Reliability and Validity Testing for Questionnaires and Scales
Show that your questionnaire is consistent and measures what it claims to measure. We assess reliability and validity and write the evidence up for your methods section.
Reliability: is the scale consistent?
- Cronbach’s alpha and McDonald’s omega for internal consistency
- Item-total correlations and “alpha if item deleted” to find weak items
- Split-half reliability
- Test-retest reliability using intraclass correlation (ICC)
- Inter-rater agreement using Cohen’s kappa or ICC
You can run a quick internal-consistency check yourself with our free Cronbach’s alpha calculator. We go further by checking dimensionality first, since alpha is only meaningful for a single construct.
Validity: does the scale measure the right thing?
- Content validity: expert review, content validity index (CVI) and item relevance ratings
- Construct validity: factor structure through exploratory and confirmatory factor analysis
- Convergent and discriminant validity: AVE, Fornell-Larcker and HTMT
- Criterion validity: correlation with an established measure or an outcome, using the correlation calculator approach
Pilot testing before the main survey
Reliability and validity checks are most useful on a pilot sample, before the full survey goes out. We analyse the pilot, flag items that confuse respondents or reduce consistency, and suggest revisions. This is especially valuable when you translate a scale or adapt it to a new population.
What reviewers expect to see
Examiners and journals usually want alpha or omega for every subscale, the number of items, sample size and the criteria you used (for example, 0.70 or higher as acceptable for research), together with evidence for validity. We prepare a table and a methods paragraph that give them exactly that. If the scale feeds a larger model, the next step is structural equation modelling.
How the work runs
- You send the item-level data and describe each scale and subscale.
- We check item quality and dimensionality.
- We compute reliability and validity evidence and review weak items.
- You receive a summary table, interpretation and recommendations for item changes.
Frequently asked questions
What is an acceptable Cronbach’s alpha?
For research, 0.70 or higher is commonly accepted, with 0.80 or higher preferred. Very high values (above about 0.95) can signal redundant items.
Why is my alpha low?
Common causes are too few items, items that measure different constructs, reverse-coded items that were not recoded, or a small or very uniform sample. Item statistics usually show the cause.
Alpha or omega?
Omega relaxes alpha’s assumption that all items contribute equally, so it is often more accurate. We can report both.
Can I test reliability on a small pilot sample?
Yes, but the estimate is imprecise. We report confidence intervals and treat pilot results as a guide, not a final verdict.
Do you help with translated scales?
Yes. We assess reliability and structure for the translated version and compare it with the original.
Related statistical analysis services
Factor Analysis (EFA & CFA)
EFA and CFA to find and confirm the structure behind your items.
Descriptive & Inferential Statistics
Summaries, confidence intervals and tests that turn a dataset into conclusions.
Structural Equation Modelling (SEM & PLS-SEM)
CB-SEM and PLS-SEM for complex theories with several constructs.
Statistical Analysis Services
See every statistical method we support.
Also related: SPSS and Stata analysis · Likert scale analysis