Steve Tippins walks dissertation students through mixed-methods research, combining quantitative and qualitative approaches. Key mistakes: poor integration of the two methods, misaligned research questions, and unclear sequencing. Start with committee buy-in early, use separate analysis software for each method, and plan for a longer timeline.
Questions this video answers
Quantitative studies usually use a G*Power calculation to determine the appropriate sample size, while qualitative studies typically aim for about 10‑15 participants. In mixed‑methods projects, committees often allow smaller sample sizes for both components to improve doability, but this can reduce generalizability and statistical power.
Chapters
Ready as I'm going to be. Okay, welcome everyone. It's January 19th, and the last two times we met, we talked about quantitative methodologies and qualitative methodologies. And logically, let's move forward today and talk about mixed methods. Okay. Mixed methods, quite frankly, means you're using both quantitative and qualitative approaches. Mixed methods can be a great way to go. It can really allow you to tackle a problem from multiple perspectives. That said, it's also a way that can take you longer because you're doing two different studies. So many times I try to encourage people to choose one because the goal in a dissertation is to get done. That's not saying mixed methods is bad, but if your goal is finishing as quickly as possible, then maybe one method may be better than doing two. So what are the some of the challenges questions we get pertaining to mixed methods? Well, sometimes people just say, how do I integrate both types of approaches into my study? Which kind of goes to another question is, wow, I have a research question. And how do I get both methods to get to that research question? Typically, what I've seen is multiple research questions, some of which are addressed by, let's say, the qualitative approach, others which are addressed by the quantitative approach. So this is, this can help you integrate both approaches into doing your work. Now another question that arises sometimes is, well, which do I do first? And I have seen both. I've seen people take a, let's say, qualitative approach first and then in there we've developed some themes and then go to the quantitative approach to start testing those themes with a bigger population of people. One of the limitations though sometimes of a mixed method approach is committees will allow you to get by with smaller sample sizes. Well, a smaller sample size may not allow you to test a theme that well, but it does allow you to show competency in different methodologies. So that's if you do the qualitative approach first followed by quantitative. However, I've seen people do quantitative first and see what comes up as responses from people, then use those to probe within the qualitative approach. So there value to doing it either direction or either one first, I guess. And really you want to get your committee and your chair on board and, you know, come up with whatever is agreed upon by everyone to move on first and then second. Okay, it can be overwhelming doing these if you, if you can do them simultaneously. So we talked about doing them in, in a certain order. Well, if you're doing them simultaneously, that can be overwhelming. So you're going to have to set up a system or methods in order to keep things straight between your quantitative and your qualitative. Fortunately, if you're using systems to analyze data, you're typically going to be using completely separate systems, for example, spss to do a quantitative analysis and something like in vivo or deduce or one of the other approaches or systems to do your qualitative approach. So that can help you contain, control, understand, or not be overwhelmed by the data that you're collecting. It can be a struggle for some people and in some committees to propose mixed methods and allow that to happen. Typically, more often than not, it's though it's a committee member that says, oh, you ought to take a mixed methods approach here, as opposed to a student who said, I'll do mixed methods and then try to convince a committee that that's an appropriate response. I haven't seen a lot of committees say no to mixed methods. I have seen a number of students say no to mixed methods because of the time factor. However, if you are going to do mixed methods, you really want to at the beginning have your committee and you agree upon what are the parameters. Meaning, okay, I'm going to do a qualitative approach. How many interviews do you think I'm going to do? And you want to justify how ideally as low a number as possible. And then how many data points, surveys or whatever am I going to need for my quantitative approach? And again, the lower the number, the faster you move forward. And if that's our goal, that's what we want to do. Mixed method is not done large number of times on dissertations. You know, I've served on hundreds of committees. I might have served on four, maybe five that used mixed methods. So because that's the case, it may be harder to find examples of a mixed methods approach. In fact, typically I will tell people, look, go find a copy of a dissertation your chair has approved recently so you can see what type of things are in those documents that you then think, I need to put something like that in mine. Well, if your chair hasn't done a mixed method in a long time, it's hard to find that. But at least you can find one side of the equation, quantitative or qualitative, and then maybe find another recent dissertation at your school that is done qualitative and morph those together. One of the great advantages of doing mixed methods is you're really exposing yourself and learning multiple ways to look at research. You're not just saying, I am a quant person or am I a qual person. I can do both, which can open you up to a lot of different research opportunities in the future and that can be exciting. It can also help you in your career. However, I always have to add this disclaimer. Sorry about that. With mixed methods is it may take you longer because of the collection of data issue. If you know you're going to be doing mixed methods, then just like we were saying in qualitative and quantitative work, setting things up as early as possible, understanding how you're going to do analysis, understanding how and where you're going to collect data from can be even more important here because you really, really want to be as efficient as possible in this process. And again, the goal of any of these processes is to help you understand a problem. And with a dissertation, the goal is to finish it so you become a doctor and then you can go out and change the world. Now, if there are people out there who have questions, I'm open to hearing. And if there are no questions, I'm ready to. We got a question. Great. I did not see that. So let's try it here. Sure. So in a typical quantitative study, you're typically going to use a calculation, G power calculation to find out what the appropriate sample size is. On a the qualitative side, you know, we range 10 to 15 ish is typical for a qualitative sample size. When we get to mixed methods, committees tend to be a little more flexible on the size them. Understanding that you're doing two studies. I've seen committees approve quantitative samples that are much below what the G power test says it should be. I've also seen them allow smaller qualitative sample sizes. The advantage? Doability. The disadvantage is generalizability because you may not have enough power to say that you've done enough analysis or had enough responses to be able to generalize outside of your sample. So it's a balancing act. You know, I want to finish more quickly, I want more power. And it may also depend on what you want to do in the future. You may want to put more time in now so that you have more generalizability and more publishability moving forward. So. But as far as numbers go, that's going to vary every study and every committee. Does that answer your question? Great. Good. Any other questions? Well then where I am right now in Wisconsin, it's really cold. So I hope wherever you are, you're warm and can get back to work and finish your dissertation. I'll see you again soon. So long.
