Demystifying longitudinal data analyses using structural equation models in school psychology

Area of Science:

• Quantitative Psychology
• Developmental Psychology
• School Psychology

Background:

• Structural Equation Models (SEM) offer flexible latent variable analysis.
• Longitudinal SEM advancements enable novel variance decomposition and mean trend estimation.
• Current longitudinal SEM methods may lack accessibility for school psychology scholars.

Purpose of the Study:

• To highlight recent advances in longitudinal SEM.
• To clarify similarities between longitudinal SEM and other statistical methods.
• To match longitudinal SEM applications to specific school psychology research questions.

Main Methods:

• Review and synthesis of recent advancements in longitudinal Structural Equation Models (SEM).
• Comparison of longitudinal SEM with more familiar statistical techniques.
• Framework for aligning specific longitudinal SEM approaches with distinct research aims in school psychology.

Main Results:

• Recent longitudinal SEM techniques offer innovative ways to analyze developmental data.
• Careful matching of analytic approaches to research questions is crucial for meaningful insights.
• Understanding the correspondence between estimands, theory, and application enhances rigor.

Conclusions:

• Promoting accessible understanding of longitudinal SEM can advance school psychology theory and practice.
• This work encourages specificity in quantitative research by aligning methods with developmental theory.
• Rigorous evaluation of longitudinal trends is essential for evidence-based school psychology.