Structural Equation Modeling (SEM) is an advanced statistical technique that allows you to test complex relationship between variables. This is more than just simple regression by pooling measurement model (how do you measure the concept) with structural model (how the concepts are related).

In SPSS, SEM is usually run using AMO add-on (Moment Structure Analysis).

Let’s divide it into two important parts:

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1. Measurement Model

That measurement model answer: “How well do the survey items I observed represent the hidden concepts (latent variables) I want to measure?”

For example:

• Latent variables: Job satisfaction
• Measured with Likert scale items: salary satisfaction, promotion satisfaction, supervisor support.

Steps in AMOS/SPSS:

1. Draw your model: Create latent variables (ovals) and relate them to observed items (rectangle).
2. Run Confirmatory Factor Analysis (CFA): It tests whether the item truly belongs to its latent construct.
3. Inspect factor loading: Items must load >0.50 (preferably >0.70) on the desired factor.
4. Evaluate validity and reliability:
   • Alpha Reliability / Cronbach Composite (CR) > 0.7
   • Average Variance Extracted (AVE) > 0.5
   • Discriminant Validity: Constructions must be different from each other.

If the measurement model matches, you can continue. If not, you may need to remove weak items or redefine the factors.

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2. Structural Model

That structural model answer: “How do my latent variables relate to each other?”

For example:

• Hypothesis: Job Satisfaction → Organizational Commitment → Employee Performance.
• You already know how each construct is measured (from the measurement model). Now you are testing the causal path between the two.

Steps in AMOS/SPSS:

1. Add directional arrows between latent variables to represent hypotheses.
2. Run SEM analysis.
3. Evaluate model fit index:
   • Chi-square (χ²/df): Ideally <3
   • RMSEA (Root Mean Square Error of Approximation): <0.08
   • Finance (Comparative Suitability Index): > 0.90
   • TLI (Tucker-Lewis Index): > 0.90
4. Inspect path coefficient (β): This shows the strength and significance of the relationship between variables.

A significant path (p < 0.05) supports your hypothesis. Paths that are not significant may need to be revised or eliminated.

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Putting it together

• Measurement Model = Validation Stage. Ensure your survey items measure what they are supposed to.
• Structural Model = Testing stage. Examining actual hypotheses and relationships between constructs.

In SEM, you must build a solid measurement model before interpret structural models—otherwise your results will not be reliable.

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Example of Research Flow

1. Design Survey → Collect data on Likert scale items.
2. Run CFA (Measurement Model) → Confirm constructs such as job satisfaction, commitment, performance.
3. Appropriate Structural Model → Test a hypothesis (e.g., Does satisfaction drive performance?).
4. Interpreting Fit Indexes & Path Coefficients → Accept/reject the hypothesis.

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In short

Structural Equation Modeling in SPSS (AMOS) is like building a house:

• That measurement model what is your foundation (are your variables strong?).
• That structural model is the design (how the variables are connected).

When both are strong, SEM becomes a powerful tool for validating theories and uncovering insights in the social sciences, business, psychology, and beyond.