Heuristic Evaluation Quality Score (HEQS): Defining Heuristic Expertise

Peer-reviewed Article

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Abstract

This paper identifies the factors affecting heuristic expertise and defines levels of expertise permissible to conduct an evaluation. It aims to standardize skills or define heuristic expertise worldwide and also suggests ways to improve issue categorization.

An online heuristic evaluation competition was hosted on the World Usability Day website in November 2007 by Usability Professionals’ Association (UPA), Bangalore. Twenty contestants from the U.S. and India with heuristic evaluation experience ranging from 0 to 10 years participated. Contestants were judged on a quantitative framework that assigns weights to the severity of the identified issues (Kirmani & Rajasekaran, 2007). Results indicated that the group with average heuristic experience of 2 years found a mean HEQS% of 8% in 1 hour. Previous studies identified that evaluators found a mean of 34% of issues but did not address issue quality (Nielsen & Landauer, 1993). Further studies on heuristic expertise quality would make the standards more reliable.

Practitioner’s Take Away

  • A Heuristic Evaluation Quality Score (HEQS) can be used to quantify heuristic evaluation expertise to ensure evaluations of a certain standard. It is critical for evaluations to be of a certain standard to build reliability and trust among evaluators and ultimately provide the end users with high quality applications.
  • The average HEQS% is 8% for evaluators taking 1 hour with heuristic experience of 2 years.
  • Evaluations are recommended to be done by above average and exceptional evaluators. Above average evaluators have an HEQS% of 8% or more and exceptional evaluators have an HEQS% of 15% or more.

Introduction

Heuristic evaluation is a discount
usability engineering method involving a few evaluators who judge the
compliance of an interface based on a set of heuristics. It is difficult for
one evaluator to find all the usability problems with an interface hence a
few evaluators, preferably between three to five evaluators, are suggested.
This optimal range gives the best benefit-to-cost ratio (Nielsen & Landauer,
1993). Because the quality of the evaluation is highly dependent on
their skills, it is critical to measure these skills to ensure evaluations
are of a certain standard. This popular technique that is used by 76% of the
usability community (UPA Survey, 2005) and has a high a cost-to-benefit
ratio of 1:48 (Nielsen, 1994) emphasizes the assessment of heuristic
evaluation skills. More so, evaluators are extremely confident of their
abilities as experts. An online heuristic evaluation competition held in
November 2007 by UPA, Bangalore, India asked contestants to rate themselves on a scale of 5, where 5 meant that
they absolutely thought they would win the competition. Results showed that
85% of 20 contestants felt confident of winning the competition. They scored
4 or more on a scale of 5. This confidence
or the inability to authenticate it threatens the quality of
heuristic evaluation. Experts could misuse this false
confidence, intentionally or unintentionally, to provide expertise of sub
optimal standards limiting the usability of the applications that they
evaluate. This issue can pose grave risk to the users of these applications
who depend on these applications, in some cases, to save their lives.
Hence, it is critical to quantify this expertise to ensure evaluations of a
certain standard.

A framework to quantify heuristic evaluation skills was
proposed by the authors (Kirmani & Rajasekaran, 2007). Quantification is
based on the number of unique, valid issues identified by the evaluators as
well as the severity of each issue. Unique in this context refers to a problem that could be repeated in more than one
place but still counted as a single, unique problem with several instances.
Unique, valid issues are categorized into eight user interface
parameters and severity is categorized into three. The three categories of
severity are showstoppers or catastrophic issues preventing users from
accomplishing goals, major issues or issues causing users to waste time or
increase learning significantly, and irritants or cosmetic issues violating
minor usability guidelines. Weights of 5, 3, and 1 are assigned to
showstoppers, major issues, and irritants respectively. A Heuristic
Evaluation Quality Score (HEQS) is computed for each evaluator by
multiplying the weight factor with the number of issues in that severity
category. For example, Evaluator A has identified 2 showstoppers, 10 major
issues, and 20 irritants his HEQS= 2*5+10*3+20*1 =60.
A benchmark of the collated
evaluations of all the evaluators is used to compare skills across
applications as well as within applications. If the benchmark HEQS is 200
then Evaluator A identified an HEQS% of 60/200 or 30%. Skills are also
computed for eight User Interface (UI) parameters to identify strengths and
weaknesses of the evaluators. The eight parameters are Interaction Design,
Information Architecture, Visual Design, Navigation, Labeling, Content,
Functionality, and Other (for issues that do not fall into the first seven).

