20.4 Data capture: Striving for accuracy in our raw data

It is very hard to make a claim that research was conducted in a rigorous way if we don’t start with quality raw data. That is to say, if we botch our data collection, we really can’t produce trustworthy findings, no matter how good our analysis is. So what is quality raw data? From a qualitative research perspective, quality raw data means that the data we capture provides an accurate representation of what was shared with us by participants or through other data sources, such as documents. This section is meant to help you consider rigor as it pertains to how you capture your data. This might mean how you document the information from your interviews or focus groups, how you record your field notes as you are conducting observations, what you track down and access for other artifacts, or how you produce your entries in your reflexive journal (as this can become part of your data, as well).

This doesn’t mean that all your data will look the same. However, you will want to anticipate the type(s) of data you will be collecting and what format they will be in. In addition, whenever possible and appropriate, you will want the data you collect to be in a consistent format. So, if you are conducting interviews and you decide that you will be capturing this data by taking field notes, you will use a similar strategy for gathering information at each interview. You would avoid using field notes for some, recording and transcribing others, and then having emailed responses from the remaining participants. You might be wondering why this matters, after all, you are asking them the same questions. However, using these different formats to capture your data can make your data less comparable. This may have led to different information being shared by the participant and different information being captured by the researcher. For instance, if you rely on email responses, you lose the ability to follow up with probing questions you may have introduced in an in-person interview. Those participants who were recorded may not have felt as free to share information when compared to those interviews where you took field notes. It becomes harder to know if variation in your data is due to diversity in peoples’ experiences or just differences in how you went about capturing your data. Now we will turn our attention to quality in different types of data.

As qualitative researchers, we often are dealing with written data. At times, it may be participants who are doing the writing. We may ask participants to provide written responses to questions or we may use writing samples as artifacts that are produced for some other purpose that we have permission to include in our study. In either case, ideally we are including this written data with as little manipulation as possible. If we do things like take passages or ideas out of context or interpret segments in our own words, we run a much greater risk of misrepresenting the data that is being shared with us. This is a direct threat to rigor, compromising the quality of the raw data we are collecting. If we need to clarify what a participant means by one of their responses and we have the opportunity to follow up with them, we want to capture their own words as closely as we can when they provide their explanation. This is also true if we ask participants to provide us with drawings. For instance, we may ask youth to provide a drawn response to a question as an age-appropriate way to respond to a question, but we might follow-up by asking them to explain their drawing to us. We would want to capture their description as close to their own words as possible, including both the drawing and the description in our data.

Researchers may also be responsible for producing written data. Rigorous field notes strive to capture participants’ words as accurately as possible, which usually means quoting more and paraphrasing less. Of course we can’t avoid paraphrasing altogether (unless you have incredible shorthand skills, which I definitely do not), but the more interpreting or filtering we do as we capture our data, the less trustworthy it becomes. You also want to stick to a consistent method of recording your field notes. It becomes much harder to analyze your data if you have one system one day and another system another day. The quality of the notes may differ greatly and differences in organization may make it challenging to compare across your data. Finally, rigorous field notes usually capture context, as well. If you are gathering field notes for an interview or during a focus group, this may mean that you take note of non-verbal information during the exchange. If you are conducting an observation, your field notes might contain detailed information about the setting and circumstances of the observation.

As qualitative researchers, we may also be working with audio, video, or other forms of media data. Much of what we have already discussed in respect to written data also applies to these data formats, as well. The less we manipulate or change the original data source, the better. For example, if you have an audio recording of your focus group, you want your transcript to be as close to verbatim as possible. Also, if we are working with a visual or aural medium, like a performance, capturing context and description—including audience reactions—with as much detail as possible is vital if we are looking to analyze the meaning of such an event or experience.

This topic shouldn’t require more than a couple sentences as you write up your research proposal. However, these sentences should reflect some careful forethought and planning. Remember, this is the hand-off! If you are a relay runner, this is the point where the baton gets passed as the participant or source transfers information to the study. Also, you want to ensure that you select a strategy that can be consistent and part of systematic process. Now we need to come up with a plan for managing our data.