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Organizing Quantitative Results
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Today, we will explore how to organize quantitative results from our usability tests. Why do you think it's important to collect and organize data in a structured way?
So we can see what works well and what doesn't?
Exactly! Organized data allows us to identify trends, such as tasks with low completion rates or high error counts. Can anyone think of how we might display this data?
Maybe in a table format? That way itโs easy to compare results.
Very good! Using tables helps us quickly grasp which areas need more focus. Letโs remember the acronym 'DIVE'โto Data, Identify, Visualize, and Evaluateโto help us remember this process.
So we dive into the data to find the important parts?
Exactly! Now, let's summarize this: Organizing data is essential for identifying trends and effective prioritization.
Evaluating Severity
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Now, let's talk about evaluating severity. When we find issues, how can we determine if theyโre critical, major, or minor?
I think critical issues need to be fixed right away because they prevent users from completing tasks.
Correct! Critical issues should be prioritized first. Can anyone give me an example of a critical issue?
If a user canโt log in at all, that's critical!
Great example! Remember the phrase 'C-M-M'โCritical, Major, Minorโto categorize issues based on their severity. It helps us focus on what's most important to fix.
And we can fix minor issues later since they arenโt as urgent?
Exactly! Let's wrap up: We categorize issues as Critical, Major, or Minor to help us prioritize effectively.
Determining Priorities
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Next, we need to determine the priorities of the issues we identified. How do you think we can prioritize them?
Maybe we can look at how often each problem occurred?
Yes! Plotting issues on a graph with Frequency on one axis and Impact on the other helps visualize the most pressing problems. Who remembers how this plotting helps us?
It helps us focus on the ones that are common and have a big impact!
Exactly! The goal is to tackle those 'high-frequency, high-impact' issues first. Remember the phrase 'Fix the Big Problems First.' Great job! Let's conclude by summarizing that prioritizing involves evaluating both frequency and impact.
Root Cause Investigation
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Letโs dive into why itโs essential to perform a root cause investigation. Why not just fix the symptoms of a problem instead of looking deeper?
If we only fix the symptoms, the problem might come back, right?
Youโve got it! By digging deeper, we can identify the real issue behind user frustrations. Can anyone suggest ways to uncover these root causes?
We can ask users more questions about their experience!
Exactly! Asking engaging questions can reveal usersโ mental models and expectations. Let's remember this concept with the acronym 'D.I.G.'โDig Inquire Gather.
So, we need to dig deep to understand their thoughts?
Yes! And remember, understanding the root cause leads to more effective solutions.
Brainstorming Data-Driven Solutions
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Last, letโs talk about brainstorming solutions. Why is it important to base our improvements on user feedback?
Because it ensures that what we change actually helps the users!
Exactly! This is where we become data-driven designers. Can anyone suggest how we might brainstorm solutions?
We could use sticky notes to jot down ideas quickly!
Great idea! Remember the strategy 'S.O.L.V.E.'โSolutions Organized, Listed, Verified, and Executedโto help facilitate brainstorming sessions.
So, weโll make sure our solutions are verified before we put them into practice?
Exactly! Let's summarize our session: Brainstorming should be rooted in user feedback to ensure targeted solutions.
Introduction & Overview
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Quick Overview
Standard
It discusses how to organize quantitative results, evaluate severity, and determine priorities based on user feedback in usability testing. The focus is on understanding tasks users struggled with, identifying underlying causes, and brainstorming actionable solutions.
Detailed
In this section, we delve into the critical aspect of analyzing user feedback gathered from usability tests and prioritizing improvements accordingly. The first step involves organizing quantitative dataโsuch as task success rates and user satisfaction scoresโinto clear tables to identify patterns and trends. Next, issues are categorized based on their severity, distinguishing between critical, major, and minor problems. After establishing the severity of issues, they are plotted based on frequency and impact to prioritize 'high-frequency, high-impact' problems for immediate attention. Furthermore, the section emphasizes conducting a root cause analysis to uncover why users faced certain issues, which leads to more effective problem-solving. Lastly, brainstorming sessions for data-driven solutions are encouraged to ensure that improvements directly address user concerns and enhance overall usability.
Audio Book
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Organizing Quantitative Results
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Compile data into a table:
Task Participant Success Time (s) Errors Satisfaction
Find chapter Sam Yes 32 1 4
Highlight tool Maya No 75 3 2
Look for trends:
โ Tasks with low completion rates
โ High average time or error counts
โ Low satisfaction scores
Detailed Explanation
In this step, you'll organize the results from your user testing. Start by compiling all the quantitative data into a table, which allows you to see the success rate for each task, the average time taken to complete each task, the number of errors encountered, and the satisfaction scores of each participant. By looking for trends in this data, you can identify specific tasks that users struggled with, such as those that had a low completion rate, took a long time, or resulted in frustrated feedback.
Examples & Analogies
Imagine you're a coach reviewing the performance of athletes during a race. By timing each athlete and noting how many finished the race without stumbling (errors), you can see patterns in their performance. If one athlete consistently takes longer to finish, you might notice they need more practice on certain techniques.
