Compare methods

	Multidimensional Unfolding	Multilevel Modeling
Field	Political Science	Research Statistics
Family	Latent structure	Process / pipeline
Year of origin	2000	1992
Originator	Keith T. Poole (nonparametric optimal classification and unfolding)	Anthony Bryk and Stephen Raudenbush
Type	Latent-space scaling model placing individuals and stimuli in a joint space	Method
Seminal source	Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Aliases	Unfolding analysis, Optimal classification, Preference unfolding, Joint-space scaling	HLM, mixed-effects models, random effects models, MLM
Related	5	3
Summary	Multidimensional unfolding places both individuals and the stimuli they evaluate — candidates, parties, bills — in a single joint low-dimensional space, so that each person's preferences are explained by their proximity to the stimuli. In political science it underlies Keith Poole's nonparametric optimal classification of roll-call votes and the unfolding of thermometer ratings and rank orders, recovering legislators' and bills' positions from nothing but the pattern of choices. Unlike correlation-based scaling, unfolding treats preference as a single-peaked function of distance: you like what is close to you and dislike what is far.	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.