3.2 Research methods
3.2.2 Mono versus Mixed-Methods
Many authors have presented typologies of mixed-methods research design with the aim to better classify mixed-methods designs (Greene and Caracelli, 1997; Teddlie and Tashakkori, 2006; Teddlie and Tashakkori, 2009). Creswell et al. (2003, p. 203) have developed a de- cision model for the selection of a rigorous mixed-method research de- sign, while the text highlighted in bold represents the attributes imple- mented for this thesis.
Table 3.1: Decision model
Implementation Priority Integration
Concurrent Quantitative and qual- itative
Data collection
Sequential Qualitative First
Qualitative Data analysis Data interpretation Sequential
Quantitative First
Quantitative Different Steps
The first step is the implementation step, which is the question of when qualitative and quantitative data will be collected. Under the next step, Creswell et al. (2003) subsume the degree of priority under which the researcher regards the collection of data. The last step reflects how the different methods are combined with each other.
Quantitative work corresponds to figures rather than to words, while for qualitative research, the opposite is true (Thietart, 2001). Quantitative methods imply significant advantages in comparison to qualitative ap- proaches. This is due to the fact that quantitative methods rely on tan- gible and thus measurable data, which are comparable to each other. Quantitative methods focus or numbers (“hard science”) and can pro- vide advantages in terms of time savings for the data collection and analysis. Another advantage that quantitative methods reveal is that they are relatively easy for researchers to use. There are a lot of analyt- ical tools and a variety of software exists that supports quantitative methods. However, quantitative methods also face several shortcom- ings, as they focus only on numbers, which can limit objectivity. Relying only on numbers can prevent observations from being considered and can thus lead to research gaps or even a misinterpretation of results. In contrast to the above, qualitative methods focus on intangible data and argue that qualitative data exceed numbers in terms of importance (“soft science”). Qualitative methods focus on human behaviour, mainly using interviews. Qualitative methods require considerable investment in time and effort to analyse unstructured data. The coding of interviews is a necessary task for the qualitative researcher, as interviews involve unstructured data. The limitations of interpretation are also blurred with- in qualitative research. In contrast to the quantitative method, there are fewer accepted qualitative standards and tools.
Mixed–method designs can be differentiated into four major types, ac- cording to Creswell (2006):
Table 3.2: Four major types of mixed-method design Design
Type
Variants Timing Weighting
Triangu- lation Convergence Data transfor- mation Validating quanti- tative data Multilevel Convergence Data transformation Validating Quantita- tive data Multilevel Usually Equal Embed- ded Embedded experimental Embedded correla- tional Concurrent or Sequential Unequal Explana- tory Follow-up Participant Expla- nations Selection Sequential:
First quantitative then qualitative Usually quantitative Explora- tory Researcher Tax- onomy develop- ment Sequential:
First qualitative then quantitative
Usually Qualitative
(Source: Creswell, 2006)
A mixed-method or combined method approach can be differentiated in the sequence of the implementation of the different methods. This is either known as concurrent design or sequential design (Driscoll et al., 2007). The concurrent design is used to compare one form of data with another. The concurrent mixed-method approach is based on the con- cept of triangulation, which suggests that with the simultaneous combi- nation of qualitative and quantitative methods, biases inherent in any single method could be neutralized or cancel the biases of other meth- ods (Jick, 1979).
The exploratory design consists of a two-phase mixed-method, and is used predominantly when an exploration is needed, according to Cre- swell (2006):
• Instruments for measurement are not available or are incomplete • Variables are not documented
• A guiding framework or theory is not available
Exploratory designs are best suited for exploring a phenomenon (Creswell et al., 2003), testing a new instrument and generalizing to different groups and will be applied for this thesis. The “instrument de- velopment model” and the “taxonomy development model” are two kinds of exploratory model (Doyle et al., 2009, p. 176). Each of these models starts with an initial qualitative phase and finishes with a quanti- tative phase. Differences exist with regard to the researcher’s connec- tion of the two phases and the relative emphasis of the two methods. Researchers use the instrument development model in cases where they need to develop and implement a quantitative instrument based on qualitative findings. In this design, the researcher starts to qualitatively explore the research topic with a few participants. The qualitative find- ings provide guidance for the development of elements and scales for a quantitative survey instrument.
The taxonomy development model is used in this thesis. According to this model, the initial qualitative phase delivers specific categories for CGR. These categories or relationships are used in the following step to appoint the research questions as well as data collection utilized in the second, quantitative phase. For this thesis, the researcher will identify emergent categories from the qualitative data and then consider the quantitative phase to test the existence and replicability of these cate- gories within the sample (Morse, 1991).
lection technique. While the mono-method approach follows only one data collection technique, such as one qualitative study, multi-method approaches combine either qualitative and quantitative methods or mul- tiple quantitative or multiple qualitative research techniques. The mixed- method approach provides advantages for practicing researchers, as it combines the strengths of both methods and minimizes their weak- nesses (Johnson and Onwuegbuzie, 2004). Similarly, Brewer and Hunter conclude that the multi-method approach allows researchers to “attack a research problem with an arsenal of methods that have no overlapping weaknesses in addition to their complementary strength” (Hunter and Brewer, 2005, p. 74). According to Tashakkori and Teddlie (1998, p. 413) mixed methods “are being used extensively to solve practical research problems”, which is also a reason why mixed meth- ods are applied for this thesis.