Sammenlign metoder

	Spatial Voting Model	NOMINATE
Fagfelt	Political Science	Political Science
Familie	MCDM	Latent structure
Opprinnelsesår	1957	1985
Opphavsperson	Harold Hotelling, Duncan Black & Anthony Downs	Keith T. Poole and Howard Rosenthal
Type	Formal model of electoral and legislative choice	Spatial scaling model of roll-call voting
Opprinnelig kilde	Downs, A. (1957). An Economic Theory of Democracy. Harper & Row. ISBN: 9780060417505	Poole, K. T., & Rosenthal, H. (1985). A Spatial Model for Legislative Roll Call Analysis. American Journal of Political Science, 29(2), 357–384.
Alias	Spatial Theory of Voting, Downsian Model, Proximity Voting Model, Median Voter Model	DW-NOMINATE, W-NOMINATE, Nominal Three-Step Estimation, Poole-Rosenthal scores
Relaterte	4	3
Sammendrag	The spatial voting model represents voters and political alternatives as points in a common geometric policy space and assumes that each voter supports the alternative nearest to their own ideal point. Rooted in Hotelling's location theory, Duncan Black's 1948 single-peakedness result, and Anthony Downs's 1957 economic theory of democracy, the model yields two foundational results: the median voter theorem, which identifies the equilibrium policy in one dimension, and the Downsian prediction that two vote-seeking parties converge toward the center. It is the workhorse formalism behind modern empirical estimation of political positions.	NOMINATE — Nominal Three-step Estimation — is the family of spatial scaling procedures developed by Keith Poole and Howard Rosenthal to recover legislators' ideological positions from roll-call votes. Each legislator and the yea and nay outcomes of each vote are placed in a low-dimensional space, and a normal (Gaussian) deterministic utility plus a random shock governs choices. Fitted by maximum likelihood, NOMINATE produces the canonical ideal-point coordinates used to chart polarization across two centuries of the U.S. Congress, with the dynamic DW-NOMINATE variant allowing positions to drift smoothly over time.