เปรียบเทียบวิธี

	Hierarchical Linear Modeling	Mixed Effects Model
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Hypothesis test	Regression model
ปีกำเนิด	1986	1982
ผู้ริเริ่ม	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Laird & Ware
ประเภท	Parametric nested-data regression	Mixed effects regression
แหล่งต้นตำรับ	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
ชื่อเรียกอื่น	HLM, MLM, multilevel modeling, multilevel analysis	LME, LMM, mixed model, random effects model
ที่เกี่ยวข้อง	4	4
สรุป	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.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.