“Defining Goals and Concerns” and “Deciding Who Should Be Participants” – Joseph Dumas & Janice Redish

Date : October 26, 2009

Before you begin testing it is important to set your primary evaluation goals and concerns. Once those have been defined, it will be possible to plan a usability test accordingly. Dumas & Redish raise the following processes: making choices among goals and concerns; moving from general concerns to specific ones; and understanding sources of goals and concerns.

Making Choices Among Goals and Concerns
Depending on the stage at which a product has been designed and prepared for testing, goals and concerns will differ. In any case, they should be defined in order to uncover the appropriate problems.

Moving From General Concerns to Specific Ones
The authors suggest both looking at general and specific concerns, those that help frame a type of user and those that may suggest specific tasks for evaluation.

Understanding Sources of Goals and Concerns
– Concerns from task analysis and quantitative usability goals: Quantitative performance data may, for instance, refer to how much time participants will spend on a given task.
– Concerns from a heuristic analysis or expert review: Local problems raised by heuristic analyses or expert reviews help guide some of the questions you may want answered during an evaluation.
– Concerns from previous tests: In the context of iterative testing, questions may concern the problems revealed in previous tests.

Selecting who are the most appropriate participants for testing can be challenging. At this point you should seek representative users. The authors suggest: developing user profiles, selecting subgroups for a test, defining and quantifying characteristics for each subgroup, and finally deciding how many people to include in a test.

Developing user profiles

User profiling is assessed collectively within the team, and involves marketers, engineers, and designers. Profiles will differ depending on what type of product is to be tested, and whether it is a novel or a upgraded version of an existing product.
– Relevant characteristics are found in two categories: those that are found to be common to all users and those that may be specific to some users. – Grouping the target audience into different subgroups helps you decide on which factors matter most. Those subgroups may concern background information such as: users’ specializations, their general and specific computer skills (if any), their knowledge of the product at test, and their experience with similar products. – It is also important when testing the usability of a product to think broadly about users and to seek non-representative users as well. These type of participants will give an insight as to how efficiently this product might correspond to non-expert users.

Selecting subgroups for a test
Dumas & Redish define subgroups as “people who share specific characteristics that are important in the user profile.” (p.123) They propose beginning with a pretest questionnaire or survey for defining those different categories or subgroup users. A detailed understanding of user characteristics can be found through collecting quantitative responses. This helps dividing users into subgroups with limited and measurable requirements which include novice, experienced, and intermediate users.

Deciding how many people to include in a test
Finally, it is important to limit the number of subgroups and the amount of participants within each subgroup before you begin testing. As suggested in Nielsen and Molich 1990’s study, between 4 to 10 participants would suffice to uncover 80-90% of usability problems. Dumas & Redish propose to test 6 to 12 users to make up 2 to 3 subgroups of at least 3 users per subgroup to avoid collecting idiosyncratic data. While testing you will find that similar problems within subgroups as well as across subgroups will emerge, which help determine a number of global problems to consider when redesigning.

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Frog’s Research Learning Spiral (How the Why of data wins over the What)

Category : Designing for Usability, Interface Research, Research in Design Practice

As clients are becoming increasingly aware of ethnographic-ish research being an essential and integral part of the design process for a high quality user experience offer, it is important for us (designers) to learn an appropriate format for approaching and customizing research on a project basis.

Frog’s Research Learning Spiral, as David Sherwin names it in A Five-Step Process for Conducting User Research, allows us to think of research as not only a set of insight-focused methodologies and collaborative practices, but also very much so as a process of articulating and defining the focus area and scope of the research itself through its 5 learning stages: Objectives, Hypotheses, Methods, Conduct, and Synthesis.

I would like to focus on the the 3 early stages of the spiral. These are fundamental in situating the research area and addressing design questions with methodologies geared towards feeding our knowledge lexicon of people and things in their habitual contexts according to pre-defined objectives and hypotheses.

1 • Objectives focus on the framing of questions following the 3 Ws and an H structure: Who What When Where Why and How. These together help us define who the demographic user base is, what activities they might be involved in while using our service or product, when they would be engaged in such activities, where these activities would take place, under which emotional or rational states (why), and using which processes. These questions are in turn reformulated into simple statements of research objectives, which outline the scope of the research effort.

2 • Hypotheses are assumption packed opinions or suppositions we have about a product or service, its users, and the contextual settings in which the product acts, which are meant to be tested and challenged. Sherwin lists 3 types of hypotheses: attitude (what users would like to get out of a service), behavior (what users would like to do with the service), and feature (which feature users would most enjoy using).

3 • Methods — such as contextual enquiry, surveys, interviews, and benchmarking — can help prove or disprove these hypotheses by revealing key data about a demographic user, their contextual environments, and identify leverage points wherein design can affect their everyday and provide positive change or support. Other more participatory activities which involve probing users — such as diary studies, card sorting, and paper prototyping — can serve as experiential idea generating methods with a capacity for drawing design solutions and concepts that meet the user’s needs and mental models. Finally, evaluative methods — such as usability testing, heuristic evaluations, and cognitive walkthroughs — will demonstrate whether these ideas are effective, useful, and desirable.

