MeasuringU @MeasuringU
User Experience Research & Software. Tweets by Jeff Sauro, Jim Lewis & MeasuringU Team: Authors of Quantifying the User Experience + 8 Other UX Books. measuringu.com Denver, CO US Joined January 2010-
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6 Differences between B2B and B2C #Usability. bit.ly/2UcgGKV
Are top box scores a better predictive of behavior? We set out to find out. Here’s what we learned: bit.ly/3CJCnTS
An implication of Campbell’s Law is that mismanagement leads to mismeasurement. But there are strategies to avoid the corruption of high-stakes measures such as the Net Promoter Score. bit.ly/3hV0K8p
Simplifying the UMUX-Lite. bit.ly/3h6b2SI
In a way reminiscent of beer foam or the quantum foam from which matter is created at the edge of a black hole, the #usability problems that occur with some consistency across participants seem to float on a sea of usability problem foam. bit.ly/3istJ2S
Although it is possible that scale orientation could have a large effect on rating behaviors, that does not appear to be the case. At least, the effect is inconsistent. bit.ly/3u4uF3o
In current practice, study-based #UX #metrics include measures of satisfaction, loyalty, brand perception, usability/usefulness, delight, trust, visual design, and special-purpose questionnaires. bit.ly/3yTVVpa
Rating Scales: Myth vs. Evidence. bit.ly/3zj2cYu
When planning an unmoderated study, in the absence of historical data about the task, we generally recommend using a conservative estimate (e.g., the 75th percentile) for the planned research protocol (think-aloud, non-think-aloud, tree test). bit.ly/44ibVPp
In this article, we review how task success is assessed in moderated and unmoderated studies, then we provide guidance on how to approximate completion rates when they can’t be collected. bit.ly/3zj7mUm
Given Campbell’s Law, how might you avoid the corruption of high-stakes #metrics such as the NPS? Consider audits, education, third-party data collection, transparency, timing, and decoupling monetary incentives from subjective customer measures. bit.ly/3hV0K8p
Want to write clearer #survey questions? Keep them short, use simple language, use high-frequency words, match the respondents’ vocabulary, minimize the use of acronyms, avoid complex syntax, and generally avoid passive voice. bit.ly/3l35rPZ
Study-level #metrics are likely more generalizable than task-level metrics. bit.ly/3IepIL3
When asked to interpret cumulative graphs, we found that people often misinterpret them, and do so with confidence. bit.ly/36Tr2SC
Why #survey questions are answered incorrectly #3: Misinterpreting the question. bit.ly/3ykSIvM
Your Guide to Hamburger Menus. (Interaction Design Foundation) bit.ly/4cTauL4
Just because attitudes aren’t a perfect predictor of behavior (that’s an unrealistic bar) doesn’t mean they aren’t useful. Don’t be afraid to ask what people think or do (but know the limitation of asking versus observing). bit.ly/3eFQX4G
Why interview target respondents when crafting #survey questions? The interview step provides the opportunity to validate that the people taking the survey will understand the words and phrases you plan to use. bit.ly/37mcUBI
When planning unmoderated #usability tests, researchers should be aware that think-aloud tasks will usually take more time than non-think-aloud, and tree test tasks are much faster than non-think-aloud tasks. bit.ly/44ibVPp
A review of five studies that compared the effect of think-aloud and non-think-aloud protocols on task completion times found all three possible outcomes (no difference, faster completion with TA, and slower completion with TA). bit.ly/3DPPwfE
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320 Followers 47 Following The Journal of Usability Studies is a peer-reviewed, international, online publication dedicated to promote the practice, research, and education of UX
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@albanvillamil @leeloowrites All behind NDAs. So, sorry, I don't give a shit what you think Jared if I can't validate your methodology myself. The biggest mistake UX made IMO was breaking off from human factors and the strong research culture. Now no one knows anything and management rules all.
