Structural Equation Modeling Techniques in Psychological Research
Summary
Structural equation modelling (SEM) encompasses a family of statistical methods that integrate factor analysis, path analysis and regression to examine complex relationships among observed and latent variables. In psychological research SEM is employed to test theoretical models of cognition, emotion, personality and behaviour while accounting for measurement error. Core approaches include confirmatory factor analysis for validating measurement instruments, multiple‐group SEM for assessing measurement invariance across populations or time, and cross‐lagged panel models for disentangling reciprocal effects in longitudinal data. Recent advances have introduced Bayesian estimation, regularisation techniques for high‐dimensional data and robust protocols for ordinal indicators. Practical applications range from evaluating the structure of psychopathology to modelling mediation and moderation in developmental studies. Emphasis on sample size determination, power analysis and model fit evaluation has strengthened the rigour of SEM studies and enhanced the reproducibility of psychological science.
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Structural Equation Modeling Techniques in Psychological Research publication trend
The graph below shows the total number of articles in structural equation modeling techniques in psychological research across all publications each year (not limited to Nature Index journals).
Technical terms
Latent variable: Unobserved construct inferred from multiple observed indicators.
Confirmatory factor analysis: Technique for testing how well a prespecified measurement model fits observed data.
Measurement invariance: Property that a measurement model operates equivalently across groups or time points.
Model fit indices: Statistical criteria that evaluate the correspondence between a specified model and the data.
Cross‐lagged panel model: Longitudinal design estimating directional influences between variables over successive time waves.
Statistical power: Probability of correctly detecting a true effect given the model, sample size and significance level.
References
- Structural Equation Modeling With Many Variables: A Systematic Review of Issues and Developments. Frontiers in Psychology (2018).
- Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis. Educational and Psychological Measurement (2023).
- semPower: General power analysis for structural equation models. Behavior Research Methods (2023).
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