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 factors. This method helps researchers confirm or reject hypotheses about the structure of data and the relationships among variables, making it crucial for validating measurement models and ensuring that constructs accurately represent theoretical concepts.

5 Must Know Facts For Your Next Test

1. CFA is primarily used in situations where researchers have specific hypotheses about the factor structure they want to test, as opposed to exploring data without prior assumptions.
2. This analysis allows researchers to evaluate the fit of their proposed model against observed data using fit indices like Chi-square, RMSEA, and CFI, indicating how well the model explains the data.
3. One of the key advantages of CFA is its ability to assess measurement invariance, which checks whether the same construct is being measured across different groups or conditions.
4. CFA can also help in identifying poorly performing items in a survey or questionnaire by revealing which indicators do not align well with the proposed factor structure.
5. The results from CFA inform decisions regarding scale development and refinement, ensuring that constructs are accurately represented and reliable for future research.

Review Questions

• How does confirmatory factor analysis differ from exploratory factor analysis in terms of its purpose and application?
  • Confirmatory factor analysis is designed to test specific hypotheses about the relationships between observed variables and underlying latent factors. In contrast, exploratory factor analysis is used to discover these relationships without prior assumptions. CFA confirms whether the data fits a specified model, while EFA seeks to explore potential structures within the data. This distinction makes CFA crucial for validating measurement models that are based on theoretical foundations.
• What role does confirmatory factor analysis play in assessing measurement invariance across different demographic groups?
  • Confirmatory factor analysis plays a critical role in assessing measurement invariance by determining if a construct is measured consistently across different demographic groups. Researchers can test if the same factor structure applies across populations, which ensures that comparisons made between groups are valid. If invariance holds, it indicates that any observed differences can be attributed to true differences in the constructs rather than discrepancies in measurement.
• Evaluate how confirmatory factor analysis can impact the development of a new measurement scale in social sciences research.
  • Confirmatory factor analysis significantly impacts the development of new measurement scales by providing a rigorous method for testing whether proposed constructs align with observed data. By evaluating model fit and identifying any problematic items, researchers can refine their scales to enhance validity and reliability. This process leads to more accurate representations of theoretical concepts, ultimately improving research outcomes and findings in social sciences.

Related terms

Exploratory Factor Analysis:

A technique used to identify the underlying relationships between variables without imposing a preconceived structure, allowing researchers to discover how many factors are needed to explain the data.

Latent Variables: Variables that are not directly observed but are inferred from other measured variables, often representing theoretical concepts in social science research.

Measurement Model: A model that specifies the relationships between latent variables and their observed indicators, crucial for ensuring the validity and reliability of measurements in research.