Foundations of HR Research & Validity
Good data isn’t just about numbers—it’s about how we collect, frame, and interpret information. Solid HR research design avoids false signals and builds real insights.
Even the most advanced HR dashboards are useless if the data behind them is weak. At the heart of any evidence-based HR approach is the ability to ask the right questions, collect relevant data, and interpret results responsibly. That’s where research design and validity come in.
Why HR Needs Research Thinking
- To assess what works—and what doesn’t
- To test HR interventions before scaling them
- To reduce noise, bias, and misinterpretation
- To support strategic credibility with business leaders
Core Concepts in HR Research
Hypothesis Testing
Forming a testable prediction:
“We believe that offering remote work flexibility will reduce voluntary turnover among high performers.”
Then designing a way to test that assumption using data, control groups, or comparison periods.
Validity: Are We Measuring What We Think?
- Face validity – does it look like it measures what it should?
- Content validity – does it cover the whole concept?
- Construct validity – is it related to what theory predicts?
- Criterion validity – does it correlate with outcomes we care about (e.g., performance)?
Reliability: Are the Results Consistent?
- Test-retest reliability – similar results over time
- Inter-rater reliability – different evaluators, same outcome
- Internal consistency – e.g., Cronbach’s alpha in surveys
Quantitative vs. Qualitative Research in HR
| Type | Use Case | Strengths | Cautions |
|---|---|---|---|
| Quantitative | Surveys, turnover data, A/B tests | Generalizable, scalable | Can miss nuance |
| Qualitative | Interviews, focus groups | Depth, context, emotional tone | Harder to scale or compare |
Best practice: use mixed methods where possible.
Sampling, Bias & Representation
- Sample size affects statistical confidence
- Sampling method (random vs. convenience) matters
- Selection bias and nonresponse bias distort results
How to Apply These Concepts Practically
- Review the tools you use: are they validated?
- Question the sample: who’s missing from the data?
- Pilot test new surveys or tools before scaling
- Seek support from internal analysts or external experts
🎉The original Myers-Briggs Type Indicator has poor test-retest reliability—yet it’s still widely used in corporate training.
Conclusion: Strong Foundations, Better HR
HR research doesn’t require a PhD, but it does require discipline. Thinking critically about what we measure and how we measure it is the foundation of trust—both in the data and in the people who use it.