Detecting Construct-Irrelevant Variance: A Comparison of Network Psychometrics and Traditional Psychometric Methods Using the HEXACO-PI Dataset
Bibliographic record
Abstract
Construct-irrelevant variance (CIV), defined as excessive variance that is unrelated to the intended construct, poses a significant threat to the validity of test interpretations and applications. CIV can arise from two notable sources: construct-irrelevant items, which include items with content unrelated to the construct being measured, and redundant items, which repeat information already captured by other items, leading to individual fatigue and inflated reliability estimates. This study explores the detection of CIV using network psychometrics in comparison with traditional psychometrics methods. The study utilizes the HEXACO-PI dataset, focusing on six personality dimensions, and applies network psychometrics techniques such as Exploratory Graph Analysis and Unique Variable Analysis. These techniques are compared against traditional methods, such as reliability and factor analysis, to assess their effectiveness in identifying CIV items. Results highlight the potential of network psychometrics as a complementary approach for enhancing the structural integrity of psychological instruments, with 89% agreement in the number of identified CIV items. This comparison provides insights into alternative methods for improving psychological instruments, with implications for the future of psychometric evaluation.
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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 | 0.028 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".