Why Yet Another Usability Questionnaire?
Most usability evaluation in practice leans on metrics like conversion rates, task success, or time on task. Those numbers are easy to collect, but they do not capture usability as a whole. Questionnaires such as SUS, UMUX, and SUMI do a better job, but they share a structural weakness: their items describe the effects of usable or unusable design ("I felt very confident using the system") rather than the causal aspects ("the text was hard to read"). That makes them poor at telling you what to fix.
Inuit — short for Interface Usability Instrument — was built to close that gap. It is a seven-item questionnaire with a score normalized to 0–100, calibrated to be more diagnostic than SUS while imposing less response burden. Its items target seven distinct factors: informativeness (Q1), understandability (Q2), confusion (Q3), distraction (Q4), readability (Q5), information density (Q6), and reachability (Q7).
The instrument was developed in a two-step process. First, the authors reviewed more than 250 usability guidelines and checklists, filtering them against the goals of actionability and brevity. The resulting draft was refined through expert interviews with nine usability professionals. Finally, a confirmatory factor analysis (CFA) was run on data from a study in which 81 participants evaluated four news websites. The fit indices came back solid: CFI was 0.971 (good fit is 0.95 or above) and RMSEA was 0.063 (acceptable range is 0.05–0.08). In plain terms, the data fit the hypothesized model — Inuit does measure usability.
The Questionnaire
Respondents rate each item on a scale from 1 ("completely disagree") to 5 ("completely agree"). The bracketed parts are meant to be adapted to your specific interface, e.g., products in an online shop, articles on a news website, or results in a search engine.
| Q1 | I found [the information] I was looking for. |
| Q2 | I could easily understand [the provided information]. |
| Q3 | I was confused using [the interface]. |
| Q4 | I was distracted by elements of [the interface]. |
| Q5 | Typography and layout added to readability. |
| Q6 | There was too much information presented in too little space. |
| Q7 | [My desired information] was easily reachable. |
The Inuit score is computed as follows:
(Q1 + Q2 + Q5 + Q7 - Q3 - Q4 - Q6 + 11) * 100/28
The constant 11 and the divisor 28 come from the response scale. Q1, Q2, Q5, and Q7 are positively worded, so higher ratings are better. Q3, Q4, and Q6 are negatively worded, so lower ratings are better. Subtracting the latter from 6 flips them, producing a raw sum between 7 and 35. Subtracting 7 yields a range of 0 to 28, and dividing by 28 followed by multiplying by 100 normalizes the result to a 0–100 scale.
Diagnostic Value in Practice
Because each item maps to a specific usability factor, the results tell you where to intervene. A poor score on readability (Q5) points at typography or contrast; a poor score on reachability (Q7) suggests that important content or functions are hard to get to. This design also makes Inuit suitable for generating training data for machine-learning models that predict usability from user interactions or web analytics.
The first field test took place at Unister GmbH, which ran travel search engines like fluege.de and reisen.de and was building a new semantic search engine called BlueKiwi. In a study with 81 participants, the average Inuit score for BlueKiwi's results page was 59.9. The lowest ratings were for informativeness (Q1), information density (Q6), and reachability (Q7).
Those results drove a redesign. The number of advertisements was cut, search results were made more concise, and layout elements were more clearly aligned and separated. A follow-up study measured an overall Inuit score of 67.5 — an 11% improvement — with gains across every single item.
Testing Inuit In The Wild
Despite being published in 2014, Inuit remained largely confined to academic circles. The research–practice gap is real: work tied to a Ph.D. project often gets set aside after it has served its purpose in a paper, thesis, or defense. That is exactly what happened to Inuit, but there was a second reason for the delay in broader promotion.
The authors wanted to see the instrument perform in real industry settings over time. They ran a longitudinal study across nearly two years, conducting quarterly usability benchmarks of several e-commerce websites using both Inuit and the System Usability Scale (SUS). A total of 6,368 users participated. Benchmark results were tracked in product KPI dashboards and shared with a team of six product managers. After about two years, the product managers were interviewed about how they used the data, their challenges, and what could be improved.
High-level results were telling. All six product managers independently described Inuit as more intuitive, less abstract, and more actionable than SUS. They found most Inuit items more specific and easier to interpret, and therefore more relevant to their work. SUS, by comparison, was considered “good for [the] overall score” and offering a bird’s eye view. A common wish from the product managers was even more granular insight into where on a page usability problems occurred — one suggested hybridizing items from SUS and Inuit for an optimal tool.
Reliability And Correlation Data
Statistical checks were run on the accumulated data. Cronbach’s α, a measure of internal consistency indicating how well items in a test measure the same underlying variable, was computed for Inuit (3,190 answers) and SUS (3,178 answers).
