Reliability and Validity Analysis
Assess every construct in your questionnaire in one step. A CFA estimates standardized loadings, from which you get Cronbach’s alpha, composite reliability, McDonald’s omega, AVE and MSV, plus the Fornell–Larcker and HTMT tests of discriminant validity.
Updated · By Bibin XavierData 180 × 12
| Respondenttext · 180 levels | TR1numeric · coded | TR2numeric · coded | TR3numeric · coded | TR4numeric · coded | SAT1numeric · coded | SAT2numeric · coded | SAT3numeric · coded | SAT4numeric · coded | LOY1numeric · coded | LOY2numeric · coded | LOY3numeric · coded |
|---|---|---|---|---|---|---|---|---|---|---|---|
| R001 | 2 | 2 | 1 | 2 | 3 | 1 | 3 | 3 | 4 | 2 | 3 |
| R002 | 5 | 4 | 5 | 4 | 3 | 4 | 3 | 4 | 4 | 3 | 4 |
| R003 | 4 | 4 | 3 | 4 | 1 | 2 | 2 | 4 | 2 | 3 | 1 |
| R004 | 1 | 2 | 3 | 2 | 4 | 4 | 4 | 4 | 3 | 4 | 4 |
| R005 | 3 | 4 | 2 | 3 | 3 | 3 | 3 | 4 | 3 | 2 | 2 |
| R006 | 5 | 4 | 5 | 4 | 5 | 5 | 4 | 4 | 4 | 3 | 4 |
| R007 | 3 | 4 | 4 | 4 | 3 | 3 | 2 | 4 | 1 | 3 | 3 |
| R008 | 3 | 3 | 4 | 4 | 5 | 4 | 5 | 5 | 5 | 5 | 4 |
Variables and options
Trust =~ TR1 + TR2 + TR3.Results
180 rows · example dataConstruct reliability and convergent validity
| Construct | Items | Cronbach’s α | CR (ρc) | McDonald’s ω | AVE | √AVE | MSV | Verdict |
|---|---|---|---|---|---|---|---|---|
| TR | 4 | .838 | .838 | .838 | .565 | .751 | .332 | Supported |
| SAT | 4 | .816 | .817 | .818 | .529 | .727 | .432 | Supported |
| LOY | 3 | .846 | .846 | .846 | .646 | .804 | .432 | Supported |
Note. Thresholds (Hair et al., 2019; Fornell and Larcker, 1981): α, CR and ω ≥ .70 (≤ .95); AVE ≥ .50; MSV below AVE for discriminant validity. CR and AVE use standardized loadings; ω uses the unstandardized solution.
Fornell–Larcker criterion
| TR | SAT | LOY | |
|---|---|---|---|
| TR | .751 | ||
| SAT | .576 | .727 | |
| LOY | .516 | .657 | .804 |
Note. Diagonal: √AVE. Off-diagonal: latent correlations. Discriminant validity holds when each √AVE exceeds that construct’s correlations.
Heterotrait–monotrait ratio (HTMT)
| TR | SAT | LOY | |
|---|---|---|---|
| TR | — | ||
| SAT | .584 | — | |
| LOY | .510 | .648 | — |
Note. HTMT below .85 (strict) or .90 (liberal) supports discriminant validity (Henseler, Ringle and Sarstedt, 2015). Computed from absolute item correlations.
Standardized loadings
| Construct | Item | λ | λ² | p |
|---|---|---|---|---|
| TR | TR1 | .730 | .532 | fixed |
| TR | TR2 | .754 | .569 | < .001 |
| TR | TR3 | .749 | .562 | < .001 |
| TR | TR4 | .772 | .595 | < .001 |
| SAT | SAT1 | .723 | .523 | fixed |
| SAT | SAT2 | .771 | .594 | < .001 |
| SAT | SAT3 | .772 | .595 | < .001 |
| SAT | SAT4 | .636 | .405 | < .001 |
| LOY | LOY1 | .795 | .632 | fixed |
| LOY | LOY2 | .786 | .617 | < .001 |
| LOY | LOY3 | .830 | .689 | < .001 |
CFA model fit (for reference)
| Index | Value | Threshold | Verdict |
|---|---|---|---|
| χ² / df | 1.23 | ≤ 3 (≤ 5 acceptable) | Good |
| CFI | .989 | ≥ .95 (≥ .90 acceptable) | Good |
| TLI | .985 | ≥ .95 (≥ .90 acceptable) | Good |
| RMSEA | .036 [.000, .065] | ≤ .06 (≤ .08 acceptable) | Good |
| SRMR | .037 | ≤ .08 | Good |
| p-close (RMSEA ≤ .05) | .755 | > .05 | Good |
| GFI | .952 | ≥ .90 | Good |
| AIC / BIC | 4986.9 / 5066.7 | lower is better (compare models) | — |
Note. Thresholds follow Hu and Bentler (1999). With small samples or many indicators, judge fit on several indices together.
Interpretation
All 3 constructs meet the thresholds for reliability (CR ≥ .70) and convergent validity (AVE ≥ .50). Discriminant validity is supported by both the Fornell–Larcker criterion and HTMT.
How to cite this tool
Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.
APA 7
Xavier, B. (2026). Reliability and Validity Analysis [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/reliability-and-validity-analysis/
BibTeX
@software{xavier_reliabilityvalidity_2026,
author = {Xavier, Bibin},
title = {Reliability and Validity Analysis},
publisher = {FreeStatisticalTools.com},
year = {2026},
url = {https://freestatisticaltools.com/reliability-and-validity-analysis/},
note = {Web application}
}Suggested sentence for your methods section: “The analysis was carried out with the free Reliability and Validity Analysis tool on FreeStatisticalTools.com (Xavier, 2026).”
What is reliability and validity analysis?
Survey research has to show that each construct is measured reliably and validly. This tool fits a confirmatory factor model to your items and reports internal consistency (Cronbach’s alpha, composite reliability, McDonald’s omega), convergent validity (AVE) and discriminant validity (MSV, Fornell–Larcker and HTMT) in publication-ready tables.
When to use reliability and validity analysis
Use it in the measurement-model section of any survey-based study to establish reliability, convergent validity and discriminant validity before testing hypotheses.
Assumptions
- Reflective constructs with at least two, preferably three or more items.
- Items keyed in the same direction (reverse-code first).
How to use this tool
- Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
- Choose the variables. Select the constructs and their items.
- Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.
How to report reliability and validity analysis in APA style
Report a table with α, CR, AVE (and ω) for each construct, then a Fornell–Larcker or HTMT matrix.
Frequently asked questions
What is the difference between reliability and validity?
Reliability is consistency: the items of a construct agree with each other. Validity is accuracy: the items measure the intended construct and not another one.
What are the thresholds for CR and AVE?
Composite reliability should be at least .70 (and below .95), and AVE at least .50 (Hair et al., 2019; Fornell and Larcker, 1981).
Fornell–Larcker or HTMT?
HTMT detects discriminant validity problems more reliably (Henseler et al., 2015), so report HTMT, optionally with Fornell–Larcker.
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