enjoy this recommendation from Jeremy Hulette, Design Advisor at Propportunity AG, who summarized it thus: “A short but thoughtful book on the beauty of imperfection, impermanence, and incompleteness.”

• Financial Intelligence: A Manager's Guide to Knowing What the Numbers Really Mean, by Karen Berman and Joe Knight. A recommendation from Haden Yonce, Head of Product Design at TD. If you struggle to parse the financials discussed in your company's all-hands meetings, or how you can connect the work you do to the numbers most important to the business, this book breaks things down. 

Bonus personal recommendations

These responses show how well-read UX professionals are as a group. As an avid reader myself, I'd like to offer three books that didn't turn up in the thread but that I think fit the theme well, offering powerful lessons for our work:

• Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts, by Annie Duke. A cognitive psychologist turned champion poker player describes how probabilistic thinking can help people to face situations of uncertainty and make better decisions.

• When: The Scientific Secrets of Perfect Timing, by Daniel H. Pink. Billed as the world's first “when-to” book in a world of “how-to” books, Pink describes how our internal chronotype influences daily cycles of energy peaks and troughs, and offers actionable strategies like the Nappuccino to boost your productivity each week.

• The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution, by Walter Isaacson. The biographer of Benjamin Franklin, Steve Jobs, and Elon Musk tells the story of computing and Silicon Valley, beginning with Ada Lovelace. Understanding how our profession fits into this broader history will help you contextualize your work.

The bottom line

If you’re fortunate to have a professional development budget as a benefit through your employer, this is a great way to use it up. You may be able to convince a few colleagues to pick them up so you can discuss them together.

Whoever foots the bill, a great book is a bargain — often summarizing years of an author’s work and research for the price of a modest restaurant meal.

This has been a selected list — many other books were mentioned in the threads. One thoughtful reader even compiled them all into a public spreadsheet. But the 25 listed here should get your reading list for the remainder of the year set for now.

Special thanks to all who contributed to the threads. This article contains no affiliate links.

ANOTHER THOUGHT…

Getting more out of what you read

It’s February! How are your New Year’s resolutions going?

Many people resolve to read more than they did the year before. This year, I’m trying to get more out of the books I read. For example, I’m revisiting some old favorites in the hopes that their lessons will sink in a bit deeper.

Here are a few things I’m trying that may be helpful for you:

• Best-selling author (and bookstore owner) Ryan Holiday lists his 38 rules for reading. Here’s an example: “In every book you read, try to find your next one in its footnotes or bibliography. This is how you build a knowledge base in a subject—it’s how you trace a subject back to its core.”

• The classic book How to Read a Book describes four different levels of depth that you can apply to reading, and argues you should choose the right level for what you hope to learn from the book. If you don’t have time for the full book, I recommend this Farnam Street summary.

• If you have trouble taking notes on what you read, scope it down. Commit to jotting down just the three to five biggest takeaways from each book somewhere. You’ll often find yourself inclined to write a few more after you’ve met this smaller goal.

• Further solidify your learning by turning those notes into flashcards to test your understanding of concepts and their connections. Using spaced repetition software like Anki is a simple way to regularly bring the most interesting or important lessons to mind over time. Here’s a primer for getting started.

After a year of economic turmoil in the tech sector, some User Experience (UX) Researchers are wondering if it’s time to consider a career change.

While these are concerning circumstances, layoffs and hiring freezes are coarse tools applied across companies, and may not take into account the longer-term need for our contributions. In the economy more broadly, people tend to delay major purchases during recessions, which in turn leads to a surge in spending during the subsequent economic recovery—a phenomenon known as "pent-up demand." Is it possible that companies are in a similar position, holding back for the moment on a costly albeit necessary investment in user research?

This article looks at historical data sources and at a recent survey of professionals to describe the evolving market for UX research over recent years and examine the different scenarios that teams are facing this year. For those on an extended job search, those contemplating alternative careers, and team leaders shaping their vision for the year, these data-driven insights are intended to help.

From excess demand to hiring freezes and layoffs

Although it can seem to some like a distant memory, the desire for UX research was at fever pitch just a few short years ago.

Whether and how we would continue to meet the increasing need was a constant topic of discussion. For example, UserZoom’s annual State of UX report in 2022 found that there were many more consumers of research than producers, concluding that “demand outpaces supply.” People debated a number of proposed solutions, including democratizing or automating research, standardizing our credentials, and creating new subspecialties.

But in 2022, the end of stringent pandemic policies and low interest rates made companies emphasize cost-cutting over digital transformation, leading to layoffs and hiring freezes that may have had a heavier impact on UX teams than expected. This is also reflected rather dramatically in the steady drop in open UX Researcher roles during 2022, which then remained low throughout 2023. As of this month, there were only 335 open roles, compared to all-time highs of 2,990 in February 2022.

As this economic cycle reached full momentum in early 2023, User Interviews ran its annual State of User Research survey, finding that “half of researchers were directly or indirectly affected by layoffs in the last 12 months.”

Yet even during this painful period, there were hopeful signs that our work as researchers has stayed relevant. Among their 930 respondents, nearly half (43%) agreed or strongly agreed that leadership valued research, and a majority (65%) agreed or strongly agreed that their peers valued research.

Respondents’ perceptions of leadership buy-in were understandably lower if they had experienced layoffs or hiring freezes. But interestingly, their perceptions of peer buy-in were less affected by these factors. Indeed, when asked about the future of user research, all of the roles that consume user research had stronger optimism than the producers themselves.

In terms of layoff impact and open role inventory, there are objective indicators that demand for our work has decreased over time. But less obvious measures like history and industry perceptions suggest that a need still exists. How might this translate into hiring reality in 2024?

About half of teams need help or plan to hire soon

In early January 2024, I shared a brief 7-question survey on social media channels, which was then reshared by participants. The goal was to understand two questions:

• Do respondents perceive a need for additional support on their teams to handle the workload?

• Do respondents believe their teams are hiring now or in the foreseeable future (i.e., during the first half of 2024)?

Over two weeks, 73 self-identified UX Researchers responded.

A bit over half (56%) of respondents believed their teams would hire sometime before July 2024. In open-ended comments from respondents whose teams aren’t currently hiring, a few mentioned that they had recently hired. For example: “The team just hired for two new UX roles which is why we won’t be hiring for the foreseeable future.” And nearly half (48%) of respondents felt their teams needed additional resources to meet the demand for research that they currently have.

But respondents’ answers can be further classified into four groups that describe the current state of their teams:

• Stabilizing (don’t need help and aren’t hiring): These teams are equipped for their current workload, which can be a healthy sign that the team is right-sized. As one participant said: “It’s a neutral holding pattern, no layoffs expected but we are at capacity bandwidth-wise.” However, it may be symptomatic of a larger problem if work went away when others (e.g. non-specialists or vendors) took it on, or if the team is bloated with little to do. 

• Stretching (need help but aren’t hiring): These teams have more work to do than people to do it, but can’t grow right now. It’s normal for teams to experience this from time to time, but it can be stressful and even demoralizing if it becomes a chronic condition. One participant described such a situation: “We desperately need researchers, but our department budget for hiring always seems to go to design roles.” When hiring freezes are lifted, leaders can remedy this situation by opening new roles.

• Rebuilding (don’t need help and are hiring): When asked about their team’s need for support, these respondents were ambivalent, with an average of 3.6 on a 7-point scale. So why are their teams looking to hire? Several open-ended comments clarified that these roles have been opened to backfill vacated positions. As backfills are often paused during hiring freezes, it’s encouraging that these teams are returning to previous levels.

• Expanding (need help and are hiring): These teams are looking to grow this year. This may be because they are supporting additional workgroups, or they may plan to add specializations like Quantitative UX Research or Research Operations to the team. Even here, there may be some caution, as one participant said: “We grew our team fairly significantly year on year, pausing recruiting intermittently at times depending on the wider market outlook.”

None of the percentages reported here should be considered a precise estimate. At this sample size, the margin of error, even at 90% confidence, overlaps such that none of the differences are significant.

But the melancholy tenor of discussions dominating social media paints a picture of a field where a majority of teams are unable to hire and unable to handle their workload. At least from these data, we don’t have good evidence that this is the case. Instead, there’s a mix of teams adapting to meet demand in different ways.

A few words of caution: this survey measures participants’ perceptions, which may not square with reality. For instance, junior researchers may not be qualified to comment on the need for support or the likelihood that the team will hire soon. Nevertheless, 74% of respondents did self-identify as leaders or senior individual contributors.

In the interest of keeping a voluntary survey brief, I didn’t ask questions that could show how the story changes by geographical region. And we don’t have data from another time period to compare against or understand what a “normal” mix might look like.

These data suggest that far from being a minority, roughly half of respondents felt their teams have more work than they can adequately handle with current resources. And roughly half of UX research teams are looking to grow or fill vacated positions sometime in 2024.

Summary and implications for professionals

Has the demand for UX research dried up, or have companies decided to delay investing in research for better times?

The answer is nuanced. In recent years, demand for UX research has soared to all-time highs, then dramatically dropped during tech’s economic downturn in 2022 and 2023. Layoffs and hiring freezes have created intense competition over significantly fewer roles.

Nevertheless, there are signs our work as UX Researchers remains important and may be rebounding:

• A large 2023 survey conducted by User Interviews showed many UX professionals perceive that leadership and especially peers still value research.

• An original January survey suggests that approximately half of respondents believe their teams will hire in the first half of 2024, indicating a need for additional resources.

• The same recent survey found a roughly even distribution of teams stabilizing, stretching, rebuilding, or expanding to meet current levels of need, rather than an outsized group of overburdened teams unable to grow.

A word of advice to the many who are disheartened from an extended job search: hang in there, especially if you believe yourself to be a competitive applicant. After a long trough period, we may reasonably expect the UX Researcher job market to improve this year as the stock market hits all-time highs.

I’ve also spoken with leaders who have expressed concerns about the available opportunity before their teams. Based on these data, I’d suggest looking for ways to make latent needs manifest as your peers across working groups plan for the quarter and year. Evaluate where your team sits among the four groups identified and set a course for moving into the place you want it to be.

Even amidst an ongoing and painful correction, the sun may be dawning on a bright new era for user research.

ANOTHER THOUGHT…

Create a breakthrough with a brainstorm

We sometimes divide UX into a more “creative” specialty (design) and a more “analytical” specialty (research). It’s a false dichotomy: both roles require a mix of the two skills. 

UX Researchers must often consider creative ways to interrogate a research question, solve a logistical problem, or express an idea or finding. It’s easy to become “stuck” or to fall back on familiar approaches. Getting feedback from a peer or superior can be helpful, but many researchers find themselves alone or on strapped smaller teams.

One underutilized tool we can borrow from our design colleagues is brainstorming. Here’s an approach, adapted from James Altucher, that I’ve found useful:

• Start with a prompt to ideate upon. For example, a slide heading to describe a finding in a memorable way.

• Write down the numbers 1–10, then generate 10 ideas.

• If you’re struggling to come up with 10, your new assignment is to come up with 20. Why? Altucher explains: “You are putting too much pressure on yourself. … You have to shut your brain off to come up with bad ideas. The way to shut the brain off is by forcing it to come up with bad ideas.”

• Only after you have your list should you start to evaluate and implement the ideas. Some of the “bad ideas” might look better on second glance, or could be easily reworked into a good one.

The whole process takes me about 5 to 10 minutes. Altucher suggests building up an “idea muscle” by practicing this at least once daily. The prompts you choose may be personal or creative if you don’t have an immediate professional problem to solve.

Even the least naturally creative person will find that it gets easier with time and practice. And you may find it revolutionizes your life and work.

Facebook connects you, your friends, and your community. In exchange, it serves you advertisements it believes will be of interest to you based on your activities in the platform. It’s a common, lucrative business model — and most of the time, users are happy with the trade.

