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Mixed Methods Research is the product of such evolution. It is a relatively new kind of research methodology, whereby the benefits of both qualitative and quantitative research are combined for better and reliable results. This paper discusses the scope of mixed methods research, its characteristics, types; and evaluates it as a research methodology.
Mixed methods research is a growing area of methodological choice for many academics and researchers from across a variety of discipline areas. With the development and perceived legitimacy of both quantitative and qualitative research in the social and human sciences, mixed methods research, employing the combination of both quantitative and qualitative research, has gained popularity.
This popularity is because research methodology continues to evolve and develop and mixed methods research is another step forward, utilizing the strengths of both qualitative and quantitative methods.
Mixed methods research is basically defined as the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study.
It is inclusive, pluralistic and complementary. Mixed methods research focuses on collecting, analyzing and mixing both quantitative and qualitative data in a single study or series of studies. Its central premise is that the use of quantitative and qualitative approaches in combination provides a better understanding of research problems that either approach alone.
This better understanding results because mixed methods offer strengths that offset the weaknesses of separately applied quantitative and qualitative research methods [ii]. It also encourages the collection of more comprehensive evidence for study problems; helps answer questions that quantitative or qualitative methods alone cannot answer.
Mixed methods research is important today because of the complexity of problems that need to be addressed, the rise of interest in qualitative research and the practical need to gather multiple forms of data for diverse audiences. As per the definition, mixed methods research involves both collecting and analyzing quantitative and qualitative data.
Quantitative data includes closed-ended information such as that found on attitude, behaviour, or performance instruments. Sometimes quantitative information is found in documents such as census records or attendance records. The analysis consists of statistically analyzing scores collected on instruments, checklists or public documents to answer research questions or to test hypotheses. In contrast, qualitative data consists of open-ended information that researcher gathers through interviews with participants.
The general, open-ended questions asked during these interviews allow the participants to supply answers in their own words. Also, qualitative data may be collected by observing participants or sites of research, gathering document from a private or public source, etc.
The analysis of the qualitative data word or text or images typically follows the path of aggregating the words or images into categories of information and presenting the diversity of ideas gathered during data collection [v]. The mixing of data is a unique aspect of the definition of the mixed methods research.
By mixing the datasets, the researcher provides a better understanding of the problem than if either data set had been used alone. There are three ways in which the mixing occurs: merging or converging the two data sets by actually bringing them together, connecting the two datasets by having built on the other, or embedding one dataset within the other so that one type of data provides a supportive role for the other dataset [vi].
In short, it is not enough to simply collect and analyze qualitative and quantitative data; they need to be mixed in some way so that together they form a more complete picture than they do when standing alone. The following are the some basic features of the mixed methods research:. The mixed methods research process model comprises eight distinct steps:. There are six major strategies for inquirers to choose from in while designing a research proposal.
These are: sequential explanatory strategy, sequential exploratory strategy, sequential transformative strategy, concurrent triangulation strategy, concurrent embedded strategy and concurrent transformative strategy. It is the most popular strategy for mixed methods research that often appeal to researchers with strong quantitative leanings. It is characterized by the collection and analysis of quantitative data in the first phase of research followed by the collection and analysis of the qualitative data in the second phase that builds on the results of the initial quantitative results.
The mixing of the data occurs when the initial quantitative results informs the secondary qualitative data collection [vii]. Thus, the forms of the data are separate but connected. A sequential explanatory design is typically used to explain and interpret quantitative results by collecting and analyzing qualitative data.
It can be useful when unexpected results arise from a quantitative study [viii]. In this case, the qualitative data collection that follows can be used to examine these results in detail. This strategy may or may not have a specific theoretical perspective [ix]. However, it is easy to implement because the steps falls into clear and separate stages.
In addition, this strategy makes it easy to describe and to report. The main weakness of this design is the length of time involved in data collection with the two separate phases. This especially a drawback if the two phases are given equal priority.
This strategy is similar to the explanatory sequential approach except that the phases are reversed. The sequential exploratory strategy involves a first phase of qualitative data collection and analysis, followed by a second phase of quantitative data collection and analysis that builds on the results of the first qualitative phase.