Evaluating Severity
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Categorize issues:
โ Critical: prevents successful task completion
โ Major: causes confusion or difficulty
โ Minor: tends to cause minor annoyance or cosmetic issues
Detailed Explanation
Next, categorize the issues that arise from user feedback based on their severity. Issues are classified into three categories: Critical issues are those that completely prevent a user from completing a task, Major issues cause significant confusion or difficulty, and Minor issues might only cause slight annoyance or are cosmetic. Understanding the severity of each issue helps you decide which problems need your immediate attention and which can be addressed later.
Examples & Analogies
Think about a school project where you get feedback from classmates. If they say your project doesn't help them learn anything (critical issue), that's something you need to fix right away. If they mention a section is just a little boring (minor issue), that's something you could work on after fixing the most important parts.
Determining Priorities
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Plot issues along two axes: Frequency (how often they occurred) and Impact (severity). Prioritize โhigh-frequency, high-impactโ issues first.
Detailed Explanation
After categorizing your issues, visualize them on a two-dimensional graph with Frequency on one axis (how often the issue occurred) and Impact on the other axis (the severity of the issue). This plotting helps to prioritize issues, allowing you to focus on the ones that occur frequently and have high impact. These are the issues that most need to be addressed to improve overall user satisfaction and functionality.
Examples & Analogies
Consider a restaurant receiving feedback. If many customers complain about slow service (high frequency, high impact), thatโs a problem to solve quickly. However, if only a few customers mention an unpleasant color on the walls (low frequency and low impact), itโs something that can wait until more pressing issues are resolved.
Root Cause Investigation
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Ask:
โ Why did users behave that way?
โ What mental model did they have?
โ What interface element misled them?
โ How can the design better match their expectations?
Detailed Explanation
To effectively address issues, it's crucial to investigate their root causes. Ask questions to gain insights into why users struggled. Consider the mental models they had while interacting with your design. Identify which elements of the interface may have misled them, and think about how the design can be adjusted to better align with user expectations. This process ensures that your solutions tackle the actual problems rather than just their symptoms.
Examples & Analogies
Imagine a teacher analyzing why students didn't perform well on a test. Instead of just looking at their wrong answers, the teacher might ask why they found certain questions confusing. Maybe the wording was unclear or a key term was unfamiliar, which gives the teacher a clearer view of how to improve future tests.
Brainstorming Data-Driven Solutions
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Based on each issue, propose improvements:
โ Redesign unclear icons (e.g., label settings gear โSettingsโ).
โ Move hidden buttons to more visible areas.
โ Simplify navigation: fewer steps, clearer paths.
Sketch quick low-fidelity updates noting connections to observed issues.
Detailed Explanation
Now, using the insights gathered from the previous steps, start brainstorming potential solutions to each identified issue. This could involve redesigning aspects of the product, such as labeling confusing icons, repositioning buttons that are difficult to find, or simplifying the overall navigation process. Create sketches of low-fidelity updates that illustrate these proposed changes. This approach allows for quick iterations based on user feedback, ultimately leading to a more user-friendly design.
Examples & Analogies
If you were reworking a sports play that didnโt work well during a game, you might re-evaluate the strategies that caused confusion. Youโd look at how players could be better positioned for clarity during play, drawing up alternative plans to enhance teamwork and execution.
Definitions & Key Concepts
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Key Concepts
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Organizing Quantitative Results: Using tables to identify trends in user feedback.
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Evaluating Severity: Categorizing issues as Critical, Major, or Minor to prioritize fixes.
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Determining Priorities: Plotting issues based on Frequency and Impact to focus on significant problems.
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Root Cause Investigation: Understanding the underlying reasons for user issues to design effective solutions.
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Data-Driven Solutions: Proposing improvements grounded in actual user feedback.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
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If users need more than three attempts to log in, this indicates a critical issue with the login process.
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High error rates on a specific task suggest that the design may be confusing or misleading.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
๐ต Rhymes Time
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To find out what's wrong, donโt go along, just dig real deep to find the cause we seek.
๐ Fascinating Stories
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Picture a doctor diagnosing a hidden illness. They donโt treat the symptoms without investigating further; similarly, we must uncover root causes for design flaws.
๐ง Other Memory Gems
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Remember the acronym 'D.I.G.' for Dig Inquire Gather when performing root cause investigations.
๐ฏ Super Acronyms
'C-M-M' stands for Critical, Major, Minor for issue categorization.
Flash Cards
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Glossary of Terms
Review the Definitions for terms.
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Term: Quantitative Results
Definition:
Data that can be measured and expressed numerically, such as task success rates and time taken.
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Term: Severity
Definition:
The degree of impact or disruption an issue causes to a user's ability to complete a task.
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Term: Frequency
Definition:
How often a particular issue occurs during testing sessions.
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Term: Impact
Definition:
The effect or consequence of an issue on the overall user experience.
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Term: Root Cause
Definition:
The underlying reason for a problem, which may contribute to the issue's recurrence.
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Term: DataDriven Solutions
Definition:
Improvements or changes based on concrete data and user feedback rather than assumptions.