Salīdzināt metodes

	Hierarhiskā lineārā modelēšana (HLM / daudzlīmeņu modelēšana)	ANOVA ar atkārtotiem mērījumiem	Strukturālā vienādojumu modelēšana (SEM)
Nozare	Statistika	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test	Latent structure
Izcelsmes gads	1986	1992	1970
Autors	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Girden (textbook treatment); Field (2013)	Karl Jöreskog (LISREL framework, 1970s)
Tips	Parametric nested-data regression	Parametric within-subjects mean comparison	Latent variable / causal modeling
Pirmavots	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Citi nosaukumi	HLM, MLM, multilevel modeling, multilevel analysis	within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Saistītās	4	4	5
Kopsavilkums	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.	Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013).	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.