Object – Analysis

Analyzing data is harder for participants than collecting it, but because of the

study. Senior interviewees moved easily through this stage and described good questions and designs as making this stage clear and easy to perform. However, newer interviewees had a more complex experience, wherein earlier ignored uncertainties might lead to a complete barrier at this stage.

Hurried or imprecise navigation through uncertainties earlier in the activity process came to a head for participants when they tried to analyze data. Participants had sometimes collected data in hopes of finding something or figuring it out later, putting off thinking about analysis until the analysis is in process. Putting off thinking about the analysis allowed participants to move through previous steps such as data collection without in-depth planning for how to analyze and draw meaning from the data. Causes of this putting-off described by interviewees mostly focused on lack of confidence in analytical skill. However, several interviewees also mentioned colleagues who they believed had put off thinking about analysis due to time pressure and/or lack of interest in rigor. Interviewees who discussed colleagues’ putting-off of analytical planning believed that time pressure was a primary culprit. As one researcher described a colleague’s research,

[s/he] was in a hurry and so places where I might have made a different choice about taking more time to make sure the questions were effective for getting findings with the survey, [s/he] was more interested in pushing it through to get done. (A1).

Putting off analytical design worries until after data is collected therefore appears to have a pragmatic advantage, especially to interviewees’ colleagues. At participating sites where rigor is not an evaluative criterion, this putting-off process may actually promote completion of studies. On the other hand, putting off thinking about analysis until after data is collected led some interviewees to problematic, incomplete, or uninformative analyses. Participants indirectly described a cost/benefit trade-off between the benefits of getting a presentation or article

finalized for sharing even if there was relatively little analysis and the benefits of doing rigorous research that has a positive long-term impact even if it delays publishing or presenting some tangible product.

The desire for training was mentioned by several interviewees. The research methods class in library school may either be inadequate or too far removed in time or context to have been a sufficient analytical preparation; one senior interviewee speculated about data analysis, “I’m not sure it’s always well explained even for people who might have done a thesis or dissertation” (C3). On the other hand, socializing students in the details of rigorous research might lose the pragmatic advantages mentioned in the previous paragraph, in terms of finishing projects quickly. Momentum (see 4.2.3.3 above) is an important part of participants’ mindsets. Resources also might in some way mediate the impact of analytical training.

Resources for analyzing data could either speed or slow interviewees’ progress.

Interviewees benefit from being able to use familiar, simple tools for data analysis. Successful tactics among the interviewees included using nothing but word processing and focusing on primary source analysis, using pre-made graphs in survey software, and using spreadsheets such as Excel. Using the cameras in mobile phones to collect document data was another valuable tactic, for the few that were performing document analysis. Interviewees who had found their first studies to be going or to have gone well used familiar software and tools for analysis, unless there was an experienced co-author to take on the burden of less familiar software. Lucky participants worked with co-authors in a way that they could balance different campus strengths when each has access to a software the other does not:

So essentially we broke the analysis into two parts. Because I had a Qualtrics license I did all the quantitative kind of analysis of the explicit categories, and then for the

qualitative analysis but it’s been a really long time ago, so I was kind of grateful not to have to deal with that piece of it. (A1)

When there is no expert for a certain tool, the librarian can lose the study thread entirely. Discussions of failed studies included loading data into software suites and then not being sure what to do with it. This was most likely to happen when both the tool and the method were sources of uncertainty. For example:

I just didn’t know what to do with the data. I would import my transcripts into NVivo, like I said. I was just thinking, ‘Okay, go in and code,’ but I didn’t know what codes to use, I didn’t know how do you make links between one transcript to another, it just felt so disconnected to one another and not really any themes and so I just thought I’ll just set it aside for now. (E1)

As this quote shows, unfamiliar tools presented a few interviewees with serious barriers to developing through their uncertainties about analysis. These tool barriers were particularly pronounced then the tool was tied to a new methodological concept, such as coding in the preceding quote. Participants who had been successful with their earliest attempts at research studies had focused on good descriptive and correlational studies that could be done in Excel, or on good primary source analyses that can be done in hard copy combined with word processing. The choice of which approach these early successes had used was also aligned with the

interviewee’s undergraduate background in either social sciences (for descriptive studies) or humanities (for working with primary source documents).

I conclude that using familiar tools and simple analytical approaches that align with undergraduate epistemologies is a way of making this stage’s Zone of Proximal Development smaller the first time, so that it is easier to navigate and supports researcher development. It is less ambitious than a more complex development strategy (with a correspondingly greater uncertainty, envisioned as a larger Zone of Proximal Development). However, balancing

ambition in development versus management of uncertainty appears to have some key impacts in moving through the activity process.