Conclusion

Key Takeaways

Quantitative research uses numerical data and statistical analysis to examine relationships and cause-and-effect between variables.
It is objective, systematic, and rigorous, helping reduce bias and produce reliable, generalizable findings.
While valuable, it can be resource-intensive, require large samples, and offer less depth than qualitative approaches.
Observational studies involve no intervention and include:
- Descriptive studies (e.g., case reports, cross-sectional studies) that describe characteristics
- Analytical studies (e.g., cohort, case-control) that examine associations
Experimental studies involve interventions to test cause-and-effect, including:
- Randomized controlled trials (RCTs)
- Non-randomized (quasi-experimental) designs
Experimental research explains causality, while non-experimental research describes or predicts relationships in natural settings.
Variables include:
- Numerical (discrete, continuous)
- Categorical (nominal, ordinal)
Extraneous and confounding variables can influence results and threaten validity.
Sampling methods influence generalizability:
- Probability sampling supports broader application
- Non-probability sampling is more limited
Descriptive statistics summarize data (e.g., mean, median, standard deviation).
Inferential statistics test hypotheses and draw conclusions about populations (e.g., p-values, significance).
Correlation identifies relationships, while regression predicts outcomes and examines variable influence.

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