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'''Remote usability testing''' (also known as unmoderated or asynchronous usability testing) involves the use of a specially modified online survey, allowing the quantification of user testing studies by providing the ability to generate large sample sizes. Similar to an in-lab study, a remote usability test is task-based and the platforms allow you to capture clicks and task times. Hence, for many large companies this allows you to understand the WHY behind the visitors intents when visiting a website or mobile site. Additionally, this style of user testing also provides an opportunity to segment feedback by demographic, attitudinal and behavioural type. The tests are carried out in the user’s own environment (rather than labs) helping further simulate real-life scenario testing. This approach also provides a vehicle to easily solicit feedback from users in remote areas. '''Remote usability testing''' (also known as unmoderated or asynchronous usability testing) involves the use of a specially modified online survey, allowing the quantification of user testing studies by providing the ability to generate large sample sizes. Similar to an in-lab study, a remote usability test is task-based and the platforms allow you to capture clicks and task times. Hence, for many large companies this allows you to understand the WHY behind the visitors intents when visiting a website or mobile site. Additionally, this style of user testing also provides an opportunity to segment feedback by demographic, attitudinal and behavioural type. The tests are carried out in the user’s own environment (rather than labs) helping further simulate real-life scenario testing. This approach also provides a vehicle to easily solicit feedback from users in remote areas.

Popular remote usability testing companies include , , and .


==How many users to test?== ==How many users to test?==

Revision as of 23:53, 17 September 2010

Usability testing is a technique used to evaluate a product by testing it on users. This can be seen as an irreplaceable usability practice, since it gives direct input on how real users use the system. This is in contrast with usability inspection methods where experts use different methods to evaluate a user interface without involving users.

Usability testing focuses on measuring a human-made product's capacity to meet its intended purpose. Examples of products that commonly benefit from usability testing are foods, consumer products, web sites or web applications, computer interfaces, documents, and devices. Usability testing measures the usability, or ease of use, of a specific object or set of objects, whereas general human-computer interaction studies attempt to formulate universal principles.

History of usability testing

A Xerox Palo Alto Research Center (PARC) employee wrote that PARC used extensive usability testing in creating the Xerox Star, introduced in 1981.] Only about 25,000 were sold, leading many to consider the Xerox Star a commercial failure.

The Inside Intuit book, says (page 22, 1984), "... in the first instance of the Usability Testing that later became standard industry practice, LeFevre recruited people off the streets... and timed their Kwik-Chek (Quicken) usage with a stopwatch. After every test... programmers worked to improve the program.") Scott Cook, Intuit co-founder, said, "... we did usability testing in 1984, five years before anyone else... there's a very big difference between doing it and having marketing people doing it as part of their... design... a very big difference between doing it and having it be the core of what engineers focus on.

Goals of usability testing

Usability testing is a black-box testing technique. The aim is to observe people using the product to discover errors and areas of improvement. Usability testing generally involves measuring how well test subjects respond in four areas: efficiency, accuracy, recall, and emotional response. The results of the first test can be treated as a baseline or control measurement; all subsequent tests can then be compared to the baseline to indicate improvement.

  • Performance -- How much time, and how many steps, are required for people to complete basic tasks? (For example, find something to buy, create a new account, and order the item.)
  • Accuracy -- How many mistakes did people make? (And were they fatal or recoverable with the right information?)
  • Recall -- How much does the person remember afterwards or after periods of non-use?
  • Emotional response -- How does the person feel about the tasks completed? Is the person confident, stressed? Would the user recommend this system to a friend?

What usability testing is not

Simply gathering opinions on an object or document is market research rather than usability testing. Usability testing usually involves systematic observation under controlled conditions to determine how well people can use the product.

Rather than showing users a rough draft and asking, "Do you understand this?", usability testing involves watching people trying to use something for its intended purpose. For example, when testing instructions for assembling a toy, the test subjects should be given the instructions and a box of parts. Instruction phrasing, illustration quality, and the toy's design all affect the assembly process.

