Comparing Out-of-Sample Predictive Ability of PLS, Covariance, and Regression Models
Bibliographic record
Abstract
Partial Least Squares Path Modelling (PLSPM) is a popular technique for estimating structural equation models in the social sciences, and is frequently presented as an alternative to covariance-based analysis as being especially suited for predictive modeling. While existing research on PLSPM has focused on its use in causal-explanatory modeling, this paper follows two recent papers at ICIS 2012 and 2013 in examining how PLSPM performs when used for predictive purposes. Additionally, as a predictive technique, we compare PLSPM to traditional regression methods that are widely used for predictive modelling in other disciplines. Specifically, we employ out-of-sample k-fold cross-validation to compare PLSPM to covariance-SEM and a range of a-theoretical regression techniques in a simulation study. Our results show that PLSPM offers advantages over covariance-SEM and other prediction methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".