Quantitative Research Methodology SOC 509

Quantitative Research Methodology SOC 509. Lesson 16: Mixed Method Research Design-Part 2.

Mixed M ethods Research Designs. There are different types of mixed methods research designs. The differences between them relate to the aim of the research, the timing of the data collection, and the importance given to each data type. As you design your mixed methods study, also keep in mind: Your research approach (inductive vs deductive ) Your research questions What kind of data is already available for you to use What kind of data you’re able to collect yourself. The four major mixed methods designs are identified below and compared in terms of their purposes, strengths and weaknesses. Examples of each design are also described..

Convergent parallel. In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions. In this design only one data collection phase is used, during which quantitative and qualitative data collection and analysis are conducted separately yet concurrently. The findings are integrated during the interpretation phase of the study. Usually, equal priority is given to both types of research. When to use it? To develop a more complete understanding of a topic or phenomenon. To cross-validate or corroborate findings..

Strengths Provides well-validated and substantiated findings. Compared to sequential designs, data collection takes less time. Weaknesses Requires great effort and expertise to adequately use two separate methods at the same time. It can be difficult to compare the results of two analysis using data of different forms. It may be unclear how to resolve discrepancies that arise while comparing the results. Given that data collection is conducted concurrently, results of one method (e.g., interview) cannot be integrated in the other method (e.g., survey)..

Example 1: The researcher uses a survey to assess people’s self-reported food safety practices and also observes those practices in their natural environment. By comparing the two types of data, the researcher can see if there is a match between what people think they are doing and what they are actually doing in terms of food safety practices. Example 2: In your research on cycling safety in Amsterdam, you undertake both sides of your research simultaneously: On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why. On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city. When you finish your data collection and analysis, you then compare results and tie your findings together..

Embedded Research Design. In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other. This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design. In this design only one data collection phase is used, during which a predominant method (quantitative or qualitative) nests or embeds the other less priority method (qualitative or quantitative, respectively). This nesting may mean that the embedded method addresses a different question than the dominant method or seeks information from different levels. The data collected from the two methods are mixed during the analysis phase of the project..

When to use it? To gain broader and in-depth perspectives on a topic. To offset possible weaknesses inherent to the predominant method. Strengths Two types of data are collected simultaneously, reducing time and resources (e.g., number of participants). Provides a study with the advantages of both quantitative and qualitative data. Weaknesses The data needs to be transformed in some way so that both types of data can be integrated during the analysis, which can be difficult. Inequality between different methods may result in unequal evidence within the study, which can be a disadvantage when interpreting the results..

Example 1: The researcher collects data to assess people’s knowledge and risk perceptions about genetically modified food by using a survey instrument that mixes qualitative (open-ended) and quantitative (closed-ended) questions, and both forms of data are integrated and analysed . Example 2: As part of a quantitative study testing whether the number of cyclist complaints about an area correlates with the number of accidents, you could “embed” a series of qualitative interviews with cyclists who submitted complaints to further strengthen your argument. The bulk of your research remains quantitative..

Explanatory sequential. In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis. You should use this design if you think your qualitative data will explain and contextualize your quantitative findings. This design involves the collection and analysis of quantitative data followed by the collection and analysis of qualitative data. The priority is given to the quantitative data, and the findings are integrated during the interpretation phase of the study. When to use it? To help explain, interpret or contextualize quantitative findings. To examine in more detail unexpected results from a quantitative study..

Strengths: Easy to implement because the steps fall into clear separate stages. The design is easy to describe and the results easy to report. Weaknesses : Requires a substantial length of time to complete all data collection given the two separate phases. Example 1: The researcher collects data about people’s risk and benefit perceptions of red meat using a survey and follows up with interviews with a few individuals who participated in the survey to learn in more detail about their survey responses (e.g., to understand the thought process of people with low risk perceptions). Example 2: You analyze the accident statistics first and draw preliminary conclusions about which areas are most dangerous. Based on these findings, you conduct interviews with cyclists in high-accident areas and analyze complaints qualitatively. You can utilize the qualitative data to explain why accidents occur on specific roads, and take a deep dive into particular problem areas..

Exploratory sequential. In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis. You can use this design to first explore initial questions and develop hypotheses. Then you can use the quantitative data to test or confirm your qualitative findings. In this design, qualitative data collection and analysis is followed by quantitative data collection and analysis. The priority is given to the qualitative aspect of the study, and the findings are integrated during the interpretation phase of the study..

When to use it? To explore a phenomenon and to expand on qualitative findings. To test elements of an emergent theory resulting from the qualitative research. To generalize qualitative findings to different samples in order to determine the distribution of a phenomenon within a chosen population. To develop and test a new instrument Strengths: Easy to implement because the steps fall into clear, separate stages. The design is easy to describe and the results easy to report..

Weaknesses: Requires a substantial length of time to complete all data collection given the two separate phases. It may be difficult to build from the qualitative analysis to the subsequent data collection. Example 1: The researcher explores people's beliefs and knowledge regarding nutritional information by starting with in-store interviews and then uses an analysis of the information to develop a survey instrument that is administered later to a sample from a population. Example 2: You first interview cyclists to develop an initial understanding of problem areas, and draw preliminary conclusions. Then you analyze accident statistics to test whether cyclist perceptions line up with where accidents occur..

Advantages of Mixed Method Research. Using a mixed methods study has several advantages, which we discuss below. Compares quantitative and qualitative data . Mixed methods are especially useful in understanding contradictions between quantitative results and qualitative findings. Reflects participants’ point of view . Mixed methods give a voice to study participants and ensure that study findings are grounded in participants’ experiences. Fosters scholarly interaction . Such studies add breadth to multidisciplinary team research by encouraging the interaction of quantitative, qualitative, and mixed methods scholars ..

Provides methodological flexibility . Mixed methods have great flexibility and are adaptable to many study designs, such as observational studies and randomized trials, to elucidate more information than can be obtained in only quantitative research. Collects rich, comprehensive data . Mixed methods also mirror the way individuals naturally collect information—by integrating quantitative and qualitative data. For example, sports stories frequently integrate quantitative data (scores or number of errors) with qualitative data (descriptions and images of highlights) to provide a more complete story than either method would alone..

Disadvantages of Mixed Method Research. Mixed methods studies are challenging to implement, especially when they are used to evaluate complex interventions such as a PCMH model. Below we discuss several challenges. Increases the complexity of evaluations . Mixed methods studies are complex to plan and conduct. They require careful planning to describe all aspects of research, including the study sample for qualitative and quantitative portions (identical, embedded, or parallel); timing (the sequence of qualitative and quantitative portions); and the plan for integrating data. Integrating qualitative and quantitative data during analysis is often a challenging phase for many researchers..

Relies on a multidisciplinary team of researchers . Conducting high-quality mixed methods studies requires a multidisciplinary team of researchers who, in the service of the larger study, must be open to methods that may not be their area of expertise. Finding qualitative experts who are also comfortable discussing quantitative analyses and vice versa can be challenging in many environments. Given that each method must adhere to its own standards for rigor, ensuring appropriate quality of each component of a mixed methods study can be difficult . Requires increased resources . Finally, mixed methods studies are labor intensive and require greater resources and time than those needed to conduct a single method study..

References. Creswell, J. W. & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications..

Feel free to ask question if you have any confusion.

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