Methods

Setting up a usability test involves carefully creating a scenario, or realistic situation, wherein the person performs a list of tasks using the product being tested while observers watch and take notes. Several other test instruments such as scripted instructions, paper prototypes, and pre- and post-test questionnaires are also used to gather feedback on the product being tested. For example, to test the attachment function of an e-mail program, a scenario would describe a situation where a person needs to send an e-mail attachment, and ask him or her to undertake this task. The aim is to observe how people function in a realistic manner, so that developers can see problem areas, and what people like. Techniques popularly used to gather data during a usability test include think aloud protocol and eye tracking.

Hallway testing

Hallway testing (or Hall Intercept Testing) is a general methodology of usability testing. Rather than using an in-house, trained group of testers, just five to six random people, indicative of a cross-section of end users, are brought in to test the product, or service. The name of the technique refers to the fact that the testers should be random people who pass by in the hallway.

Remote testing

Remote usability testing (also known as unmoderated or asynchronous usability testing) involves the use of a specially modified online survey, allowing the quantification of user testing studies by providing the ability to generate large sample sizes. Similar to an in-lab study, a remote usability test is task-based and the platforms allow you to capture clicks and task times. Hence, for many large companies this allows you to understand the WHY behind the visitors intents when visiting a website or mobile site. Additionally, this style of user testing also provides an opportunity to segment feedback by demographic, attitudinal and behavioural type. The tests are carried out in the user’s own environment (rather than labs) helping further simulate real-life scenario testing. This approach also provides a vehicle to easily solicit feedback from users in remote areas.

How many users to test?

In the early 1990s, Jakob Nielsen, at that time a researcher at Sun Microsystems, popularized the concept of using numerous small usability tests—typically with only five test subjects each—at various stages of the development process. His argument is that, once it is found that two or three people are totally confused by the home page, little is gained by watching more people suffer through the same flawed design. "Elaborate usability tests are a waste of resources. The best results come from testing no more than five users and running as many small tests as you can afford." . Nielsen subsequently published his research and coined the term heuristic evaluation.

The claim of "Five users is enough" was later described by a mathematical model which states for the proportion of uncovered problems U

U = 1 ( 1 p ) n {\displaystyle U=1-(1-p)^{n}}

where p is the probability of one subject identifying a specific problem and n the number of subjects (or test sessions). This model shows up as an asymptotic graph towards the number of real existing problems (see figure below).

In later research Nielsen's claim has eagerly been questioned with both empirical evidence and more advanced mathematical models . Two key challenges to this assertion are:

  1. since usability is related to the specific set of users, such a small sample size is unlikely to be representative of the total population so the data from such a small sample is more likely to reflect the sample group than the population they may represent
  2. Not every usability problem is equally easy-to-detect. Intractable problems happen to decelerate the overall process. Under these circumstances the progress of the process is much shallower than predicted by the Nielsen/Landauer formula .

Most researchers and practitioners today agree that, although testing 5 users is better than not testing at all, a sample size larger than five is required to detect a satisfying number of usability problems.

See also

References

  1. Nielsen, J. (1994). Usability Engineering, Academic Press Inc, p 165
  2. http://interactions.acm.org/content/XV/baecker.pdf
  3. http://news.zdnet.co.uk/itmanagement/0,1000000308,2065537,00.htm
  4. http://jerz.setonhill.edu/design/usability/intro.htm
  5. http://www.useit.com/alertbox/20000319.html
  6. http://www.useit.com/alertbox/20000319.html
  7. Virzi, R.A., Refining the Test Phase of Usability Evaluation: How Many Subjects is Enough? Human Factors, 1992. 34(4): p. 457-468.
  8. http://citeseer.ist.psu.edu/spool01testing.html
  9. Caulton, D.A., Relaxing the homogeneity assumption in usability testing. Behaviour & Information Technology, 2001. 20(1): p. 1-7
  10. Schmettow, Heterogeneity in the Usability Evaluation Process. In: M. England, D. & Beale, R. (ed.), Proceedings of the HCI 2008, British Computing Society, 2008, 1, 89-98

External links

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