Mixed Methods Design

This research is using a mixed methods design. Mixed methods mean that the research has both quantitative and qualitative data. Creswell( 2015, p. 2) defines mixed methods as “An approach to research in the social, behavioural, and health sciences in which the investigator gathers both quantitative (closed-ended) and qualitative (open-ended) data, integrates the two, and then draws interpretations based on the combined strengths of both sets of data to understand research problems”. Robson (2011) and Bryman (2004) stress that it can also be called “multi-strategy designs”, since this method includes more than one strategy. Many researchers, such as Tashakkori & Teddlie ( 2003) give more advice on how to use this kind of research. Creswell (2015) explains that mixed methods research is not just about gathering quantitative and qualitative data together. There should be a strong rationale for using mixed methods, and quantitative and qualitative data should be integrated.

3.2.1 Reasons for Using a Mixed Methods Design

Many researchers believe in using a mixed methods design. Bryman (2006) identifies many of these reasons. For example, triangulation is used to relate quantitative to qualitative data. Denzin (1970) identifies four types of triangulation: data triangulation,

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investigator triangulation, theoretical triangulation and methodological triangulation. Data triangulation is more about collecting data in different contexts with different participants. Investigator triangulation is what happens when more than one researcher works on the same study. Using different theories to explain the data is called theoretical triangulation. Finally, using more than one methodology in the same research is called methodological triangulation.

However, Bryman( 2004) concludes that triangulation follows the realism view where there is one single approach to interpreting the data in social science, while

constructionism assumes that research findings could be explained using more than one approach and that triangulation can enrich the research findings. Furthermore,

triangulation explains the data collected using different methodologies to answer a research question in the same way, while there could be different social circumstances associated with each methodology.

The second reason could be complementarity, by using the results of one method to explain the results of the other method used in the research. Development is also used when the results of one method are used to develop the results of the other one. In addition, when the results of one method are used to find the contradiction to the results of the other method, then this is called initiation. Finally, expansion could be one of the reasons for using mixed method research when we are interested in increasing the range that the study covers.

There could also be other reasons for using mixed methods, such as combining the strengths of both methods to overcome the weaknesses, which is called offsetting. Furthermore, we sometimes need to combine both methods since the quantitative method provides structure data while the qualitative method will be more about process data. The results of one method could be used to explain the results of the other. Furthermore, if we get unexpected results from one method, then the other method could be used to clarify the reasons for such results. In addition, a mixed method could be used to develop the instruments used in the research, such as better questionnaires or scales. Using mixed methods also helps to sample the results and increases their credibility. Mixed methods also helps to better understand the context when quantitative data does not cover all the

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needed information and this will also help to illustrate the quantitative data. Results coming from mixed methods will also be more practical to apply. Moreover, when a qualitative method is used to generate a hypothesis, then the quantitative method can be used to test this hypothesis. Using mixed methods also helps to join different quantitative and qualitative views and to enhance the research by having data from more than one approach.

3.2.2 Arguments against a Mixed Methods Design

In contrast, other researchers argue that quantitative and qualitative methods use two different paradigms, so they cannot be combined. Sale, Lohfeld, & Brazil( 2002, p. 43) claim that “Because the two paradigms do not study the same phenomena, quantitative and qualitative methods cannot be combined”. On the other hand, Howe( 1988, p. 10) affirms that “there are important senses in which quantitative and qualitative methods are inseparable”. This is because the similarities between quantitative and qualitative

research are greater than the differences. Johnson, Onwuegbuzie, & Turner ( 2007) explain that there are three research paradigms: quantitative, qualitative and mixed

methods. In this research, the “quantitative dominant mixed method” is defined as a “type of mixed research in which one relies on a quantitative, post-positivist view of the

research process, while concurrently recognising that the addition of qualitative data and approaches are likely to benefit most research projects” (Johnson et al., 2007, p. 124). The following graph explains the types of mixed methods research.

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Figure 3.1Graphs of the Three Major Research Paradigms, Including Subtypes of Mixed Methods Research, cited from (Johnson et at., 2007, p.124)

This type of methodology is also called “sequential explanatory design”, where the study depends mainly on the quantitative data, while the qualitative data are used to explain the findings of the quantitative data in an integrative way. Creswell ( 2015) calls it “explanatory sequential design”, since the qualitative data are used to gain deeper understanding of the quantitative data.

3.2.3 Things to be Considered before Using a Mixed Methods Design

In contrast, there are many things a researcher should consider before using the multi- strategy design. For example, skills and training are needed for both quantitative and qualitative data analysis. In addition, it could be time consuming, since qualitative data may need more time to collect and analyse than the quantitative data. Moreover, the

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qualitative data may not add any further explanation to the quantitative data. For

example, qualitative data should not be used just to illustrate the findings of quantitative data. Finally, the findings of quantitative and qualitative data may not integrate with each other. This is why a researcher using mixed methods should be given enough time, resources and training.

However, Moffatt, White, Mackintosh, & Howel (2006) acknowledge some strategies to deal with the differences between quantitative and qualitative findings. For example, each method’s findings can be analysed differently to show that there are different views of real-world research in social science and to try to identify the reasons for getting differences between quantitative and qualitative data. For example, the number of participants or type of required participation. Furthermore, it is possible to compare the two data groups to find if there are big differences in their outcomes. Another suggestion would be collecting more quantitative and qualitative data to get more support for the findings of each method. Moreover, the conditions where each method was used should be revised to find out if they are related to such differences. Finally, findings of quantitative and qualitative data should be explored. This is because they would result in different outcomes, since some factors could be measured or answered by one method, but not the other. Some researchers believe that either the similarities or the differences between quantitative and qualitative data would enrich the recommendations of the study.