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Wordfish×Pemodelan Persamaan Struktur Eksploratori×
BidangPsikometrikPsikometrik
KeluargaLatent structureLatent structure
Tahun asal20082009
PengasasJonathan Slapin, Svenja-Sophia ProkschTihomir Asparouhov, Bengt Muthén
JenisGenerative text model for dimension reductionHybrid exploratory-confirmatory factor modeling
Sumber perintisSlapin, J. B., & Proksch, S. O. (2008). A scaling model for estimating time-series party positions from texts. Journal of Politics, 70(3), 554-569. DOI ↗Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗
AliasESEM
Berkaitan55
RingkasanWordfish 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.
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