Occasionally, however, there’s public outcry, usually in response to a scandal over data misuse. And whenever that’s happened, Meta CEO Mark Zuckerberg has defended their policy, stating that years of internal research shows that users overwhelmingly prefer relevant ads. But a few years ago, when Apple built stronger user controls over data sharing into iOS,  most users chose to opt-out of Facebook’s data collection. For Meta, this meant pivoting to other ways of serving relevant ads — but it also took a huge chunk out of their stock value.

What happened? After all, Meta is known as a prestigious employer, having both a large research staff and a highly competitive hiring process. The internal research that Zuckerberg references has never been publicly shared, but it’s fair to assume it was done to a high standard of quality. Yet repeated findings in one context — internal research — failed to replicate in another, and this had huge consequences for the company.

As UX Researchers, our goal is to inform decisions with quality research. But even with the best of intentions, research sometimes fails. Depending on the severity of the failure, and how integral those findings were to company strategy, the consequences can be grave. 

There are three major ways a UX research study can fail: the study wasn’t sound, the findings were wrong, or the findings don’t apply as widely as hoped. In this article, we’ll look closely at common mistakes under each of these categories and explore ways to address them.

The study wasn’t sound

In 2008, Walmart ran a customer survey. There was one clear takeaway: customers wanted less clutter in the stores.

The company went right to work, simplifying store layouts and presenting streamlined options. They cleared and slimmed the aisles, reduced displays, and minimized selection. Having spent millions to refurbish stores, customer satisfaction soared — but there was a swift and dramatic loss in sales. By some estimates, it ended up costing Walmart almost $2 billion in potential revenue.

One analyst suspected that Walmart leadership was fishing for data to support a hypothesis they had: to compete with Target, with its sleek and sophisticated stores, Walmart needed to follow suit. And if a researcher is motivated to do so, it’s not hard to design a study that will guarantee a predetermined result.

Placing one’s thumb on the scale is one example of this first category of error: conducting a study that has fundamental flaws. This can manifest in other ways. You may have unclear objectives. You could have too few participants, or you may have recruited a biased sample. We can also introduce measurement error by asking flawed, inappropriate, or leading questions.

In a technical sense, we would describe all of these as problems related to internal validity. They compromise the integrity of the research.

Of the three major failures, this sin is the deadliest. It doesn’t matter how good your analysis or report is if the study is flawed from the beginning. And though unforeseen issues can later compromise the later stages of a project, the researcher has the most control at this point — and therefore, the most responsibility.

Researchers need to develop both an intuitive feel as well as a clear process for nipping these mistakes in the bud. Formal instruction on experimental design and threats to internal validity can help. 

There was a problem with the findings

During the 2008 and 2012 US presidential elections, Nate Silver accurately forecasted not just Obama’s victory, but how many individual districts and states would vote. Historically, pollsters would look at one or a few recent polls. Silver’s probabilistic approach involved including as many as possible and weighting each by past accuracy and poll quality.

In 2016, however, Silver’s publication 538 gave Hillary Clinton a 71% chance of winning the presidency. They summarized their forecast saying, “Clinton is probably going to win, and she could win by a big margin.” When Trump won, Silver pointed out that events that are only 30% likely to happen will still happen 30% of the time. Unfortunately, many of 538’s readers misinterpreted the forecast, and Silver’s credibility suffered.

Communicating the bounds of one’s certainty is difficult — and the time to get it right is before the forecasted event. 

Our work can also suffer from issues related to probability and uncertainty. And there are many ways to draw or convey an incorrect conclusion from an otherwise solid study. We might think we’ve found something that was never really there (a false positive), or we might fail to find something that we should have (a miss). And when we describe a real finding, we might think it’s more or less important than it actually is.

When our takeaways don’t reflect the underlying reality, it leads stakeholders into bad decisions. Researchers must be vigilant in their analyses, challenging their findings and considering alternative explanations. And we should be careful to explain how certain or uncertain a finding is.

The study and findings were sound, but only in its context

In the early 1980s, Coca-Cola was steadily losing market share to its sweeter competitor, Pepsi. To try and boost sales, they decided to test a refreshed recipe for their flagship beverage. The results from extensive market research were conclusive: consumers had an overwhelming preference for the new, sweeter Coke. It seemed so obvious that the company should move forward with the refresh that some distributors even threatened to protest if they didn’t.

Upon release, the market reacted strongly to New Coke — but not in the way the company had anticipated. The outrage and boycotts were so intense that Coca-Cola quickly back-pedaled, retracting the product and putting the original back on shelves.

Many have since speculated on how Coke’s research could have misled them so badly. Some point to the nature of the taste tests, where participants got what amounted to a single sip, rather than the full serving of a can or bottle. Others point to customer loyalty. New Coke was released as a wholesale replacement of the original recipe rather than another option, when customers had built up nearly a century of brand associations.

In either case, what was true in the laboratory didn’t remain true in the real world. 

UX research studies and their findings can be sound in themselves, but if they don’t carry over into the context we’re interested in — that of our actual users — our work has little value. Just about any element of our study could differ in a meaningful way from real life: its timing, the way we framed our questions and tasks, our participants’ goals and motivations, the presence of a researcher-observer, or the experience itself.

When findings fail to generalize, the problem is rooted in the study’s external validity. Researchers must carefully consider the context of their studies and replicate the conditions they are interested in as much as possible. And when we’re reporting our findings, we must consider the possible contexts in which they may not remain true.

Conclusion and summary

There are many ways a UX research study can go wrong, but the root cause of the failure rolls up into one of three major categories:

• Problems related to the research plan and design. A study with unclear or biased objectives or measurements is flawed from the beginning. Here, the researcher has the most control and responsibility.

• Problems of analysis and communication. We can fail to find something important, or report the illusion of a finding that wasn’t truly there. We can also misjudge the strength of a finding and mislead others in how we present it.

• Problems of generalization. The research design and analysis may be technically flawless, but the findings may never occur outside of our study context. It fails to tell us anything meaningful about our actual users.

In a technical sense, these relate to internal validity, probability and uncertainty, and external validity, respectively. Whenever and however a UX research study fails, it can undermine our credibility and harm our organizations.

To prevent these problems, UX Researchers should consider them during each phase of the project and use systems (like checklists and getting feedback) to keep them in check. If a past study has suffered from one of these problems, diagnosing it can help you decide whether to toss or redo it.

Links for January

• Donna Tedesco, author of classic texts Beyond the Usability Lab and The Moderator’s Survival Guide, is exploring practical approaches to using LLMs in UX Research in a series on Medium.

• With the New Year, many of us are setting intentions for our work. Thomas Stokes answers a wide range of career planning questions in his year-end AMA.

• Human factors expert Rebecca Grier shares a primer on models of cognition for UX practitioners in a two-part series on Medium.

There are many ways to learn a lesson — by formal instruction, from a book, by watching someone’s example, or by making a mistake, to name a few — but one underappreciated tool is the proverb. A well-worded turn of phrase can capture the essence of a challenging topic and stick in your mind for decades. 

UX research is a complex multidisciplinary domain. Holding all the necessary considerations in your mind can feel daunting. A handful of carefully chosen maxims and aphorisms can help you clarify your task to yourself, your team, and your stakeholders.

Let’s look at eight such adages that I’ve found useful in my own practice.

"The first principle is that you must not fool yourself—and you are the easiest person to fool."

Most afternoons, I like to have a break with a bit of dark chocolate. It’s delicious, and touts a few health benefits — but is weight loss one of them? Many of us would certainly like to think so.

A study suggesting just that became a viral sensation back in 2015, getting coverage from dozens of news outfits worldwide. Unfortunately, the study was an intentional hoax designed to show how credulous popular reporting about science often is. The author had used both a false name and institution, and paid to have it published in a predatory journal. Despite these warning signs, few journalists even bothered to contact the researcher with questions.

Since Kahneman and Tversky’s groundbreaking work on cognitive biases, there has been greater understanding that we humans are not creatures of pure reason. Instead, we are emotional, self-interested, and apply a biased and motivated lens to much of the information we receive and process.

Though researchers are often especially aware of such biases, we’re not immune to them as we carry out our work. Proceed with a healthy dose of mistrust in yourself.

“If you torture the data long enough, it will confess.”

That bogus study about eating chocolate gets worse. It was also designed to get a fraudulent outcome.

The author set out to achieve this in a few ways. He measured 18 outcome variables. This made a false positive — where a result is caused by random chance — much more likely, since the standard significance threshold for academic publishing is just 1 in 20 (i.e., p < .05). The data was then given to a statistician instructed to massage the data as needed. 

There are many ways we can mistreat data. We can run dozens of analyses and cross our fingers until one sticks. We can work backwards from the results to research questions that seem prescient. We can ignore the places where responses might be missing in a meaningful way and use sophisticated ways of filling in the gaps, perhaps under the banner of artificial intelligence. Intentional or not, these compromise both the findings and our credibility.

Ultimately, data isn't an infallible guide to the truth. It's something that we must treat with care and respect.

“Correlation does not imply causation.”

In 1999, researchers from the University of Pennsylvania Medical Center published an article in the prestigious peer-reviewed journal Nature, suggesting that young children who sleep with a light on at night are more likely to develop vision problems later in life.

Parents everywhere whose toddlers require night lights were put on alert. But it was later found that the original study did not account for the vision of the parents, which ultimately explained both the poor vision and the nighttime lighting.

Our minds are prone to find patterns anywhere, even where they don’t exist. So it can be counterintuitive to learn that finding two related variables doesn't mean that one causes the other. It’s also possible that something else causes them both, or it could be a simple coincidence.

We often need to dig a bit deeper to understand why we see the relationship. It’s important to leave our minds open to possibilities before jumping to any conclusions.

“Absence of evidence is not evidence of absence.”

Many of us spent the early days of the pandemic glued to news feeds, updating our mental models of this strange new disease and our risk mitigation strategy in real-time as new information came in. Since the virus had no track record and there had been little time for formal scientific inquiry, there was little that we could say conclusively that we “knew.”

This led to headlines like the following:

In the spring of 2020, we lacked reliable evidence demonstrating that many of these propositions are true as we now believe them to be. The evidence we had was at best plausible based on anecdote or conjecture, and it’s sensible for scientifically-minded individuals to withhold some judgment until higher quality data comes.

But simply lacking evidence isn’t the same as evidence that the proposition is untrue. For researchers, it’s often an invitation to investigate further.

“The plural of anecdote is not data.”

“But my sister didn’t have any problems using it!” “I sent it to all my friends and they thought it was great.”

As researchers, we do our best to conduct high quality research and communicate the findings as effectively as possible, but ultimately what our stakeholders do with the finding is up to them. When our findings contradict stakeholders’ expectations, they will sometimes lean upon personal experiences or those of friends and family members.

In situations like these, it’s our job to explain the relative weight we give to data from a well-designed study compared to stories about people who may or may not represent our user base and may or may not have been using the product in a realistic context. One such anecdote, or even several of them together, shouldn’t overshadow or remove the need for proper user research.

“Be approximately right rather than exactly wrong.”

If hurricanes are a part of life in your area, you may be familiar with visualizations projecting the storm’s path.

Starting from the storm’s present location, these figures plot a line over the next several days, bounded on both sides by a “cone of uncertainty” that expands as time goes on. Although such visualizations are imperfect and can be misinterpreted, they help the intended audience to think as scientists are trained to do — that is, probabilistically. 

We work under conditions of uncertainty, often trying to extract a useful insight about an entire user base from just a small sample of them. Beyond a certain point, reducing uncertainty is impractical for cost and timing reasons, and can still yield an inaccurate result.

Instead, we seek to communicate our findings in a probabilistic way, sharing with our audiences the bounds of our certainty and where the likely outcomes might lie.

“All models are wrong, but some are useful.”

The world isn’t flat — but it isn’t round either. In fact, the Earth is an oblate spheroid: squashed at the poles and swollen at the equator. It’s not a perfect one at that, having lots of pits and bumps called valleys and mountain ranges.

When Copernicus placed the Sun at the center of the solar system, one reason that people resisted his model was that the old one was working well enough. With the Earth at the center and celestial bodies moving around it in circles within circles (or epicycles), astronomers of the age could fairly accurately project the motion of Mars and Jupter. 

It was useful. Nevertheless, it was wrong.

The process of science depends on the gradual improvement of inaccurate models. When we use models in our research (like those for technology adoption), or create them for our stakeholders' use (like personas), our goal is usefulness, not perfect accuracy.

Did it help us design a valid study, better understand our users, or produce a better-informed decision? Then it did its job, even if it turns out to be wrong. 

“Research is formalized curiosity. It is poking and prying with a purpose.”

Both halves of this formulation explain something profound about the work that we do.

Researchers are curious people, but our profession takes that natural proclivity and places a formal discipline and practice around it. We do so not for curiosity’s sake, but for the benefit of both our users and our organizations. 

Sometimes research is uncomfortable, both for the researcher and for the participants. In fact, it's often more uncomfortable for participants than we realize. Part of our job is to ensure that the poking and prying that we do truly benefits the larger user base, and that it’s properly balanced with the incentives and benefits we offer those who participate in our studies.

In summary

Across the eight, a few themes emerge:

• We love data — but data can mislead, especially if abused. UX Researchers should take care to avoid both bias in study design and shoddy analysis practices.

• Before jumping to conclusions, explore alternative hypotheses. Think of different ways to explain an observed relationship (or lack thereof) in your user research data.

• Even the best findings are probabilistic and provisional. We can’t give stakeholders absolute certainty; instead, we share the context and bounds around our uncertainty.

But this synthesis doesn’t do justice to the punchy memorability of the proverbial phrases.

Committing a few timeless quotes to memory can help UX Researchers adopt the right mindset as they approach study design, analysis, and communicating findings. And they can help to communicate that approach to stakeholders and partners who lack our research background, or to juniors still developing the fundamentals.

Which have you used in your practice?

Thanks to Jeff Scott for reviewing a draft of this article.

In order of appearance, these quotes have been attributed to: Richard Feynman, Ronald Coase, various authors including Karl Pearson, William Wright (and popularized by Carl Sagan), Kenneth Kernaghan and P. K. Kuruvilla, John Tukey, George Box, and Zora Neale Hurston.

Is the UX party officially over?

A year ago, I found some evidence that UX — and UX Research, in particular — may have been disproportionately impacted by layoffs. Since then, many companies have had further reductions in force. It’s fair to say that most UX professionals have either been personally affected or know colleagues who have.

It’s a discouraging time. Many of us are looking for direction and explanations. Some of you have even reached out to me hoping to find some clarity. I’ll be the first to admit that I don’t have all the answers, but I’ve continued collecting data on the state of the field. For whatever it’s worth, here’s my perspective on several questions I’ve gotten since last year.

Have you updated your UX layoffs data? How have the findings changed?

In November 2022, I used layoff support directories as a primary source of data. Though convenient, these public spreadsheets suffer from two major drawbacks:

• Participation was voluntary and thus self-reported, creating potential sampling bias. In other words, some groups may be more or less likely to enter their information, creating an illusion of how well represented they are.

• Such directories have since grown less popular. Malicious actors have abused them as a source of personal information. Plus, increased awareness of the tight job market makes their purpose less clear.

I’ve since tried to replicate those findings using public records submitted by companies in compliance with employment laws. Under many circumstances, companies are required to release detailed information about planned layoffs or else face legal scrutiny. I updated my analysis using data collected by WARN Tracker, a service that tracks and compiles these records.

Comparing these data to team size and ratio benchmarks from Nielsen Norman Group, I again found that more Researchers were laid off than expected. Instead of 1 Researcher to every 5 Designers, as in my 2022 data findings, it was closer to a 1:1 ratio. On the bright side, nominally fewer Designers were laid off than expected — about 1 for every 12 Engineers, rather than the previous 1:10 ratio.

Although these data are an improvement over self-reported stats, they have their own limitations. They’re somewhat dated, ending in April — though the size and frequency of layoffs have stabilized since then. They’re also largely limited to employees laid off in California, which may not represent the entire market of UX professionals despite being a key location for many.

Nevertheless, both sets of data suggest that UX Research may have been disproportionately affected by layoff decisions.

Has the job market for UX Research picked back up yet?

Jobs plummeted across tech in 2022, and have remained low all year.

My own data on UX Research role inventory specifically shows a similarly dramatic dropoff from all-time highs in February 2022.

The disproportionality between available UX jobs to other available Product positions is similar to what we saw in my layoffs data. That is, there are only about 70% as many open Research roles as might be expected from open Design roles. Worse, there are only 33% as many open Design roles as would be expected from open Engineering roles.

Although open UX roles are still available, they have been slower to recover than Engineering.

Why has tech taken such a huge hit?

Noted industry analyst Ben Thompson suggested that a “covid hangover” and higher interest rates are two key drivers of the tech recession.

Lockdowns and other pandemic mitigation strategies meant that customers couldn’t easily transact with businesses in-person. So the years-long gradual shift to web and mobile channels suddenly and dramatically accelerated. As restrictions loosened up and consumers returned to familiar habits, that growth rate proved to be unsustainable.

Unfortunately, many companies projected recent trends indefinitely into the future.

In the midst of all this, economic stimulus payments and low interest rates flooded the market with cash. Many businesses used this as an opportunity to scale up teams, creating a bidding war for talent. Across Big Tech, including UX Research, there was a massive hiring spree. Indeed, there’s evidence that the number of laid off employees is small compared to the new roles created.

Tech companies had staffed up and planned for a vision of a rapidly growing future that didn’t end up materializing. Inflation began to grow, consumer spending fell, and interest rates were raised in response. It becomes more difficult for organizations to justify spending for potential future rewards once large interest payments begin to accrue.

In other words, recent events helped tech to grow, but it may have grown too fast.

But why would UX Research be disproportionately impacted?

The short answer is that no one really knows.

Organizations don’t share their layoff decision making process to avoid liability and excessive scrutiny. And each layoff is in some ways an independent event with its own motivation and selection methods.

Nevertheless, people have theories. One controversial school of thought, popular on social media, says our wounds are self-inflicted: UX Research cuts can be blamed on our failure to deliver valuable insights. Though I’ve alluded to that possibility myself, it’s mere conjecture without any data to back it up.

The truth may be more complex. I previously suggested that perhaps companies hope to lean more on agencies and freelancers for UX work. It’s also possible that organizations defer to the advice of consultants whose methodologies and benchmarks may unintentionally target UX. Since there’s little data that layoffs improve company financials, it may be the case that companies imitate each other in these decisions.

Unfortunately, UX may simply be a luxury in leaner times. Engineering teams are responsible for the functionality and reliability of products — the most foundational layers in Aarron Walter’s hierarchy of user needs. As in Maslow’s hierarchy of needs, lower needs must be satisfied before the higher ones that UX is responsible for — like usability and delight — can be addressed.

In the absence of data, the simplest answer is usually best.

Should I give up on breaking into UX? Or, should I look for a job in another field? 

Most people don’t have the luxury of indefinitely waiting out an economic downturn.

There are many valid reasons why one might have a greater sense of urgency, whether personal finances, or an expiring visa. It may be a practical necessity to at least temporarily pivot so that you can pay the bills and keep a roof over your head. For the time being, it might be easier to look outside of tech, since the barrier to entry will be higher for any role. But I’m far from qualified to give individual advice here. 

All of that said, UX isn’t a fad. There have been professionals contributing value to organizations in this field for decades.

And there are more such professionals working today — and more open UX Researcher roles — than there were before the pandemic. Ironically, the accelerated digital transformation that has caused growing pains has also left the field stronger than it was a few years ago.

Demand for UX is there, and will eventually continue to grow.

When will things turn around?

If I could predict this with any great accuracy, I’d be managing a hedge fund, not a team of UX Researchers. 

That said, there are some encouraging signs:

• The US Federal Reserve may soon stop raising interest rates. That means the cost of businesses investing in themselves (e.g., by hiring) won’t continue rising so steeply.

• Many Big Tech stock values are rebounding. That’s one indicator that investors are now confident that the spending excesses of the pandemic have been corrected.

• There’s been a recent uptick in tech IPOs. This could show that investors have an appetite to fund growth in the tech sector.

Many companies set their financial targets and budgets for the next calendar year in the final quarter (i.e., now). If those projections are looking more rosy, it’s possible we’ll start to see more job openings when the 2024 budget opens up in January.

Hope springs eternal.

In Summary

This year hasn’t been the brightest for tech in general or UX specifically.

Many of us have lost our jobs, and many others have friends or colleagues who have been affected, or have even experienced organizational layoffs themselves. Enough readers have reached out for answers about the market that I felt compelled to share my perspective more broadly. In short:

• As we saw last year, a more recent look at layoffs data from public records suggests that UX Research may have been harder hit than Design.

• Open positions in tech — including UX Research — fell dramatically in 2022 and haven’t recovered in 2023, though there are still more now than before the pandemic.

• Why? The tech sector grew quickly and dramatically in response to Covid restrictions and low interest rates, but has contracted somewhat since those headwinds turned direction.

• No one can say for sure why UX may have been disproportionately impacted, but the simplest explanation is that engineering responsibilities like functionality and reliability are essential.

• Your immediate career strategy will depend on personal factors, but the number of open roles and working UX professionals indicate a strong field that will eventually resume its growth.

• It’s hard to predict when markets will turn, but a few encouraging economic indicators suggest that jobs could tick up again in the new year.

There’s nuance here, and things aren’t as dire as they might first seem, but we’re not out of the woods yet. If you’re fortunate to find yourself gainfully employed, more context on the market can help you give your work greater impact. If you’re still searching for a role, temper expectations over the coming months — but look for the opportunities that are still available. Things could turn around soon.

Though the tech and UX landscapes have been turbulent, maintaining a realistic perspective will be key to making informed decisions for your career.

Thanks to Danny Spitzberg for reviewing a draft of this article.

From the archives

ANOTHER THOUGHT…

What UX professionals should do in tough markets

I recently read an insightful essay on this topic. It begins:

“The economy stinks. Regardless of where you live in the world, you’ve probably been impacted by the current economic downturn. Most of us know people who have lost their jobs or even their homes. This is probably the worst downturn that most of us have experienced in our lifetimes.”

That was the late Tom Tullis writing in February 2009.

In times like these, it’s helpful to zoom out and recognize that our current problems, though painful, aren’t unique or unprecedented. But his essay goes a step further and offers 10 practical suggestions for professionals in our field to focus on, all of which remain relevant today.

Read the whole thing.

“Will anyone actually use this?” It’s a common question from collaborators—and with good reason.

A startup’s product might have the best user experience (UX) in the world, but that’s cold comfort when they’re still searching for product-market fit. Even for established products, research from Pendo suggests that 80% of features added are rarely or never used.

UX Researchers don’t often ask users to predict their future behavior, since those answers can be unreliable. But for decades, academic researchers have been systematically predicting technology adoption, and you can use the best of what they’ve learned in your practice.

In this article, we’ll take a look at how the study of technology adoption has developed over time. And you’ll learn some tools that you can start implementing in your research practice today.

Diffusion of Innovations theory

In the early 1960s, Ohio State University sociologist Everett Rogers published a book titled Diffusion of Innovations.

In the book, he synthesized the findings of over 500 multidisciplinary studies covering a wide range of contexts, such as agriculture (e.g., hybrid seed and automated harvesters) and healthcare (e.g. hospital hygiene and cancer screenings). He then distilled major findings into three groups of five: the five categories of adopters, the five characteristics of an innovation that adopters consider, and the five stages of adoption.

Rogers found that over time, the groups adopting the innovation have meaningful differences: notably, that they are distinguished by their risk tolerance, financial liquidity and resilience, and their status and persuasiveness in the community. Members of each group share similarities in age, education, and conservatism.

Of the five groups of adopters Rogers identified, the earliest adopters are the innovators and early adopters, followed by the early and late majorities. The laggards are the last to adopt after finally being convinced to incorporate the innovation.

Rogers then found that each group considers five characteristics of the innovation when choosing whether to adopt it:

1. Compatibility: how well does the innovation work with existing tools, practices, and values?

2. Trialability: before you buy it or commit to it, can you try it out?

3. Relative advantage of the innovation over alternatives

4. Observability: can you see others using the innovation, and are the benefits noticeable?

5. Simplicity or complexity: Innovations that are easier to use spread more quickly. 

With a better understanding of an innovation and its potential users, Rogers’ framework can then be used to consider what he termed the five stages of acceptance: 

1. Awareness, or initial exposure to an innovation without the benefit of much information.

2. Persuasion, when a potential adopter becomes interested in and begins seeking information about the innovation.

3. Decision, the adopter makes an initial determination to adopt or reject the innovation.

4. Implementation, if adopted, where the innovation is used and evaluated.

5. Continuation, or an ongoing decision to continue using or ultimately reject the innovation. 

Diffusion of Innovations theory has been highly influential as a sophisticated and pioneering work in this domain. It’s valuable to understand users’ needs and contexts, especially for products seeking to create network effects—such as a social network or dating app—where the value increases with the number of users. An important note: as a sociological model it is best used to describe population-level trends rather than predict individual choices. 

A few suggested takeaways for UX practitioners:

• Select research participants, using behavioral and demographic screeners, who mirror the characteristics and traits of the adopter groups you are aiming to reach with your product or service. 

• Assess how your product measures up against competing solutions across the five characteristics that adopters consider through competitive analysis and benchmarking.

• Address the changing needs of potential adopters moving through the stages of adoption in your product and accompanying materials. You can find areas for improvement here using qualitative interviews.

Technology Acceptance Model

In the mid 1980s at MIT, Fred Davis wanted to understand how to get professionals to start using personal computing systems in the workplace as part of his dissertation. Combining key elements of Diffusion of Innovations theory and psychological models of behavior, he proposed and evaluated the Technology Acceptance Model (TAM) across a series of studies.

Davis and the other principal investigators in this line of research come from information systems, an academic sister to UX typically part of a university’s business department. Though its original context was institutional technology adoption—think the internal apps you use for operational and logistical tasks at work—it has since been used across many other areas, including consumer technology.

Davis identified two primary factors with impressive explanatory power:

• Perceived usefulness: The belief that using the new technology would enhance performance on a job or task.

• Perceived ease of use: The extent to which using the new technology would be free from effort.

A consistent finding across thousands of replications and applications of the model since then is that although perceived ease of use is a meaningful influence, perceived usefulness plays a more important role. So UX practitioners should give the “effectiveness” dimension of usability extra scrutiny in any adoption-related projects.

One of TAM’s enduring strengths is its simplicity: you assess the product or feature on two key dimensions. You need not use the multi-item batteries from the original studies, as the UMUX-LITE is a short UX-specific instrument based on this research. 

Once you measure your product’s perceived ease of use and usefulness, you may want to consider plotting the data along four quadrants with stakeholders. Successful products hope to achieve "Super Tool" status. Determine where your product currently stands and work with your team to develop a plan to move it closer to that goal.

In short, UX teams should:

• Measure perceived ease of use and usefulness (e.g., with UMUX-LITE) in usability studies to find opportunities for improving adoption rates along each dimension.

• Give added attention to perceived usefulness in adoption-related studies, highlighting its pivotal role in reports and study deliverables.

Other noteworthy developments

Although TAM remains simple and effective, you can make more accurate predictions with a more robust and theoretically sophisticated model.

That’s why Viswanath Venkatesh and colleagues extended the model with TAM 2 and 3, including other relevant elements like prior experience, independent choice, subjective norms, self-efficacy and anxieties related to the technology, and even objective usability. These models retain the essence of TAM in their final stages, but help researchers to better understand the drivers of perceived ease of use and usefulness.

For those in entertainment or leisure spaces, where perceived usefulness may seem less relevant to your work, researchers developed the Hedonic-Motivation System Adoption Model (HMSAM). This model targets contexts where practical utility doesn’t make sense as a primary driving motivator, such as games or gamified systems, shopping, learning/education, dating, music, or even social networking. Though perceived usefulness and ease of use remain important, this model introduces curiosity, joy and control as key considerations.

Venkatesh and colleagues developed the Unified Theory of Acceptance and Use of Technology (UTAUT) 1 and 2 as an attempt to integrate findings from across the domain. It incorporates many similar and overlapping elements from other models described here, and goes a step further with factors like gender, age, price, and habit. The latest version of UTAUT is simpler than TAM 3, and remarkably, predicts 70–74% of usage intentions and 35–52% of usage.

There are a few ways UX teams can apply these newer models:

• The latest versions of TAM point to concrete suggestions for improving perceived ease of use and usefulness, if next steps are unclear after initial measurement.

• HMSAM is better for entertainment and leisure products where usefulness is less relevant.

• UTAUT is the best predictive model if there’s adequate time and resources to measure all its elements.

TL;DR

Understanding technology adoption is crucial for product development, and UX Researchers can use validated models to help. This article took a look at five:

People’s own predictions about their future behavior are imperfect, but the best of these models can reliably predict 35–52% of later usage. Two key factors that UX teams can influence are ease of use and usefulness, common threads across these models.

By applying these models and tools, UX and product teams can better understand user behavior and make informed decisions about how to improve adoption rates.

Thanks to Hallie MacEldowney and Brian Utesch for reviewing a draft of this article.

From the archive

Considering the UX of emerging technologies as a path towards greater adoption:

• Unlocking the potential of AI tools with UX research

• Crypto is still confusing for novice users

ANOTHER THOUGHT…

What’s an insight?

What's our "value proposition" as UX Researchers? Many answers I've heard offer some variation of: "we provide insights to product teams."

But that raises another question. What exactly is an "insight?"

That's the title of a compelling literature review recently released by researchers Leilani Battle and Alvitta Ottley. They examined 125 scholarly articles on the topic. Here are some of the competing definitions they found:

• "data utterances," or any observation about data made by an individual

• "data facts," or computable descriptions and relationships that support a hypothesis

• "hypotheses and/or evidence," or a synthesized observation from multiple data points

• "knowledge links," or connecting units between data, findings, and knowledge

How do the authors adjudicate these differing ideas? They propose a new definition that encompasses them all: insights are collections of knowledge. It's an interesting read, and offers some compelling ideas for future research.

It’s a classic “chicken-and-egg” problem. Which comes first: your first UX Research role, or your first experiences doing UX Research?

With many job descriptions asking for some combination of work experience or case studies, it can seem a daunting — even impossible — paradox for folks seeking a first role.

The good news is that you can develop a compelling portfolio of case studies even before starting a job. While you can’t fake years of experience, you can bolster the experiences you already have and simulate experiences close to those needed for a new role. And getting hands-on experience with practice projects is a valuable early indicator that you’re suited to doing the kind of work you’ve been considering for a career.

In this article, we’ll look at five ways to crack the problem, listed in a roughly ascending order of lesser to greater difficulty or time commitment. Several strategies involve raising your hand for volunteer work — which might feel like a step backwards, but one that will hopefully enable you to take two steps forwards. Whichever methods you choose, I encourage you to work with a mentor in the field who can evaluate your work and guide you in the right direction.

1. Give new emphasis to old work

If you’re coming to UX Research (UXR) from a graduate program or adjacent career, you likely already have work that you can repurpose.

Although academic work differs from user research in various ways, both subtle and significant, what they do share will be of interest to hiring managers. For instance, while you may never use a sonogram or EEG in industry as you did for your thesis data collection, you can talk about how you designed a study with the right methodology and appropriate sample size to address your research questions. The manuscript you submitted to your field’s flagship journal might look nothing like a typical UXR report — but you might be able to repurpose a slide deck from a conference talk or the elevator pitch you gave in an interview with the popular press. Similar examples would apply for those transitioning from another career, like market research.

In any case, be careful to share just the information that’s relevant to the role you’re seeking and provide any context a UX hiring manager might need that your original audience would have already understood.

2. Conduct research to improve unfriendly designs

There’s an interesting phenomenon among medical school students called “second year syndrome.”

As they begin studying the symptoms of a range of diseases, they start to report experiencing them themselves. Much like the cocktail party effect, in which one can pick one’s name out across a loud and crowded room, researchers believe this a normal function of observing newly salient stimuli that was there all along, rather than unleashing a latent tendency towards hypochondria.

I’ve observed that something similar, though less stressful, happens when folks begin to learn about and internalize ideas and concepts from user experience. Their perspectives on frustrating technological interactions suddenly shift. Rather than being caused by their own lack of skill or understanding, these problems were actually the predictable result of poor design! And they begin to see this everywhere.

I’ve encouraged mentees to harness that energy by keeping a list of the unfriendly designs they encounter as the seedbed for sample project ideas. Then, whenever you’re developing a practice project with an unfamiliar research methodology, consider whether it might be an appropriate tool for finding problems with or improvements to one of those unfriendly designs you encountered.

3. Volunteer UXR skills for a cause that’s meaningful to you

Although you may lack the funds to financially support the causes closest to your values, you can still lend the skills you’re developing as a valuable means of support.

Nonprofits and charitable organizations are often spread thin and strapped for resources. Many have websites with lots of room for improvement. If your research could help them convert more visitors to donors or volunteers, you’ll have made a tangible impact that translates directly to the kind of work that UX Researchers do every day.

Reach out to the organization you’re interested in helping and schedule time to discuss what might be most valuable given their current initiatives. Then propose an engagement with a fixed term that details your research approach and what you’ll need from them. For example, they may be able to connect you with volunteers and donors as study participants. If not, you can try the classic guerilla method of offering gift cards to the patrons of your local coffee shop in exchange for 15 minutes of their time.

By the end of the project, you’ll have gained practice working with a real stakeholder under constraints, getting scrappy to answer research questions meaningful to their organizational objectives, and putting together an actionable report — all ingredients for a powerful case study.

4. Get involved with a tech apprenticeship group

Believe it or not, most folks trying to break into tech aren’t looking for user research roles. Many of them are seeking positions in engineering, design, product management, or even project management.

Fortunately for everybody, there are apprenticeship groups that attract talented transitioners to do valuable projects on a deadline for real stakeholders at startups and other worthy causes. You’ll be placed on a cross-functional team, often under the oversight of an experienced advisor. This allows you to simulate a realistic working group and develop communication and collaboration skills. Plus, you won’t be flying solo — you’ll have others to filter and sharpen your ideas. And unlike working as a volunteer or with a local startup, as discussed in the next suggestion, these apprenticeship groups find the projects and facilitate the introductions and handoffs so you don’t have to.

If you’re interested in pursuing this route, look into groups like Hack for LA, UX Rescue, Catchafire, and Tech Fleet.

5. Work with local startups

Reid Hoffman, the founder of LinkedIn, once likened life at an early-stage startup to “jumping off a cliff and assembling a plane on the way down.”

These companies may be searching for product-market-fit, or ramping up to their first product launch. What little cash they have on hand might be claimed for the foreseeable future. Even so, they’re one of the groups with the most to gain from user experience work. By volunteering to share your research skills and insights with them, you could have a huge potential impact on the future of a fledgling company. It’s also possible you could translate the gig into a larger role or some sweat equity — but temper your expectations, since most startups fail.

You can explore your local startup scene through tech incubators, conferences, and meetups. Further afield, you could scour Wellfound for ideas that look promising.

Bonus: Seek freelance projects

Let’s say you’ve already done one, two, or perhaps all of the above. And now, you’ve put together a diverse portfolio of case studies, but for whatever reason, a full-time role hasn’t materialized yet.