This metric has been used to compare the heuristic expertise of individual evaluators with
other evaluators across or within applications to base evaluations on
individual strengths. It has also been used to identify weaknesses and train
evaluators in those skills. Measuring improvement based on training and
tailoring training programs to groups or individuals based on this
methodology are some other applications.

What has not
been addressed in the previous study is a definition of heuristic expertise
at a global level. This study aims to define these standards for the world
wide usability community. It is also known that many such competitions will
need to be conducted before these results can be generalized and this is a
first attempt to do so.

In particular the following questions are addressed:

  • What are the factors affecting heuristic evaluation expertise?
  • What is the average expertise of heuristic evaluators?
  • What level of expertise is required for one to conduct a heuristic evaluation?

Method

A world wide heuristic evaluation competition that was
part of Usability Professionals’ Association’s (UPA) World Usability Day was
hosted by UPA, Bangalore online in November 2007. The competition details were as follows:

  • Nature of the website: A healthcare site where evaluators put in symptoms and the website provides advice.
  • Scope of the evaluation: Five scenarios were given to evaluators.
    • Find all conditions related to a cough.
    • Check all symptoms associated with a cough.
    • Edit the symptoms.
    • Find related articles.
    • E-mail an article to yourself.
  • Time to do the evaluation: 1 hour
  • Demographic data collected: age, gender, experience, location, and confidence to win

Contestants had to sign an honor statement stating that
they would not take more than an hour to complete the evaluation and that
the evaluation was the sole effort of the contestant. The competition lasted
for 2 weeks and entries were submitted in a particular format (see Table 1).
The UI parameters and severity categories were taken from the initial HEQS
paper (Kirmani & Rajasekaran, 2007).

Table 1. The Evaluation Format.
Issue The other 2 steps in the process of getting advice for a particular symptom are barely visible.
UI Parameter Visual Design
Severity Major Issue

Contestants were encouraged to participate via various
methods such as sending links to the competition via individual e-mails,
blogs, social networking sites, and usability communities. Anyone could
participate. They were all directed to the World Usability Day website (see
Figure 1) where the event was hosted online. The event page had a
downloadable presentation with the demographic data collection form,
evaluation format, scope of the evaluation, and judgment criteria. Each
contestant used their own evaluation criteria to conduct the 1 hour
evaluation. They had to e-mail their entries along with the filled
demographic form to a given e-mail address.

Figure 1. Competition details and instructions hosted on the World Usability Day website.

Figure 1. Competition details and instructions hosted on the World Usability Day website.

The competition was judged the same way as described in
the previous HEQS paper (Kirmani & Rajasekaran, 2007). The judges were given
a benchmark of a collation of all the issues (200 plus issues) of all the
contestants. The three judges who were heuristic experts followed the
process listed below.

  1. Each judge rated each issue of the 200 plus issues as valid or invalid individually.
  2. Each judge categorized the severity and UI parameter of each valid issue individually.
  3. They got together to discuss each issue and its severity and UI parameter categorization.
  4. If they did not agree on the issue validity, severity, or UI parameter categorization they discussed it together to arrive at a final consensus.
  5. This finalized list of valid issues with their appropriate severity and categorization was used to judge each contestant.
  6. Judges were requested to write down their thoughts on the categorization process to later discuss ways to improve it.
  7. Each issue of a contestant’s entry was matched to the finalized list and weights of 5, 3, or 1 were awarded based on the severity of the issue (5 for every showstopper, 3 for every major issue, and 1 for every irritant). Summing up the scores for all the issues one arrived at the HEQS for each contestant.
  8. If contestants incorrectly categorized issues for severity or UI parameter they were re‑categorized to arrive at their scores. Contestants were asked to enter the severity and UI parameter to gain insight into the categorization process. For example, the way the issue was worded depended to a great extent to the way it was categorized. This qualitative data helped to improve the categorization process.

