Linganisha mbinu

	Mixed Effects Model	Uchanganuzi wa Faulo wa Njia Moja	ANOVA ya Vipimo Rudia
Nyanja	Takwimu	Takwimu	Takwimu
Familia	Regression model	Hypothesis test	Hypothesis test
Mwaka wa asili	1982	1925	1992
Mwanzilishi	Laird & Ware	Ronald A. Fisher	Girden (textbook treatment); Field (2013)
Aina	Mixed effects regression	Parametric mean comparison	Parametric within-subjects mean comparison
Chanzo asilia	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.	Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185
Majina mbadala	LME, LMM, mixed model, random effects model	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA	within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA
Zinazohusiana	4	4	4
Muhtasari	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.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.	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).