The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Teori tindak balas item (IRT) digunakan secara meluas untuk analisis ujian. Kebanyakan model IRT menganggap bahawa respons subjek terhadap item yang berbeza dalam ujian adalah bebas dari segi statistik. Walau bagaimanapun, situasi sebenar sering melanggar andaian ini. Oleh itu, ujian kebebasan bersyarat (CI) antara item yang diberi pembolehubah keupayaan terpendam diperlukan, tetapi ujian CI tradisional mengalami bias. Kajian ini menyiasat ujian kebebasan bersyarat terpendam (LCI) yang diberi pembolehubah terpendam. Keputusan menunjukkan bahawa ujian LCI boleh mengesan CI diberikan pembolehubah terpendam dengan betul, manakala ujian CI tradisional sering gagal untuk mengesan CI. Penggunaan ujian LCI kepada data ujian matematik mendedahkan bahawa item yang berkongsi alternatif biasa mungkin bergantung kepada syarat.
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Salinan
Takamitsu HASHIMOTO, Maomi UENO, "Latent Conditional Independence Test Using Bayesian Network Item Response Theory" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 4, pp. 743-753, April 2011, doi: 10.1587/transinf.E94.D.743.
Abstract: Item response theory (IRT) is widely used for test analyses. Most models of IRT assume that a subject's responses to different items in a test are statistically independent. However, actual situations often violate this assumption. Thus, conditional independence (CI) tests among items given a latent ability variable are needed, but traditional CI tests suffer from biases. This study investigated a latent conditional independence (LCI) test given a latent variable. Results show that the LCI test can detect CI given a latent variable correctly, whereas traditional CI tests often fail to detect CI. Application of the LCI test to mathematics test data revealed that items that share common alternatives might be conditionally dependent.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.743/_p
Salinan
@ARTICLE{e94-d_4_743,
author={Takamitsu HASHIMOTO, Maomi UENO, },
journal={IEICE TRANSACTIONS on Information},
title={Latent Conditional Independence Test Using Bayesian Network Item Response Theory},
year={2011},
volume={E94-D},
number={4},
pages={743-753},
abstract={Item response theory (IRT) is widely used for test analyses. Most models of IRT assume that a subject's responses to different items in a test are statistically independent. However, actual situations often violate this assumption. Thus, conditional independence (CI) tests among items given a latent ability variable are needed, but traditional CI tests suffer from biases. This study investigated a latent conditional independence (LCI) test given a latent variable. Results show that the LCI test can detect CI given a latent variable correctly, whereas traditional CI tests often fail to detect CI. Application of the LCI test to mathematics test data revealed that items that share common alternatives might be conditionally dependent.},
keywords={},
doi={10.1587/transinf.E94.D.743},
ISSN={1745-1361},
month={April},}
Salinan
TY - JOUR
TI - Latent Conditional Independence Test Using Bayesian Network Item Response Theory
T2 - IEICE TRANSACTIONS on Information
SP - 743
EP - 753
AU - Takamitsu HASHIMOTO
AU - Maomi UENO
PY - 2011
DO - 10.1587/transinf.E94.D.743
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2011
AB - Item response theory (IRT) is widely used for test analyses. Most models of IRT assume that a subject's responses to different items in a test are statistically independent. However, actual situations often violate this assumption. Thus, conditional independence (CI) tests among items given a latent ability variable are needed, but traditional CI tests suffer from biases. This study investigated a latent conditional independence (LCI) test given a latent variable. Results show that the LCI test can detect CI given a latent variable correctly, whereas traditional CI tests often fail to detect CI. Application of the LCI test to mathematics test data revealed that items that share common alternatives might be conditionally dependent.
ER -