Confirmatory factor analysis

Definition

Confirmatory factor analysis (CFA) is a statistical technique used to test whether a set of observed variables can be explained by a smaller number of underlying latent variables or factors. This method allows researchers to validate the hypothesized relationships among measured variables and confirm the structure of a proposed model. It is widely applied in social sciences for assessing construct validity, especially during scale development and structural equation modeling.

5 Must Know Facts For Your Next Test

1. CFA differs from exploratory factor analysis (EFA) because it tests a specific hypothesis about the structure of the data rather than exploring potential patterns.
2. In CFA, researchers specify the number of factors and which observed variables load onto which factors based on theoretical expectations.
3. Goodness-of-fit indices, such as Chi-square, RMSEA, and CFI, are crucial in CFA for evaluating how well the proposed model fits the observed data.
4. CFA can be used as part of structural equation modeling (SEM) to assess both measurement and structural relationships in complex models.
5. The results of CFA help in refining measurement instruments, ensuring that they accurately reflect the constructs they intend to measure.

Review Questions

• How does confirmatory factor analysis differentiate itself from exploratory factor analysis?
  • Confirmatory factor analysis (CFA) is focused on testing specific hypotheses about the relationships between observed variables and underlying latent factors, whereas exploratory factor analysis (EFA) is used to discover potential patterns without predefined expectations. In CFA, researchers begin with a clear theoretical model that defines which factors are expected to influence which variables, making it a more stringent test of construct validity compared to the open-ended nature of EFA.
• Discuss the importance of goodness-of-fit indices in confirmatory factor analysis and their role in evaluating model adequacy.
  • Goodness-of-fit indices play a critical role in confirmatory factor analysis as they help assess how well the specified model aligns with the observed data. Indices like Chi-square indicate whether there is a significant difference between the observed and expected covariance matrices, while RMSEA assesses fit per degree of freedom. CFI measures how well the specified model fits compared to a baseline model. These indices provide essential insights into whether the hypothesized relationships are valid and if adjustments are needed for better model fit.
• Evaluate how confirmatory factor analysis can contribute to scale development and its implications for research accuracy.
  • Confirmatory factor analysis significantly enhances scale development by validating the dimensionality and structure of measurement instruments. By confirming that a set of observed items accurately reflects intended latent constructs, researchers can ensure that their scales are both reliable and valid. This rigorous approach minimizes measurement error and increases confidence in research findings. As a result, effective use of CFA leads to improved accuracy in psychological testing, surveys, and assessments, ultimately influencing theories and practices across various fields.

Related terms

Latent Variables: Latent variables are unobserved variables that are inferred from observed measurements and are used in models like CFA to represent underlying constructs.

Construct Validity: Construct validity refers to the degree to which a test measures what it claims to measure, often assessed through techniques like CFA.

Measurement Model: A measurement model is a type of model in structural equation modeling that specifies the relationships between observed variables and their underlying latent factors.