How to conduct path analysis through a structural equation model (SEM)?

Path analysis through structural equation modeling (SEM) evaluates direct and indirect causal pathways among observed or latent variables, enabling hypothesis testing about complex relationships. Key principles include specifying a theoretical path diagram to outline expected relationships, ensuring model identification through sufficient constraints. Necessary conditions involve multivariate normality and adequate sample sizes (N>200) for reliable estimation. The method applies broadly in social sciences but requires caution: assess model fit using indices like CFI >0.90, RMSEA <0.08, and evaluate paths for statistical significance to avoid mis-specification. Implementation involves: first, formulating a path model based on theory. Second, collecting and preparing data (e.g., survey responses). Third, estimating parameters using maximum likelihood in software like AMOS or Lavaan. Fourth, testing fit and path coefficients; refine models using modification indices if needed. This reveals causal mechanisms, supporting evidence-based decisions in fields like psychology or marketing.