Estimating and Reporting Structural Equation Models with Behavioral Accounting Data
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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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 | 0.122 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".