Conceptual, methodological, and measurement factors that disqualify use of measurement invariance techniques to detect informant discrepancies in youth mental health assessments
Bibliographic record
Abstract
On page 1 of his classic text, Millsap (2011) states, “Measurement invariance is built on the notion that a measuring device should function the same way across varied conditions, so long as those varied conditions are irrelevant [emphasis added] to the attribute being measured.” By construction, measurement invariance techniques require not only detecting varied conditions but also ruling out that these conditions inform our understanding of measured domains (i.e., conditions that do not contain domain-relevant information). In fact, measurement invariance techniques possess great utility when theory and research inform their application to specific, varied conditions (e.g., cultural, ethnic, or racial background of test respondents) that, if not detected, introduce measurement biases, and, thus, depress measurement validity (e.g., academic achievement and intelligence). Yet, we see emerging bodies of work where scholars have “put the cart before the horse” when it comes to measurement invariance, and they apply these techniques to varied conditions that, in fact, may reflect domain-relevant information. These bodies of work highlight a larger problem in measurement that likely cuts across many areas of scholarship. In one such area, youth mental health, researchers commonly encounter a set of conditions that nullify the use of measurement invariance, namely discrepancies between survey reports completed by multiple informants, such as parents, teachers, and youth themselves (i.e., informant discrepancies). In this paper, we provide an overview of conceptual, methodological, and measurement factors that should prevent researchers from applying measurement invariance techniques to detect informant discrepancies. Along the way, we cite evidence from the last 15 years indicating that informant discrepancies reflect domain-relevant information. We also apply this evidence to recent uses of measurement invariance techniques in youth mental health. Based on prior evidence, we highlight the implications of applying these techniques to multi-informant data, when the informant discrepancies observed within these data might reflect domain-relevant information. We close by calling for a moratorium on applying measurement invariance techniques to detect informant discrepancies in youth mental health assessments. In doing so, we describe how the state of the science would need to fundamentally “flip” to justify applying these techniques to detect informant discrepancies in this area of work.
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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 | 0.323 | 0.464 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".