方法对比

	Wordfish	Latent Transition Analysis
领域	心理测量学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2008	2002
提出者	Jonathan Slapin, Svenja-Sophia Proksch	Linda M. Collins, Stephanie T. Lanza
类型	Generative text model for dimension reduction	Markovian transition between latent states
开创性文献	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.	Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Wiley. ISBN: 9780470228395
别名	—	LTA
相关	5	4
摘要	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.	Latent Transition Analysis (LTA) is a method for studying transitions between latent classes over time, developed by Collins and Lanza (2010). LTA combines latent class analysis (grouping individuals into classes) with Markovian transition models to understand how people move between qualitatively distinct states across time periods.