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 is often used to confirm hypotheses about the structure of data, ensuring that the proposed model fits well with the observed data. CFA is particularly important in validating measurement instruments and understanding the relationships among variables in research.

5 Must Know Facts For Your Next Test

1. CFA allows researchers to test specific hypotheses about the structure of data and determine if the model fits the observed relationships.
2. This analysis requires a well-defined model based on theory or prior research, differing from exploratory methods that do not impose a specific structure.
3. In CFA, it is crucial to assess model fit using various indices, such as the Chi-square statistic, Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA).
4. CFA can help in validating measurement scales by confirming whether items load on their intended factors as predicted.
5. The technique is widely used in social sciences, psychology, and marketing research to confirm theories and improve questionnaire design.

Review Questions

• How does Confirmatory Factor Analysis differ from Exploratory Factor Analysis in terms of its purpose and methodology?
  • Confirmatory Factor Analysis is focused on testing a pre-specified model based on theory or prior research, whereas Exploratory Factor Analysis aims to discover potential underlying structures without an initial hypothesis. CFA requires researchers to propose a model before analysis and assesses how well the data fits this model. In contrast, EFA allows for flexibility in identifying patterns within the data, making it useful for generating new hypotheses.
• What role does Goodness-of-Fit play in Confirmatory Factor Analysis, and which indices are commonly used to measure it?
  • Goodness-of-Fit is crucial in Confirmatory Factor Analysis as it evaluates how well the proposed model aligns with the observed data. Commonly used indices for measuring goodness-of-fit include the Chi-square statistic, which tests the null hypothesis that the model fits perfectly; Comparative Fit Index (CFI), which compares the fit of the target model against a baseline model; and Root Mean Square Error of Approximation (RMSEA), which assesses how well the model predicts the covariance among observed variables. A good fit suggests that the model adequately represents the data.
• Evaluate the implications of using Confirmatory Factor Analysis in validating measurement instruments within social sciences research.
  • Using Confirmatory Factor Analysis to validate measurement instruments has significant implications for social sciences research as it enhances the reliability and validity of findings. By confirming that items load appropriately onto their intended factors, researchers can ensure that constructs are accurately measured, leading to more credible results. This not only strengthens theoretical frameworks but also aids in refining questionnaires and scales used for data collection. The rigorous testing provided by CFA ultimately contributes to better decision-making and insights based on empirical evidence.

Related terms

Latent Variable: A latent variable is an unobserved variable that is inferred from observed variables, representing underlying constructs in a model.

Exploratory Factor Analysis:

Exploratory Factor Analysis (EFA) is a technique used to identify the underlying relationships between observed variables without a pre-specified structure.

Goodness-of-Fit: Goodness-of-Fit refers to statistical measures used to assess how well a model explains the observed data, including indices like Chi-square and RMSEA.