Evaluating the Performances of Missing Data Handling Methods in Ability Estimation From Sparse Data
Bibliographic record
Abstract
Large amounts of missing data could distort item parameter estimation and lead to biased ability estimates in educational assessments. Therefore, missing responses should be handled properly before estimating any parameters. In this study, two Monte Carlo simulation studies were conducted to compare the performance of four methods in handling missing data when estimating ability parameters. The methods were full-information maximum likelihood (FIML), zero replacement, and multiple imputation with chain equations utilizing classification and regression trees (MICE-CART) and random forest imputation (MICE-RFI). For the two imputation methods, missing responses were considered as a valid response category to enhance the accuracy of imputations. Bias, root mean square error, and the correlation between true ability parameters and estimated ability parameters were used to evaluate the accuracy of ability estimates for each method. Results indicated that FIML outperformed the other methods under most conditions. Zero replacement yielded accurate ability estimates when missing proportions were very high. The performances of MICE-CART and MICE-RFI were quite similar but these two methods appeared to be affected differently by the missing data mechanism. As the number of items increased and missing proportions decreased, all the methods performed better. In addition, the information on missing data could improve the performance of MICE-RFI and MICE-CART when the data set is sparse and the missing data mechanism is missing at random.
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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 | 0.122 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".