The Neat Equating Via Chaining Random Forests in the Context of Small Sample Sizes
A Machine-Learning Method
Bibliographic Data
The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be managed to a planned missing scenario. In the context of small sample sizes, we present a machine learning (ML)-based imputation technique called chaining random forests (CRF) to perform equating tasks within the NEAT design. Specifically, seven CRF-based imputation equating methods are proposed based on different data augmentation methods. The equating performance of the proposed methods is examined through a simulation study. Five factors are considered: (a) test length (20, 30, 40, 50), (b) sample size per test form (50 versus 100), (c) ratio of common/anchor items (0.2 versus 0.3), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 0.5), and (e) three different types of anchors (random, easy, and hard), resulting in 96 conditions. In addition, five traditional equating methods, (1) Tucker method; (2) Levine observed score method; (3) equipercentile equating method; (4) circle-arc method; and (5) concurrent calibration based on Rasch model, were also considered, plus seven CRF-based imputation equating methods for a total of 12 methods in this study. The findings suggest that benefiting from the advantages of ML techniques, CRF-based methods that incorporate the equating result of the Tucker method, such as IMP_total_Tucker, IMP_pair_Tucker, and IMP_Tucker_cirlce methods, can yield more robust and trustable estimates for the “missingness” in an equating task and therefore result in more accurate equated scores than other counterparts in short-length tests with small samples
Context (archaeology) · Equating · Imputation (statistics) · Missing data · Rasch model · Sample size determination · Statistics · Advanced Statistical Methods and Models · Advanced Statistical Modeling Techniques · Mathematics · Psychometric Methodologies and Testing
Statistical analysis with missing data
Statistical Theories of Mental Test Scores
Test Equating, Scaling, and Linking
Statistics versus machine learning
Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using Mice
MissForest—non-parametric missing value imputation for mixed-type data
Random Forests
Nominal Weights Mean Equating
The Delta-Scoring Method of Tests With Binary Items
Rasch Versus Classical Equating in the Context of Small Sample Sizes
| Unique citing works | 1 |
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| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |