What is a Research Methodology?
Research methodology refers to the overall framework that guides how a study is planned, conducted, and analyzed. It explains the philosophical foundation, research design, approaches, tools, and techniques used to collect and interpret data. Methodology is essential because it provides transparency and rigor, ensuring that the findings of the study are credible, reproducible, and grounded in scholarly practices.
5 Reasons Why Research Methodology is Important in Academic Research
- It situates your research within the broader academic conversation by summarizing key developments and perspectives on your topic.
- It reveals what has not yet been explored or adequately addressed, allowing you to position your study as a valuable contribution.
- A thorough review shows that you are well-informed about the topic and aware of key theories, findings, and scholarly debates.
- It helps ensure that your study doesn’t replicate existing research unnecessarily, instead building on or challenging previous work.
- It helps shape your research questions, design, and approach based on what has or hasn’t worked in past studies.
The 19 Components of a Good Research Methodology Section
Lets use an example topic; “The impact of Employee motivation on Employee performance”
1. Research Design
Describes the type of design used—quantitative, qualitative, or mixed methods—and explains how it fits the study’s objectives.
Example: This study employed a cross-sectional quantitative design to explore the relationship between motivation and employee performance at a single point in time.
2. Research Philosophy
Outlines the philosophical stance (e.g., positivism, interpretivism, pragmatism) that guides the study’s worldview.
Example: A positivist philosophy was adopted to objectively measure and analyze relationships using observable data.
3. Research Approach
Refers to whether the study is inductive, deductive, or abductive.
Example: A deductive approach was used, beginning with existing motivation theories and testing their applicability to real-world performance outcomes.
4. Study Setting
Details the environment or context where the research takes place (e.g., field, lab, office, school, hospital).
Example: The study was conducted in the corporate offices of three service-oriented companies in Nairobi, Kenya.
5. Study Population
Describes the group from which the sample will be drawn.
Example: The population included full-time employees from the finance, HR, and operations departments of the selected firms.
6. Sampling Techniques
Explains the strategy for selecting participants—random, purposive, stratified, etc.
Example: A stratified random sampling technique was used to ensure representation across departments.
7. Sampling Procedure
Outlines the steps taken to select the sample from the population.
Example: Employees were first grouped by department, then randomly selected from each group using a random number generator.
8. Sample Size Determination [Sampling Frame]
Explains how the number of participants was calculated and identifies the sampling frame.
Example: Using Yamane’s formula, a sample size of 150 was determined from a total workforce of 320 employees, with a 95% confidence level.
9. Inclusion and Exclusion Criteria
Specifies who was eligible or ineligible to participate.
Example: Employees with at least one year of service were included, while part-time, temporary, or probationary staff were excluded.
10. Data Collection Methods
Outlines how data was gathered—surveys, interviews, focus groups, etc.
Example: Data were collected using self-administered questionnaires distributed via email and in person.
11. Data Collection Instruments
Describes the tools used to gather information.
Example: The questionnaire contained three sections: demographic information, motivation factors (based on Herzberg’s scale), and performance metrics (using a 5-point Likert scale).
12. Data Quality Control
Explains the measures taken to ensure accuracy, validity, and reliability of data.
Example: A pilot study was conducted with 15 employees, and Cronbach’s alpha was used to test reliability (α = 0.82).
13. Research Rigors
Highlights the trustworthiness of the methodology, including validity and reliability.
Example: Construct validity was ensured through expert review, and internal consistency was measured using reliability coefficients.
14. Data Management
Describes how data was stored, processed, and protected.
Example: All completed questionnaires were securely stored in password-protected folders and backed up on encrypted drives.
15. Data Analysis Procedures
Explains how data were analyzed statistically or thematically.
Example: SPSS Version 26 was used for analysis. Descriptive statistics were calculated, followed by Pearson correlation and linear regression to test the relationship between motivation and performance.
16. Ethical Considerations
Addresses issues like informed consent, confidentiality, and data protection.
Example: Ethical approval was obtained from the university ethics board. All participants signed consent forms, and anonymity was guaranteed.
17. Limitations
Acknowledges the weaknesses or constraints in the methodology.
Example: The study’s cross-sectional design limits the ability to infer causality between motivation and performance.
18. Potential Biases in Research
Identifies possible sources of bias and how they were minimized.
Example: Response bias was mitigated by assuring participants of confidentiality and emphasizing the voluntary nature of the study.
19. Results Dissemination
Describes how findings will be shared with relevant stakeholders.
Example: The findings will be published in academic journals and shared with the participating organizations through summary reports and presentations.
