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Longitudinal Model Testing Research

Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables.

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Sources

  1. Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968
  2. Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344

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Referenced by

ScholarGateLongitudinal Model Testing Research (Longitudinal Model Testing Research). Retrieved 2026-06-04 from https://scholargate.app/en/research-design/longitudinal-model-testing-research