Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Wordfish× | Parciální metoda nejmenších čtverců v modelování pomocí strukturálních rovnic× | |
|---|---|---|
| Obor | Psychometrika | Psychometrika |
| Rodina | Latent structure | Latent structure |
| Rok vzniku≠ | 2008 | 1985 |
| Tvůrce≠ | Jonathan Slapin, Svenja-Sophia Proksch | Herman Wold |
| Typ≠ | Generative text model for dimension reduction | Component-based structural equation model |
| Původní zdroj≠ | 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. DOI ↗ | 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 |
| Další názvy≠ | — | PLS-SEM, PLS path modeling |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. |
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