Confirmatory Factor Analysis

Confirmatory Factor Analysis

Confirmatory Factor Analysis (CFA) is a statistical technique used to test the hypothesis that a relationship exists between observed variables and their underlying latent constructs. Unlike Exploratory Factor Analysis (EFA), which explores the data to identify potential underlying structures without preconceived notions, CFA confirms whether the data fit a hypothesized measurement model. This technique is widely used in social sciences, psychology, and educational research to validate the structure of constructs like intelligence, personality traits, or satisfaction.

In CFA, researchers start with a theoretical model specifying the number of factors and the loadings of observed variables (indicators) on these factors. The model is then tested against the actual data to see how well it fits. Key components in CFA include:

– **Latent Variables (Factors)**: These are the unobserved constructs inferred from the observed variables.

– **Observed Variables (Indicators)**: These are the measured variables that are presumed to reflect the underlying latent constructs.

– **Factor Loadings**: These indicate the strength of the relationship between each observed variable and its corresponding latent factor.

The primary goal of CFA is to assess the fit of the hypothesized model and refine it if necessary, ensuring that the observed data adequately represent the theoretical constructs.

Assumptions of Confirmatory Factor Analysis

For CFA to produce valid and reliable results, several fundamental assumptions must be met:

1. **Multivariate Normality**: The observed variables should follow a multivariate normal distribution. This assumption is crucial for the maximum likelihood estimation method commonly used in CFA.

2. **Linearity**: The relationships between the observed variables and the latent constructs are assumed to be linear. Non-linear relationships can lead to poor model fit and incorrect conclusions.

3. **No Multicollinearity**: The observed variables should not be too highly correlated. High multicollinearity can make it difficult to distinguish each variable’s unique contribution to the latent construct.

4. **Sufficient Sample Size**: CFA requires a relatively large sample size to produce stable and generalizable results. A common rule of thumb is at least 200 observations. Still, the required sample size can vary depending on the complexity of the model and the number of parameters to be estimated.

5. **Correct Model Specification**: The hypothesized model should be specified correctly based on theory or prior research. Incorrect model specifications can lead to biased estimates and poor model fit.

6. **Independence of Observations**: The data should be collected to make the observations independent. Violations of this assumption can lead to biased parameter estimates and incorrect conclusions.

How to Interpret the Results of Confirmatory Factor Analysis

Interpreting the results of CFA involves several steps:

1. **Model Fit Indices**: Before interpreting the individual parameters, it’s crucial to assess the model’s overall fit. Standard fit indices include:

   – **Chi-Square Test**: A non-significant Chi-Square indicates a good fit. However, it is sensitive to sample size.

   – **Root Mean Square Error of Approximation (RMSEA)**: Values less than 0.06 indicate a good fit.

   – **Comparative Fit Index (CFI)** and **Tucker-Lewis Index (TLI)**: Values greater than 0.95 indicate a good fit.

2. **Factor Loadings**: Evaluate the factor loadings to understand the strength of the relationship between each observed variable and its corresponding latent factor. High factor loadings (typically greater than 0.5) suggest that the observed variables are good indicators of the latent construct.

3. **Modification Indices**: If the model fit is unsatisfactory, modification indices can suggest potential changes to improve the model. These indices indicate the expected decrease in Chi-Square value if a particular fixed parameter is freed. However, any modifications should be theoretically justified and not purely data-driven.

4. **Standardized Residuals**: Examine the standardized residuals to identify areas where the model does not fit the data well. Large residuals indicate that the model is not accurately capturing the relationship between the observed variables and the latent constructs.

5. **Reliability and Validity**: Assess the reliability and validity of the constructs. Reliability can be evaluated using measures like Cronbach’s alpha or composite reliability, while validity can be assessed through convergent and discriminant validity. Convergent validity ensures that the indicators of a construct are highly correlated, while discriminant validity ensures that constructs that should not be related are indeed distinct.

For example, suppose you are conducting CFA to validate a psychological scale measuring depression, anxiety, and stress, and you find high factor loadings for each indicator on its respective factor along with good fit indices (e.g., RMSEA < 0.06, CFI > 0.95). In that case, the scale is a valid and reliable measure of these constructs. If the initial model fit is poor, modification indices suggest adding correlations between specific indicators, provided these adjustments are theoretically justifiable.

By carefully interpreting these components, researchers can confirm the validity of their theoretical constructs and ensure that their measurement instruments are reliable and accurate.


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