[F]inding meaning in your data […] can mean reading between the lines and not taking a quote or something observed at face value. The why behind a piece of data is always more important than the what.” — Lauren Serota, Associate Creative Director at Frog Design

According to Sherwin, data tells us what and when users do things, but not why. Context is in fact king. Integrating such framework for user research helps provide us with the contextual understanding — the understanding of given demographics’ everydayness — for making more informed design decisions.

I am particularly interested in the name “learning spiral”: a looping process that doesn’t need to be lengthy, costly, and a unique event. It is spiral and has the potential of being a cyclical and iterative process, which can be applied as needed at different stages of a design process and with a different scope. That spiral from which I can learn allows me to investigate more specific areas of my users’ everyday by defining learning objectives.

While every research endeavour has a plan and objectives, i particularly found this interpretation because it gives importance to the planning and framing of research and integrates objectives definition as part of the research itself. Typically research seems to begin with contextual inquiry and interviewing right away as a recourse to inform the design approach and concept which does not necessarily end up being a solution that is desirable. Involving research participants in the framing of the research seems to be a more inclusive and humane approach that is bound to have a worthwhile and desirable quality.

— source: http://bit.ly/1eCoUyH

“A Human-Centered Technology.” – Don Norman

Category : Designing for Usability

When we speak of technology what we are often referring to are communication platforms, such as computers and cellular phones. For Don Norman (1993), anything that is created by human beings is a technological artifact, whether physical (tangible objects, i.e. paper) or mental (information structures, i.e. language). Norman divides technological innovations into good and bad, those that “make us smart” by extending human capabilities and those that “make us stupid” by frustrating, captivating, enslaving. His focus involves the understanding of both technologies that extend cognition and those that manipulate cognition.

While innovation has a historical contribution in terms of providing appropriate and efficient tools for society that help enrich human knowledge and enhance mental capabilities (memory, thought, reflection), it has however created modes of entertainment that increasingly promote a consuming rather than a productive people and is accountable for the apparent social divider between the haves and havenots of technology.

Moreover, new technologies create new social practices, specializations and knowledge requirements. This in turn adds a new set of social dividers between those who possess the required skills and those who don’t. The societal implications of new technologies have become more and more complex with the advent of computational systems. Norman’s aim is as follows: “If we learn the reasons for and the properties of these various technologies, then maybe we can control the impact.” His aim is to influence a human-centered approach to technology; a quest for technologies that demonstrate humane properties.

“Toward a Human-Centered View of Technology and People”

As technology becomes increasingly immaterial, its function concerns more and more the fulfillment of abstract needs, such as time-management, decision-making, methodologies, etc. Norman’s critique is that rather than aiding human cognition, technologies today add confusion. He notes that “distractibility”, “illogical” behaviour, and other concurrent characterizations are responses to the rigidness of the way in which computers operate. Those characterizations are related to the constant need for technology to impose a certain type of behavioral and mental effort (i.e. to pay attention, to speak grammatically, etc.). In addition, the information overload creates a feeling of inadequacy in society; such as, the inadequacy of the mind the memorize a certain amount of information, the incapacity to cope with innovation, etc. His premise is that “people err,” and computers don’t, instead they are faulty and the blame is made on the human.

‘To say that people often act illogically is to say that their behavior doesn’t fit the artificial mathematics called “logic” and “decision theory.” ‘

Hence, this adds yet another social divide: machine-centered behavior and human-centered behavior. For Norman, the problem lies in the way technology inadequacy to adapt to the human. Technology would benefit society if it understood more the way in which human cognition works. Norman also criticizes technology for creating an information saturation wherein humans are exposed to too much information that results in a so-called mental “burnout”. To illustrate this side-effect, Norman distinguishes between what technology is able to measure (i.e. hours worked per week) and what it can’t (i.e. satisfaction, rupture, experience), which he respectively refers to as “hard sciences” and ‘ “soft” sciences.’ The “hard sciences” make simple and objective measurements, leaving out what it cannot measure; whereas the ‘ “soft” sciences’ concerns itself with the complexity of subjective measurements, thereby making a link between technology and the living system.

“Two Kinds of Cognition”

The author identifies two kinds of cognition: experiential and reflective. Experiential cognition refers to the way we acquire expert skills through the use of our bodily skills; reflective cognition refers to mental thought processes that inform the development of new concepts and appropriate structures that serve as guides for action. Those are complementary and essential in the sense that they include both inner (mental) and outward (physical) practices.

The point here is that technologies that excite the mind (such as, TV) cannot be a substitute for experience, and hence cannot provide adequate modes of cognitive enhancement. “Experiential thought” is only possible through an active participation and engagement of the world.

Source:
Norman, Don. “A Human-Centered Technology.” Things That Make Us Smart: Defending Human Attributes in the Age of the Machine. Perseus Books, U.S.A., 1993.

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