The Role of Multiple-Group Measurement Invariance in Family Psychology Research.
Bibliographic record
Abstract
Measurement invariance (MI) is a property of measurement that is often implicitly assumed, but in many cases, not tested. When the assumption of MI is tested, it generally involves determining if the measurement holds longitudinally or cross-culturally. A growing literature shows that other groupings can, and should, be considered as well. Additionally, it is noted that the standard techniques for investigating MI have been focused almost exclusively on the case of 2 groups, with very little work on the case of more than 2 groups, even though the need for such techniques is apparent in many fields of research. This paper introduces and illustrates a model building technique to investigating MI for more than 2 groups. This technique is an extension of the already-existing hierarchy for testing MI introduced by Meredith (1993). An example using data on father involvement in 5 different groups of families of children with and without developmental disabilities from the Early Childhood Longitudinal Study-Birth Cohort dataset will be given. We show that without considering the possible differential functioning of the measurements on multiple developmental groups, the differences present between the groups in terms of the measurements may be obscured. This could lead to incorrect conclusions.
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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 | 0.117 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".