Compara mètodes

	Wordfish	Modelització d'equacions estructurals per a mínims quadrats parcials
Camp	Psicometria	Psicometria
Família	Latent structure	Latent structure
Any d'origen	2008	1985
Autor original	Jonathan Slapin, Svenja-Sophia Proksch	Herman Wold
Tipus	Generative text model for dimension reduction	Component-based structural equation model
Font seminal	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.	Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445
Àlies	—	PLS-SEM, PLS path modeling
Relacionats	5	5
Resum	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.	PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data.