There's no hard and fast rule for qualitative versus quantitative research, and it's often taken for granted. It is claimed here that the divide between qualitative and quantitative research is ambiguous, incoherent, and hence of little value, and that its widespread use could have negative implications. This conclusion is supported by a variety of arguments. Qualitative researchers, for example, have varying perspectives on fundamental problems (such as the use of quantification and causal analysis), which makes the difference as such shaky. In addition, many elements of qualitative and quantitative research overlap significantly, making it difficult to distinguish between the two. Practically in the case of field research, the Qualitative and quantitative approach can't be distinguished clearly as the study pointed. The distinction may limit innovation in the development of new research methodologies, as well as cause complication and wasteful activity. As a general rule, it may be desirable not to conceptualise research approaches at such abstract levels as are done in the context of qualitative or quantitative methodologies. Discussions of the benefits and drawbacks of various research methods, rather than general research questions, are recommended.

Research design methods, such as qualitative, quantitative as well as mixed methods were introduced and subsequently each method was discussed in detail with the help of literature review as well as some personal and live examples to substantiate the findings of various literature. From various literature as well as from the own experiences, it is concluded that both qualitative research design method and quantitative research design method are equally important. It is not fair to criticize one method as the researcher is inclined towards the other method. It is practically evidenced that usage of both methods in the research, the researcher can substantiate the case better. However, duration part while using mixed methods to be kept in mind as it will take more time compared to the qualitative and quantitative methods. Hurrying and aborting in the middle due to time constraint ultimately result in poor research. It would be better if the world view towards these methods changes from criticizing mode to effective utilization mode, which will help research community in focusing and bring up better research outcomes rather than wasting time in arguing which method is scientifically acceptable and which method is biased. While I agree that the ontological, epistemological, axiological, and methodological assumptions for qualitative research method and quantitative research method, researchers should know fully about these methods and keep them as effective tools to utilize them in mixed mode, wherever it is appropriate and required to arrive at adequate research findings.

In this paper, as a practitioner, I describe quantitative and qualitative inquiries. I explain some of their differences and similarities. I emphasize most important differences that researchers should consider when selecting one of the methods or when running a mixed study. I list differences in worldviews, research design, research processes, reliability assurance, and validity assurance. I propose a process flowchart for each type of inquiry. The purpose of the essay is to give researchers and primarily doctorate students a review of differences and similarities between the two methods of inquiry.

Much of the literature on research in applied linguistics views quantitative and qualitative research as distinct entities embodying contrasting philosophies. In this paper, however, we present an example of how preliminary quantitative analyses of data can inform a qualitative discourse analysis study. Data from an online discussion forum concerning the Thai political crisis of 2010 were initially analysed quantitatively to identify keywords, word clusters, length of postings and user ratings of postings for the contributions from the opposing political factions. Each posting was also rated for level of antagonism and credibility of argumentation. These quantitative data provide an overview of the discussion forum and the patterns of discussion within it which was then used as a framework to guide the main qualitative analysis ensuring that the key revealed meanings and functions were covered in the analysis and reducing potential bias in data analysis and presentation. Introduction Mixed methods research has a long history in disciplines that attempt to explain behavior and social phenomena (Dörnyei, 2007). The practice includes a mix or qualitative and quantitative methods, a mix of quantitative methods or a mix of qualitative methods. The type of mixed methods approach that is most popular and is increasingly employed is the first, the mix of the two, which are often based on different research paradigms. Single method research is normally criticised by their opposition as inferior and insufficient. In a pure quantitative study, with the focus on theory or hypothesis testing, the researcher may not be sensitive on contextual details. Moreover, it requires a large amount of data to be able to give an effective ground. A qualitative method, on the other hand, is prone to high subjectivity of the researcher and is unlikely to be generalisable. It can only deal with a small size of data, which makes decision making of the overview and conclusive deduction an ordeal. Four models of mixed methods design are proposed including concurrent design, explanatory sequential design, exploratory sequential design and embedded sequential/concurrent designs (Creswell & Zhang, 2009). The first model, the concurrent design compares and contrasts the results between the two methods to present evidence. Second, the explanatory sequential design utilises the explanation of one set of results to support the other's. In an exploratory sequential design, one method's results are followed by the other's to strengthen the claims made, in the name of generalisability, for instance. In the last design, embedded sequential/concurrent design, a small database is made part of the big database and is used to experiment or enhance the major findings. Typically, a mixed method research deals with different types of data but it may also mean applying different methods to investigate the same data. Two dimensions of advancements that mixed methods data analyses (MMDA) have to offer can be considered as 1) the design virtue and b) research expertise. The research virtue obtained from mixed methods may refer to the strategies that are used to display trustworthiness of the research such as triangulation, complementarity, development, initiation and expansion (Green et al, 1989). In greater details, four major advantages of MMDA have been put forward (Dörnyei, 2007). First, oversimplification, decontexualisation and reduction of the quantitative analysis can be disputed by in depth meaningful qualitative analysis, while content-specificity and unrepresentativeness can be overcome by the generalisable quantitative analysis. Second, for multi-level analyses, MMDA can add more meaning by converging numbers into words and vice versa. Third, triangulation through multi-methods analyses means increasing validity of the study of the results. Fourth, a study with mixed methods analyses tends to attract more attention from the