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- 52. Semi-structured qualitative studies
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Such considerations imply the need a to review data gathering techniques to maximise the likelihood of gathering valid data see Section 6 and b to reflect on the data quality and implications for the findings see Section There are at least two aspects to expertise: that in qualitative research and that in the study domain. There is no shortcut to acquiring expertise in qualitative research. Courses, textbooks and research papers provide essential foundations, and different resources resonate with — and are therefore most useful to — different people. Corbin and Strauss Corbin and Strauss , p.
Often these persons are disappointed when their participants are less than informative and the data are sparse, at best. As noted elsewhere, there is an interdependence between methods, research questions and resources; fixed methods have their place, but can rarely be applied cleanly to address a real research problem Furniss et al. As well as expertise in qualitative methods, the level of expertise in the study context can have a huge influence over the quality and kind of study conducted.
When the study focuses on a widely used technology, or an activity that most people engage in, such as time management e. Kamsin et al.
Curzon et al. Where the study is of a highly specialised device, or in a specialist context, the expertise of the researcher s can have a significant effect on both the conduct and the outcomes of a study. At times, naivety can be an asset, allowing one to ask important questions that would be overlooked by someone with more domain expertise. At other times, naivety can result in the researcher failing to note or interpret important features of the study context.
Pennathur et al. Domain expertise may also cause the researcher to become drawn into the on-going activity, potentially limiting their ability to record observations systematically — effectively becoming a practitioner rather than a researcher, insofar as these roles may conflict. In preparing to conduct a study, it is important to consider the effects of expertise and to determine whether or not specific training in the technology or work being studied is required. There will be other resources and constraints that create and limit possibilities for the research design. These include the availability of equipment, funding — for example, for travel and to pay participants —, time, and suitable places to conduct research.
Where a study takes place can shape that study significantly. Rogers, Of course, there are also study types where researcher and participant are at-a-distance from each other, such as diary studies and remote interviews.
52. Semi-structured qualitative studies
Rogers et al. All of these can be useful tools for data recording, depending on the situations in which data is being gathered.
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For instance, still photographs of equipment that has been appropriated by users, or a record of the locations in which technology was being used or how it was configured, provide a permanent record to support analysis, and to illustrate use in reports. As an example, Figure 2 shows how a home haemodialysis machine was marked up to remind the user to change a setting every time the machine was used.
Jack Sanger (Author of Young Children, Videos and Computer Games)
Screen capture software can give a valuable record of user interactions with desktop systems. Particular qualitative methods such as the use of cultural probes Gaver and Dunne, or engaging participants in keeping video diaries, or testing ubiquitous computing technologies, may require particular specialist equipment for data gathering. When it comes to data analysis, coloured pencils, highlighter pens and paper are often the best tools for small studies. For larger studies, computer-based tools to support qualitative data analysis e. NVivo or AtlasTI can help with managing and keeping track of data, but require an investment of time to learn to use them effectively.
Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below. The strips hanging at the bottom also show how the machine is being used as a temporary place to store cut strips for future use.
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In addition to the costs of equipment, the other main costs for studies are typically the costs of travel and participant fees. Within HCI, there has been little discussion around the ethics and practicality of paying participant fees for studies. In disciplines where this has been studied, most notably medicine, there is little agreement on policy for paying participants e. Grady et al. The ethical concerns in medicine are typically much greater than those in HCI, where the likelihood of harm is much lower.
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In HCI, it is common practice to recompense participants for their time and any costs they incur without making the payment, whether cash or gift certificates, so large that people are likely to participate just for the money. Traditionally, ethics has been concerned with the avoidance of harm, and most established ethical clearance processes focus on this. Willig Willig , p. However, the work of the author and co-workers with clinicians and patients Furniss et al. It should be about doing good, not just avoiding doing harm. This might require a long-term perspective: understanding current design and user experiences to guide the design of future technologies.
That long-term view may not directly address the desire of research participants to see immediate benefit. What motivates an individual technology user to engage with research on the design and use of that technology? This is not, however, universally the case. For example, in one of our studies of medical technologies Rajkomar et al.
For others, there is an indirect pay-back in terms of having their expertise and experience recognised and valued, or of being listened to, or having a chance to reflect on their condition or their use of technology. Some people will be attracted by financial and similar incentives. There are probably many other complex motivations for participating in research.
As researchers, we need to understand those motivations better, respect them, and work with them. Finally, Rogers et al. They suggest that requiring participants to sign an informed consent form helps in achieving this: true in some situations, but not in others, where verbal consent may be less costly and distracting for participants. The most common techniques for data gathering in SSQSs are outlined below: observation; contextual inquiry; semi-structured interviews ; think-aloud; focus groups ; and diary studies. The increasing focus on the use of technologies while mobile, in the home, and in other locations are leading to yet more ways of gathering qualitative data.
As Rode Rode , p.
The possibilities are seemingly endless, and growing. The limit may be the imagination of the research team. Whatever methods of data gathering are employed, it is wise to pilot test them before launching into extensive data gathering — both to check that the data gathering is as effective as possible and to ensure that the resulting data can be analysed as planned to address the purpose of the study. If the study design is highly iterative e.
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If the data gathering and analysis are more independent, as in some other research designs, it is more important to include an explicit piloting stage to check that the approach to data gathering is working well: for example, to ensure that interview questions are effective or that participant instructions are clear. There are many possible forms of observation, direct and indirect. Flick Flick , p. In other words: there is no single right way to conduct an observational study.
These phases — particularly selective observations and theoretical saturation — convey a particular view of observation as developing a focused theory, much in the style of Grounded Theory Section 1. Nevertheless, the broader idea of careful preparation for a study and recognition that the nature of observations will evolve over time are important for nearly all observational studies. She also notes that data collection and analysis may be more or less tightly integrated — a theme to which we return in Section 9. Contextual inquiry Beyer and Holtzblatt, is a widely reported method for conducting and recording observational studies in HCI, as a stage in a broader process of contextual design.
In other words, contextual inquiry involves interleaving observation with focused, situated interview questions concerning the work at hand and the roles of technology in that work. More importantly, Holtzblatt and Beyer present clear principles underpinning contextual design, and a process model for conducting design, including the contextual inquiry approach to data gathering. This includes a basic principle of the relationship between researcher and participants: that although the researcher may be more expert in human factors or system design, it is the participants who are experts in their work and in the use of systems to support that work.
Holtzblatt and Beyer present five models, flow, cultural, sequence, physical, and artefact, that are intermediate representations to describe work and the work context, and for which contextual inquiry is intended to provide data. Although contextual inquiry is often regarded as a component of contextual design, it has been applied independently as an approach to data gathering in research e.
Blandford and Wong, In contextual inquiry, the researcher is clearly present, shaping the data gathering through the questions he or she asks; in contrast, in a think-aloud study the researcher retreats into the background. Think aloud involves the users of a system articulating their thoughts as they work with a system. It typically focuses on the interaction with a particular interface, and so is well suited to identifying strengths and limitations of that interface, as well as the ways that people structure their tasks using the interface. Think aloud is most commonly used in laboratory studies, but also has a valuable role in some situated studies, as people demonstrate their use of particular systems in supporting their work e.
Makri et al.