Your conceptual framework has five constructs, eight arrows and a mediator. Which test answers all of it? Running a separate regression for each arrow ignores measurement error and cannot test the model as a whole. Structural equation modeling was designed for this situation: it estimates the measurement of latent constructs and the relationships between them in one model.
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This guide is written for students meeting SEM for the first time. It explains the vocabulary, the two-step approach, six practical steps, how to read the results, and the decisions you must justify, including the choice between covariance-based SEM and PLS-SEM.
What is structural equation modeling? It is a family of multivariate techniques that combines factor analysis and regression. It measures latent constructs through several observed indicators (the measurement model) and estimates directional and correlational relationships among those constructs (the structural model), including indirect effects, while accounting for measurement error. Covariance-based SEM tests how well the whole model reproduces the observed covariances.
What are the basic terms of structural equation modeling?
| Term | Meaning | Drawn as |
|---|---|---|
| Observed (manifest) variable | A measured item, such as a questionnaire question | Rectangle |
| Latent variable (construct) | An unobserved concept measured by several items, such as job satisfaction | Oval or circle |
| Exogenous variable | A construct not predicted by any other in the model | Has no incoming arrows |
| Endogenous variable | A construct predicted by at least one other | Has incoming arrows and a residual |
| Path | A hypothesized directional effect | Single-headed arrow |
| Covariance | A non-directional association | Double-headed arrow |
| Error / residual | Unexplained variance of an item or an endogenous construct | Small circle |
Why use SEM instead of several regressions?
- It corrects for measurement error, because constructs are modeled through several items rather than averaged scores.
- It tests the whole model and reports overall fit.
- It estimates indirect (mediated) effects and their significance directly.
- It can compare models, for example full versus partial mediation, or the same model across groups.
The cost is complexity: larger samples, more assumptions and more decisions to justify.
A simple test helps you decide. If your framework has one outcome, a few predictors measured as averaged scale scores and no indirect effects, multiple regression may be enough and easier to defend. If it has several outcomes linked in a chain, constructs measured by several items each, and hypotheses about mediation, a structural model answers the questions more directly and with fewer separate tests. Discuss the choice with your supervisor before collecting data, because the sample size and the number of items per construct depend on it.
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What is the two-step approach?
Anderson and Gerbing (1988) recommended testing the measurement model first, with a confirmatory factor analysis that establishes fit, reliability and validity, and only then estimating the structural model with the hypothesized paths. If the measurement model fails, any path coefficient is uninterpretable. This two-step logic is now standard in structural equation modeling studies, and examiners look for it.
How do you carry out structural equation modeling in 6 steps?
- Specify the model from theory. Every arrow must be supported by literature and stated as a hypothesis. Draw the diagram before collecting data.
- Check identification and plan the sample. Each construct needs enough indicators (three or more is the usual advice) and the model needs fewer free parameters than available data points. Kline (2023) discusses sample size in terms of model complexity; 200 or more cases is a common minimum for moderate models.
- Prepare the data. Screen for entry errors, missing values, outliers and multivariate normality before estimation.
- Estimate and evaluate the measurement model (CFA): fit indices, standardized loadings, composite reliability, AVE and discriminant validity.
- Estimate the structural model. Read overall fit, path coefficients, their significance and the R² of each endogenous construct.
- Test indirect effects and report. Use bootstrapped confidence intervals for indirect effects and report every modification made.
How do you read the results?
Fit. The same indices as in CFA: χ² with df, CFI, TLI, RMSEA with its confidence interval and SRMR. Common guidelines are CFI and TLI at about .95, RMSEA at or below .06 to .08 and SRMR at or below .08 (Hu & Bentler, 1999). Good fit means the model is consistent with the data, not that it is the only or the true model.
Paths. Report the standardized coefficient (β), its standard error or confidence interval and p. A standardized path of .35 means that a one standard deviation increase in the predictor is associated with a .35 standard deviation increase in the outcome, holding the other predictors constant.
R². The proportion of variance in each endogenous construct explained by its predictors.
Indirect effects. In a model where training affects performance through motivation, the indirect effect is the product of the two paths. Its sampling distribution is not normal, so test it with a bootstrapped confidence interval (for example 5,000 samples); an interval that excludes zero indicates a significant indirect effect.
CB-SEM or PLS-SEM: which one should a beginner choose?
| Aspect | Covariance-based SEM (AMOS, lavaan, Mplus) | PLS-SEM (SmartPLS) |
|---|---|---|
| Main aim | Testing and confirming theory | Prediction and exploratory theory development |
| Model fit | Global fit indices are central | Global fit is limited; focus on R², Q² and predictive tests |
| Sample size | Generally larger samples | Works with smaller samples, though not as a cure for tiny ones |
| Distribution | Maximum likelihood assumes normality (robust options exist) | No normality assumption; bootstrapping for significance |
| Construct type | Mainly reflective constructs | Reflective and formative constructs |
Choose the method that matches your research aim, then justify it with references in the methodology chapter. Choosing a method only because it produces significant results is a weakness committees notice.
Which mistakes do beginners make?
- Estimating the structural model before validating the measurement model.
- Adding arrows or error covariances to improve fit without theoretical justification.
- Claiming causality from cross-sectional survey data. Structural equation modeling estimates relationships consistent with a causal model; it does not prove causation.
- Testing mediation with the Sobel test alone instead of bootstrapping.
- Reporting only significant paths and omitting fit, R² or non-significant hypotheses.
- Using single-item constructs everywhere, which removes the main advantage of SEM.
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Frequently asked questions
Is structural equation modeling the same as path analysis?
Path analysis is a special case that uses only observed variables. Full SEM adds latent constructs measured by several indicators, which corrects for measurement error.
Which software should I use?
AMOS is common in social sciences because of its graphical interface; lavaan in R is free and transparent; SmartPLS is used for PLS-SEM. The choice should follow the method, not the reverse. Our comparison of AMOS and SmartPLS covers this in more detail.
How many items should each construct have?
Three or more is the usual recommendation for identification and stable estimates. Constructs with two items can cause estimation problems, especially in small samples.
Can I use SEM with a small sample?
Covariance-based SEM with fewer than about 150 to 200 cases often gives unstable estimates or convergence problems. Simplify the model, use parcels with caution, or consider whether PLS-SEM fits your research aim, and discuss the limitation openly.
How do I present the results?
Include the final model diagram with standardized coefficients, a fit table, a table of hypotheses with β, *p* and decision, and a table of indirect effects with bootstrapped confidence intervals.
Sources
- Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling (5th ed.). Guilford Press.
- Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
- Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
- Byrne, B. M. (2016). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming (3rd ed.). Routledge.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). SAGE.
Sources last checked: November 2026. This article is general educational guidance and does not replace your university regulations or your supervisor’s instructions. How we prepare our content.