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Predictive vs. Structural Modeling: PLS vs. ML
Abstract
The Latent Variables Path (LVP) model and also the first viable estimation technique were developed by Karl Jöreskog (from 1973 onwards). Right from the beginning his LISREL method was of the utmost elegance and perfection, as regards the deductive properties of the model. The LISREL program has since been gradually improved (LISREL III: Jöreskog & van Thillo 1973; LISREL VII: Jöreskog & Sörbom 1987), as regards convenience for the user, additional estimation criteria and model diagnostics. However, one inconvenient side of LISREL, well known to all users, is incurable: LISREL tends to produce improper solutions characterized by negative variance estimates and LV correlations greater than unity (van Driel 1978, Jöreskog 1981, Fornell & Bookstein 1982, Rindskopf 1983, 1984). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Cited by
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“SRMR values below 0.08 mean that the data fit the model (Hu and Bentler, 1999; Henseler et al., 2014), whereas for NFI values (which are between 0 and 1), the closer the value is to 1, the better the fit (Lohm ¨ oller, 1989).”
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Metadata
- Authors
- Lohmöller, J.-B.
- Venue
- Latent Variable Path Modeling with Partial Least Squares
- Publisher
- Physica-Verlag HD
- Published
- 1989
- Pages
- 199-226
- OpenAlex
- W160454418
- Cited by
- 284
- References
- 0
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Lohmöller, J.-B. (1989). Predictive vs. Structural Modeling: PLS vs. ML. Latent Variable Path Modeling with Partial Least Squares. https://doi.org/10.1007/978-3-642-52512-4_5
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