Mixed Method Research Designs: Combining Qualitative And Quantitative Research

Research on Mixed Methods 

Research on mixed methods has been widely used in health care research for various reasons. The integration of qualitative and quantitative approaches is an interesting question and continues to be one of the many debates (Bryman, 2004; Morgan, 2007; Onwuegbuzie and Leech, 2005). In particular, the various hypotheses and epistemological and ontological paradigms associated with qualitative and quantitative research had a major influence on discussions on the question of whether the integration of the two is possible, and even less desirable (Morgan, 2007; Sale et al., 2002). 

Supporters of research on mixed methods suggest that the vision of purists, that quantitative and qualitative approaches cannot be merged, constitutes a threat to the progress of science (onwuegbuzie and Leech, 2005) and that if the commitments Epistemological and ontological can be associated with certain research methods, connections are not necessary deterministic (Bryman, 2004). 

Rather than pursuing these debates in this article, we aim to explore the approaches used to integrate qualitative and quantitative data into health care research. Consequently, this article focuses on practical issues in the management of mixed methods and the need to develop a rigorous framework to design and interpret mixed methods to advance the field. 

In this article, we will try to provide advice for research on intermediate methods interested in means of combining qualitative and quantitative methods. The concept of mixing methods was introduced for the first time by Jick (1979), as a means of seeking convergence through qualitative and quantitative methods within social science research (Creswell, 2003). 

It has been advanced that the search for mixed methods can be particularly useful in health care research because only a range of perspectives can do justice to the complexity of the phenomena studied (Clarke and Yaros, 1988; Foss and Ellefsen, 2002; Steckler et al., 1992). By combining qualitative and quantitative results, a global or negotiated report of the results may be forged, not possible using a singular approach (Bryman, 2007). 

Mixed methods can also help highlight the similarities and differences between specific aspects of a phenomenon (Bernardi et al., 2007). The interest and expansion of the use of mixed methods have been recently fueled by pragmatic problems: growing demand for profitable research and the remoteness of research theoretically motivated to research that meets the needs of decision-makers and practitioners and growth in competition for research financing (Brannen, 2009; O'Cathain et al., 2007). 

Tashakkori and Creswell (2007) largely define research on mixed methods as "research in which the investigator collects and analyzes data, integrates results and draws inferences using both qualitative and quantitative approaches" (2007: 3). In any study of mixed methods, the aim of mixing qualitative and quantitative methods should be clear to determine how analytical techniques relate to each other and how, if necessary, the results must be integrated (O'Cathain et al ., 2008; Onwuegbuzie and Teddlie, 2003). 

It was argued that a characteristic of really mixed methods is that which implies the integration of qualitative and quantitative results at a certain stage of the research process, whether During the collection, analysis, or the research phase of research (Kroll and Neri, 2009). An example of this is found in mixed methods that use a simultaneous data analysis approach, in which each data set is integrated during the analytical phase to provide a complete image developed from the two data sets after the qualified or quantified data (that is, where the two of the data forms have been converted into qualitative or quantitative data so that they can be easily merged) (onwuegbuzie and Teddlie, 2003).

Other analytical approaches that include; Analysis of parallel data, in which the collection and analysis of both data sets are carried out separately and the findings are not compared or consolidated until the interpretation stage, and finally the analysis of sequential data, in which the Data are analyzed in a particular sequence with the purpose of informing, instead of being integrated with the use or findings of the other method (Onwuegbuzie and Teddlie, 2003). 

An example of sequential data analysis could be where quantitative findings are intended to lead to a theoretical sampling in a deep qualitative investigation or where qualitative data is used to generate elements for the development of quantitative measures. When qualitative and quantitative methods are mixed in a single study, one method generally has priority over the other. 

In such cases, the objective of the study, the justification for using mixed methods, and the weighting of each method determine if the empirical findings will be integrated. This is less challenging in the studies of sequential mixed methods where an approach clearly informs the other, however, the orientation to combine qualitative and quantitative data of equal weight, for example, in studies of mixed methods with current, is much less clear ( Foss and Ellefsen, 2002). 

This becomes even more challenging by a common defect that is insufficient and inexplicitly identifying the relationships between epistemological and methodological concepts in a particular study and the theoretical propositions on the nature of phenomena under investigation (Kelle, 2001). An approach to combine different data of equal weight and facilitates the clear identification of the links between the different levels of theory, epistemology and methodology could be to frame triangulation as a "methodological metaphor", as Erzberger and Kelle (2003) argue. 

This can help; Describe the logical relationships between qualitative and quantitative findings and theoretical concepts in a study; Demonstrate the way in which qualitative and quantitative data can be combined to facilitate an improved understanding of particular phenomena; And can also be used to help generate a new theory (Erzberger and Kelle, 2003) (see Fig. 1). The points of the triangle represent theoretical propositions and empirical findings from qualitative and quantitative data, while the sides of the triangle represent the logical relationships between these propositions and findings.

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Reference

Östlund, U., Kidd, L., Wengström, Y., & Rowa-Dewar, N. (2011). Combining qualitative and quantitative research within mixed method research designs: a methodological review. International journal of nursing studies, 48(3), 369-383.

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