Neurophysiological Correlates of Information Systems Commonly Used Self-Reported Measures: A Multitrait Multimethod Study
Bibliographic record
Abstract
Given the importance and criticality of instrument validation in IS research, the objective of this study is to provide a systematic assessment of IS construct validity via a multitrait multimethod (MTMM). In doing so, this paper uses structurally different methods -- neurophysiological and self-reported scales - to measure three commonly used IS constructs: engagement, arousal and cognitive load. The study's results generally support MTMM expectations and shed light on the complexity of detecting the nature of mono-method bias. More specifically, the study's results show that primitive perceptual IS constructs such as arousal are unlikely to suffer from mono-method bias, whereas more complex perceptual constructs such as engagement or cognitive load have higher within method correlations. There are two alternative explanations for the within method correlations: a) a method bias, or b) a combination between trait and method.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".