The Importance Of The Mixed Method Approach?

3.1 Methodology
3.1 Research design
According to Inkoom (1999) A research design can be described as the logical sequence that connects the empirical data to study initial research questions and ultimately to its conclusions. Basically, it is viewed as a guiding plan for the process of collecting, analyzing and interpreting observations and a logical model of proof that facilitates the drawing of inference by the research concerning causal relations among the variables under study. Thus, the main objective of the research design is to ensure that evidence collected addresses the initial research questions and make appropriate inferences (Kaleem, 2011). Researchers, at least of the social persuasion, have long agreed that qualitative data can
An important reason why this study chose mixed methods was to help expand the scope of and deepen our insights from the study (Sandelowski 2000). Again, by mixing methods alone, it was possible to make substantial theoretical propositions. For instance, in triangulating various strategies, a better overall view of the reality on the ground was achieved when the social survey component was effectively linked to some unstructured questioning and participant observations. Mixing qualitative and quantitative approaches is not new to social research, where this is done; the researcher seeks breadth and depth of understanding and corroboration (Onwuegbuzie et al. 2010; Scerri and James 2010; Tashakkori and Tenddlie 1998; Tenddlie and Johnson 2009). In addition to the above, the interest of this research in mixing methods was to unlock the reliability of the data acquired (Tilly 2004; Scerri and James 2010). After all, Delanty (2002) opines that science is quickly and progressively becoming communicative, hence a researcher must interact reflexively with society which makes an understanding of scientific knowledge both in its natural and social form important (Sanyare,
In order to facilitate the intention of the study to generate simple statistical inferences, it adopted a probabilistic sampling technique to select respondents for inclusion in the community based survey component. The literature suggests a choice of four possible probability sampling designs. Choosing of any brings along certain advantages and disadvantages. These probability sampling designs includes: the simple random sampling, stratified sampling, systematic sampling, and cluster sampling (Robson 2002; Doherty 1994). This study opted for the simple random sampling technique to select participant communities from randomly identified local government jurisdictions. While in the communities, the study also employed the same approach to select households from which adult individuals were randomly chosen for