La diferencia como metáfora de la emergencia de la alteridad

Participants talked about different initial selection criteria for choosing data for their research. While the initial data selection process was closely related to the initial trust judgment (section 3.2.2.), participants discussed two conditions (or preferences) for data to be reusable for their research: relevancy and usability. Those were not directly relevant to their trust judgments but were essential to the selection process In addition, the initial selection criteria may influence the participants’ level of acceptance of trust violations later in their data reuse experiences. Relevancy, the appropriateness of the data for the research project and how well it meets the researcher’s need, is a foundational criterion for data reuse, as data reusers choose only data that can solve their research problems. Usability criteria are relevant to the technical elements influencing reusers’ ease of using data, which include availability, accessibility, ease of use of the data and its associated software, and the reuser’s familiarity with the data type or software necessary to view and process it.

Relevancy

Participants sought data that could answer their research questions or solve their research problems. Thus, whether data met their research needs (e.g., “It had the vast majority of

demographic characteristics that I was interested in (PP03),” “It had a best measure of what I’m looking for (PP13)”) or was relevant to the topic of research (e.g., “all the variables in the study answered my questions (PP02)”) was a common criterion. Only after participants identified the data that were relevant to their research problems did they start examining its other aspects.

Relevance to the research problem means two things to the participants of this study: variables of interest and sample size. The variables can be a measure of interests (e.g., the concept of hope, poverty, and various scales), demographic characteristics (e.g., race or ethnicity), or a population of interest (e.g., LGBT (Lesbian, Gay, Bisexual, and Transgender), over 65). Depending on the research problem, participants checked whether the data was nationally representative or whether the sample size was large enough to run a statistical

analysis. Although an adequate sample size was important for some types of analysis, not all data met this criterion. PP14 talked about “encounter[ing] quite a few times with data sets that I’ve been very, very interested,” but being unable to use the data since “they have such a small number of group[s].”

Usability

Participants expressed a preference for data that were easily accessible and freely available. As discussed in an earlier section, one motivation for participants to use secondary data was saving time and money. For example, for PP02, the rule was to use publicly available data that was free and easy to use, and for PS15, the time and effort involved in obtaining data

were important criteria:

PP02: Proprietary is also important. I, as a rule, don’t spend a lot of money on purchasing secondary data sets.

PS15: It’s one of those cost-benefit analyses kinds of things, right? (…) There’s a good number of hoops to jump through to get access to [the data] (…). And it can be one of those things where if it’s too difficult of a process or too lengthy of a process (…) Cause sometimes, people have very short timetables they’re working on for whatever reason, it might not be worth it to invest all that time and energy to gaining access and getting set up with the data.

Even data that are publicly available may include portions that are restricted, requiring a special application. This extra step may not entirely prevent use of the data, as “it becomes that trade- off,” and the decision depends on “how important it is to you, to look at Y, when you’re really interested in X, and how much time and effort you are really willing to put into looking into [it]” (PS15). However, some participants expressed a strong preference for finding data that are fully publicly available to save time and money.

Data formats and software were other elements that influenced the usability of data. Several participants reported difficulties with formats, and others shared difficulties using unfamiliar data software or analytics programs. For instance, PP01 struggled to download some data because of the format:

downloaded and functioning. [These data] have different data formats (…). [I]t just becomes a huge headache.

PP11 was also unable to open data because “there’s a kind of, some process involved.” She was not sure of the format of the data or if she needed a special program to open and run the data. She needed “some assistance from a statistician or other experts,” but she ended up abandoning the data, thinking, “It’s just too much,” without any institutional support or data services available to her. PP12 had tried to use some data from Europe: “You can’t just use regular SPSS [Statistical Package for the Social Sciences] for that data. You have to use programs that are set up for [it]” (PP12). She had to use the analytic program designed for the data format but was not sure if the new software program was worth her time and money. Thus, participants sought data that were “at least transfer[able] pretty easily” (PP05), and they were “probably a little reluctant to use [some data] because you do have to use [special] statistical software…” (PP12), depending on the research support level at their school or institution.