The good news is that you can start to earn some money while you continue looking. Reach out to your network and let them know that you’re open to freelance UX Research projects. You can also join a project marketplace like Upwork or Fiverr to begin taking on engagements.

These projects will give you not only some cash flow, but also the confidence that you can make a career out of UX work. And completed projects can be sanitized to further extend your portfolio. Just be careful not to overcommit yourself while you continue applying and interviewing for positions.

Summary and conclusion

You need not have prior UX Research experience on your resume to build a portfolio that will help you stand out for entry-level roles. This article discussed a few ideas:

• Repurpose existing work from graduate programs or adjacent careers to highlight relevant skills and experiences for UX research roles.

• Keep a list of unfriendly designs you encounter and use them as a starting point for practice projects to develop your research skills.

• Offer your research skills to nonprofits or charitable organizations to help improve their websites and make a tangible impact while gaining experience.

• Join apprenticeship groups that work on real projects for startups and other causes, which provide opportunities to simulate a realistic working environment.

• Volunteer your research skills and insights to early-stage startups, potentially making a significant impact on their future while gaining valuable experience.

• Start earning money by reaching out to your network for freelance UX research projects or joining project marketplaces like Upwork or Fiverr.

These options vary in difficulty and time commitment, allowing you to gain meaningful experiences while you seek a full-time UX research role. 

Regardless of the pathway you take, your goal is to create realistic experiences that you can share with a hiring team. Working with other product team members, reframing business objectives into research questions, designing studies, recruiting participants, collecting and analyzing data, and presenting it to stakeholders are all key aspects of a UX research job that you can get a taste of before you ever land your first role.

Thanks to the many mentees who have asked for advice on this topic, and to Summer Harvey for sharing her experiences with me.

ANOTHER THOUGHT…

Academic vs. industry reports

New UX Researchers out of grad school have written a lot of reports already. But those reports were different in tone, style, and format from UXR reports.

Academic reports are written for an audience of scholarly peers, and are expected to be thorough and rigorous. They document prior work on the topic by extensively citing sources, and give detailed information about the methods used so that others may critique or replicate them. The tone is typically formal and objective, with the findings presented in a cautious and tentative manner.

UX research reports, on the other hand, are typically written for a more general audience of product team members. They are designed to be clear and concise, and they focus on sharing the key findings, necessary context, and implications of the research. The tone needs to be more informal and engaging, with the findings presented in a more confident and assertive manner.

But perhaps the biggest difference is the goal: academic reports contribute to the body of knowledge on a particular topic, while UX research reports inform decision-making to improve the user experience.

One's not better than another. Each has its conventions for good reasons. But just as you learned the rules for academic writing, you'll need to learn a new set for UX research. Understanding the differences will help you better tailor your reports to the proper audience and their outcomes.

Poll

From the archive

• Create a realistic UXR case study by avoiding this approach (Medium)

• 3 reasons grad school won’t count as UXR work experience (and what you can do) (Medium)

Many UX Researchers — especially those seeking a first role — put their hearts and souls into producing and polishing a portfolio that shows off their skills and experiences. But questions remain about what makes a good portfolio and just how necessary they are. 

To help hiring managers and potential job seekers, we’ve collected several years worth of data1 in the hopes of gaining some clarity on this topic. If you’re an individual contributor preparing to apply for your next role, understanding common expectations and strategies can help you to job hunt more effectively. And for managers who often lack training on best hiring practices, having a clearer portfolio strategy can help you help candidates put their best foot forward.

Let’s look at the data

As we saw two years ago, many respondents either have never maintained a portfolio (16%) or have an outdated one (31%). The biggest reasons are practical concerns: they’re time consuming to maintain (70%) and it’s a challenge to include confidential work (65%).

Among those who have a portfolio, 41% had nothing more sophisticated than a slide deck or PDF on hand. One participant described investing time and money into a portfolio website on the advice of a mentor, only to find that it was hard to keep polished and representative of current skills and experiences. “Sometimes people would find my website and pull up something outdated,” he said. “That’s not good! Eventually, I just scrapped the website.” 

Portfolios are occasionally required when submitting an application, but how they’re used that early on in the process is unclear. One participant frustratedly likened these “gate-clearing artifacts” to “an exercise in mind-reading.” Many (41%) were unsure if they were ever reviewed by employers, with a small minority (5%) saying their portfolios had never been discussed during an interview.

Which raises another question: how often are portfolios explicitly called out within UX Researcher job descriptions?

When we last examined this question two years ago, the word “portfolio” appeared in 29% of 1,316 job descriptions sampled. We’ve since continued to sample job descriptions on a near-monthly basis (20 samples in 25 months). Including these additional 27,388 job descriptions, the word “portfolio” appeared in only 21%, varying somewhat by job title. This updated figure is substantially fewer than initially reported.

In other words, only about 1 in 5 UXR job descriptions talks about portfolios in any way.

Yet when we recently surveyed the state of UXR hiring and interviewing practices, most participants (69%) indicated that they have presented or expect to present past case studies in later rounds as a work sample test. So, although these materials often aren’t required up front, they will likely come into play later on. 

What do hiring managers look for in portfolios? It depends in large part on the target role and its expected competencies. One manager we spoke with explained that she is “really looking for their experience with certain methodologies and how they frame their findings” in junior portfolios, but acknowledged that she employs different criteria for more senior candidates.

Nevertheless, job seekers we spoke with had a strong desire for more explicit guidance on what will be evaluated so that they can share the most relevant work. As one participant put it: “It would be nice to know what an employer is looking for in your portfolio. For example, insight into process, insights, repertoire of methods, lessons learned — I hear different things from different people … I’ve tested mine with a few dozen hiring managers in various sectors and have found the whole topic wildly subjective.” 

These findings lead to a few practical suggestions for both job seekers and hiring managers.

Key points for job seekers

1. Skip the custom website or public portfolio. Unlike design, the work UX Researchers do for our organizations seldom gets wide release, even when the products we’ve supported do. That makes it difficult to put case studies that may contain confidential information in a public forum. Web design and development is also much further removed from our core competencies. As we’ve previously suggested, a slide deck or PDF that repurposes materials from existing deliverables is often best for convenience’s sake.

2. Make sanitizing and archiving part of your project close-out process. One participant said: “Every time I do a project that I'm like, 'that was really cool' or 'that had some interesting challenges that were noteworthy,' I'll basically rebuild those slides … so I keep them handy.” With any hygiene practice, small consistent habits, though inconvenient at first, are more effective than heroic periodic efforts. Only 12% of UX Researchers in our sample did this, so adopting this strategy could be an opportunity to stand out.

3. When asked, be ready to select and share 2 or 3 relevant case studies from a range of sanitized work samples. If possible, showcase your breadth across the case studies by highlighting a variety of products, methodologies, time-frames, and/or deliverable formats. What you choose should be based either on specific guidance provided by the hiring manager, or, absent that, cues from the job description and any notes you’ve taken about the role during your interviews. 

Key points for hiring managers

1. Don’t ask for materials until they’re needed to respect candidates’ time. Specifically, don’t make a portfolio an application requirement if, like most of the hiring managers we spoke with, you don’t spend much time reviewing portfolios before inviting candidates to a phone screen.

2. Communicate how and when portfolios will be used. If you require one with the application as a screening tool to assess some minimal level of research competence, some indication can be given so that candidates won’t spend excessive time preparing a customized portfolio. If the portfolio will come into play in later rounds, give candidates a preview of what the whole cycle will look like.

3. Give candidates guidance on how portfolios will be evaluated. As they put together materials relevant to the role you’re filling, make yourself available for clarifying follow-up questions. Getting clear about what you’re looking for will not only help candidates prepare to make a favorable impression, but will help the hiring team to structure its assessments of the candidate.

Summary and conclusion

For a few years now, we’ve been tracking questions surrounding what makes a good portfolio and how necessary they are in job interviews. We’ve consistently found that only about 1 in 5 job descriptions explicitly call for them. And many practitioners don’t actively maintain one after their first few years on the job. Nevertheless, a case study presentation is common to most UX Research interview cycles. 

Savvy researchers regularly sanitize and archive interesting projects so that they can select from a breadth of relevant work samples for the specific role they’re interviewing for, using pre-existing deliverable materials rather than fancy custom websites. But hiring teams should do a better job of telling candidates how and when portfolios are used in their process, and what they are expecting to see.

If you’re actively pursuing the next chapter in your career, hopefully these findings will help you show your best work. And if you’re trying to get the best UX Research talent for your team, these recommendations should help you improve your chances at finding them.

ANOTHER THOUGHT…

Should you take that management role?

Wondering if becoming a UX Research Manager is the right path for your career progression? Or have you been asked to step into that role? Here are a few questions I’d recommend considering, which I thought about before making the transition:

• Are you motivated to learn a new skill set? You’ve likely become highly skilled as an individual contributor, but research competencies have little overlap with people management. Be prepared to go from feeling like a rockstar at your job to a total beginner.

• Do you have prior experiences or aptitudes to prepare you for the work? Often, new managers will have spent time mentoring other researchers on the team, or helping to create or optimize team processes. Look for opportunities to try out some of the role responsibilities before formally taking them on.

• Do you have the time and support you’ll need to grow into the role? I found it essential to have a mentor or coach who can help you learn the ropes and keep you moving in the right direction. Take advantage of any training or development opportunities your organization offers, and look for external resources you can leverage.

If the answers are “yes,” you have a strong basis for giving it a shot. But if not, don’t feel pressured to make it work for the sake of your career. Many mature organizations offer parallel paths for individual contributors to continue growing in their craft.

From the archive

• Should you have a UX Research portfolio? (Archived version, originally published on UX Booth)

• Create a realistic UXR case study by avoiding this approach (Medium)

If you’ve interviewed for at least a few different UX Researcher (UXR) roles, you’ve probably noticed some common themes.

Most begin with a phone screen, involve some level of interaction with the hiring manager, and include some kind of work sample test. But what other activities is one likely to encounter, and which activities are more unusual? And, if folks interviewing for a position had their way, what would the ideal interview process look like?

Getting a bird’s eye view of the typical process can better prepare job seekers for those activities. And understanding both candidate preferences and how other organizations find the best candidates for UXR roles can help hiring managers evaluate their own processes.

So we designed a study to paint a picture of how the UXR interview experience unfolds today.

Our study and approach

To get as comprehensive an understanding as possible, we used an exploratory-sequential mixed methods design.

That began with qualitative interviews, using a semi-structured format that covered the entire interview cycle from end to end. Through these interviews, we spoke with both hiring managers and job seekers to capture their perspectives and experiences. We then transitioned into a quantitative survey to gauge the prevalence and impact of the issues identified during the interviews.

Using a rolling recruit method, we sought volunteers to participate in the survey through various channels, including social media and word-of-mouth referrals. Remarkably, it attracted a response far surpassing our expectations: we heard from 160 participants, consisting of 56 hiring managers and 104 job seekers. The enthusiasm and passion this topic evoked was evident in the extensive open-ended comments respondents shared.

Throughout the process, we also conducted secondary research, drawing from industry-specific resources and broader academic literature to ensure we asked the right questions and captured the breadth of the topic.

Let's take a look at what we found, starting with the activities selected during the UX Research interview cycle.

Which activities should be included in the interview?

In our survey, we asked hiring managers to outline their interview process, specifically identifying the activities used at each stage.

We found the initial phone screen was a widely recognized and common starting point for interviews. Computer/virtual interviews, where candidates respond to preset questions via webcam recording, though less prevalent, were also typically employed early in the process.

The next most commonly observed step involved one-to-one interviews with hiring managers. This clusters together with other frequently chosen activities, including technical interviews, behavioral questions, portfolio reviews, and additional one-to-one interviews with members of the research team.

