Advanced factor analysis goes beyond exploratory techniques to test hypotheses about the relationships between observed variables and their underlying latent constructs. Unlike exploratory factor analysis (EFA), which identifies potential structures without prior assumptions, Confirmatory Factor Analysis (CFA) allows researchers to confirm whether a pre-specified factor structure fits the data. This makes CFA ideal for validating measurement models, ensuring that instruments measure what they are intended to measure.
Confirmatory Factor Analysis (CFA) in SPSS
CFA is used to assess how well measured variables represent latent constructs. In SPSS, CFA is performed through the AMOS module, where researchers specify a model by drawing relationships between latent variables (factors) and observed variables. Fit indices such as CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), and RMSEA (Root Mean Square Error of Approximation) are used to evaluate model adequacy. A good model fit indicates that the hypothesized factor structure aligns well with the data.
Structural Equation Modeling (SEM) in SPSS
Structural Equation Modeling (SEM) extends CFA by allowing the analysis of complex relationships between latent variables, including mediation and causal paths. SEM integrates measurement models (CFA) with structural models (hypothesized causal relationships). Using SPSS AMOS, researchers can visualize paths, estimate coefficients, and test hypotheses about direct and indirect effects. SEM is particularly useful in social sciences, psychology, and market research, where multiple variables interact simultaneously.
Steps to Perform CFA and SEM in SPSS
The typical workflow in SPSS AMOS includes: (1) Defining latent constructs and mapping observed variables, (2) Specifying the model with hypothesized paths, (3) Estimating the model using maximum likelihood or other estimation methods, (4) Assessing model fit through indices like CFI, RMSEA, and Chi-square, and (5) Refining the model if needed, including adding covariances or removing weak indicators. Proper model specification ensures reliable and valid results.
Practical Example: Understanding Student Motivation
Imagine a researcher studying student motivation using a survey with items measuring intrinsic motivation, extrinsic motivation, and self-efficacy. Using CFA in SPSS AMOS, the researcher can confirm whether the survey items accurately reflect these three factors. Then, using SEM, the researcher can explore how intrinsic motivation and self-efficacy influence academic performance, while extrinsic motivation acts as a mediator. This approach provides a clear, data-driven picture of the relationships between motivation factors and outcomes.
