Qualitative HCI Research - Ann Blandford Synthesis Lectures on Human-Centered Informatics

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complement the qualitative in some studies (see discussion of mixed methods in Chapter 6).

      When it comes to data analysis, colored pencils, highlighter pens and paper are often adequate for studies that involve only a few hours of data. For larger studies, computer-based Qualitative Data Analysis tools (e.g., NVivo, MaxQDA, Dedoose or ATLAS.ti) can help with managing and keeping track of data, but require time to learn to use effectively. These tools can help track large quantities of quotations, codes, links and memos. They can also speed up the process of analysis; for example, they allow you to rapidly change the name of every instance of a particular code, or list every quotation with a particular code. However, they do not actually do any of the sense making themselves—that is left to the researcher.

      As well as 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.g., Grady et al., 2005; Fry et al., 2005). The ethical concerns in medicine are typically much greater than those in HCI due to the level of potential harm. In HCI, it is common practice to recompense participants for their time and any costs they incur, with cash or gift certificates, without making the payment so large that people are likely to participate just for the money.

      Often, the biggest constraint is access to a study setting or availability of suitable participants; we devote the next chapter to this topic.

      Traditionally, ethics has been concerned with the avoidance of harm, and most established ethical clearance processes focus on this. “VIP” is a useful mnemonic for the main considerations:

      • Vulnerable participants

      • Informed consent

      • Privacy and confidentiality

      Particular care needs to be taken when recruiting participants from groups that might be regarded as vulnerable, such as children, the elderly or people with a particular condition (illness, addiction, etc.).

      In providing informed consent, participants should be told the purpose of the study, and made aware of their right to withdraw at any time without reason and without them being at any disadvantage. If it is not possible to inform participants of the full purpose of the study at the outset (e.g., because this might bias their behaviour and defeat the object of the study), then they should be debriefed fully at the end of the study.

      It is common practice to provide a written information sheet outlining the purpose of the study, what is expected of participants, how their data will be stored, used and, if applicable, shared and how findings will be reported. Depending on the circumstances, it may be appropriate to gather either written or verbal consent; if written then the record should be kept securely, and separately from data. Preece et al. (2015) suggest that requiring participants to sign an informed consent form helps to keep the relationship between researcher and participants “clear and professional.” This is true in some situations, but not in others, where verbal consent may be less disruptive for participants. For example, verbal consent may work better if observing someone briefly while they go about their work, if getting written consent would disrupt the work disproportionately.

      With the growing use of social media, and of research methods making use of such data (e.g., from Twitter or online forums), there are situations where gathering informed consent is impractical or maybe even impossible. In such situations, it is important to weigh up the value of the research and how to ensure that confidentiality and respect are maintained. Bear in mind that although such data has been made publicly available, the authors may not have considered all possible uses of the data and may feel a strong sense of ownership of it. If in doubt, discuss possible ethical concerns with experts in research ethics.

      Privacy and confidentiality should be respected in data gathering, management and reporting. Some of this is covered in data protection laws and information governance procedures. It is good practice to anonymise data as soon as is practical, i.e., when taking notes or transcribing audio. This means replacing people’s names with a participant number (e.g., “P3”) or pseudonym, and removing other proper nouns that have the potential to personally identify participants (e.g., company names, specific places, such as the name of a small town, etc.). It may be necessary to retain contact details securely so that it is possible to inform participants of the outcome of the study later, but this would normally only be done with informed consent, for participants who want to know more.

      Ethics goes beyond the principle of no harm: it should also be about doing good. There must be some value in the research, otherwise it is not worth doing. 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 give research participants immediate pay-back, but where possible there should be benefits to participating in a study. In our experience, participants have responded positively to us explaining that findings from their study will not be used to inform the design of the technology they actually use, but with the aim of making this sort of technology easier to use for people in the future.

      It is important to review the safety of the researcher as well as that of participants. This commonly involves doing a risk analysis. For example, researchers should meet participants who are not already known to them in public spaces wherever possible. For home studies, it is generally good practice to work in pairs, or to consider other ways of mitigating any risks.

      In addition to resources, constraints and ethical considerations, there are various less tangible factors that shape any study. Probably the most important are the ways that pre-existing theory can be used to inform data gathering, analysis and reporting of a study, and also the biases, understanding, and experience of the researcher(s) involved in the project (Denzin and Lincoln, 2011).

      No researcher is a tabula rasa: each comes to a study with pre-existing understanding, experience, interests, etc. Hertzum and Jacobsen (2001) studied how several analysts independently identified usability difficulties from the same video data in which other participants had been thinking aloud while interacting with a user interface. There was significant variability in what issues their participating analysts identified. They considered this to be “chilling”: that there is no objective, shared understanding, even with an activity as superficially simple as identifying usability difficulties from think-aloud data. If this is true for analysing pre-determined data with a pre-defined question, it clearly has an even greater effect when the researcher is shaping the entire study.

      For the individual, it may be difficult to identify or articulate many of the factors that shape the research they conduct, but one obvious factor is the role of theory in a study. Theory may shape the research from the outset, come into play during the analysis, or be most prominent towards the end of a research project. In Chapter 6, we discuss how theory may be introduced in an analysis, and how it can contribute to the generalisability of findings. Here, we focus on how it may be used to shape a study at the planning stage.

      Theory may be introduced early into a study: either to test an existing theory in a new context or to better understand the study context while having a focus that helps to manage its complexity. A theory can act as a “lens,” providing sensitising concepts that help to shape and focus data gathering and impose a partial structure on the data that is gathered. Similarly, a theory can help in shaping analysis.

      Where this is done, it is important not to trust an existing theoretical framework unquestioningly, but to test and extend that framework: are there counter-examples that challenge the accuracy of the existing framework? Are there examples that go beyond the framework and introduce important extensions to it? Many studies that introduce theory early end up extending or refining the theory

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