Mixed-Methods Approach
Mixed-methods approaches have gained popularity in recent years as researchers have become more willing to acknowledge the unique strengths and limitations of both qualitative and quantitative methods.
The complexity of using mixed methods requires that researchers carefully consider the planning of such studies. One major consideration is the timing of the quantitative and qualitative components. Depending on the goals of each component, the phases of data collection can be either sequential or concurrent. When sequential, the first phase of data collection can help to inform the second phase, or the second phase can be used to aid in the interpretation of data collected in the first phase. Concurrent data collection reduces the amount of time required to collect data and can therefore be more efficient. Another concern is the weight given to each phase. While the weight of each phase may be equal, it is more common that one phase is emphasized based on the primary logic that guides the mixed-method study. Studies using deductive logic will tend to weight the quantitative portion more heavily and seek to explain a phenomenon, while those employing inductive or exploratory logic will emphasize the qualitative portion.
Discover How We Assist to Edit Your Dissertation Chapters
Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.
- Bring dissertation editing expertise to chapters 1-5 in timely manner.
- Track all changes, then work with you to bring about scholarly writing.
- Ongoing support to address committee feedback, reducing revisions.
One popular mixed-methods approach is the sequential explanatory strategy. In this approach, quantitative data are collected and analyzed first and the results used to inform the subsequent qualitative phase. Often the qualitative phase is useful in helping to understand unexpected results that arise in the initial quantitative phase. This approach is commonly employed by researchers who are more comfortable with quantitative research and weight is given primarily to the quantitative findings, which explains why this strategy is considered explanatory.
In contrast, the sequential exploratory strategy places greater emphasis on an initial qualitative phase which is used to gain insight into an understudied phenomenon (hence the exploratory nature). Extensive qualitative research is employed to develop new knowledge and testable hypotheses, and the secondary quantitative phase is used to examine the phenomenon in a more generalizable fashion. A common application of this strategy is to conduct qualitative research on a particular phenomenon or with a special population, and then use this information to develop an appropriate survey instrument to collect quantitative data.
Because the goal of sequential strategies is to use one phase of the research to inform the next, sequential strategies take a long time to conduct. When time is a concern, researchers often employ the concurrent triangulation strategy in which the qualitative and quantitative phases are conducted at the same time. Ideally equal weight is given to each phase, with the results of both interpreted concurrently to determine whether there is agreement in the data collected through each approach. In reporting the results of such a study, researchers generally present the statistical results first with quotes from the qualitative phase used to flesh out the statistical information. Though this is the most common approach to mixed-methods research it can be challenging for researchers to design two equally-strong phases of research, and the integration of results can be difficult especially when contradictions emerge from the data. In such cases, additional data collection can help to clarify the results.
The above descriptions lay out the phases of large-scale mixed-methods studies, but mixed methods are often used by individual researchers conducting their own investigations as well. Again, the goal is to draw on the unique strengths of each approach to provide a more complete understanding than would be possible using only one approach. A common study design is to integrate the results of analysis of a large-scale data set with results from in-depth interviews or focus groups. A mixed-methods study that truly incorporates the strengths of each will do so at each step, from the research question through data collection to analysis. An example can help to illustrate the multiple considerations that must be addressed in a mixed-methods study.
A researcher sought to address the following research question: How do gender expectations (normative beliefs about what is appropriate for women and men) shape adolescents’ decisions regarding sexual risk behavior? Clearly, some aspects of this question lend themselves to survey data – the timing and frequency of sexual risk behavior among adolescents. But the idea of gender expectations is less concrete – it is unlikely that an existing data source will include questions that directly measure this concept. The researcher therefore decides to use a nationally-representative data set to explore risk behavior and to concurrently conduct in-depth interviews with adolescents to understand how they view gender expectations and how these relate to their sexual decision making. In the course of the interviews, the researcher finds that the opinions of peers with regard to appropriate sexual behavior appear to operate very differently for female and male adolescents. This leads the researcher to return to the data set to find a variable that captures such concerns, which is then incorporated into the analysis. The final report includes both statistical information on the association between perceptions of the consequences of violating gender expectations and sexual risk behavior, along with quotes from adolescents about how they perceive gender expectations relating to sexual behavior. Together the two components provide greater insight than either alone, with the quantitative phase providing generalizability and the qualitative phase giving context to the findings.