Weight is generally placed on the first phase and data are mixed through being connected between the qualitative data analysis and the quantitative data collection. However, the design may or may not be implemented within an explicit theoretical perspective. The purpose of this strategy is to use quantitative data and results to assist in the interpretation of qualitative findings. Unlike the sequential explanatory approach, which best suited to explaining and interpreting relationships, the primary focus of this model is to initially explore a phenomenon [x].
It has been suggested that this design is appropriate to use when testing elements of an emergent theory resulting from the qualitative phase and that it can also be used to generalize qualitative findings to different samples [xi]. Similarly, Morse cited one purpose for selecting this approach: to determine distribution of a phenomenon within a chosen population [xii]. The sequential exploratory strategy is often discussed as the procedure of choice when a researcher needs to develop an instrument because existing instruments are inadequate or not available.
The sequential exploratory strategy has many of the same advantages as the sequential explanatory model. Its two-phase approach qualitative research followed by quantitative research makes it easy to implement and straightforward to describe and report.
It is useful to a researcher who wants to explore a phenomenon but also wants to expand on qualitative findings. But as with sequential explanatory approach, the sequential exploratory model requires a substantial length of time to complete both data collection phases, which can be a drawback for some research situations.
In addition, the researcher has to make some key decisions about which findings from the initial qualitative phase will be focussed on in the subsequent quantitative phase e. This final sequential approach has two distinct data collection phases, one following other as in the first two strategies described.
The sequential transformative strategy is a two-phase design which has an initial phase either quantitative or qualitative followed by second phase either qualitative or quantitative that builds on earlier phase. In this design, researcher may use either method in the first phase of research and the weight can be given to either or distributed evenly to both phases.
The mixing is connected as in all sequential designs. Unlike the sequential exploratory and explanatory approaches, the sequential transformative model has a theoretical perspective to guide the study [xiii]. By using two phases, a sequential transformative researcher may be able to give voice to diverse perspectives, to better advocate for participants or to better understand a phenomenon or process that is changing as a result of being studied.
The sequential transformative model shares the methodological strengths and weaknesses of the other two sequential approaches. Its use of distinct phases facilitates its implementation, description and sharing of results, although it requires time to complete two data collection phases. Unfortunately, because little has written to date on this approach, one weakness is that there is little guidance on how to use the transformative vision to guide the methods. Likewise, as with all sequential strategies, key decisions need to be made about what findings in the first phase will be the focus of the second phase.
This approach is probably the most familiar of the six major mixed methods models. In a concurrent triangulation approach, the researcher collects both quantitative and qualitative that concurrently and then compares the two databases to determine if there is convergence, differences or some combination.
This model generally uses separate quantitative and qualitative method as a means to offset the weaknesses inherent within one method with the strengths of the other or conversely, the strength of one adds to the strength of the other [xv].
In this approach, the quantitative and qualitative data collection is concurrent, happening in one phase of the research. Ideally, the weight is equal between two methods, but often in practice, priority may be given to one or the other. The mixing during this approach, usually found in an interpretation or discussion section, is to actually merge the data i. This side-by-side integration is often seen in published mixed methods studies in which a discussion section first provides quantitative statistical results followed by qualitative quotes that support or disconfirm the quantitative results.
This traditional method is advantageous because it is familiar to most researchers and can result in well-validated and substantiated findings. In addition, concurrent data collection results in shorter data collection time period as compared to the sequential approaches because both the qualitative and quantitative data are gathered at one time at the research site.
However, this model has a number of limitations. It requires great effort and expertise to adequately study a phenomenon with two separate methods. It also can be difficult to compare the results of two analyses using data of different forms. In addition, a researcher may be unclear how to resolve discrepancies that arise in comparing the results, although the procedures are emerging in the literature, such as conducting additional data collection to resolve the discrepancy, revisiting the original database, gaining new insight from the disparity of the data or developing a new project that addresses the discrepancy [xvi].
Like the concurrent triangulation approach, the concurrent embedded strategy of mixed methods can be identified by its use of one data collection phase, during which both the qualitative and quantitative data are collected simultaneously [xvii]. In this method, one is embedded i. In this design, priority is given to primary data collection approach with less emphasis placed on the nested approach and data are mixed during the analysis phase [xviii].
