Linganisha mbinu

	Multilevel Modeling	Regresheni ya Logistiki
Nyanja	Takwimu za Utafiti	Takwimu za Utafiti
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1992	1958
Mwanzilishi	Anthony Bryk and Stephen Raudenbush	David Roxbee Cox
Aina	Method	Method
Chanzo asilia	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Majina mbadala	HLM, mixed-effects models, random effects models, MLM	logit model, binomial logistic regression, LR
Zinazohusiana	3	3
Muhtasari	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.