Other less frequently used activities occurring later in the process included panel interviews, where multiple members of the hiring team interview a single applicant, and alternative options to the portfolio review, such as whiteboarding activities or take-home test projects. Additionally, one-to-one interviews with members of other teams, such as developers or product managers, are occasionally conducted to assess cross-functional collaboration.

Overall, six key activities were widely favored and frequently part of UX Research interview cycles: initial phone screens, one-to-one interviews with hiring managers, technical and behavioral interviews, portfolio reviews, and panel interviews.

We presented the same set of interview activities to job seekers and asked them to indicate their preferences, expectations, and any activities they would be reluctant to participate in or that would cause them to remove themselves from the process altogether.

When these responses were juxtaposed with the activities hiring managers reported using, we found broad agreement between both parties with minimal differences in absolute ranking. Job seekers expressed lower expectations for, and enjoyment in, the activities at the lower end of the rankings that hiring managers were likewise less likely to include.

Having examined the activities that both hiring managers and job seekers prefer, let's consider the duration of the interview cycle.

How many interview rounds and activities?

A strong consensus emerged when job seekers were asked about the ideal number of rounds in an interview cycle: over 80% of survey respondents expressed a preference for a streamlined process of two to three phases.

Hiring managers selected an average of 5.5 total activities, although we didn’t ask any direct question about how those activities might be combined in distinct interview rounds. Nevertheless, open-ended comments suggest that these activities often are consolidated. As one hiring manager respondent said: “Some of these were combined into one, so I would not want to give the impression that there were 8 interviews… more like 3 rounds.”

Some interview cycles, particularly in Big Tech companies like Google, have gained a reputation for their extended duration. Yet even in these organizations, there’s been a trend towards shorter cycles in recent years. According to author Laszlo Bock, who served as Google's Senior Vice President of People Operations, hiring at Google in the mid-2000s could take up to six months and involve as many as 25 interviews. Time spent on each hire, he stated, has since been reduced by approximately 75%.

Surprising alignment on interview structure

We didn’t necessarily expect the broad agreement we observed between hiring managers and job seekers on both the number and types of activities they preferred to see in UX Research interviews. Upon reflection, we suggest three key reasons that might explain it.

First, both hiring managers and job seekers have a vested interest in the outcome of the interview process.

Job seekers face the prospect of either securing employment or needing to continue their job search elsewhere, while hiring managers aim to find the right candidate to fill the role. The shared stake in this outcome drives a common understanding in the interview process.

Furthermore, both hiring managers and job seekers value the opportunity to assess and demonstrate competency.

Hiring managers seek to evaluate a candidate's abilities to determine their suitability for the role and gauge the potential they bring. Similarly, job seekers aspire to showcase their skills and capabilities, eager to prove themselves in the context of the specific role and organization. This shared desire for competency evaluation could manifest in the activities they selected during the interview cycle.

Lastly, every hiring manager has been a job seeker at some point in their career, which gives  them a firsthand understanding of the job-seeking process.

This perspective allows them to empathize with candidates and recognize the importance of fair and comprehensive evaluation methods. This shared experience contributes to similar preferences and the desire for a rigorous yet fair interview process.

The bottom line and next steps

There’s a common narrative about job interviews that portrays hiring managers and interviewees as conflicting forces, pitting their interests against each other. But is that in fact the case? We wanted to see what the data has to say about the current state of UX Research interviews:

• Six activities were particularly popular: initial phone screens, one-to-one interviews with hiring managers, technical and behavioral interviews, portfolio reviews, and panel interviews.

• There was consensus that interview cycles should be streamlined into approximately three major rounds, which reflects broader trends in the industry towards less time spent per candidate.

• Ultimately, hiring managers and job seekers share some mutual interests in the outcome of the hiring process and desire to demonstrate and uncover competency, which is strengthened by their shared experiences going through interviews. 

Despite these shared preferences, our research also revealed potential friction points within the actual interview process itself, starting with its early stages.

In the second part of this series on our findings — to be published later this month on — we’ll take a deeper look at those pain points and explore strategies for creating a more effective and mutually beneficial UX Research interview process.

These findings were first presented at UXPA 2023 in Austin, TX.

June is upon us again! Meaning: conference season for UX researchers is at its crest. In the span of a month, we get UXRConf and Quant UX Con among many others.1

I hope you’ve had the chance to benefit from all the knowledge and experiences being shared. This week, I’m headed to Austin to deliver two talks at UXPA — so we’ll be resuming our annual tradition of a special link-fest2 edition of The ¼" Hole.

Next month, we’ll resume our regularly scheduled programming with write-ups related to those talks. Until then, sit back, relax, and queue up some tabs for your reading breaks between conference sessions — and if you’re hungry for more, check out last year’s roundup.

There is no one way to do UX research (Gregg Bernstein)

Layoffs have hit the tech sector hard over the past 12 months, and there is some evidence that UX has had an even rougher go. One explanation going around is that we’ve largely brought that upon ourselves, so UX research is facing a reckoning. I’m not convinced by this narrative, as I’ve written elsewhere — and I think Gregg adds valuable perspective to the conversation.

The not-so-subtle art of job searching in the open (The Career Whispers)

An unfortunate consequence of the layoffs and corresponding reduction in role availability is that job seekers are spending more time on the market. During veteran UX leader Andy Warr’s search, he’s shared a wealth of his past and present experiences with the discipline, and we’re all better for it. Others may do well to consider the “do things, tell people” approach.

Research is a job that benefits businesses first, users second (Tech Workers Coalition)

Many of us enter UX with the ideal of helping people, removing friction and frustration from their lives. Unfortunately, what’s best for users sometimes conflicts with what’s best for the business, and the final decisions are ultimately out of our hands. Part of our role is balancing these two competing goals — but as Claire writes, that’s not always possible.

Calendar analysis: my day-to-day as a Principal UX Researcher (Lisa Koeman)

When job candidates are given the opportunity to ask questions about the role, they often want to know what a typical day looks like. There are lots of articles that answer that question in a general way, but comparatively few that take Lisa’s approach: using actual weekly schedules as data. I’m inspired (and encourage others) to do something similar sometime.

No one cared about my spreadsheets (Econlib)

Writer Bryan Caplan makes an interesting observation about the most common criticisms of one of his popular books: nobody actually checked his math. Though we in UX research also draw conclusions from data, it is likely also true that our conflicts over those conclusions aren’t based on data. (Previously on The ¼" Hole: what actually persuades.)

The Kano model, revisited (P. 25, Proceedings of the Sawtooth Software Conference)

Since 1984, Dr. Noriaki Kano’s feature prioritization model has been popular among product teams. But like any methodology, it has its shortcomings — and quant UXR pros Chris Chapman and Mario Callegaro point out some serious ones, including questionable item validity and low small-sample reliability. As a Kano fan, I’ll follow this debate with interest.

Human factors guidelines for presenting quantitative data (Human Factors) 

Doug Gillan (my advisor) and colleagues note with some irony that many submissions to a journal about designing with human capabilities in mind include visualizations that were seemingly designed without the reader in mind. Using that rubric, they propose principles for effective data presentation, along with the helpful flowchart above.

Fifty psychological and psychiatric terms to avoid (Frontiers in Psychology)

Far from dogmatic language policing, the authors have compiled a compelling list of “inaccurate, misleading, misused, ambiguous, and logically confused words and phrases,” including some common items like: operational definition, statistically reliable, and scientific proof.

Cognitive bias in LLMs (ArXiv preprint)

Large language models are AI tools that have been trained to find relationships between words using a vast corpus of human-produced texts. And though it is true that these systems are in some sense intelligent, it is also true that they may reproduce flaws in human intelligence like our many well-documented cognitive biases, as was demonstrated in this clever study by Alaina Talboy and Elizabeth Fuller.

Lightning round

The last man without a cell phone • Hollywood professionals give casually sophisticated commentary on the UX of streaming (h/t Steve Portigal) • When do visualizations persuade? • A primer on designing mixed methods studies • What’s the deal with curved lines in design? • The value of inconvenient design • HCI research through litigation • How UX research teams allocate budgets towards their hierarchy of needs • A community for UX freelancers •  Greater variability in workplace rhythms may reduce emotional distress in knowledge workers •  The seven levels of busy

“If I have seen further, it is by standing on the shoulders of giants.”

In that simple but memorable phrase, Isaac Newton poetically described the way that science advances: incrementally, by building upon the work of others. User experience (UX) is no different. And many of our practices spring from a rich tradition, being rooted in debates and insights wrought from academic literature.

Yet much of the work we do as UX researchers never reaches a broad audience. Why is that, and what would it look like if we put more into the public sphere? Let’s look at the history and benefits of engaging in public-facing UX research — and how you can get started today.

Establishing our foundations in public

For over four decades, professionals have been practicing in the field of user experience, though the discipline has been known by various names.

In the early days, engineers made most or all decisions about product experience, including its interaction and interface design. It wasn’t at all clear why professionals with backgrounds in the social sciences might have an important role to play in the process. So our practices first earned their legitimacy in debates carried out in academic history, such as human factors and human-computer interaction (HCI).

Many of our methods, theories, approaches, and philosophies were discussed and refined in academic journals and debates like those held at academic conferences.

We owe foundational techniques and concepts such as heuristic evaluation, mental models, diary studies, cognitive walkthroughs, naturalistic observation, inspection methods, and best practices on sample sizes to researchers who published their work through peer-reviewed journals and conference proceedings. They even sought to demonstrate the financial return of investing in usability.

This transparency allowed practitioners from different parts of the world to see how others were working, test it themselves, and, if needed, challenge it. It also gave the field credibility and legitimacy as a proper scientific endeavor.

Insights locked away

Although as many as three-quarters of working UX researchers possess some graduate training, most of our work is confidential and serves only the private organizations that employ us.

This system has some advantages. It protects company intellectual property and trade secrets and distinguishes innovative, usable experiences as a competitive advantage. It also shields companies from some liability that public research institutions face.

Of course, organizations like Nielsen Norman Group and Baymard Institute stand out as examples to the contrary, as they regularly publish independent research that drives the field forward in blog posts and white papers. However, this kind of work is sometimes intended for lead generation purposes, and may thus be limited in scope or paywalled.

Scholars in the fields of human factors and HCI continue to research and publish. But in recent years, the topics that interest academic researchers and attract grant funding have had less overlap with those that are most important for UX researchers working in the industry.1 Though sharing a common heritage, the two disciplines have evolved independently.

Unfortunately, this means that UX doesn’t benefit as broadly as it once did from emerging discussions on methodologies, approaches, and innovations. It also means that the best and brightest minds working in the industry often have little opportunity to collaborate and learn from each other unless they happen to work for the same company.

The benefits of sharing your work

We can learn from the way other disciplines balance public and private research.

In medicine, while private companies conduct their own pharmaceutical research and patent drugs and vaccines for their own benefit, many researchers also contribute to the public store of information. This advances the innovation taking place in private industry and prevents wasted, duplicative efforts.

Artificial intelligence researchers often have the responsibility of sharing and disseminating research. Employees of OpenAI, Meta, and Google all publish findings in academic journals, which advances the field and prevents stagnation. For example, the recent boom in generative artificial intelligence, largely popularized by OpenAI, was first seeded by research published by Google.

Processes like these can also improve the quality of the research itself. For example, anonymous peer review is uncommon for independent researchers or those working in companies. Except in companies with very large research teams, where researchers are unlikely to know each other, it’s just not practical. Nevertheless, replicability and validity are legitimate concerns with any research study — including UX research, which is subject to a significant evaluator effect.

Individual UX researchers may also benefit from engaging the public. Visibility of their work, and the feedback and collaboration resulting from their papers can have long-term advantages for careers.

How you can start

Whatever your idea — for example, a generalizable finding from a usability test, a new methodology, or a critique of an old one — there are many ways to get it out there.