Demographic Data

Twenty contestants took part in the competition. Table 2 summarizes the demographic data.

Table 2. Demographic Data of the Contestants.
Parameter Average Range
Age 28.4 years 22-34 years
Time spent as heuristic evaluator 23.7 months 0-120 months
Time spent as a usability practitioner 30.7 months 0-144 months
Time spent as a domain expert (i.e. healthcare) 4.2 months 0-24 months
Confident of winning (self rating on a scale of 5 where 5=absolutely win and 1=never win) 4.1 1-5
Gender 6 females and 14 males4
Location 6 states and 2 continents

Karnataka, India
Mumbai, India
Gujarat, India
Chennai, India
New Jersey, US
California, US

Results

Overall results indicated that the group found an average
HEQS% of 8% in 1 hour ranging from 2% to 17% (see Figure 4). This compares
with previous studies of evaluators finding 24% and 25% (highest in both
cases being 38%) in 2 hours (Kirmani & Rajasekaran, 2007). The numbers are
slightly higher for the previous case studies as the average heuristic
experience was higher (more than 30 months) than the average heuristic
experience of the contestant group (23.7 months). The contestant group also
included 4 contestants who have never been exposed to heuristic evaluations.
Evaluators can be compared and their performance can be studied by looking
at the issues identified based on UI parameter (see Figure 2) and severity
(see Figure 3). For example, Evaluator 3 found twice as many interaction
design issues, thrice as many content issues, and five times as many
navigation issues as Evaluator 18. However, Evaluator 18 found five times as
many showstoppers as Evaluator 3 indicating that Evaluator 3 is good at
covering the breadth of issues (across UI parameters) while Evaluator 18 is
good at covering severe issues.

Figure 2. HE skills based on UI parameters.

Figure 2. HE skills based on UI parameters.

Figure 3. HE skills based on severity.

Figure 3. HE skills based on severity.

Figure 4. HEQS%.

Figure 4. HEQS%.

What is the average expertise of heuristic evaluators?

The average HEQS% is 8% for evaluations of 1 hour
conducted by a group of evaluators with an average heuristic experience of 2
years and an average usability experience of 2.5 years.

What are the factors affecting heuristic evaluation expertise?

The following factors affect heuristic evaluation expertise:

  • Usability experience: The relationship between usability experience and heuristic evaluation expertise is significant (see Table 3). Thirty percent of the variation between usability experience and heuristic evaluation expertise is related. The more usability experience the better is the quality of the evaluation.
  • Heuristic experience: The relationship between heuristic experience and heuristic evaluation expertise is significant. Seventeen percent of the variation between heuristic experience and heuristic evaluation expertise is related. The more heuristic experience the better is the quality of the evaluation.
  • Domain experience: Domain experience in this study did not significantly impact expertise. This could be due to the non-technicality of the website. Identifying conditions for a set of symptoms is understood world wide and does not require a lot of learning but other studies have shown that domain experts are better evaluators (Anthanasis & Andreas, 2001).
  • Training: Training does impact heuristic evaluation experience. Quality of the evaluation improves with training. A 48.4% improvement was seen in a study conducted on a group of 26 evaluators (Kirmani & Rajasekaran, 2007).

The following factors do not affect heuristic evaluation expertise:

  • Age: Age does not affect heuristic evaluation expertise.
  • Gender: Gender does not affect heuristic evaluation expertise.
  • Self rating: Self rating or self proclamation of calling oneself an expert does not corroborate with heuristic expertise. Eighty-five percent of 20 contestants felt confident of winning the competition and rated themselves 4 or higher on a scale of 5.