A theoretical perspective may or may not guide the design. The primary purpose of the concurrent embedded strategy is gaining a broader perspective than could be gained from using only the predominant data collection method.
It is also used to address different research questions or garner information from different groups or levels within an organization. The strength of this approach is that researcher is able to collect two types of data simultaneously getting advantages of both methods. For instance, Morse noted that a primarily qualitative design could embed some quantitative data to enrich the description of the sample participants.
A concurrent embedded model may be employed when a researcher chooses to utilize different methods to study different groups or levels [xix]. Tashakkori and Teddlie described this approach as a multilevel design. Finally, one method could be used within a framework of the other method, such as if researcher designed [xx]. However, the various weaknesses of this approach are: data need to be transformed to allow integration during analysis which may lead to issues in resolving discrepancies that occur between different data types; moreover, there is little literature in this area providing little guidance for the researcher.
This perspective can be based on ideologies such as critical theory, advocacy, participatory research or a conceptual or theoretical framework. The choice of a concurrent model, whether it is triangulation or embedded design, is made to facilitate this perspective.
For example, the design may have one method embedded in other so that diverse participants are given a voice in the change process of an organisation. It may involve a triangulation of quantitative and qualitative data to best converge information to provide evidence for an inequality of policies in an organization.
Thus, the concurrent transformative model may take on the design features of either a triangulation or an embedded approach [xxi]. The mixing of data would be through merging, connecting or embedding the data.
Because the current transformative model shares features with the triangulation and embedded approaches, it also shares their specific strengths and weaknesses.
Mixed methods research actually has a long history in research practice. It is now time that all researchers and research methodologists formally recognize the third research paradigm and begin systematically writing about it and using it.
Design-oriented research has a long tradition in Europe. In many other European countries, however, design-oriented IS research is less visible. As a consequence, high-quality design science research has not only to be relevant, but also to be rigorous. The rigour of design-oriented IS research, however, is less well defined and less commonly accepted than its behavioural counterpart. Methodology research in social sciences has matured over decades.
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This chapter uses an emphasis on research design to discuss qualitative, quantitative, and mixed methods research as three major approaches to research in.
Research design refers to the overall strategy utilized to carry out research  that defines a succinct and logical plan to tackle established research question s through the collection, interpretation, analysis, and discussion of data. The methodologies and methods incorporated in the design of a research study will depend on the standpoint of the researcher over their beliefs in the nature of knowledge see epistemology and reality see ontology , often shaped by the disciplinary areas the researcher belongs to. The design of a study defines the study type descriptive, correlational, semi-experimental, experimental, review, meta-analytic and sub-type e. There are many ways to classify research designs. Nonetheless, the list below offers a number of useful distinctions between possible research designs.
Research design is the framework of research methods and techniques chosen by a researcher. The design allows researchers to hone in on research methods that are suitable for the subject matter and set up their studies up for success. There are three main types of research design: Data collection, measurement, and analysis. The type of research problem an organization is facing will determine the research design and not vice-versa.
Before beginning your paper, you need to decide how you plan to design the study. The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you can use, not the other way around! Kirshenblatt-Gimblett, Barbara. Part 1, What Is Research Design?
Kemper, Springfield and Teddlie () define mixed methods design as a method that includes both qualitative and quantitative data collection and analysis in.
Mixed Methods Research is the product of such evolution. It is a relatively new kind of research methodology, whereby the benefits of both qualitative and quantitative research are combined for better and reliable results. This paper discusses the scope of mixed methods research, its characteristics, types; and evaluates it as a research methodology. Mixed methods research is a growing area of methodological choice for many academics and researchers from across a variety of discipline areas. With the development and perceived legitimacy of both quantitative and qualitative research in the social and human sciences, mixed methods research, employing the combination of both quantitative and qualitative research, has gained popularity. This popularity is because research methodology continues to evolve and develop and mixed methods research is another step forward, utilizing the strengths of both qualitative and quantitative methods.
Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes. Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight.
Before beginning your paper, you need to decide how you plan to design the study. The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that the research problem determines the type of design you should use, not the other way around!
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