Compare methods

	Cosponsorship Network Analysis	Ideal Point Estimation	Manifesto Coding
Field	Political Science	Political Science	Political Science
Family	Process / pipeline	Latent structure	Process / pipeline
Year of origin	2006	2004	2001
Originator	James H. Fowler	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)
Type	Social-network analysis of legislative collaboration	Latent-variable spatial model of binary choice data	Quantitative content analysis of party manifestos
Seminal source	Fowler, J. H. (2006). Connecting the Congress: A Study of Cosponsorship Networks. Political Analysis, 14(4), 456–487.	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.	Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press. ISBN: 9780199244003
Aliases	Cosponsorship networks, Legislative collaboration networks, Bill cosponsorship analysis, Co-sponsorship network analysis	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding
Related	3	4	4
Summary	Cosponsorship network analysis treats legislative collaboration as a social network: when legislators cosponsor one another's bills, they form ties, and the resulting web of connections can be measured with the tools of network science. Introduced to congressional studies by James Fowler in 2006, it turns the public record of who signed on to whose bills into a graph among lawmakers, revealing who is central and influential, how connected the chamber is, and which clusters of legislators form coalitions. With inferential network models such as ERGMs, researchers move from describing the network to explaining why ties form.	Ideal point estimation recovers the latent policy positions — ideal points — of political actors from their observed binary choices, most often legislators' yea/nay votes on roll calls. Building on the spatial theory of voting and formalized as a Bayesian item-response model by Clinton, Jackman, and Rivers in 2004, it places each legislator and each bill in a low-dimensional policy space and estimates positions so that the probability a legislator votes yea increases as the bill's 'yea' outcome moves closer to that legislator's ideal point.	Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945.