Estimating and Reporting Structural Equation Models with Behavioral Accounting Data
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
ABSTRACT Despite prior research explaining the benefits of using structural equation modeling (SEM) for analyzing accounting behavioral data, SEM remains underutilized in accounting behavioral research relative to related and reference domains such as psychology, information systems, and management. Prior research posits the frequency with which accounting behavioral data violate SEM assumptions as one probable reason for this underutilization. Accounting behavioral researchers may be unfamiliar with the techniques and approaches available to develop and estimate structural models when data violate SEM assumptions. Given this unfamiliarity, researches may opt to use less informative techniques. The purpose of this paper is to provide guidance on the testing, judgment, and decision-making processes that influence SEM estimation, analysis, and reporting with accounting behavioral data. A structural model is developed, tested, and evaluated using accounting behavioral data that violate, to varying degrees, the assumptions of SEM.
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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 | 0.031 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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