My new essay is out in @tabletmag Masking school kids is based on the lowest level of evidence imaginable. Contradicted by data from Spain. It is astonishing we have done this for 2+ years, and run 0 cluster RCTs. History will judge us poorly tabletmag.com/sections/scien…
When the history books are written, we will look worse than our ancestors who survived the great plagues of Europe Because their superstition did not disproportionately harm kids My new essay in @tabletmag tabletmag.com/sections/scien…
Some people like to boost 80 year olds in nursing homes Others like to mask 2-7 year olds I prefer to boost 80 year olds just because I like to do things that actually work But you do you tabletmag.com/sections/scien…
putting this out there bc 1) it’s really important to understand this while looking at all of these covid 19 studies, or any public opinion polling currently happening and 2) it’s relevant to my talk that went up today, for anyone looking for further reading
The problem of confounded variables has affected research in a variety of areas, including social policy (wire vs. terry cloth mothers for monkeys), ergonomic keyboards (split vs. standard), and survey design (labeled vs. unlabeled item response options). ow.ly/toZY50zWDV7
Way too many misleading stories like these based on one day's worth of figures. Wisconsin reported 22 deaths on Wednesday but that was after three very slow days (3, 4, 3) Sunday thru Tuesday, which likely reflected lags because of the 3-day weekend. nbcnews.com/news/us-news/w…
There are still *way* too many stories about big spikes in cases when the cause of those spikes was a big increase in tests. And remember, it's a good thing when states start doing more tests!
People are having trouble with this, so let me explain. If a caseload in a state is constant, and you test more people, you're going to appear to get more cases. If it's declining and you test a *lot* more people, same effect. 7/6
Peter's corrections are spot on but I will say this about him... he is way too polite. The trend of national reporters dripping out stats without context is lazy at best and pandering for apocalypse dollars at worst.
Another mistake is not understanding lags in the data. If someone gets infected, it will likely take ~2 weeks before they get symptoms, get tested, and the test shows up in the data. Yet many articles assume that cases will *instantly* rise when regulations are changed.
It's not a question of innumeracy because the math isn't complicated. It's more in the category of "too good to check", with not bothering to be rigorous when the surface (but perhaps misleading upon even mild scrutiny) interpretation of the data meets a certain narrative.
One basic mistake is in not accounting for tests. If a state doubles its testing it is going to see far more cases. Many articles about big spikes don't even bother noting when they were caused by tests increasing. The math isn't hard. It's a basic failure to provide context.
@DougCollinsUX @MeasuringU has a great quant masterclass I am planning to attend.
@alanklement @MeasuringU I guess Twitter isn’t a medium for reasoned or nuanced discussion. You can take a strong stance without demonizing those who disagree, so I’m not going to mudsling with you. I will just note that your claim is at odds with both classical test theory and item response theory.
@Laghari_UX @JeffSauro @MeasuringU It’s an excellent book! Enjoy!
@jmspool @Pasquale_Stella @MeasuringU @Luis_Parker I think almost all tools fall into this category: Interviews Focus groups Surveys Card Sorting Personas Mental Models Context Scenarios Requirements Frameworks Sprints User Testing All tools that can lead to good design that go horribly awry constantly.
@jmspool @CarolynHermanUX @MeasuringU @Luis_Parker ...and TBH, I don’t really see NPS as part of my UX toolkit, more a tool for marketers etc. I’m far more interested in PURE but I haven’t had enough time with it yet!
Note to self: Don't use an analogy when discussing metrics that @jmspool has a problem with. Very (very) long thread about how NPS is, in many (many) ways not like a scientific measurement of heat. 😂
Does the NPS predict future business metrics? We found some evidence (but not a lot). ow.ly/uMre30lNfo4
@jmspool @Luis_Parker @MeasuringU There have been studies comparing NPS with other satisfaction measures. This one found a pretty strong correlation: link.springer.com/chapter/10.100… …. I don't particularly like NPS, but if all you get are 1s and no 10s, I'm not sure how you can argue it's all noise and no signal.
GREAT #UX metrics article! Highly recommend. ⭐️⭐️⭐️⭐️⭐️ x.com/measuringu/sta…
#UX metrics typically vary between 3–10% depending on the panel, but in some cases can exceed 20%. ow.ly/gQLz30kpwAN
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