Before anyone believes your findings, they need to trust your *measures*. Reliability and validity are how you earn that trust — and examiners will look for both in your methodology and results.
Reliability — is it consistent?
Reliability is consistency: would the instrument give the same result under the same conditions? The most reported form is internal consistency via Cronbach's alpha (values of 0.70+ are generally acceptable), alongside test-retest and inter-rater reliability where relevant.
Validity — is it measuring the right thing?
- Content validity — do the items cover the whole concept?
- Construct validity — does it truly capture the underlying construct (via convergent and discriminant validity)?
- Criterion validity — does it correlate with an established measure or outcome?
Reliability is hitting the same spot every time; validity is hitting the bullseye. You can be reliably wrong — consistent but measuring the wrong thing — which is why you need both, not either.
How to establish and report them
Reliability and validity aren't afterthoughts — they start with careful questionnaire design and a pilot study. You then compute and report the statistics (alpha, AVE, composite reliability, HTMT) in SPSS or your SEM tool. For choosing that tool, see SPSS vs AMOS vs SmartPLS.
Frequently asked
What is an acceptable Cronbach's alpha?+
As a common rule of thumb, 0.70 and above is acceptable, 0.80+ is good. Very high values (0.95+) can signal redundant items. Always interpret alpha alongside your construct and item count, not in isolation.
Can a measure be reliable but not valid?+
Yes — that's the classic pitfall. An instrument can produce consistent (reliable) scores while measuring the wrong construct (invalid). Establishing validity, not just reliability, is what makes your measure trustworthy.
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