A peer-reviewed study in a reputable journal is still a gold standard for some. And while there are other options in this vein, such as posting to pre-print repositories like arXiv.org, many UX researchers, having attained a graduate degree or left the academy, have no desire to return to publishing in this way.

Other options include submitting proposals to conferences like UXPA and UXRConf, or sharing more informally through a blog, podcast, or newsletter. Each method has its own benefits and drawbacks worth considering, such as accessibility, convenience, status, timeliness, reach, and engagement.

Whichever path you choose, there will be challenges to overcome, such as ethical issues, legal risks, and the cost of funding.

It may be necessary to consult with in-house legal teams, and conduct a thorough review to ensure no trade secrets or competitive advantages are shared. If budget’s unavailable to fund the study, researchers may need to find scrappy ways to address their hypotheses. Perhaps there will one day be a organization to sponsor this kind of work, similar to ACX or Helium Grants.

Summary and conclusion

Though our first responsibility as UX researchers is conducting high quality research for those we immediately serve, our field progresses when our work enters the public forum.

• Our history: Debates in academic disciplines, such as human factors and HCI, helped legitimize UX practices as a proper scientific endeavor. Many foundational techniques and concepts were developed through peer-reviewed journals and conference proceedings, providing transparency and credibility to the field.

• The trade-offs: Private research protects corporate intellectual property and trade secrets, it can also hinder collaboration and the sharing of innovations in methodologies and approaches. Other fields, such as medicine and AI research, benefit from a mix of private and public research, but UX research has yet to strike a similar balance.

• How you can share: UX researchers can engage with the public through various forms of dissemination, such as peer-reviewed journals, pre-print repositories, conferences, blogs, podcasts, and newsletters. Each route has unique advantages and shared challenges, such as ethical issues, legal risks, and funding limitations.

As a UX researcher, you have a unique opportunity to advance our field with your insights and questions. Whether you want to explore a new topic, challenge a common assumption, or share a best practice, there are many ways to contribute to the public discourse of UX.

The future of our discipline needs your input.

ANOTHER THOUGHT…

Yes, UX researchers are creative

There’s an old cliché that researchers are the analytical, logical ones, while designers are the creative, artistic ones — almost like the left- vs. right-hemisphere of the brain. Of course, it’s an oversimplification (as is that description of brain lateralization), but one that many take at face value.

In fact, both specializations require a blend of rational and intuitive thinking. And UX researchers could especially benefit by more intentionally leaning into the creative aspects of their work. A few examples:

• Simplifying complex information: The data we plumb are messy, but the insights we extract from them should be clear. It takes some ingenuity to explain a concept in a pithy way or to find the right analogy or metaphor.

• Working under constraints: Poetry can be beautiful and profound — but poems are often an exercise in writing under rules of form, rhyme, or meter. In the same way, every study is conducted with trade-offs. The trick is finding reliable information in those circumstances.

• Problem-solving through divergent thinking: We often make conscious efforts to examine our biases in framing questions, but we are prone to others — like functional fixedness — which limit our view of the possible. Some problems can only be solved with an expansive or novel approach.

Becoming more comfortable with creativity will make you a more effective, well-rounded researcher.

Is the field of user experience struggling, or is it thriving?

With heated debates about the direction of our discipline and a flood of newcomers entering the profession, it's easy to feel uncertain about UX's future. However, by taking a closer look at the history, current state, and potential of the field, we can gain a more nuanced and optimistic perspective.

It’s worth taking a step back to get some perspective on how far our field has come, its challenges and successes, and how we’re continuing to develop.

Real challenges facing us

People who argue that our field is under attack — perhaps even on the verge of extinction — aren’t being dishonest; they’re hypothesizing from the data before them.

It is true that we have, in some sense, been the victims of our own success. Over the years, as UX has proven valuable to various industries, demand for our work has ebbed and flowed, sometimes soaring far beyond what we can adequately provide. This can lead to a glut of open positions, with no one but underqualified juniors to fill them.

A career in UX can be life-changing for many, offering a lucrative salary and work-life balance that’s hard to find in other fields with similar qualifications. Even when demand is low, there’s almost always a steady stream of people eager to transition in. When the economy inevitably dips, or our industry seems disproportionately affected by layoffs, some worry if they’ve changed their career trajectory for a passing trend.

The fast-paced growth of the field can also be overwhelming for new professionals, who may feel discouraged by the messy reality of how UX is practiced in the business world. The truth is many organizations are still low on the UX maturity scale, so our processes aren’t fully integrated into their way of doing business. This can lead to an “us versus them” mindset, where UX’s successes are seen as zero-sum against haters who want us out of the picture.

Some may view UX as a relatively new field without established methodologies or principles. Added to the confusion are frequent debates on social media over terminology, methods, key phrases, and the very name of our field. Hardline, unnuanced stances on the validity of various approaches — such as quantitative versus qualitative research, the use of specific scales, or approaches to deliverables — take center stage.

While these challenges are real, we can look at them from another perspective.

Tracing our roots to today

The field of UX has a rich history, dating back to the design of cockpits for military airplanes during World War II.

The term "user experience" was first coined by Don Norman in 1993, but it has also been referred to as user-centered design, experience design, user research, and usability engineering, among others. The discipline is rooted in academic fields like human factors, engineering psychology, and human-computer interaction.

Today, UX professionals are represented in many industries and countries globally. With the accelerated transition to digital caused by the COVID-19 pandemic, almost any company can now benefit from user experience practices. The ways that user experience fits into and contributes to an organization can look very different, but the underlying philosophy and approach are the same.

Despite controversial viewpoints that might dominate social media, there are many reputable sources that defend the value of UX. Professional organizations such as local meetups, UXPA chapters, and UX Research & Strategy offer mentorship, conferences, networking, job opportunities, education, and training. Publications like the Journal of User Experience, Smashing Magazine, UX Magazine, UX Booth, User Weekly, and UX Collective are edited by seasoned professionals and regularly promote valuable content.

UX is anything but a small, young field. Rather, it’s a mature one with a rich history and a wealth of resources. Social media might give a distorted perspective, but by returning to our roots and sources, we can continue to develop and grow our profession.

The field continues to evolve

Even among the challenges our field faces, unique opportunities lie ahead.

By definition, half of everything is below average — including UX professionals and the companies we work for.

Many of us in the bottom half of the bell curve won’t remain there forever. Hopefully, our personal lowest point was on our first days in UX roles. As we continue to mature in our craft, we move towards the other end of the spectrum. Some of those in the below-average cohort may be bad actors. But with proper leadership and accountability, they’ll be removed from the pool of UX professionals as if by natural selection.

Organizations will also vary in their UX maturity. Some may be unaware or barely aware of our profession, while others have a deeper integration with our work. Indeed, research by NNgroup suggests that the average organization is quite low on the absolute scale of maturity. This need not discourage us as practitioners: it’s an opportunity in disguise. As these companies become more mature, we’ll see even more demand for our work.

And even though some cynical seniors bemoan a flood of “unqualified juniors” entering our ranks, rarely does a hiring manager give an offer to an applicant with zero relevant experiences or promising qualities. When demand is so great that we seek candidates from tangential disciplines, we benefit from a diversity of perspectives that further enriches the discipline.

In the same way that sand inside an oyster becomes a pearl, these challenges may prove to be our strength.

Zoom out, then zoom in

Is UX a young, beleaguered field on the verge of extinction — or a strong, mature field with a pathway for development and future opportunities? Without downplaying the challenges we face, it can be helpful to take a broader perspective:

• UX isn’t a buzzword. Folks have worked in this specific space for over forty years, with academic underpinnings stretching farther back.

• Reputable voices aren’t that hard to find. Scratch the surface, and you’ll see industry veterans who compile, publish, or write from a perspective rooted in this history and their experiences.

• There’s opportunity even in hardships. Individual contributors grow and mature over the years — as do the organizations we serve. That means more senior professionals and more demand in years to come.

Obsessing over the thoughts, opinions, and actions of others can be counterproductive and unhealthy. Above all, we owe it to ourselves and our own mental health to focus on what’s in our control.

This means continuing to learn, develop professionally, and provide even greater value to the organizations we serve. We can also contribute to the positive development of our field by mentoring and educating others.

Far from being a fragile discipline, UX practitioners should take pride in the robustness of their profession and look forward to even better years ahead.

Further reading on the history of UX

• A Great Leap Forward: The Birth of the Usability Profession (1988-1993) by Joe Dumas (Journal of User Experience)

• 40 Years in UX by Jakob Nielsen (YouTube)

• A Brief History of Usability by Jeff Sauro (MeasuringU)

• The Waves of Research Practice by Dave Hora (Dave’s Research)

False dichotomies and bimodal distributions

Back to those infamous social media debates…

• Are surveys dangerous?

• Is there such a thing as too many scale points?

• Is the Net Promoter Score harmful?

• Is democratization cheapening our discipline?

• Do AI tools have a place in our process?

For the moment, let's set aside our own opinions on these topics. Instead, let's consider the discussions themselves: have they been nuanced or one-sided?

In my experience, people tend to feel strongly one way or the other. This pattern is known in statistics as a bimodal distribution, where two distinct groups are represented in the data by two peaks.

Social media platforms are designed to amplify this pattern. People are more motivated to write when they feel strongly, and extreme opinions are easier to express than subtle ones, especially in a short form factor. Plus, social algorithms deliver content that users are more likely to engage with, like attention-grabbing hot takes.

Unfortunately, this can create a skewed perception of reality. The true distribution of public opinion might actually be multimodal — having a third, larger peak in the murky middle. People who see arguments on both sides and prefer a compromise or a third way often get edged out of the conversation.

So don't fall into the bimodal trap or rush to select an opinion from the limited menu presented to you. Instead, talk with colleagues and mentors outside of social media. Exercise critical thinking and creativity. Look for more context and alternative points of view.

Our field is complex and constantly evolving. We need to keep an open mind and engage in constructive dialogue to move forward.

A UX research presentation is not a dry reporting of facts.

Yes, we communicate findings and recommendations. But few fates are worse for a report than sitting on a shelf, unused. To have a real impact on the products we support and the people who use them, we must persuade stakeholders to our point of view on the data.

Effective communicators throughout the ages — from Cicero to Churchill — have used principles of persuasion known since antiquity. Following principles first articulated by the Greek philosopher Aristotle in his Rhetoric, good speakers build convincing arguments by establishing their credibility and trustworthiness, guiding their audience toward their logic and reasoning, and connecting with them on an emotional level.

Let’s look at how researchers can practice and master these skills to give presentations that have an impact.

Building trust with your audience

Picture two people giving talks on the same topic.

One reads verbatim from slides full of dense, technical jargon in a dull, monotonous tone — often hesitating, making mistakes, or getting confused about what comes next. The other has clearly rehearsed the material, shows enthusiasm for and mastery of the subject, and is able to explain complex problems in a way that even an outsider can understand.

All things being equal, the clearer, more engaging speaker will be seen as more reliable and authoritative. A researcher summarizing the literature put it this way: “A highly confident speaker is viewed as being more accurate, competent, credible, intelligent, knowledgeable, likable, and believable than the less confident uncertain speaker.”

And we find credible experts more persuasive than the uninitiated.

This pillar of persuasive speech is known as ethos, or the ethical appeal.

One of the first and most important ways to establish your ethos is being professional and well-prepared. So know your material and rehearse it before the talk.  Avoid typos and other embarrassing mistakes.

If possible, start establishing your credibility before the presentation, or even before the research engagement. Each interaction with your stakeholders is an opportunity to build trust and rapport. Do you have a long history of research in this problem space, for instance? Then draw from prior studies to showcase your experience. Having conducted the study, you know more than your audience about the participants' experiences within this specific context, regardless of whether you consider yourself an expert on the topic. 

Walking through the method and participants can also help establish your reliability and credibility. What research questions was this study designed to answer, and how? Perhaps just as importantly, what questions were left off the table? Note the language stakeholders use to frame their problems and questions, and mirror it — even if you feel another phrasing would be more technically accurate. For better or worse, we trust people who are like us. 

