Compara mètodes

	Investigació sobre proves de models basats en panells	Modelatge Multillivell
Camp	Disseny de recerca	Estadística per a la recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	1970s–1980s (panel econometrics and SEM matured in parallel)	1992
Autor original	Developed across econometrics (Hsiao, Hausman) and psychometrics (Jöreskog, Bollen)	Anthony Bryk and Stephen Raudenbush
Tipus	Quantitative longitudinal research design	Method
Font seminal	Bollen, K. A. (1989). Structural Equations with Latent Variables. Wiley. ISBN: 978-0471011712	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Àlies	panel SEM, longitudinal model testing, panel structural equation modeling, panel-based hypothesis testing	HLM, mixed-effects models, random effects models, MLM
Relacionats	4	3
Resum	Panel-based model testing research combines the longitudinal power of panel survey designs with the confirmatory rigor of structural model testing — such as structural equation modeling (SEM), path analysis, or confirmatory factor analysis — applied to data collected from the same units (individuals, firms, countries) across multiple time points. This approach enables researchers to test theoretically specified causal and mediation structures while controlling for unobserved unit-level heterogeneity and examining how relationships unfold over time.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.