The Item Response Theory (IRT) evaluates the relationship between people’s ability and test items, and it includes unidimensional and multidimensional models. One key assumption for the unidimensional IRT model is that only one dimension of ability should be tested. However, since people’s abilities are latent, many datasets fitted with the unidimensional IRT model reflect abilities from more than one dimension in fact. To identify the consequence of fitting the unidimensional IRT model on correlated abilities, this research focuses on when the correlated abilities can be treated as a single ability, the possible pattern of misfit, and if it is reduced by higher correlated abilities. In the research, the misfits are evaluated by applying unidimensional 2-parameter logistic (2PL) IRT model while the datasets are simulated with items testing two different correlated. The dimensionalities are examined with abilities correlated to different degrees, and the misfit of using the unidimensional IRT model is tested by comparing the item difficulties and item discriminations from the fitted model and the true parameters. The results show that when the correlation between abilities is higher than 0.95, the unidimensional model can be fit without bias. But for all simulated datasets with correlated abilities below 0.95, the estimated item parameters using the unidimensional model are biased and the biases are not reduced with increasing correlation if multiple factors are identified for abilities.
Published in | American Journal of Theoretical and Applied Statistics (Volume 11, Issue 4) |
DOI | 10.11648/j.ajtas.20221104.11 |
Page(s) | 109-113 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2022. Published by Science Publishing Group |
The Item Response, People’s Ability, The IRT Model, Item Dimensionality, Probability and Statistics, The Factor Analysis
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APA Style
Xiong Rao. (2022). Effect of Correlation Between Abilities Under Between-Item Dimensionality. American Journal of Theoretical and Applied Statistics, 11(4), 109-113. https://doi.org/10.11648/j.ajtas.20221104.11
ACS Style
Xiong Rao. Effect of Correlation Between Abilities Under Between-Item Dimensionality. Am. J. Theor. Appl. Stat. 2022, 11(4), 109-113. doi: 10.11648/j.ajtas.20221104.11
@article{10.11648/j.ajtas.20221104.11, author = {Xiong Rao}, title = {Effect of Correlation Between Abilities Under Between-Item Dimensionality}, journal = {American Journal of Theoretical and Applied Statistics}, volume = {11}, number = {4}, pages = {109-113}, doi = {10.11648/j.ajtas.20221104.11}, url = {https://doi.org/10.11648/j.ajtas.20221104.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20221104.11}, abstract = {The Item Response Theory (IRT) evaluates the relationship between people’s ability and test items, and it includes unidimensional and multidimensional models. One key assumption for the unidimensional IRT model is that only one dimension of ability should be tested. However, since people’s abilities are latent, many datasets fitted with the unidimensional IRT model reflect abilities from more than one dimension in fact. To identify the consequence of fitting the unidimensional IRT model on correlated abilities, this research focuses on when the correlated abilities can be treated as a single ability, the possible pattern of misfit, and if it is reduced by higher correlated abilities. In the research, the misfits are evaluated by applying unidimensional 2-parameter logistic (2PL) IRT model while the datasets are simulated with items testing two different correlated. The dimensionalities are examined with abilities correlated to different degrees, and the misfit of using the unidimensional IRT model is tested by comparing the item difficulties and item discriminations from the fitted model and the true parameters. The results show that when the correlation between abilities is higher than 0.95, the unidimensional model can be fit without bias. But for all simulated datasets with correlated abilities below 0.95, the estimated item parameters using the unidimensional model are biased and the biases are not reduced with increasing correlation if multiple factors are identified for abilities.}, year = {2022} }
TY - JOUR T1 - Effect of Correlation Between Abilities Under Between-Item Dimensionality AU - Xiong Rao Y1 - 2022/07/29 PY - 2022 N1 - https://doi.org/10.11648/j.ajtas.20221104.11 DO - 10.11648/j.ajtas.20221104.11 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 109 EP - 113 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20221104.11 AB - The Item Response Theory (IRT) evaluates the relationship between people’s ability and test items, and it includes unidimensional and multidimensional models. One key assumption for the unidimensional IRT model is that only one dimension of ability should be tested. However, since people’s abilities are latent, many datasets fitted with the unidimensional IRT model reflect abilities from more than one dimension in fact. To identify the consequence of fitting the unidimensional IRT model on correlated abilities, this research focuses on when the correlated abilities can be treated as a single ability, the possible pattern of misfit, and if it is reduced by higher correlated abilities. In the research, the misfits are evaluated by applying unidimensional 2-parameter logistic (2PL) IRT model while the datasets are simulated with items testing two different correlated. The dimensionalities are examined with abilities correlated to different degrees, and the misfit of using the unidimensional IRT model is tested by comparing the item difficulties and item discriminations from the fitted model and the true parameters. The results show that when the correlation between abilities is higher than 0.95, the unidimensional model can be fit without bias. But for all simulated datasets with correlated abilities below 0.95, the estimated item parameters using the unidimensional model are biased and the biases are not reduced with increasing correlation if multiple factors are identified for abilities. VL - 11 IS - 4 ER -