The Effect of the Number of Observations per Parameter in Misspecified Confirmatory Factor Analytic Models
Bibliographic record
Abstract
Some authors have suggested that sample size in covariance structure modeling should be considered in the context of how many parameters are to be estimated (e.g., Kline, 2005 Kline, R. B., 2005. Principles and practice of structural equation modeling, . New York: Guilford; 2005. [Google Scholar]). Previous research has examined the effect of varying sample size relative to the number of parameters being estimated (N:q). Although some support has been found for this effect, the effect size appears to be small compared to other influences, such as indicator reliability and sample size (Jackson, 2003 Jackson, D. L., 2003. Revisiting sample size and the number of parameter estimates: Some support for the N:q hypothesis., Structural Equation Modeling: A Multidisciplinary Journal 10 (2003), pp. 128–141.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Efforts to extend this work to the case where models are intentionally misspecified are described in this article. In addition to varying the number of observations per estimated parameter, several other known influences on model fit were varied such as sample size, the degree of misspecification, number of variables per factor, and the communality of the measured variables. The results suggest that decreasing the number of parameters to be estimated while holding sample size constant can help detect misspecification errors, and some fit indexes were more sensitive to this manipulation than others. In general, the effects of N:q were small relative to other experimental effects.
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.
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 | 0.524 | 0.889 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".