An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we propose integrated generalized structured component analysis (IGSCA), which is a general statistical approach for analyzing data with both components and factors in the same model, simultaneously. This approach combines generalized structured component analysis (GSCA) and generalized structured component analysis with measurement errors incorporated (GSCAM) in a unified manner and can estimate both factor- and component-model parameters, including component and factor loadings, component and factor path coefficients, and path coefficients connecting factors and components. We conduct 2 simulation studies to investigate the performance of IGSCA under models with both factors and components. The first simulation study assesses how existing approaches for structural equation modeling and IGSCA recover parameters. This study shows that only consistent partial least squares (PLSc) and IGSCA yield unbiased estimates of all parameters, whereas the other approaches always provided biased estimates of several parameters. As such, we conduct a second, extensive simulation study to evaluate the relative performance of the 2 competitors (PLSc and IGSCA), considering a variety of experimental factors (model specification, sample size, the number of indicators per factor/component, and exogenous factor/component correlation). IGSCA exhibits better performance than PLSc under most conditions. We also present a real data application of IGSCA to the study of genes and their influence on depression. Finally, we discuss the implications and limitations of this approach, and recommendations for future research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it