This chapter discusses the philosophy and approach of the research design. It explains the overall methodology and provides details on the methods employed including the sampling techniques, data collection and procedures, data analysis, and interpretation. The qualitative and quantitative methods used in this empirical research study are discussed in the context of the research questions of the thesis.
The research questions are considered appropriate for the application of an exploratory sequential mixed methods design.
Rationale for Mixed Method Design:
This study uses mixed methods for data collection and analysis. This method is considered appropriate due to the research questions and the nature of the topic under research which is deemed under-researched. The purpose of using mixed methods is to acquire knowledge about the under-representation of females working and committing to their careers in STEM fields that is more extensive than a single approach may provide. Mixed methods design has been receiving increasing attention in recent years. It is a type of research where the researcher incorporates both qualitative and quantitative methods, approaches or conceptions into one study (Johnson & Onwuegbuzie 2004). In such research, the methods serve as means to answer the same research questions, to gather data and administer equivalent data analysis (Yin 2009). Thus, mixed methods can allow the researcher to undertake more complex and broader questions, collect deeper evidence, and provide more vigorous proof than can be acquired by a single method (Yin 2014). One of the
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strengths of using mixed method design is that it assumes that both types of data will yield distinct forms of information for the research study. Mixed methods design has been deemed appropriate for this empirical investigation since existing literature has not evidently explained the proposed research questions regarding mentoring of females, their coping self-efficacy, and occupational commitment in STEM industries. Mixed methods research has been discussed in various ways. This study adopts Creswell’s (2016) definition on mixed methods where it is considered as a method rather than, for instance, an ideology or language. Creswell defines mixed methods as a way of conducting research in several fields of study, such as behavioural and social where the researcher utilizes open and closed-ended questions in reference to quantitative and qualitative data, combines both and finally draws conclusions established according to the incorporated end results in order to reach an understanding of specified research questions. Creswell and Plano Clark (2007) introduced four main mixed design types that differ according to the timing whether sequential or concurrent, the weighing whether dominance or priority/equal or unequal, and the mixing that identifies at which phase and how the data are combined. Through this study, exploratory sequential design will be utilized where the first phase is qualitative followed by quantitative design (Creswell 2003, Patton 2002, Miles & Huberman 1994, Flick 2010). In an attempt to provide more integration to the process, further studies introduced a three-dimensional typology (Onwuegbuzie 2003). The variation in the level of mixing is one dimension which will be a partial variation in this study. The time orientation which will be sequential, and finally the emphasis which will be the dominance of the qualitative design.
Therefore, in this study a partially-mixed sequential dominant status design, referred to as QUAL→quan will be implemented. Recent studies have discussed several complex typologies of mixed methods. In this study a synergistic approach will be implemented where the resulting synergism of mixing both research designs will
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take place at the analysis and interpretation stages were conclusions drawn from both qualitative and quantitative designs are integrated at the final meta-inference stage (Teddlie & Tashakkori 2009). Hall and Howard (2008) put forward four main principles for synergetic approaches that will be followed through this research:
1.The notion that combining both qualitative and quantitative will result in a research process and findings that are far better than that created by using one of the approaches only.
2.The significant presence of multiple ideologies or viewpoints concerning mixing the two methods. This will result in efforts to accommodate opposing viewpoints that might appear at different stages of the research process.
3.Although there is a clear allocation of different weighing for each approach, where qualitative is the dominant approach in the case of this study, equal value will be attributed to both.
4.The significance of a contemplative viewpoint in balancing the possibly conflicting views that I might have in relation to data interpretation, for example.
I am hereby including a detailed diagram that would better explain the synergetic process of the mixed methods design adopted. It is borrowed from Tashakkori and Teddlie (2010) and will act as a roadmap for this section’s methodology. It clearly identifies the main method adopted (qualitative), the supplemental component (quantitative), the sequential organization of the two designs, and the point of interface in the results section. The sequential mixed design is conducted as follows:
phase one comprises the qualitative core component. After identifying the research questions and the theoretical framework (SCCT), the qualitative core method is selected, which is the multiple individual case studies. Then the sample is identified using purposive criterion sampling and the sample size is 28 participants. The data
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was collected using semi-structured interviews that assisted in exploring in more depth women’s accounts of their experiences of mentoring, coping self-efficacy, and occupational commitment. These steps are discussed in more details in the coming sections of the study. The next step was to analyse the data using Nvivo software resulting in the research findings of the QUAL. These were used to partially design the survey questionnaire. The survey helped in identifying whether relationships exists between the variables in these three areas: coping self-efficacy and mentoring, coping self-efficacy and occupational commitment, and mentoring and occupational commitment. The potential advantages of the survey are the large sample size and the advancing numerical analysis that helps with finding trends in the data (Patton 2002). Data collection for the quantitative part was done through an administered survey. The sample was made up of female alumni from universities across UAE and Lebanon who graduated with STEM majors. Data is analysed using structural equation modelling in Stata.
