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Qualitative and mixed method data strategies
The role of research in social sciences cannot be overestimated. More than before, research has provided social scientist with ways of answering questions made on various topics of interest. Research work has been valuable in identification of patterns and outcomes in various fields of interest. At the kernel, of any research work is the data that researchers manipulate to draw conclusions and insights that are related to the research. The success of research work has been largely due to the use of effective strategies that have a valuable contribution in improving the overall outcomes of the research. To be specific, various strategies have been developed to provide researchers with tools critical in succeeding in their research work. Given the significant value of data in research work, many researchers have focused on a number of data analysis strategies in order to understand their topic well. This paper will address the use of qualitative and mixed methods data strategies in understanding the depth and breadth of research topics.
Breadth and depth of research topic
Any research work is done for a purpose that is aimed at providing an answer to a phenomenon or a solution to a problem. Because of this role, the need to have topics with appropriate depth and breadth cannot be underscored. Evaluation of the depth and breadth of a research topic is possible when certain methods are used to analyze data. In much research work, researchers have utilized both qualitative and mixed method data strategies to arrive at depth and breadth of a topic. The use of any of these strategies has always been associated with problem solving research (Creswell & Clark, 2007).
Qualitative data strategies
Qualitative research refers to a type of inquiry that concerns itself with understanding data collection by means of providing meaning and generalization through abstraction of general concepts related to data. Qualitative research work and its methods are also concerned with the explanation, evaluation and description aspect of the topic under study (Bazeley, 1999). The use of qualitative strategies has been adopted by many researchers because of a number of reasons, which allow researchers to have a clear grasp of their topic under study.
Qualitative method is believed to give researchers the opportunity to look at the depth of an issue with more detail, nuance and context. Qualitative data strategy has been an advantage to the researchers who can explore the depth and breadth of any given topic under study. The success of using qualitative data has been in the ability of the researcher to transform new data into insight and simple relationships that are being studied. This is possible because this method provides agility in the transformation process where a researcher can arrive at the meaning without necessarily quantifying the data.
The result of qualitative data analysis is central to the utilization of this method in generating new meaning from data. In some cases, the use of qualitative data strategies lays emphasis on the importance of inductive reasoning, which is fundamental in understanding the depth and breadth of a research topic (Blake, 1989). Because qualitative methods produce data defined by subjects, it is possible to use these techniques in evaluating topics and report on the breadth and depth. Most important, use of qualitative methods rests on its ability to allow evaluation of topics are higher levels because it establishes an interpretive philosophy that allows topic under study to be interpreted from a number of perspectives.
The process of data analysis in qualitative process follows an iterative process, which gives researchers the avenue to conduct inquiries that can yield valuable information. This information influences how the researchers conceptualize the research process in terms of the breadth and depth of the research topic at hand (Johnson & Onwuegbuzie, 2004).
How qualitative data strategies are used
Qualitative data analysis strategy is a process with several subtasks, all aimed at giving more meaning to data. The interim analysis is the first step that is used to analyze data. This step is often carried out with the aim of having a deeper view of data. Memoing is the second step that concerns that use of reflective notes for the purpose of keeping track of the data. Transcption is the next step that follows allowing researchers to put data in a format that can allow analysis to be carried out. Segmentation is the next step that enables researchers to divide their data in meaningful ways that they can be used for computer analysis. After the segmentation process, Data is often coded to indicate the themes it has. This step is then succeeded by the enumeration step that makes the data be quantified (Roberts, 2000). Hierarchical categorization is then done to organize themes and patters displayed in the data. This result of this will be a relation between the various themes and patterns in the data. The result of qualitative data analysis strategy underscores the flexibility of this method in shaping the evaluation of breadth and depth of research topics (Johnson & Christensen, 2004).
The process of qualitative data strategy encompasses data collection and analysis, which take place at the same time. Other than viewing the two processes as happening at different times, the two should be seen as taking place concurrently. The above steps are central to the evaluation of the breadth and depth of the topic being studied. A step like coding gives researchers the chance to simplify data, which is a necessary step in the understanding of the breadth and depth of study. Classification of data into themes is also a critical step that allows researchers to understand the context of the paper. This step is essential in providing a basis that researchers can evaluate a named topic that is being studied. Certainly, the flexibility of the qualitative data strategy is one of the reasons that allow researchers to iterate evaluation process when determining the breadth and depth of a research topic (Sandelowski, 2000).
Mixed Methods data strategies
The application of mixed method data strategies has made significant inroads in research work due to its suitability. The mixed method research data integrate the qualitative approach and qualitative approach into a single module that reduces the limitation of both techniques. The success of mixed method studies rests on the application of multiple research techniques to achieve the high possible result. In combining two research methods with some unique shortcoming, the mixed methods have emerged as a research paradigm that offset the weakness of the two methods while leveraging on the strengths. The mixed method approach is also called the multi-strategy research thought the application of the specific approaches differ. For instance, mixed method data strategy can be executed by using the two techniques at the same time or one at a time. Nonetheless, the use of this approach is beneficial in the evaluation of breath and depth of research topic under study (Tashakkori & Teddlie, 2003).
There are many reasons that make the mixed method be suitable in the evaluation of the breadth and depth of research topics. First, mixed method research strategy is believed to provide researchers with avenues to triangulate their research work and thus having the ability to look at the research topic from a different dimension. The kernel of triangulation allows researchers to conceptualize different research problems. In so doing, it is possible to gain insight that can manifest the breadth and depth of the topic being addressed by the researcher. The use of simultaneous approaches is the main factor that makes the mixed approach data strategy of immense value in drawing immense attributes from research topics, and depicting the breadth and depth of a given research topic. Even when this approach can be done in two different ways indicated below, these techniques still bring the best attribute of quantitative and qualitative technique to make produce insight on the breadth and depth of a given research topic.
How to use mixed method data strategy
There are two approaches that can be used to evaluate topics using the mixed method approach. These methods are;
a) Sequential design
This technique is used to collect data following an interactive process that allows the data to be merged from one phase with those collected in another. This process has the value of enabling researchers collect more data and then earlier phases and thus improving data available of analysis. Because of this technique, it is possible to evaluate data and generalize findings, while verifying result in order to arrive at the breadth and the scope of study.
The approach in mixed method data strategies ensures that statistical methods are employed first before other these data are sent to the next phase of the process for analysis. This approach thus allows the determination of various findings, which can be augmented as the process proceeds. In addition to the above benefit, the use of phases in the sequential approach provides more time for a researcher to carry out analysis and evaluation. This is because the process is done in two phase reducing the overall time used in carrying out surveys.
b) Concurrent mixed method data approach
The mixed method data strategy can also be done through the concurrent mixed method data approach. In this approach, the one form of data can be used to provide evaluation on another data. In addition, it allows comparison of data between different questions that make up the research topic. This step is thus paramount in arriving at qualitative and quantitative data that can be used to compare the data with ease. It is possible for researchers to evaluate their data with more precision thus arriving at the breadth and depth of the research topic.