So sánh phương pháp

	Mô hình hiệu ứng hỗn hợp	Mô hình Tuyến tính Phân cấp (HLM)
Lĩnh vực	Thống kê	Thống kê
Họ	Regression model	Regression model
Năm ra đời	1982	1992
Người khởi xướng	Laird & Ware	Bryk & Raudenbush
Loại	Mixed effects regression	Multilevel linear regression
Công trình gốc	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049
Tên gọi khác	LME, LMM, mixed model, random effects model	HLM, multilevel linear model, nested data model, random coefficient model
Liên quan	4	4
Tóm tắt	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.	The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data.