How to Write Empirical Research Thesis: From Research Design to Data Presentation
This guide explains in detail how to write empirical research papers, including research hypothesis derivation, variable measurement, data collection and analysis, and result reporting.
What this page helps you do first
- Complete empirical research writing process
- Research hypothesis derivation and verification
- Standard data result presentation
What is empirical research
Empirical research is a research method that uses observable evidence and logical reasoning to verify theoretical hypotheses. Its core is the "hypothesis-verification" model where researchers first propose theoretical hypotheses, then collect data to test whether these hypotheses hold.
Empirical research is widely used in economics, management, sociology, psychology and other social sciences, as well as some science and engineering research.
Empirical research writing framework
- Research background: Why study this issue
- Literature review: What existing research has found, what gaps remain
- Research hypothesis: Hypotheses to be verified based on theoretical derivation
- Research design: Explain variables, measurement methods, sample selection
- Data analysis: Use statistical methods to test hypotheses
- Conclusions and discussion: Explain theoretical significance and practical value of results
How to propose research hypotheses
Good hypotheses should: ① Be based on existing theory with literature support; ② Have clear variable relationships that can be tested with data; ③ Be specifically stated, avoiding vague concepts.
For example, "H1: Employee job satisfaction has a significant positive effect on organizational commitment" is better than "H1: Job satisfaction affects organizational commitment" because it specifies the variable relationship and direction.
What to note in variable measurement
- Choose validated scales: Prefer scales verified by existing research, cite sources
- Report reliability: Calculate Cronbach s α coefficient, usually required above 0.7
- Report validity: Content validity, structural validity, convergent validity, discriminant validity
- Pilot test: Conduct small-scale pilot testing before formal survey to verify questionnaire quality
Data analysis and result presentation
- Descriptive statistics: Report mean, standard deviation, correlation matrix
- Hypothesis testing: Choose appropriate statistical methods (regression analysis, SEM, etc.)
- Result reporting: Report standardized coefficients, t-values, p-values, R² and other key indicators
- Result interpretation: Explain whether results support or reject hypotheses, and provide theoretical explanation
Frequently asked questions
- Does empirical research require questionnaire survey?
- Not necessarily. Data for empirical research can come from questionnaire surveys, experimental data, secondary data (such as listed company financial reports), field observations, etc. Questionnaire survey is just one commonly used method.
- How large should the sample size be?
- This depends on model complexity. Simple regression analysis typically requires sample size 10-20 times the number of variables; complex models like SEM may require 200-500 samples.
- What if results are not significant?
- Non-significant results are also valid research findings. First report honestly, then discuss possible reasons from sample characteristics, variable measurement, research design perspectives, and explain research limitations.