Cronbach’s α is a statistical measure for the internal consistency of an instrument, which can be interpreted as “the extent to which all of the items of a test measure the same latent variable [i.e., usability].”
— Wikipedia
Values of 0.7 or above are generally deemed acceptable. Inuit achieved 0.7; SUS 0.8.
When comparing overall scores aggregated across the different websites and tasks, Inuit and SUS showed a substantial (Pearson’s r = 0.53) and highly significant correlation (p < 0.001). Practically, when SUS scores go up, Inuit scores go up with them. The rough linear relationship is approximately INUIT = 0.6 × SUS + 17, meaning both measure the same underlying construct.
These encouraging findings prompted the authors to publish this practice-oriented overview now, with plans to report the full dataset in greater detail later.
SUS Or Inuit — Which One To Pick?
A question that naturally arises is whether Inuit is meant to replace SUS. The authors’ position is clear: no. Both instruments measure usability, but they were built for different contexts and goals. The choice truly “depends.”
SUS has existed since the 1990s and remains the most established and heavily validated usability questionnaire. Its longevity is unmatched, and it is ideal for high-level score comparisons or referencing public benchmarks. Inuit cannot yet claim similar validation breadth.
It does, however, offer two structural advantages:
- Efficiency. Inuit has only seven items, making it a “complete” usability instrument with 30% fewer questions. This matters when users have short attention spans or when researchers aim to reduce participant fatigue in remote studies.
- Actionability. Inuit items were designed from the outset to be more diagnostic for practitioners. In the case study interviews, product managers noted that Inuit pointed to more concrete areas for improvement, while SUS was perceived as too vague for specific fixes.
If the goal is trending over time or comparing against an established baseline, SUS is likely the safer bet. For regular, detailed iterations with specific UX teams, Inuit’s sharper diagnostic lenses may be the difference.
Applying Inuit And Adapting Its Items
Inuit works in any scenario pairing an interface with a task — either one defined by the researcher or by users themselves. It has been successfully demonstrated in controlled settings and natural usage environments, across news sites, search engines, and e-commerce.
No usability tool can be tested against every possible interface, which is another reason the authors are publishing this. Inuit has been publicly available since 2014, yet the authors have no knowledge of external usage. They are looking for reports from practitioners who have used it.
Core Inuit items lean heavily on information-seeking since that is the historical root of the instrument and covers most user activity. Note that most information is non-textual. The questions can be tailored to many tasks so long as the core usability aspects — informativeness, understandability, confusion, distraction, readability, information density, and reachability — are preserved.
Consider an e-learning module consisting of annotated video and a quiz. Q1 could be rephrased to “I had all the information necessary to complete the module” and Q7 to “All the information necessary to complete the module was easily reachable.”
Where Inuit Fits
Inuit is one more tool in the usability measurement pool — developed because the existing options, like SUS, do not always fit perfectly. What Inuit brings is the ability to be diagnostic about breakpoints in an interface, while relying on fewer questions than most. Officially, it is valid, based on confirmatory factor analysis (CFA), and correlates well with SUS reliability data from a large-scale, longitudinal study.
It is worth running side-by-side with SUS if your team is debating whether to make the switch. If SUS is the baseline for public benchmark comparisons, Inuit can add targeted, day-to-day diagnostic value. Cost of testing is low, validation data exists, and its design at a scale of seven items makes it a pragmatic choice for conversion-rate-obsessed growth teams.
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References
- “SUMI Questionnaire Homepage,” Jurek Kirakowski
- “10 Things to Know about the Single Ease Question (SEQ),” Jeff Sauro (MeasuringU)
- “Measuring Usability: From the SUS to the UMUX-Lite,” Jeff Sauro (MeasuringU)
- “Ensuring Web Interface Quality through Usability-Based Split Testing,” Speicher, Maximilian, Andreas Both, and Martin Gaedke (International Conference on Web Engineering)
- “Inuit: The Interface Usability Instrument,” Speicher, Maximilian, Andreas Both, and Martin Gaedke (Design, User Experience, and Usability: Design Discourse)
- “S.O.S.: Does Your Search Engine Results Page (SERP) Need Help?” Speicher, Maximilian, Andreas Both, and Martin Gaedke (Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems)
- “So, How Can We Measure UX?” Maximilian Speicher (ACM Interactions)
- “Growth Marketing Considered Harmful,” Maximilian Speicher
- “Current Practice In Measuring Usability: Challenges to Usability Studies and Research,” Kasper Hornbæk
- Latent variable, Wikipedia
- Confirmatory factor analysis, Wikipedia
- Internal consistency, Wikipedia
Copyright © 2023 by Maximilian Speicher & Johanna Jagow. Originally published by Smashing Magazine
