手法を比較

	Wordfish	探索的構造方程式モデリング (Exploratory Structural Equation Modeling)
分野	心理測定学	心理測定学
系統	Latent structure	Latent structure
提唱年	2008	2009
提唱者	Jonathan Slapin, Svenja-Sophia Proksch	Tihomir Asparouhov, Bengt Muthén
種類	Generative text model for dimension reduction	Hybrid exploratory-confirmatory factor modeling
原典	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.	Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438.
別名	—	ESEM
関連	5	5
概要	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.	Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories.