Mix Methods research involves the combination of qualitative and quantitative approaches to addressing a research problem. Generally, qualitative and quantitative methods have separate philosophical positions when it comes to how to uncover insights in addressing research questions.
For many, mixed methods have their own philosophical position, which is pragmatism. Pragmatists believe that if it works it’s good. Therefore, if mixed methods lead to a solution it’s an appropriate method to use.
This post will try to explain some of the mixed method designs. Before explaining it is important to understand that there are several common ways to approach mixed methods
- Qualitative and Quantitative are equal (Convergent Parallel Design)
- Quantitative is more important than qualitative (explanatory design)
- Qualitative is more important than quantitative
Convergent Parallel Design
This design involves the simultaneous collecting of qualitative and quantitative data. The results are then compared to provide insights into the problem. The advantage of this design is the quantitative data provides for generalizability while the qualitative data provides information about the context of the study.
However, the challenge is in trying to merge the two types of data. Qualitative and quantitative methods answer slightly different questions about a problem. As such it can be difficult to paint a picture of the results that are comprehensible.
This design puts emphasis on the quantitative data with qualitative data playing a secondary role. Normally, the results found in the quantitative data are followed up on in the qualitative part.
For example, if you collect surveys about what students think about college and the results indicate negative opinions, you might conduct an interview with students to understand why they are negative towards college. A Likert survey will not explain why students are negative. Interviews will help to capture why students have a particular position.
The advantage of this approach is the clear organization of the data. Quantitative data is more important. The drawback is deciding what about the quantitative data to explore when conducting the qualitative data collection.
This design is the opposite of explanatory. Now the qualitative data is more important than the quantitative. This design is used when you want to understand a phenomenon in order to measure it.
It is common when developing an instrument to interview people in focus groups to understand the phenomenon. For example, if I want to understand what cellphone addiction is I might ask students to share what they think about this in interviews. From there, I could develop a survey instrument to measure cell phone addiction.
The drawback to this approach is the time consumption. It takes a lot of work to conduct interviews, develop an instrument, and assess the instrument.
Mixed methods are not that new. However, they are still a somewhat unusual approach to research in many fields. Despite this, the approaches of mixed methods can be beneficial depending on the context.