Mixed Feelings About Mixed Methods? Not any more, says Minhui Wei
I’m Minhui, a fourth-year DPhil student from the Department of Education, and my doctoral research is a mixed-methods study of vocabulary learning, but my interest in mixed methods goes back a long way.
As an undergraduate in China, I learned that the most common approach to social science research, at least in my country, is quantitative. Even if your research questions didn’t necessarily need to be answered with statistics, you were still advised to include some stats analysis, so your study would be considered “rigorous”.
Through my reading, I observed that researchers tended to use quantitative methods as the main data collection method, and then collect some qual data to enrich or illustrate the quant data. So, in this vein, the first piece of empirical research I conducted had a mixed-methods orientation, without my having a thorough understanding of the methodology itself. I didn’t know that mixed-methods design can have different aims and different ways of collecting data.
As soon as I started my empirical research, I saw how interviews could elicit rich information from students on why they answered the questionnaire in a particular way. Some answers were within my expectations, but unanticipated ideas also emerged, which was exciting. It wasn’t an entirely smooth process however; I also experienced difficulties in conducting my study. During the interview, many new questions emerged outside my expectations, while some planned questions did not triangulate with the questionnaire answers and became useless. All of these factors combined and gave me mixed feelings about mixed methods: it could generate rich data but it was hard to make different methods resonate with each other to answer the research questions.
During my master’s study, I took a research methods course through which I gained a deeper understanding of the approach. I found out that a mixed-methods study might have a linear design with sequential qual and quant components , but also a parallel form (in which they occurred concurrently), and the more complicated multilevel nested mode (like the qual to quant to qual I use in my current study). Moreover, the mixing of the different methods had to be carried out carefully and purposefully to successfully collect the needed data.
Now I’ve conducted several studies using the mixed-methods approach, I believe it to be a powerful methodology, which I still find challenging from time to time when deciding which research tools I should use and, more importantly, how to present different types of data coherently. I relish the challenges, they keep the methods elements of my work engaging and stimulating!