This study did not shed light on site complexity or
previous experience on the site. Future research should look at more complex
examples and prior experience with the site.

Table 3. Correlation Analysis of Demographic Data.
Parameter Range Average Median Significant/Not
(at significance level of 0.1)
Gender Female, Male
Age 20 – 34 years 28.4 years 29 years
Usability Experience 0 – 144 months 30.7 months 15 months Significant
Heuristic Experience 0 – 120 months 23.7 months 13 months Significant
Domain Experience 0 – 24 months 4.2 months 0
Self rating and Confidence to win 1 – 5
(5 being “I will absolutely win”)
4.1 4

What level of expertise is required for one to conduct a heuristic evaluation?

Expertise can be divided into three levels:

  • Below average evaluators: Evaluators finding an HEQS% of less than 8% are below average performers.
  • Above average evaluators: Evaluators finding an HEQS% of 8% or more are above average performers.
  • Exceptional evaluators: Evaluators finding an HEQS% of 15% or more are exceptional performers. Fifteen percent has been arrived at by selecting the top 5%, given the highest performers have been identifying HEQS% of 17% – 19% of issues in 1 hour.

It is known that any evaluator who identifies issues to
improve the usability of an application is better than none, but I recommend
that you choose 3-5 above average or exceptional evaluators to see an
evaluation of high quality.

Improving severity and UI parameter categorization

From the inter-rater reliability in Table 4 we see that
evaluators can categorize showstoppers consistently but are not consistently
categorizing major issues and irritants.

Table 4. Inter-rater Reliability for Issues Based on Severity.
Benchmark Showstopper Major Issue Irritant Non-issue
Number of unique issues 21 153 33 49
Complete consensus 91% 66% 55% 86%

Currently descriptions of severity (Nielsen, 1994) are seen in Table 5.

Table 5. Current Descriptions of Severity.
Severity Description
Showstopper A catastrophic issue that prevents users from using the site effectively and hinders users from accomplishing their goals.
Major Issue An issue that causes a waste of time and increases the learning or error rates.
Irritant A minor cosmetic or consistency issue that slows users down slightly. It minimally violates the usability guidelines.

After judging the competition notes on categorization were
compared and we arrived at the following grid to improve the categorization
by adding two dimensions: the user and the environment (see Table 6).

Table 6. Revised Severity Ratings.
Severity About the issue Different users Different environments Yes/No*
Showstopper Does the issue stop you from completing the task? Can colorblind users interpret a colorful graph to complete a task? Does the issue create an unstable environment?
Showstopper Example The “Submit” button is not working and hinders users from sending their forms. If colors are the only form of communicating critical data to complete an online transaction, colorblind users are forced to abandon the task. For a healthcare site it is critical that advice given pertains to the conditions chosen. Incorrect association can cause harm.
Major Issue Does the issue cause you a major waste of time, increase your learning, increase the error rate, or violate a major consistency guideline? Does the issue increase errors for older adults? Does the issue increase learning for all users? Does the issue create an environment with a higher error or learning rates?
Major issue Example Using an “X” as an icon to zoom out breaks user mental models and increases errors considerably, especially in an environment where close is also denoted by an “X”. A low contrast between font and the background can cause an increase in error rates for older adults. Providing smaller than usual buttons on a mobile interface where people are always moving can increase error rates considerably.
Irritant Does the issue involve a cosmetic error, slow you down slightly, or violate a minor consistency guideline? Does the site not have visual appeal to teenagers? Does the issue create an environment that slows you down slightly?
Irritant Example The label is “Symptom” when it actually should be plural as it has many symptoms. If the colors are not young and vibrant (e.g., pink and yellow) for a site catering to teenagers it violates a cosmetic error. If you are checking symptoms for your daughter, changing the content to cater to a different environment (third person) is helpful.

