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Balancing Between Categorical and Dimensional Assessment in Short-Scale Construction Using Ant Colony Optimization

Priscilla Achaa-Amankwaa et al.

European Journal of Psychological Assessment2025https://doi.org/10.1027/1015-5759/a000892article
AJG 2ABDC B
Weight
0.41

Abstract

Abstract: Language proficiency assessment poses particular challenges for test developers in selecting items that allow for a clear assignment of individuals to language proficiency levels (categorical assessment), while at the same time providing a reliable and comprehensive dimensional assessment of language proficiency. We show how Ant Colony Optimization (ACO) can be used to achieve a balance between these measurement goals, using a German entry-level language assessment as a working example. We tailored competing ACO algorithms to develop short scales of different lengths that met several pre-specified criteria, including model fit, composite reliability, and criterion validity. In optimizing the short scales, we favored either accurate dimensional assessment (model fit and composite reliability), between-category classification accuracy (a high polychoric correlation between model-predicted and independently assessed proficiency levels), or a balance of both. We argue that scale optimization strategies such as ACO are essential for balancing conflicting measurement goals such as optimizing between categorical and dimensional assessment.

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https://doi.org/https://doi.org/10.1027/1015-5759/a000892

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@article{priscilla2025,
  title        = {{Balancing Between Categorical and Dimensional Assessment in Short-Scale Construction Using Ant Colony Optimization}},
  author       = {Priscilla Achaa-Amankwaa et al.},
  journal      = {European Journal of Psychological Assessment},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1027/1015-5759/a000892},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.