A Comparison of Different Methods for Measuring Individual Change in Kindergarten Children
Bibliographic record
Abstract
Measuring progress in students is an important consideration when making decisions in education, clinical practice, and research. However, change due to learning over time at the group level cannot be applied to interpret individual change. Therefore, the current study compared four methods for measuring individual change: reliable change index controlling for practice effects (RCI), standardized individual difference (SID), estimated standardized regression-based (SRB) change, and a normalization approach. Participants included 157 children (4 years initially and 5 years at follow-up) who completed measures of language, reading, and mathematics and were tested 1 year apart. We measured individual differences in children as they developed academic-relevant competencies. The RCI and SID indices yielded the same results. While group-based statistics did not find a change overall, the RCI/SID and SRB methods identified 7.64% and 8.28% of students as having changed, respectively. Further, in a subgroup of 54 low scorers, the RCI/SID and SRB methods indicated that 14.81% and 16.67% of students changed, respectively, whereas the normalization method identified a higher rate at 24.07%. The RCI, SID, and SRB methods showed similar results, whereas the normalization method differed from the others. Finally, a practical tool (Excel-based Growth Calculator) is provided to assist practitioners in evaluating individual change. Overall, these methods provide starting points for measuring change in individuals.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".