Exploring Three Research Designs

The epistemological foundation for the selection of methodologies for data collection and analyses of the eight research questions provided the opportunity to explore an array of research approaches. In the quest for the most appropriate research method, the following quality criteria were sought: transparency to produce rigorous analytical results; robustness in terms of validity and reliability of its design; and a method congruent with the research questions (Bryman, Becker, & Sempik, 2008) but culturally acceptable to the target population. Three genres of research approaches were explored to facilitate these criteria for the current study: qualitative, quantitative, and mixed methods. The characteristics of these methods will be described first followed by the rationale for the selection of the most appropriate research design for this study.

3.3.1 Qualitative research design.

Qualitative research is rigorous and has advanced significantly to a carefully planned research design. It involves all aspects of the study, commencing from the research questions to sampling to data collection and moving to the analysis, utilising procedures and techniques which ensure the trustworthiness of the findings (Frankel &

Devers, 2000). This method is characterised by the collection and analyses of textual data such as surveys, interviews, focus groups, conversational analysis, questionnaires and ethnographies (Olds, Moskal, & Miller, 2005) and focuses on the context within which the study occurs. Qualitative designs answer ‘how’, ‘what’ and ‘why’ questions especially when the researcher has little control over the events (Yin, 2003), thus generalizing through thick descriptions of the context, allowing the reader to make connections between the study and his or her own situation. Given qualitative work is considered to be more inherently interpretive research, the biases, values, and

judgments of the researchers need to be more explicitly acknowledged so they are taken into account in data presentation (Creswell, 2009).

The debate on qualitative analysis is not to provide a broad, generalizable description which is representative of most situations, but rather to describe a particular situation in enough depth that the full meaning of what occurs is made apparent.

Another perspective described by Foor et al. (2007)examined qualitative analysis through the lens of statistical analyses which can bury the voices of underrepresented groups through surveys and questionnaires and which do not describe marginalized individual’s experiences. Giving voice to the “other” is a key component in qualitative research which allows the respondents to discuss the topic in their own words, free of constraints from fixed-response questions found in quantitative studies.

On the other hand, Crotty (2003) explained qualitative research as reality; it is related to a particular epistemology, or way of understanding reality. Thus it is evident there are many epistemological findings pertaining to qualitative analysis. The

researcher has to sift through all the debates relating to qualitative research and select the most appropriate approach which matches his/her research questions.

3.3.2 Quantitative research design.

Quantitative research design is quite different from the qualitative paradigm. Quantitative design is a good fit for deductive approaches, in which a theory or hypothesis justifies the variables, the purpose statement, and the direction of the research questions. A hypothesis is typically formulated as a research question, and then data are collected such as from a locally developed and validated survey or commercial instruments, frequently using experimental designs. Rigorous statistical analysis is essential in quantitative research to ensure reliability and generalizability. Also different statistical analyses are used to examine the data (Creswell, 2002).

Descriptive statistics such as percentages, means and standard deviations are utilized for illustrating various points and for describing a situation, particularly one that has not been studied previously (Hodge & Steele, 2002; Todd, Magleby, Sorensen, Swan, & Anthony, 1995). Significant differences between groups on various indicators (variables), for example, cause and effect can be examined (Carpenter, Harding, Finelli, Montgomery, & Passow, 2006). The researcher has the opportunity to generalize and/or make inferences. Additionally, the results are interpreted to determine the probability that the conclusions will be replicated within the larger population through an objective process.

3.3.3 Mixed methods research design.

Mixed methods research design is gaining popularity exponentially and has been described as the third methodological movement following quantitative and qualitative oriented approaches (Tashakkori & Teddlie, 2003). Scholars highlighted that strategies can be used for linking qualitative and quantitative methods in a complementary fashion (Miles & Huberman, 1984; Patton, 2002; Sandelowski, 2000). Collection, analysis, and

integration of quantitative and qualitative data are possible in a single or multiphase study.

Integration of the results from a dataset provides a better understanding and a broader picture of the problem than if either set of quantitative or qualitative data was used separately. According to Creswell and Clark (2007, p. 7) there are three possible ways of integrating the dataset:

Merging or converging the two datasets by actually bringing them together; connecting the two datasets by having one build on the other; or embedding one dataset within the other so that one type of dataset provides a supportive role for the other dataset.

The advantage of mixed methods research provides strength to offset the weaknesses of quantitative and qualitative approaches. The line of argument claims quantitative approach is weak in understanding the full context of the study (Jick, 1979). Participants’ verbal viewpoints, biases, and interpretations are excluded from the study. Also a qualitative approach by itself could be inadequate because the interpretations and viewpoints of participants can influence generalization which may be formulated from the biases of information given.