*Answering positively to one or more questions is Yes.

From the inter-rater reliability in Table 7 we see that
evaluators are not consistently categorizing information architecture
issues.

Table 7. Inter-rater Reliability for Issues Based on UI Parameters.
Benchmark Information Architecture Navigation Labeling Other Visual Design Interaction Design Content Functionality
Number of unique issues 13 20 16 12 52 52 33 9
Complete consensus 70% 85% 100% 92% 80% 92% 88% 78%

This could be due to the poor labeling of the group as
Information Architecture in usability circles denotes structure and
organization, navigation, and labeling. Hence, we have decided to re-label
it as Structure and Organization (see Table 8).

Table 8. Revised UI Parameter.
Current UI Parameter Current Description Redefined UI Parameter New Description
Information Architecture Accurate structuring of information into groups best matching the mental model of users. Structure and Organization Accurate structuring of information into groups best matching the mental model of users.

Limitations of this study

It is known that this study and its results are limited to
the small sample size that has been used. Generalizing these results will
require many more competitions with a diverse and larger sample size.

Conclusion

Further research with a more diverse and larger group
would help in sharpening the reliability of these results. Tighter controls
on variables affecting heuristic expertise to measure definite impact on
heuristic expertise will also increase reliability.

Acknowledgements

A special thanks to Intuit and Infosys Technologies Ltd. for sponsoring this competition. I would also like to thank Shanmugam Rajasekaran, Deepa Bachu, Amit Pande, Muthukumar, Anand Almal, Rajavel Manoharan, and Amit Bhatia for helping make the competition a success. Last but not least, I would like to thank the contestants who set aside valuable time and participated in the competition.

References

Andreas, P. & Athanasis, K. (2001, November). Heuristically Evaluating Web-Sites
with Novice and Expert Evaluators. Workshop on HCI.
8th Panhellenic Conference on Informatics, Nicosia, Cyprus.

Bailey, B. (2005, October). Judging the Severity of Usability Issues on Web
Sites: This Doesn’t Work. Retrieved March 2008, from

http://www.usability.gov/pubs/102005news.html

Chattratichart, J. & Brodie, J. (2002, September). Extending the heuristic
evaluation method through contextualization. Proceedings of the 46th Annual Meeting of the Human Factors and
Ergonomics Society, HFES
(pp. 641-645) Baltimore, Maryland.

Jacobsen, N. E., Hertzum, M., & John, B. E. (1998, October). The evaluator
effect in usability studies: problem detection and severity judgments.
In Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting (pp. 1336-1340) Chicago, Illinois.

Kirmani, S. & Rajasekaran, S. (2007). Heuristic Evaluation
Quality Score (HEQS): a measure of heuristic evaluation skills,Journal of Usability Studies, 2
(2), pp 61-75.

Lindgaard, G., Chatrattichart, J., Rauch, T., & Brodie, J. (2004). Toward
increasing the reliability of expert reviews.Proceedings UPA.

Hertzum, M., Jacobsen, N. E., & Molich, R. (2002). Usability Inspections by
Groups of Specialists: Perceived Agreement in Spite of Disparate
Observations. CHI2002 Extended Abstracts (pp 662-663).

Molich, R. (2006). Comparative Usability Evaluation–CUE. Retrieved March
2008, from http://www.dialogdesign.dk/cue.html

Nielsen, J., & Landauer, T. K. (1993, April). A mathematical model of the finding of usability problems.
Proceedings ACM/IFIP INTERCHI’93 Conference (pp. 206-213) Amsterdam, The Netherlands.

Nielsen, J. (1992, May). Reliability of severity estimates for usability problems found by heuristic evaluation.
Posters and short talks of the 1992 SIGCHI conference on Human factors in computing systems. Monterey, California.

