Examining the Measurement Invariance of TIMSS 2015 Mathematics Liking Scale through Different Methods
Bibliographic record
Abstract
Studies aiming to make cross-cultural comparisons first should establish measurement invariance in the groups to be compared because results obtained from such comparisons may be artificial in the event that measurement invariance cannot be established. The purpose of this study is to investigate the measurement invariance of the data obtained from the "Mathematics Liking Scale" in TIMSS 2015through Multiple Group CFA, Multiple Group LCA and Mixed Rasch Model, which are based on different theoretical foundations and to compare the obtained results. To this end, TIMSS 2015 data for students in the USA and Canada, who speak the same language and data for students in the USA and Turkey, who speak different languages, are used. The study is conducted through a descriptive study approach. The study revealed that all measurement invariance levels were established in Multiple Group CFA for the USA-Canada comparison. In Multiple Group LCA, on the other hand, measurement invariance was established up to partial homogeneity. However, it was not established in the Mixed Rasch Model. As for the USA-Turkey comparison, metric invariance was established in Multiple Group CFA whereas in Multiple Group LCA it stopped at the heterogeneity level. Measurement invariance for data failed to be established for the relevant sample in the Mixed Rasch Model. The foregoing findings suggest that methods with different theoretical foundations yield different measurement invariance results. In this regard, when deciding on the method to be used in measurement invariance studies, it is recommended to examine the necessary assumptions and consider the variable structure.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".