A NOTE OF CAUTION ON COVARIANCE - EQUIVALENT MODELS IN INFORMATION SYSTEMS
Bibliographic record
Abstract
Covariance-based structural equation modeling is a popular statistical technique in information systems research, providing a stringent test of model fit and allowing researchers to test multiple hypotheses in the same model. Structural regressions in such models are often assumed to represent the causal nature of the underlying reality as expressed by theory. The validity of conclusions drawn from covariance-based analysis is, however, challenged when models can be constructed that fit the observed covariances equally well as the tested model, but which have a different structure, expressing different underlying causal relationships. This research shows that a large proportion of studies in IS exhibit this issue. The dangers posed by covariance-equivalent models are highlighted using an example in the published literature, and recommendations are provided to IS researchers to address the problem.
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 | 0.168 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.019 | 0.008 |
| Research integrity | 0.016 | 0.059 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".