Review:
Cfa (confirmatory Factor Analysis)
overall review score: 4.2
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score is between 0 and 5
Confirmatory Factor Analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables. It allows researchers to test whether measures of a construct are consistent with a preconceived theory or model, helping to confirm hypotheses about relationships among latent variables and their indicators.
Key Features
- Model specification based on theoretical expectations
- Assessment of the goodness-of-fit between data and hypothesized model
- Estimation of factor loadings, variances, and covariances
- Use with large sample sizes for reliable results
- Application in psychometrics, social sciences, marketing research, and other fields
Pros
- Provides rigorous validation of measurement models
- Allows for testing complex relationships between variables
- Supports hypothesis-driven research
- Widely supported by statistical software packages
Cons
- Requires a clear theoretical model beforehand
- Sensitive to sample size and data quality
- Can be complex to implement and interpret for beginners
- Assumes multivariate normality; violations can affect results