Instructions:
This journal measures your mastery of ULO 1.4 and 2.2 and CLOs 3,
Instructions:
This journal measures your mastery of ULO 1.4 and 2.2 and CLOs 3, 4, 5 and 6.
Use the embedded methods argument and the paradigm argument to persuade a researcher to avoid using a mixed methods research (MMR) strategy.
Then, reflect on how you can apply the concepts learned in this course to your current or future career. How might the lessons you have learned positively impact your career success?
Your journal entry must be at least one page in length, not counting the required references page. Please thoroughly address all areas listed above and include at least one credible source. Use APA compliant headings and sub-headings that align with the individual assignment requirements. Adhere to APA Style, including in-text citations and references for all sources that are used.
Rubric Details:
Quality of Discussion
70 possible points (70%)
Organization and Formatting
10 possible points (10%)
Writing Mechanics
10 possible points (10%)
Citations and References
10 possible points (10%)
Lesson:
Mixed Methods Research
The argument for using a mixed methods research (MMR) strategy, sometimes referred to as pragmatism, is that the researcher has access to the best of both worlds: quantitative and qualitative methodological strategies. MMR integrates the philosophical assumptions of each of these research strategies to benefit from both a breadth and depth of understanding of their research topic. This begs the question of why a researcher would not then always choose an MMR strategy over simply quantitative or qualitative. The following will summarize some of the pros and cons of MMR and answer this question.
Arguments in Favor of MMR
There are three reasons for using an MMR strategy.
The first, as alluded to, is that MMR adds both breadth and depth to the study. This can be accomplished simultaneously by asking closed-ended and open-ended questions to research participants. The data collected through close-ended questions permits the use of inferential statistics to generalize results to a population. The data collected through open-ended questions provides deeper insights not possible through self-completion questionnaires. It can also be accomplished consecutively with qualitative findings being used to create a theory, which is then tested through quantitative research. This advantage is called triangulation, which is using one research strategy to validate findings using a different strategy.
A second reason for using MMR is that the individual strategies are complementary to one another. The respective strengths of quantitative and qualitative strategies compensate for the weaknesses of the other. This results in MMR findings that are greater than the sum of the individual parts.
A third justification for MMR is that findings from the opposing methodological strategies can introduce new ways of visualizing the research problem that would not have occurred if only one strategy were used (Dawadi et al., 2021).
It is important for the researcher choosing MMR to be deliberative in the research design. Research designs are exploratory (qualitative), descriiptive (quantitative/non-experimental), and causal (quantitative/experimental). Even in the early stage of design, the complexity of MMR becomes evident as it is necessary for the researcher to prioritize things like design, analytical approach, data collection methods, sampling, and data analysis procedures. Prioritization is best done in consideration of the research problems and questions. Priority can be given to the quantitative methodology, the qualitative methodology, or equal priority between the two. Once prioritization is decided, the research must decide how much interaction there will be between the two methodologies. One straight forward approach is the keep the methodologies independent during all stages of the study through data analysis, only to be mixed during the interpretation of findings. It is possible, however, to mix the methodologies at all stages of the study, including the design, the analytical approaches, data collection methods, sampling, data analysis, and interpretation and reporting of results (Dawadi et al., 2021).
Arguments Against MMR
A practical reason for avoiding MMR is that it generally requires more time, more money, and more expertise than would be required using a quantitative or qualitative strategy alone. Data collection and data analysis are particularly lengthy endeavors in MMR. Qualitative research and interviewing participants are particularly time intensive. This leads to greater costs and budget requirements. Another reason for avoiding MMR is lack of research experience in both quantitative and qualitative strategies. Having the knowledge to correctly select from the wide array of methods available, properly implement the chosen methods, adequately collect the data, sufficiently analyze the data, and appropriately interpret the results requires expertise. While proponents of MMR argue that quantitative and qualitative methodologies are complementary, the reality for many researchers is that results are often contradictory, placing validity and reliability in jeopardy. The next MMR challenge stems from the influence of results occurring from the mixing of methodologies. For example, a sequential data collection and analysis could lead to the results of the first process influencing the results of the second process. Even results from concurrent designs have been found to influence one another. The final argument against MMR is that researchers often lack the expertise to appropriately prioritize the design and methods since they each have their own set of advantages and challenges that are dependent on the goals of the research project (Dawadi et al., 2021; Karpatschof, 2007).
In Closing
As was discussed in Unit I, one’s research tradition normally predetermines the research strategy chosen for the study of phenomena. A quantitative strategy is aligned with positivism, and a qualitative strategy is aligned with interpretivism. Although there are no codified rules about employing research strategies, it would seem inconsistent, at best, for a true positivist to use qualitative methods given their loyalty to empiricism. The critique of qualitative research as being too subjective, difficult to replicate, unable to generalize results, and lacking in transparency is completely at odds with the quantitative methodology and the positivist tradition.
Similarly, it would be difficult, if not impossible, for an interpretivist to use quantitative methods to determine the meaning observed in participant observation. An interpretivist simply could not use quantitative research to quantify the multiple meanings that are created through multiple experiences and realities. From the interpretivist’s perspective, quantitative research fails to recognize the social world as different from the natural world, forces qualitative data into numbers, relies on instruments and procedures that fail to reflect everyday life, and analyzes relationships between variables that neglect the human interaction that brings meaning to those relationships. This is completely at odds with the qualitative methodology and interpretivist tradition.
So, for every proponent that argues for the complimentary relationship between quantitative and qualitative methodologies, there are opponents that argue MMR is a non-starter due to the incompatibility of quantitative and qualitative methodologies. Researchers, especially those who are novice, should develop a level of competence with each methodological strategy before choosing to employ an MMR strategy, or at least have a compelling reason for using a dual strategy. It should not be assumed that a two-in-one approach automatically translates into a superior strategy (Bell et al., 2022).
References:
Bell, E., Bryman, A., & Harley, B. (2022). Business research methods (6th ed.). Oxford University Press. https://online.vitalsource.com/#/books/9780192640505
Dawadi, S., Shrestha, S., Giri, R. A. (2021, February 24). Mixed-methods research: A discussion on its types, challenges, and criticisms. Journal of Practical Studies in Education, 2(2), 25–36. https://doi.org/10.46809/jpse.v2i2.20
Karpatschof, B. (2007, October). Bringing quality and meaning to quantitative data – Bringing quantitative evidence to qualitative observation. Nordic Psychology, 59(3), 191–209. https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edo&AN=32170950&site=eds-live&scope=site