Usabiliy Testing

Usabiliy Testing
Usability refers to the practice of "evaluating a product or service by testing it with representative users" (Assistant Secretary for Public Affairs, 2015). The goal is to discover possible problems with product structure and design so that these can be corrected before they undergo mass production. This way, issues are identified early, hence enabling the company to save on potential costs in case of product recalls. The Usability.Gov (2015) site shows that in a usability test, participants are asked to perform several tasks while the researcher have to answer the following questions:
Are participants able to complete specified tasks successfully?
How long does it take each participant to complete the specified tasks?
Are the participants satisfied with their experience with the product?
Are there any changes that need to be implemented in order to improve product performance and function?
Does the product performance meet the usability objectives?
Needless to say, usability tests require researchers to gather quantitative and qualitative data in order to determine wheher the product is able to meet the purpose for which it was developed. It is a multi-step process, and will often generate dozens of information which can confuse the researcher if the collected data is not organized properly. An article from the Nielsen Norman Group says, "If product teams don't know what to do with usability results, they'll simply ignore them. It is up to the usability practitioners to write usability findings in a clear, precise and descriptive way that helps the team identify the issue and work toward a solution" (Shade, 2013).
Of course, the first step towards creating a usablity report is through the analysis of data and the discovery of actionable patterns and trends. The kind of analysis to be used is of course, dependent on the the kind of data available: quantitative or qualitative. Quantitative data analysis enables the researcher to create generalizations of the results in reference to a general or sample population. Because it is able to provide estimates and standardized information, it provides credibility and predictability. The difficulty with quantitative data analysis is that it can be difficult to obtain information through structured data collection instruments. Moreover, it does not consider the context of participants which may have an effect on the way they perform the tasks (US Department of State Washington DC, 2013).
Meanwhile, qualitative data analysis addresses the disadvantages of quantitative analysis. It creates holistic results because it considers various factors such as ethnicity, age, sex, educational background, etc. Moreover, it has the capacity to discover complex phenomena and will provide the results that are relevant to various contexts. The disadvantage of this approach is that it is not generalizable and would require various perspectives to address the issues it raises. Also, qualitative data analysis almost always concerns large amounts of data which means that it is diffcult to implement and would require a long time before its results can be applied.
Best Practices for Presenting Findings
When data analysis has been completed, the next step is to create a repo...