Confirmatory Factor Analysis (CFA) within the SEM Framework

Confirmatory Factor Analysis within the SEM Framework

Confirmatory Factor Analysis (CFA) is a statistical technique used to test whether the data fit a hypothesized measurement model. In Structural Equation Modeling (SEM), CFA evaluates how well the observed variables reflect the latent constructs. Unlike exploratory factor analysis (EFA), which seeks to uncover underlying structures without pre-set hypotheses, CFA is theory-driven and tests the data against a hypothesized model.

CFA starts with a clear theory-driven model of the relationships between latent variables and their observed indicators, testing how well the data aligns with the model.

Purpose of Confirmatory Factor Analysis

CFA's primary goal is to validate measurement models by confirming that the observed variables (indicators) accurately represent the latent constructs they measure. Researchers propose a specific factor structure based on theoretical expectations or prior research, which specifies which observed variables load onto which latent factors. CFA assesses the fit between the observed data and the hypothesized model.

Key Concepts in CFA

Latent Variables: Represent unobservable constructs inferred from multiple observed indicators. In psychological research, latent variables like intelligence or personality traits are crucial since they cannot be directly measured.

Observed Variables (Indicators): These measurable outcomes reflect the latent constructs. In CFA, they are test or survey items that theoretically reflect the latent factors.

Factor Loadings: Coefficients indicating the strength of the relationship between an observed variable and a latent variable. Higher factor loadings mean the indicator closely represents the latent variable.

Error Terms: Every observed variable has an associated error term that accounts for variability not explained by the latent factor. These reflect measurement errors or unmeasured influences.

CFA Model Specification

CFA requires specifying a hypothesized factor structure before analysis. The process involves determining which observed variables load onto which latent variables. Key assumptions include unidimensionality, where each observed variable is influenced by only one latent factor, and the independence of errors. Typically, factor loadings for one variable are fixed to 1 to scale the latent variable.

Evaluating Model Fit in CFA

CFA evaluates how well the hypothesized model fits the data using several fit indices:

- Chi-Square Test: Compares observed covariance matrix with the model-predicted matrix. Non-significant chi-square values suggest a good fit, though the test is sample-size sensitive.

- RMSEA: Values below 0.05 suggest close model fit; values up to 0.08 are acceptable.

- Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI): Values near 1 indicate a good fit.

- SRMR: Represents the difference between observed and predicted correlations; values below 0.08 are generally accepted as good fit.

Model Modification

If the hypothesized CFA model doesn't fit the data well, researchers can use modification indices to suggest changes that may improve fit, such as allowing correlated errors or adding paths between factors. However, these adjustments must be theory-driven rather than purely data-driven to maintain model validity.

Applications of CFA in Psychological Research

CFA is widely applied in psychological and educational research, particularly in psychometric validation. Researchers use CFA to confirm that test items reflect latent constructs in new psychological tests or questionnaires. It is also crucial in cross-cultural studies, ensuring the same factor structure holds across different groups (measurement invariance).

CFA helps establish construct validity in psychological testing, ensuring a test measures the theoretical constructs it claims to assess. By confirming a test’s factor structure, CFA supports the reliability of psychological assessments.

Limitations of CFA

CFA, while powerful, has several limitations:

- Model Specification Dependence: Poorly specified models can lead to inaccurate conclusions.

- Assumptions of Linearity and Normality: CFA assumes linear relationships and normally distributed data. Violations may affect results.

- Sample Size Requirements: Large samples are needed for reliable estimates, with smaller samples leading to bias or imprecision.