The scientific research process is a systematic and logical approach to discovering how things in the universe work. It is used to answer questions, solve problems, and generate new knowledge.
Thank you for reading this post, don't forget to subscribe!Scientific Research Process includes:
- Realizing the Problem
- Identification of the Problem
- Review of Literature
- Hypothesis Formulation
- Research Design
- Collection of Data
- Data Analysis
- Interpretation and Generalization
1.) Realizing the Problem:
This is the initial stage where a researcher becomes aware of a gap in knowledge, a problem, or an issue that needs investigation. The problem may arise from observation, experience, previous studies, or real-life challenges.
- Example: A researcher observes that students are not performing well in online learning environments.
2.) Identification of the Problem:
Once the problem is realized, it must be clearly defined and narrowed down to a specific researchable issue. This includes identifying the variables involved and the scope of the problem.
- The researcher asks: What exactly is the problem? Why is it important to solve?
- Example: “What are the factors affecting student performance in online classes?”
3.) Review of Literature:
This involves studying existing research and theories related to the identified problem. It helps in understanding what has already been studied, avoiding duplication, and building a theoretical foundation for the new research.
- Sources: academic journals, books, online databases, previous research papers.
- Helps in identifying gaps in existing knowledge.
4.) Hypothesis Formulation:
A hypothesis is a tentative explanation or prediction that can be tested through research. It connects the variables and provides a focus for the study.
- It can be null hypothesis (H₀): there is no relationship.
- Or alternative hypothesis (H₁): there is a relationship or effect.
- Example: “Students who attend interactive online classes perform better than those who attend lecture-based online classes.”
5.) Research Design:
This is the blueprint for the entire research. It includes planning how to test the hypothesis, choosing research methods, selecting the sample, and deciding tools and techniques for data collection.
- Types: descriptive, experimental, exploratory, correlational, etc.
- Ensures validity and reliability of results.
6.) Collection of Data:
Data collection is gathering the information necessary to address the research question. It can be primary data (collected directly) or secondary data (collected from existing sources).
- Methods: surveys, interviews, observations, experiments, questionnaires, etc.
- Ensures that data is accurate, relevant, and sufficient.
7.) Data Analysis:
In this step, the collected data is organized, processed, and examined using statistical or logical methods to discover patterns, trends, and relationships.
- Tools: software like SPSS, Excel, R, Python, etc.
- Statistical tests like t-test, ANOVA, regression, correlation, etc., may be used.
8.) Interpretation:
The analyzed data is interpreted to draw meaningful conclusions. The researcher determines whether the findings support the hypothesis or not.
- Interpretation explains what the data means in the context of the research question.
- It also considers limitations and unexpected findings.
9.) Generalization:
If the research is valid and reliable, the findings can be applied to a broader context or population beyond the sample studied. This step evaluates the scope of the results and how they contribute to existing knowledge.
- Also includes recommendations for future research or practical applications.
Summary Table:
| Step | Description |
|---|---|
| Realizing the Problem | Awareness of a knowledge gap or issue |
| Identification of Problem | Clearly defining a researchable issue |
| Review of Literature | Examining existing research and theories |
| Hypothesis Formulation | Developing a testable prediction |
| Research Design | Planning the method of investigation |
| Collection of Data | Gathering relevant information |
| Data Analysis | Processing and examining data |
| Interpretation | Explaining the meaning of results |
| Generalization | Applying findings beyond the study sample |