Nielsen, J. (1994, April). Enhancing the explanatory power of usability
heuristics, Proceedings of ACM CHI’94 Conference (pp 152-158) Boston, Massachusetts

Nielsen, J. (1994). How to Conduct a Heuristic Evaluation. Retrieved March 2008, from
http://www.useit.com/papers/heuristic/heurstic_evaluation.html

UPA. (2005). UPA 2005 Member and Salary Survey. Usability Professionals Association. Bloomingdale, Illinois.

Introduction

Heuristic evaluation is a discount
usability engineering method involving a few evaluators who judge the
compliance of an interface based on a set of heuristics. It is difficult for
one evaluator to find all the usability problems with an interface hence a
few evaluators, preferably between three to five evaluators, are suggested.
This optimal range gives the best benefit-to-cost ratio (Nielsen & Landauer,
1993). Because the quality of the evaluation is highly dependent on
their skills, it is critical to measure these skills to ensure evaluations
are of a certain standard. This popular technique that is used by 76% of the
usability community (UPA Survey, 2005) and has a high a cost-to-benefit
ratio of 1:48 (Nielsen, 1994) emphasizes the assessment of heuristic
evaluation skills. More so, evaluators are extremely confident of their
abilities as experts. An online heuristic evaluation competition held in
November 2007 by UPA, Bangalore, India asked contestants to rate themselves on a scale of 5, where 5 meant that
they absolutely thought they would win the competition. Results showed that
85% of 20 contestants felt confident of winning the competition. They scored
4 or more on a scale of 5. This confidence
or the inability to authenticate it threatens the quality of
heuristic evaluation. Experts could misuse this false
confidence, intentionally or unintentionally, to provide expertise of sub
optimal standards limiting the usability of the applications that they
evaluate. This issue can pose grave risk to the users of these applications
who depend on these applications, in some cases, to save their lives.
Hence, it is critical to quantify this expertise to ensure evaluations of a
certain standard.

A framework to quantify heuristic evaluation skills was
proposed by the authors (Kirmani & Rajasekaran, 2007). Quantification is
based on the number of unique, valid issues identified by the evaluators as
well as the severity of each issue. Unique in this context refers to a problem that could be repeated in more than one
place but still counted as a single, unique problem with several instances.
Unique, valid issues are categorized into eight user interface
parameters and severity is categorized into three. The three categories of
severity are showstoppers or catastrophic issues preventing users from
accomplishing goals, major issues or issues causing users to waste time or
increase learning significantly, and irritants or cosmetic issues violating
minor usability guidelines. Weights of 5, 3, and 1 are assigned to
showstoppers, major issues, and irritants respectively. A Heuristic
Evaluation Quality Score (HEQS) is computed for each evaluator by
multiplying the weight factor with the number of issues in that severity
category. For example, Evaluator A has identified 2 showstoppers, 10 major
issues, and 20 irritants his HEQS= 2*5+10*3+20*1 =60.
A benchmark of the collated
evaluations of all the evaluators is used to compare skills across
applications as well as within applications. If the benchmark HEQS is 200
then Evaluator A identified an HEQS% of 60/200 or 30%. Skills are also
computed for eight User Interface (UI) parameters to identify strengths and
weaknesses of the evaluators. The eight parameters are Interaction Design,
Information Architecture, Visual Design, Navigation, Labeling, Content,
Functionality, and Other (for issues that do not fall into the first seven).This metric has
been used to compare the heuristic expertise of individual evaluators with
other evaluators across or within applications to base evaluations on
individual strengths. It has also been used to identify weaknesses and train
evaluators in those skills. Measuring improvement based on training and
tailoring training programs to groups or individuals based on this
methodology are some other applications.

What has not
been addressed in the previous study is a definition of heuristic expertise
at a global level. This study aims to define these standards for the world
wide usability community. It is also known that many such competitions will
need to be conducted before these results can be generalized and this is a
first attempt to do so.

In particular the following questions are addressed:

  • What are the factors affecting heuristic evaluation expertise?
  • What is the average expertise of heuristic evaluators?
  • What level of expertise is required for one to conduct a heuristic evaluation?