The research approach is a plan and procedure that consists of the steps of broad assumptions to detailed methods of data collection, analysis, and interpretation. It is, therefore, based on the nature of the research problem being addressed. The research approach is essentially divided into two categories:
- the approach of data collection and
- the approach of data analysis or reasoning.
Types of research approach for data collection
Positions him- or herself
Collects participant meanings
Focuses on a single concept or phenomenon
Brings personal values into the study
Studies the context or setting of participants
Validates the accuracy of findings
Makes interpretations of the data
Creates an agenda for change or reform
Collaborates with the participants
Tests or verifies theories or explanations
Identifies variables to study
Relates variables in questions or hypotheses
Uses standards of validity and reliability
Observes and measures information numerically
Uses unbiased approaches
Employs statistical procedures
Collects both quantitative and qualitative data
Develops a rationale for mixing
Integrates the data at different stages of inquiry
Presents visual pictures of the procedures in the study
Employs the practices of both qualitative and quantitative research
|Tend to or Typically||Qualitative Approaches||Quantitative Approaches||Mixed Methods Approaches|
|Use these philosophical assumptions|
Employ these strategies of inquiry
|Constructivist/ transformative knowledge claims|
Phenomenology, grounded theory, ethnography, case study, and narrative
|Post-positivist knowledge claims|
Surveys and experiments
|Pragmatic knowledge claims|
Sequential, concurrent, and transformative
|Employ these methods||Open-ended questions, emerging approaches, text or image data||Closed-ended questions, predetermined approaches, numeric data||Both open- and closed-ended questions, both emerging and predetermined approaches, and both quantitative and qualitative data and analysis||Use these practices of research as the researcher|
Source: Creswell (2013), Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Sage Publications
The research approach for data analysis
Approaches to data analysis are of two types:
- inductive and
- deductive approach.
Qualitative data requires an inductive approach to analysis. On the other hand, quantitative data uses the deductive approach. In a mixed type of data, both inductive and deductive approaches of analysis are utilized. However, there should be some consistency between methods, methodology, and analysis. This is important in order to demonstrate logic. Thus, in order to make the research credible to the reader, the research should lead to the research findings.
It places great emphasis on the methods used to collect or generate data. However, it places less emphasis on the analytical techniques to the interpretation of data. Furthermore, an Inductive approach primarily uses a detailed reading of secondary data to derive concepts, themes, and models. Therefore, it is widely used for analyzing qualitative data. This begins with the selection of the area of study and builds a theory. The inductive approach includes:
- Combination of varied secondary data in a brief summary.
- Creation of clear links between the objectives of the research and the results from the raw data. Also, make those links clear to others and how those links will fulfill the research objective.
- Developing a theory based on the experiences and processes revealed by the text data (Jebreen 2012).
Choosing an inductive approach through thematic analysis (a ‘data-driven’ approach) for the study determines that the objective of the study is to obtain an understanding of a phenomenon. It does not focus on testing the hypothesis.
Thematic analysis can either realistically present experiences, meaning, and the reality of participants. This can also be used to examine the effects of those experiences, events, and realities operating within society.
Quantitative research often translates into the use of statistical analysis to make the connection between what is known and what can be learned by research. Consequently, analyzing data with quantitative strategies requires an understanding of the relationships among variables by either descriptive or inferential statistics. Descriptive statistics helps to draw inferences about populations and to estimate the parameters (Trochim 2000).
Inferential statistics are based on the descriptive statistics and the assumptions that generalize the population from a selected sample (Trochim 2000). Quantitative data requires statistical analysis to test hypotheses. A deductive approach is popularly used as it enables the research to reason from generic to specific. In addition deduction from general perspectives leads the researcher to develop a theoretical framework (hypothesis) and test it thereby concluding a specific conclusion. The deductive approach of analysis or reasoning consists of of the following steps:
- Exploration of theories.
- Development of theoretical framework or hypotheses.
- Observation through statistical testing of hypotheses.
- Confirmation of a specific conclusion drawn logically from premises (Soiferman 2010).
However, it appears that choosing one research approach over another severely limits the scope of the study. As Creswell & Clark (2011) observed, one approach alone cannot answer all the questions that might emerge in the course of researching a topic. In order to facilitate a more comprehensive study, researchers should have access to all available research tools. The dichotomy, therefore, should be reconsidered and researchers should become proficient in both types of approaches. While selecting the research approach aim and research problem should be taken into consideration.
- Creswell, J.W., 2013. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, SAGE Publications.
- Creswell, J.W. & Clark, V.L.P., 2011. Designing and Conducting Mixed Methods Research, SAGE Publications. Available at: https://books.google.com/books?id=YcdlPWPJRBcC&pgis=1 [Accessed May 20, 2015].
- Jebreen, I., 2012. Using Inductive Approach as Research Strategy in Requirements Engineering. International Journal of Computer and Information Technology, 01(02), pp.162–173.
- Soiferman, K.L., 2010. Compare and Contract Inductive and Deductive Research Approaches. University of Manitoba.
- Trochim, W.M.K., 2000. Introduction to Validity. Social Research Methods 2nd ed., Cincinnati, Ohio: Atomic Dog Publishing
- Conditional CAPM analysis for stock-based investing - February 20, 2021
- Short term momentum analysis of growth, income and value stocks - February 9, 2021
- Inferential analysis to compare the performance of stocks listed in the BSE (2011-2020) - January 30, 2021