Your audience will listen once you establish yourself as a relatable, credible authority.

Building a compelling argument

In a court of law, a rational argument can have a profound human impact in fines paid or time served.

Our users' and organizations' outcomes can be significantly affected by UX research, even if the result is less dramatic. As lawyers present evidence and arguments to prove their case, we must do the same.

This foundation of rhetoric is known as logos or the logical appeal.

One way to present a rational basis for your claims is through classical hypothesis testing. In this method, you state expected results up front based on prior studies or desk research and associate specific measures with those expectations. With a large enough sample size, you can then statistically test whether those measures align. This allows you to make claims such as one product being more usable than another based on the SUS or QXscore.

Although UX researchers don't usually perform controlled experiments, we often use multiple sources of evidence and make hypotheses based on observations.

Showing the validity of your claims is a critical part of building a convincing argument. To that end, supporting data points and charts or graphs that summarize and illustrate the data are crucial in building a convincing argument. You can also use reputable external sources, such as survey data from institutions like the Pew Research Center, to support your claims. 

Even if your logic is sound, it serves no purpose unless the audience understands it. Using clear, plain language and devices like analogy and metaphor helps others follow the thread of your reasoning. And be sure to anticipate objections that may linger in your stakeholders’ minds, addressing them head-on in your presentation.

Showing the validity of your claims is a critical part of building a convincing argument.

Building an emotional connection

In their Nobel Prize-winning work on human irrationality, Daniel Kahneman and Amos Tversky showed that people aren't solely creatures of reason. The framing effect — one of many reliable biases they identified — shows how word choices emphasizing the pain of loss or the thrill of gain can influence what might be straightforward, logical decisions. 

Pathos, or the emotional appeal, is a powerful persuasive tool for connecting with your audience emotionally to encourage their buy-in.

Vivid language brings findings to life. Compare the following: “Participants strongly preferred design B to design A,” vs. “Participants loved the new design.” Which is more memorable? Give thought to your headlines and titles, as evocative names can make your findings stickier and give them a longer shelf life. 

Many UX researchers use a narrative framework based on the hero’s journey — a structure familiar to many of the great stories and epics of history — to build their presentations. While this approach can work well in some cases, it’s not always the ideal format and may be too restrictive. Either way, you can make findings more relatable for the audience by incorporating stories that illustrate them.

Exploit the power of humor and surprise. Sharing an unexpected finding or funny story from a usability test or interview can illustrate a point and make it more memorable. If it doesn't induce a groan or undermine your ethos, researchers might also consider adding relevant GIFs and memes.

You should use video clips and quotes from participants to humanize the results or to highlight a particularly glaring problem. Participants often use charged language to show how a defect in the product makes them feel. They may sometimes express delight at a new feature or a proposed solution as well.

Whether by using compelling stories or evocative language to describe surprising, humorous, painful, or delightful moments, give depth and humanity to your findings to make your presentation stick. 

Bringing it all together

UX research presentations aren’t just about sharing data and results. They should also persuade stakeholders to take action based on the findings. Throughout history, effective communicators have used persuasive techniques to do just this:

While leveraging these three pillars can make findings more memorable and relatable, it does not guarantee the validity of the research. If you use these tools to amplify shoddy or half-baked work, it’s likely to backfire.

More powerful than any single persuasive pillar is the effective use of all three. By combining the classical tools of rhetoric, UX researchers can make findings more understandable and persuasive to ensure they have the desired impact.

Thanks to Summer Harvey and Thomas Stokes for reviewing drafts of this article.

A mantra for confidence

During one of the toughest courses from my time in graduate school, a student was called on to present. He got up, shuffled through a few thrown-together slides, muttering and staring at his feet. Our no-nonsense professor proceeded to humiliate him in front of the rest of the class, saying: “Face your audience and tell them what you know!”

Ironically, this turns out to be decent advice. If you know more about the subject than your audience, you shouldn't be afraid to stand in front of them and share your knowledge. The professor's rough delivery made it memorable, but the message remains clear: trust yourself and your expertise.

Recommended reading

A few longer treatments that expand upon the topics discussed here:

• Made to Stick: Why Some Ideas Survive and Others Die by Chip and Dan Heath

• Words Like Loaded Pistols: Rhetoric from Aristotle to Obama by Sam Leith

• Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

• The Minto Pyramid Principle: Logic in Writing, Thinking, & Problem Solving by Barbara Minto

• Further subcategories for each appeal discussed in this 1985 manuscript by Ulla Connor and Janice Lauer (h/t Arnas Aleksandravičius)

A bootcamp might seem like the most appealing option for launching your career in UX research.

They position themselves as a way to quickly get you prepared and make you attractive to employers. After all, even if you have a background in a similar field or some transferable skills, odds are you aren’t a perfect match for entry level roles. 

But are they the best way to get ready for UX research roles?

The answer depends on your needs and goals, and there are a variety of other options available to you. So let’s unpack some of these and weigh the relative advantages and disadvantages of each.

Different requirements for different roles

The requirements of the role will depend on the specialty you choose.

On small UX teams and in early-stage startups, it’s common to find generalists who are responsible for both design and research. As those teams grow, members tend to diverge into design- and research-focused roles. In mature teams, there are further specialties in accessibility, design systems, research operations, and content writing, among others.

After selecting a target role, you should clarify what employers want in candidates.

For research roles, you can find common themes in in-depth analyses like those by Brian Utesch and Theresa Nguyen, or by Zombor Varnagy-Toth. If you want data more relevant to your situation, you can aggregate the key responsibilities, skills, and preferred qualifications from a sample of job descriptions you intend to apply for. Then compare and collect the most common themes.

Once you have a good understanding of the qualifications and experiences required for your target role, it's time to assess how your resume compares. Then you’re ready to look at options for filling those gaps.

UX bootcamps and their appeal

Bootcamps are short, intensive programs that focus on job-specific training.

Each program is designed to help students enter a role in tech. The earliest examples were developed for software engineering roles. Today, bootcamps exist for many roles across product development, including product management, digital marketing, data science, cybersecurity, and user experience.

Selecting one tailored to your target role is crucial. While many bootcamps have “user experience” in the title, keep in mind that many are not widely applicable to any role in the field; rather, they are most suitable for aspiring designers. These tend to discuss prototyping and interface design extensively, while covering research methods superficially if at all. That said, there are a growing number of bootcamps designed specifically for UX research, which you can often identify from the title, description, or syllabus.

But even if a bootcamp fills your needs, how should you evaluate them?

Do your homework

While UX bootcamps can serve an important purpose for some jobseekers, completing one comes with some tradeoffs.

For one, bootcamps are not accredited institutions. This allows them to focus on job-specific skills, but also means that instructors may not always be qualified to convey them. Look carefully at the background and experience of the instructors you will be learning from.

They are also costly, with a typical range between $7,000–$17,000. It is common for bootcamps to offer financing options to ease that burden, but bootcamp loans are not subject to the same protections as student loans. Students can sometimes defer tuition by paying a percentage of their eventual salary as part of an Income Sharing Agreement, a controversial practice. Exercise caution before entering into any agreement if you aren’t in a secure financial situation.

To offset the cost, many bootcamps offer job placement assistance. To help you prepare, most will provide resume or portfolio reviews or mock interviews. But some even guarantee a job offer within a period of time after graduation. Be skeptical of overstated promises, and do your own independent research on alumni and the roles they later took. As the old saying goes: if it sounds too good to be true, it probably is.

Finally, it’s always worth exploring alternatives to reach your goals even if you find reputable bootcamps.

Other ways to continue your education

A successful research career requires breadth, which can be challenging to get in an intensive skills-based program.

One alternative is to pursue a degree program in a related field—for example, undergraduate degrees in the social sciences, such as psychology, or a user experience degree, or graduate programs in human factors or human-computer interaction. These programs offer both practical experience and a deeper understanding of the theory foundational to the field.

Those who don't have the funding or time to commit to a degree can also audit specific courses or take them on a pass/fail basis as a non-degree-seeking student. Local colleges or online courses can both be good options depending on the type of instruction you prefer. Additionally, some graduate programs offer non-degree certificates after taking a limited number of courses.

You can also get some of the benefits of bootcamps on an á la carte basis by looking for workshops, courses, and certifications marketed to UX professionals and those aspiring to join the field. These are generally unaccredited, but hyper-focused on the work you will be doing.

Continuing education may not be the ideal choice for everyone, but it may be an attractive alternative to bootcamps.

Summary

Bootcamps may be appealing for those transitioning into user experience roles, such as UX research.

They can train you in the primary skills for the job in a short period of time, and even offer job placement assistance. 

But bootcamps can be controversial, and aren’t the best fit for everyone, whether due to cost, financing agreements, quality of instruction, or unrealistic promises. Another issue is that many only superficially cover research topics.

Keep in mind that bootcamps aren’t the only option. Those interested in seeking another degree might consider undergraduate and graduate programs that offer relevant theory and practice, giving greater breadth and the protections of accredited institutions. Other options requiring less of a commitment include auditing local or online courses, enrolling in a graduate certificate, or taking UX-specific workshops and courses.

What’s best for you depends on your personal situation and goals. If you’re an aspiring UX practitioner, take an honest look at the gaps between the skills and experiences on your resume and those preferred for the jobs you want. Then carefully examine all the options available to you.

Potential pathways

While researching this article, I ran into some courses, bootcamps, and programs that showed some promise and may be a helpful starting point.

This list is not exhaustive. And all of the cautionary statements above still apply — I have not thoroughly vetted, or personally purchased any of these.

Online courses

• Methods & Statistics in Social Sciences via Coursera. $39/month, or audit for free; financial aid also available

• Quantitative & Qualitative Research for Beginners via EdX. $99, or audit for free

• Statistics & Probability via Khan Academy. Free

Degree programs and graduate certificates

For undergraduates

• Arizona State University offers a BS in User Experience

• Savannah College of Art and Design offers a BFA in UX Research

• Purdue offers a UX Design degree, and the University of Texas at Austin offers a UX Design concentration in their Informatics program

For graduates

• Those interested in a human factors program should choose from among those accredited by the Human Factors and Ergonomics Society

  • (I loved the program at North Carolina State University)

• I have worked with and interviewed graduates of the University of Southern California’s Applied Psychology program, and it seems top notch

• This guide to the best US HCI graduate programs is worth reviewing as well

Graduate certificates

• Human Factors Engineering from University of Michigan, 2 weeks in-person. Approximately $5,500

• Human Factors from Embry Riddle, available fully online over 1–2 semesters. Approximately $7,800 + fees for military, $9,300 + fees for civilians

• Human-Computer Interaction from IUPUI, available fully online over 1–2 semesters. Approximately $6,600 for Indiana residents, $9,500 out-of-state

• Human-Computer Interaction from Stanford, available fully online over 1–2 semesters. $20,000 – $34,000, depending on course selection

UX research courses, workshops, and bootcamps

• LinkedIn Learning Courses by Cory Lebson, all self-paced. Approximately $30 each, or available with a subscription (first month free; and Lebson suggests checking if your local library has one)

• Usability Testing Bootcamp by David Travis via Udemy, a self-paced course. $50

• User Research Mastery by Nikki Anderson-Stanier, a self-paced course. £165 (approximately $200)

• The Missing Foundations of UX Research by Noam Segal, a 2-week cohort-based course. $575

• Curiosity Tank: Ask Like a Pro by Michele Ronsen, a 9-week cohort-based course (bootcamp). $1,950 – $4,550, installment plans available, as are lower-cost individual courses and workshops

• UX Certification: UX Research Specialty from Nielsen Norman Group, a series of 5 courses and proctored exams. Approximately $5,700; prices steadily increase over time

• Zero to UX Research Masterclass by Kevin Liang, an 18-week cohort-based course (bootcamp). $9,200, installment plans available