Bandingkan kaedah

	Manifesto Coding	Wordfish	Wordscores
Bidang	Political Science	Psikometrik	Psikometrik
Keluarga	Process / pipeline	Latent structure	Latent structure
Tahun asal	2001	2008	2003
Pengasas	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)	Jonathan Slapin, Svenja-Sophia Proksch	Michael Laver, Kenneth Benoit, John Garry
Jenis	Quantitative content analysis of party manifestos	Generative text model for dimension reduction	Text analysis and dimension reduction
Sumber perintis	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	Slapin, J. B., & Proksch, S. O. (2008). A scaling model for estimating time-series party positions from texts. Journal of Politics, 70(3), 554-569.	Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2), 311-331.
Alias	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding	—	—
Berkaitan	4	5	5
Ringkasan	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.	Wordfish is a statistical model for scaling documents on latent dimensions, developed by Slapin and Proksch (2008). Unlike reference-based methods like Wordscores, Wordfish uses a Poisson generative model to jointly estimate word frequencies and document positions without requiring reference texts or manual annotation. It is particularly useful for estimating time-series changes in policy positions and can scale documents from multiple languages simultaneously.	Wordscores is a text-based scaling method developed by Laver, Benoit, and Garry (2003) that estimates the policy positions of political actors based on word frequencies in their texts. By comparing word usage in reference texts of known positions with test texts, the method infers the latent political dimension of any document without requiring manual coding or training data.