Happy holidays to library lovers and data enthusiasts everywhere! This post will take a festive approach to wrapping up our discussion on mixed methods research. Time has flown by, and this is our last post of 2022, but LRS is happy to share that our Between a Graph and a Hard Place blog series will continue in 2023 to tackle the topic of data visualization!
Introducing Data Integration
Before Thanksgiving, we took a look at three different designs for data collection in mixed methods studies (explanatory, exploratory and convergent). However, successfully completing a mixed methods study takes more than just collecting both qualitative and quantitative data. Properly integrating, or combining, the two types of data will allow the strengths of both to shine as they should in mixed methods research. Because qualitative and quantitative research operate through different methods and frameworks, mixing both types of data for a cohesive interpretation can be surprisingly tricky. This post will give you a few techniques for integrating them together.
Of course, how we decide to integrate our qualitative and quantitative data is going to depend largely on our research design and whether the two forms of data are weighted equally (often the case for convergent design) or if one data form is emphasized over the other (often the case for exploratory or explanatory research designs). Many guiding frameworks have been proposed for integrating qualitative and quantitative data, but there are not yet many universally agreed upon definitions, so this post will generally discuss a few approaches to data integration with the understanding that information on this topic is continually evolving.
Incorporating Some Festivities
One of my favorite holiday activities this time of year is wreath making, which just so happens to also work as a fun way to visualize data integration. First, let’s picture a wreath which represents the final findings or report for your mixed methods study. The wreath that comes to my mind incorporates multiple materials (evergreen branches, garland, pinecones, ribbon, glitter etc.) but there are many possibilities for how these materials can be weaved together. The same is true for data collected during mixed methods research, and the act of putting the wreath together represents the integration of qualitative and quantitative data.
Connecting the Data
When a study emphasizes qualitative data over quantitative data or vice versa, the two types of data can be connected when one informs the other. For example, if qualitative data is collected first, an analysis of this qualitative data can be used to inform the following quantitative part of the research. Returning to our example of an exploratory mixed methods design from last month, the question,“What barriers prevent people from attending summer programming?” may begin with a qualitative analysis of the barriers people face which then leads to a quantitative investigation of the extent and effect of each barrier. In this case, each data type is analyzed individually and at a different time, but you integrate them by building the qualitative findings into the quantitative portion of the study.
Building a wreath in this same manner would mean first collecting the branches you will use for the base and piecing them together then matching your decorations to the wreath based on how the wreath circle looks. The circle is your qualitative data, and the decorations are your quantitative findings. Your final product presents both together, but the decorations were added later after the base was already built. In an exploratory research design, this type of integration is called building the data.
Merging the Data
If both qualitative and quantitative data are weighted equally and collected from participants at the same time, you may choose to integrate the data by merging it together. Merging the data means comparing the two types side by side and is often completed in a convergent mixed methods design. It is also helpful when you have qualitative and quantitative data from each participant, or case, and want to consider each case individually. For example, you may choose to ask survey participants, “How many past summer programs have you attended?” and “If you have attended past programs, why did you choose to attend?” You can then examine why programs were attended and how often people attended programs on a case-by-case basis to look for patterns between these two data points.
Comparing both data forms side by side may feel overwhelming at first, and it can help to organize the data in a table or grid which includes both the qualitative and quantitative data associated with each participant. Creating a table of both qualitative and quantitative data can also help you identify where the data is fitting together and where there are contradictions or tensions between the two types, which are also important observations for your interpretation.
Weaving both qualitative and quantitative data together side by side reminds me of a wreath with multiple materials, both decorative and supportive, woven together to form the base circle instead of just gluing your decorations to the top.
Embedding the Data
If you are completing a larger experimental study or tracking change over time, you can integrate a secondary data type by embedding it at multiple places throughout your findings. Embedding the data means that you incorporate a different data type, often qualitative data, throughout a larger experimental or longitudinal study. This second data type helps develop and expand on your findings but is confined within the larger design you are working in. This creates another embedded type of mixed methods design separate from the explanatory, exploratory or convergent designs discussed previously.
If you picture a wreath that consists of weaved branches with a decorative bow, this depicts how qualitative data (the bow in this case) may be nested into a larger quantitative design. The wreath is complex enough on its own, but the bow helps to add visual interest and tie the circle together.
Gluing it all Together
We hope that this post gave you some insight on how to integrate the two data types, or at the very least, sparked your creativity for a fun holiday craft. Regardless of how you choose to integrate qualitative and quantitative data during a mixed methods study, here are four important points to keep in mind:
- Depending on your study design, integration can take place during collection, analysis, or interpretation, but regardless of when it occurs integration should be carefully carried out so as not to misrepresent or overlook aspects of your data.
- Remember that the qualitative and quantitative data may not fit together perfectly. If you are running into tensions and contradictions while integrating the data, don’t force it. Contradicting data or broken patterns can be some of the most interesting data points and may reveal questions for further study.
- As you present your findings, explain the methods you used to integrate qualitative and quantitative data. This will help your audience understand how you reached conclusions and increase the credibility of your findings.
- Integrating qualitative and quantitative data requires knowledge of both types of research, create a team experienced in both or be confident with each yourself.
In 2023, you can expect posts that contain visualizations of real world data from the Public Library Annual Report (PLAR) and step-by-step instructions on how you can create these visualizations with your library’s data. We will work on multiple platforms including Infogram, Tableau and Excel. If this sounds like a useful tool for you and your friends or colleagues please share and subscribe below to receive posts via email as soon as they are published. LRS sends you our best wishes for this holiday season, and we look forward to working with you in 2023!
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