What are the best ways to combine quantitative and qualitative data in Higher Education research?

I tend to use quant to look at the scope and character of the issue/problem. I may use extant data or survey the relevant population. Then I like to use interviews and other qualitative methods to understand how participants experience the problem. Sometimes a new component of the issue emerges that leads me to return to the quant data to analyze in a new way.

It is important to recognize mixed methods as a distinct research methodology and not merely combing quantitative and qualitative data.

Mixed methods cover a plethora of approaches and models. Many still follow the incommensurability debate and paradigmatic quarrels (Burrell and Morgan). Here are my Top 3 suggestions: 1. Convergent Parallel Design ? Example Citation: Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). SAGE Publications. 2. Explanatory Sequential Design ? Example Citation: Ivankova, N. V., Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3-20. 3. Exploratory Sequential Design ? Example Citation: Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE

My PhD Thesis based on Control of Acute Respiratory Infections children <5, data were collected quantitatively assessing children, socio-economic, environmental, healthcare backgrounds; & qualitatively - children’s mother knowledge, attitudes and practices (KAP) in control. The qualitative data - response contents analysed, selected 10% popular samples responses and using them as key and recoded the qualitative data. Both data sets were intergraded together. Even analysed the data with multiple logistic regression analysis 3 Models created and significant results achieved (PhD Thesis - Public Health, the University of Manchester, 1997).

In teaching research methods what seems like centuries ago, I'd always encouraged students to consider holistic approaches to working with data. I found that far too many were on tenterhooks to employ recently acquired tools but too rarely gave consideration to data and the stories they told organically. Irrespective of the data to be analyzed, I entreated both aspirants and colleagues to approach the endeavor with an artistic mindset--in other words to think of research as weaving a tapestry, or writing a symphony. We in research serve an important purpose as raconteurs of sorts, offering to the end user not only a set of findings and recommendations, but a more human socioemotional connection, sensemaking opportunities and meaning.

The use of mixed methods in Higher Education research is invaluable for its ability to provide a nuanced and comprehensive understanding of complex educational phenomena. By combining quantitative data, which offers generalisability and statistical rigour, with qualitative data, which provides depth and context, researchers are better equipped to capture the multifaceted nature of educational issues. This holistic approach can unearth correlations and causations while also exploring underlying mechanisms and lived experiences. In effect, mixed methods offer a more robust analytical framework, enabling researchers to address complex questions with greater validity and reliability.

I think of qualitative data as explaining what's going on behind the numbers - what are the experiences and factors that can explain quantitative trends. Qualitative data provides a more in-depth, nuanced look at higher education students, programs, challenges, and outcomes.

The major challenge in mixed methods research is the end in mind. One should start here, not by picking a method or philosophizing. What will the results look like? How does one integrate the qualitative and quantitative? How does one, from the beginning, consider validity and reliability? All these questions work backwards to answer what the design, sample, and analysis will look like.

Having to use mixed is fundamental in research in general to achieve the important concepts of research such as objectivity, testing reliability, validity, representation and for triangulation purposes. As different methods can give different insights or indepth of the research being conducted