The fact that the researcher conducting mixed methods has to be efficient in a range of both qualitative and quantitative analysis techniques is not an easy task. The researcher has also to be competent at generating findings from both research techniques to come up with substantial meta-inferences (Tashakkori & Teddlie 1998). These factors make data analysis of mixed methods a challenging task. Due to these reasons, researchers have advocated a wide range of inclusive frameworks for data analysis in mixed methods research designs. These frameworks can have practical advantages of uniting the field they represent whether it is predominantly qualitative or quantitative. They present a flexible system that other researchers can benefit from. A comprehensive research framework also offers a clear format and provides guidance for researchers on how to analyse the data and ultimately answer their research questions. An inclusive framework also enhances rigour in the analysis
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which aids in legitimizing the field of mixed methods research design. Finally, an inclusive framework supports creation of a common language for classifying and explaining the analysis process. Future research can then benefit from such consistency by expanding, modifying, or further developing existing frameworks into more advanced ones. The advantages of an integrated framework and its importance in legitimizing an accountable field has triggered a string of research (e.g. Onwuegbuzie et al. 2009, Onwuegbuzie et al. 2007, Newman and Ramlo 2010).
Despite the fact that a large amount of published work is now emerging related to mixed methods research, there still does not exist a generally acceptable framework for analysing mixed methods (Greene 2008). Many recent typologies, however, classifying mixed analysis strategies (e.g. Bazeley 2010, Creswell & Plano Clark 2007) have been highly received by scholars. Tashakkori and Teddlie’s (2010) content analysis of these typologies explains that the authors used thirteen distinct criteria to come up with these typologies. This research adopts six of these criteria to further explain and classify the mixed analysis strategy that has been used based on the mixed research design typology that was discussed earlier in this chapter.
These criteria are listed below and further discussed in the coming paragraphs.
1-The reason for conducting the mixed methods analysis.
2-Number of data types that were analysed.
3-Number of data analysis types that were used.
4-Time sequence of the mixed analysis.
5-Link to other design elements.
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6- Degree of analysis interaction between the two research designs, qualitative and quantitative.
The rationale for performing a mixed research design further aids in advancing the decision for a mixed analysis strategy. While Onwuegbuzie and Sutton (2006) assert five reasons, this research builds on one of these, namely expansion. The reason this study uses mixed methods is to expand the magnitude and dimension of the research by applying two analytical strands, one qualitative and the other quantitative, each in a different phase. The second criterion that is considered is the number of data types that will be used in the meta-inference phase when combining both results. In this study one data type is used. This type is qualitative because it is the core component of the research design. The first phase of the data analysis is qualitative, the second phase is quantitative. Then, the quantitative data, after being analysed using structural equation modelling, is qualified by changing it into narrative data that is analysed qualitatively to facilitate comparison with the findings from the qualitative study (Onwuegbuziet et al. 2007). This is presented in the Discussion Chapter which constitutes the meta-inference phase. The third criterion that this study applies to develop a mixed analysis strategy is the number of data analysis types used in each separate research study. This research involves one principal method of analysis for each design. The Gioia methodology is used for the qualitative design and structural equation modelling is used in the quantitative design. The sequence of the analysis is the fourth criterion that is considered. Since this is a sequential mixed methods design, qualitative analysis is conducted in the first phase, which then informs the quantitative design in the second phase (Teddlie
& Tashakkori 2009). The fourth criterion is the priority of a certain analytical strand.
In this research, priority is given to the core component which is the qualitative design. Therefore, complex and extensive analytical methods have been used to
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ensure in-depth investigation and understanding of the research questions as opposed to the quantitative design in the second phase. The quantitative design (which is considered supplemental) is used to further enhance the explanation resulting from the first phase. This brings us to the fifth criterion which is the link to other design elements. In summary, the mixed analysis design of this study is defined as a partially-mixed sequential dominant status design.
The last criterion considered is the level of interaction between the analysis of the two designs. This dimension is important and explains the point at which the analysis of both designs interact. The most common technique of mixed analysis is the parallel mixed analysis that has been extensively discussed in the works of Tashakkori and Teddlie (1998), Onwuegbuzie and Leech (2004), and Teddlie and Tashakkori (2009). The authors define parallel mixed analysis where, for example, both qualitative and quantitative analyses are conducted independently. However, both designs offer explanations about the phenomenon being studied. The findings of both are then joined and integrated into a meta-inference phase. This simple form of parallel mixed analysis is known as parallel tracks analysis in contrast to a more complicated form known as a cross-over tracks analysis (Li et. al 2000). The cross- over tracks form has been adopted in this study to extensively analyse the data. The findings from the two designs interlace and enhance one another throughout the research (Datta 2001). Teddlie and Tashakkori (2009) explain certain characteristics relating to these complex forms of mixed analysis strategies. In sequences of such forms, as is the case in this research, the analysis of one design informs the other before the stage of meta-inference. Based on the findings of the qualitative design, additional hypotheses were added. The conceptual framework was amended to reflect these changes in terms of additional constructs included and new relations that needed to be tested. The survey was also expanded and redesigned accordingly.
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After analysing each set of data from the two designs separately, both results are integrated and further interpreted in the Discussion Chapter of this study. The third and final characterization of cross-over tracks analysis is combining this form of analysis with other types of mixed analysis strategies. In the case of this research and as has been discussed earlier, the analysis is informed by the fact that the research design is qualitatively dominant and also sequential in nature.
As indicated by Kelle and Erzberger (2004), one of the advantages of combining both methods is that findings from both designs will focus on distinct elements of the study, yet simultaneously will also complement each other and contribute to a bigger picture. Combining both methods will lead to valid results and their limitations (Flick 2010).
Figure 5. Mixed Methods Map
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