Composite Reliability & AVE Test
Composite reliability and average variance extracted (AVE) are indices computed from standardized factor loadings that assess the internal consistency and convergent validity of a latent construct.
합성신뢰도(CR)와 평균분산추출(AVE)은 표준화 요인부하량으로 계산하여 잠재 구성개념의 내적 일관성과 수렴타당도를 평가하는 지수이다.
How do I check convergent validity and reliability of constructs in a measurement model?
측정모형에서 구성개념의 신뢰도와 수렴타당도는 어떻게 확인하나요?
Composite reliability measures the internal consistency of a construct from its factor loadings, and average variance extracted measures how much indicator variance the construct captures relative to measurement error. Both fit reporting of a confirmatory factor or SEM measurement model, with conventional cutoffs of 0.70 for composite reliability and 0.50 for AVE.
When to use Composite Reliability & AVE Test
- Reporting measurement quality after confirmatory factor analysis
- Assessing convergent validity of multi-item constructs in SEM or PLS-SEM
- Testing discriminant validity by comparing the square root of AVE with inter-construct correlations (Fornell-Larcker criterion)
When to use something else
- Only a quick reliability estimate from raw item scores is needed: use Cronbach's alpha or McDonald's omega
- Discriminant validity is the main question: also use the heterotrait-monotrait ratio (HTMT)
Assumptions and requirements
- A measurement model has been estimated with standardized loadings
- Each item loads on one construct (congeneric measurement)
- Items are reflective indicators of the construct
What the analysis reports
- Composite reliability (CR) per construct
- Average variance extracted (AVE) per construct
- Standardized factor loadings
- Fornell-Larcker table (square root of AVE versus construct correlations)
How to report it
All constructs showed adequate reliability and convergent validity, with composite reliability ranging from [value] to [value] and AVE from [value] to [value].
Related methods
- Confirmatory Factor Analysis (CFA)
- Cronbach's Alpha
- HTMT Discriminant Validity
Run Composite Reliability & AVE Test on Quorant
- Data you need
- a table (CSV or Excel) with item columns (e.g. questionnaire items)
- Price
- 400 credits for one analysis up to 10,000 rows (complexity level 3); larger or wider data costs more. Optional methodologist review: 1800 credits.
- Checking
- Python results are cross-checked against R reference outputs in every deployment
- Before you pay
- A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.