Research Methods:
At the start of any research project, you must select your research questions and overall design. This involves determining the type of research you will be conducting. While the language used to explain research varies across disciplines, your design will typically be shaped by:
the kind of knowledge you are attempting to discover,
the data you will gather and examine, and
the logistical concerns of your study, such as sampling, timeframe, and environment.
There are two general approaches utilized in data analysis that yield separate and valuable insights:
Quantitative research employs numerical data and statistical methods, and
Qualitative research deals with meaning and interpretation through textual and observational data.
*Mixed Methods research is a combination of both Quantitative research and Qualitative research.
Quantitative research employs numerical data and statistical methods, typically illustrated in the form of graphs, to test hypotheses and draw generalizable facts. Quantitative research aims to produce objective measurements and test dominant theories. Common methods involve controlled experiments, quantitative observation, and questionnaires with standardized, closed-ended questions. However, researchers must be aware of and avoid potential biases, such as information bias, omitted variable bias, sampling bias, and selection bias, that could weaken the validity of the findings.
Qualitative research looks into the language world, trying to make sense of ideas, concepts, and everyday lives. Qualitative research provides thick, descriptive insights into issues requiring close examination. Some fundamental approaches involve open-ended, face-to-face interviews, descriptive observational research, and intensive literature studies focusing on underlying concepts and theories. However, researchers need to be just as sensitive to potential biases, such as the Hawthorne effect, observer bias, recall bias, and social desirability bias, which may impact the direction of findings.
Mixed Methods research applies quantitative and qualitative methods in order to reach an improved overall understanding of a research problem than either approach could accomplish on its own. This research design brings together multiple types of data and analysis in an attempt to take advantage of the best of each without developing the worst of each. Some of the typical designs include convergent parallel (bringing in both types of data simultaneously and synthesizing results), explanatory sequential (quantitative first then qualitative to explain the results), exploratory sequential (qualitative first then quantitative to generalize themes), and embedded designs (one method primary, the other secondary). When mixed methods research is conducted, it is crucial to remain aware of biases such as design bias (inaccurate combination), sampling bias (issues with combined samples), interpretation bias (difficulty combining results), methodological bias (overdependence on known methods), and researcher bias (researchers' design or interpretation preferences).
Action Research is a circular research approach utilized to understand and solve a problem within a given setting. Action Research is characterized by the fact that it is an interactive process and addresses the simultaneous process of implementing action and research. Widely used across the social sciences, although particularly in the field of education, it seeks to require instructors to conduct organized research and thoughtful practice in their efforts to integrate theory and practice. That is a cycle that lies behind the title that the procedure has acquired and now goes under of 'cycle of action' or 'cycle of inquiry.'
Action research is usually conducted in two ways: participatory and practical.
Participatory action research is concerned with engaging the members of the community as co-researchers, enabling the people most impacted by the outcomes of the research and prioritizing their lived experiences. Practical action research is concerned with research as a process itself, aiming to solve tangible, real-time problems. Both concern building the capability of future practitioners more than adding to theoretical knowledge.