SEM is simply a method you use to achieve any of your research objectives that involve checking predictions. It is preferred over regression analysis because it controls error in better way.

A structural equation model (SEM) with latent variables accounts for measurement error in the exogeneous (predictor) and endogenous (mediator and outcome) variables, typically by using measurement models with multiple indicator (observed) variables for each construct. This can help reduce bias when estimating the regression (path) coefficients and latent correlations as well as their standard errors. An SEM also allows you to determine the reliabilities of the indicator variables as measures of latent variables.

Another advantage of SEMs is that they are typically include testable restrictions for the covariance and/or mean structure of the observed indicator variables. That is, SEM models with positive degrees of freedem can be falsified, for example, by using a chi-square test of model fit.

An SEM also allows testing complex causal models with multiple independent, intervening (mediating), and outcome variables (i.e., latent path analysis), making it possible to test complex indirect (mediated) variable effects.

@ Surej, answering your question below

SEM simultaneously solves a system of multiple equations, allowing for the estimation of relationships that are interdependent and often too complex for separate analyses.

Its primary goal is to quantitatively test a hypothesized causal structure or theory against empirical data, providing a confirmatory approach to understanding relationships.

SEM handles complex networks of relationships, simultaneous estimation of both causal and correlational links among various variables within a single model. The technique allows for the analysis of unobserved (latent) variables, such as ‘awareness’ 'self efficacy', trust etc. which are inferred from multiple observed (measured) indicators . And then quantifying relation ship between variables in equations.

One can say that SEM enables the assessment of how variables affect each other, including direct pathways and indirect effects that operate through mediating variables, .It provides a deeper understanding of multiple causal relationships simultaneously.

Surej Unnikrishnan Yes, your listed steps are theoretically valid as objectives for an SEM analysis workflow in AMOS, as they cover key phases like measurement validation, relationship testing, and model evaluation. However, they blend steps with objectives. objectives should ideally state the “why” (e.g., to test theory), while these read more like procedural goals. Here’s a quick validation and refined suggestions for clarity/strength:

1. To assess Cronbach’s alpha and CFA values of the scales: Solid for establishing reliability (alpha > 0.7 typically) and construct validity via CFA essential first step in SEM’s measurement model.

2. To assess the relationship: Valid, but vague; refine to “To assess structural relationships (e.g., paths) between latent variables” to align with SEM’s core purpose of modeling inter-variable links.

3. To assess prediction: Appropriate for predictive paths in the structural model, but specify: To assess predictive effects of exogenous on endogenous variables.”

4. To assess the Model Estimation using AMOS: Core; this involves parameter estimation (e.g., via maximum likelihood). It’s a standard step post-specification.

5. To assess the goodness-of-fit using the chi-square test: Good for initial fit, but chi-square is sample-size sensitive; add objectives for supplementary indices like RMSEA (0.95), or TLI for comprehensive evaluation.

Hi Surej Unnikrishnan ! It depends on the objective of your research. Did you use a CFA analysis for validating a survey instrument? Maybe this is why they are asking you to mention these objective.