Cognitive Component Analysis Comparing Three Chinese Remote Associates Tests: Linear Logistic Latent Trait Model Approach.

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Title: Cognitive Component Analysis Comparing Three Chinese Remote Associates Tests: Linear Logistic Latent Trait Model Approach.
Authors: Hung, Su-Pin (AUTHOR), Wu, Ching-Lin (AUTHOR)
Source: Creativity Research Journal. Jul-Sep2021, Vol. 33 Issue 3, p224-234. 11p.
Subjects: Cognitive analysis, Prefabricated buildings, Word frequency, Item response theory, Test validity
Abstract: The Remote Associates Test, generally used in creativity research, has Chinese versions for the three levels of "radical-word-vocabulary." However, research has not been conducted on the influence of the item components on the difficulties among these Chinese Remote Associates Tests (CRATs). The present study selected six item components from the construction rules for three versions of CRATs, collected 555 participants' responses to them, and analyzed the differences and similarities in the predictions of item difficulty among the six item components. The results show that item difficulty decreased if the targets are high-frequency or the stimuli are low-frequency words, but item difficulty increases if the stimulus can connect to more words, while the concreteness of the target does not significantly predict item difficulty. Moreover, the concreteness of the stimulus negatively predicts the difficulty of the CRRAT but positively predicts the difficulty of the CCRAT High association transfer increased the difficulty of the CWRAT but had no influence on the other two versions of CRAT. Therefore, these findings indicate the different influences of item components on item difficulty, strengthen the structural validity of the test, and also provide a reference for test development of the CRAT. [ABSTRACT FROM AUTHOR]
Copyright of Creativity Research Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Cognitive Component Analysis Comparing Three Chinese Remote Associates Tests: Linear Logistic Latent Trait Model Approach.
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  Data: <searchLink fieldCode="AR" term="%22Hung%2C+Su-Pin%22">Hung, Su-Pin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Ching-Lin%22">Wu, Ching-Lin</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Creativity+Research+Journal%22">Creativity Research Journal</searchLink>. Jul-Sep2021, Vol. 33 Issue 3, p224-234. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Cognitive+analysis%22">Cognitive analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Prefabricated+buildings%22">Prefabricated buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Word+frequency%22">Word frequency</searchLink><br /><searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br /><searchLink fieldCode="DE" term="%22Test+validity%22">Test validity</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The Remote Associates Test, generally used in creativity research, has Chinese versions for the three levels of "radical-word-vocabulary." However, research has not been conducted on the influence of the item components on the difficulties among these Chinese Remote Associates Tests (CRATs). The present study selected six item components from the construction rules for three versions of CRATs, collected 555 participants' responses to them, and analyzed the differences and similarities in the predictions of item difficulty among the six item components. The results show that item difficulty decreased if the targets are high-frequency or the stimuli are low-frequency words, but item difficulty increases if the stimulus can connect to more words, while the concreteness of the target does not significantly predict item difficulty. Moreover, the concreteness of the stimulus negatively predicts the difficulty of the CRRAT but positively predicts the difficulty of the CCRAT High association transfer increased the difficulty of the CWRAT but had no influence on the other two versions of CRAT. Therefore, these findings indicate the different influences of item components on item difficulty, strengthen the structural validity of the test, and also provide a reference for test development of the CRAT. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Creativity Research Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/10400419.2021.1872287
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 224
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      – SubjectFull: Cognitive analysis
        Type: general
      – SubjectFull: Prefabricated buildings
        Type: general
      – SubjectFull: Word frequency
        Type: general
      – SubjectFull: Item response theory
        Type: general
      – SubjectFull: Test validity
        Type: general
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      – TitleFull: Cognitive Component Analysis Comparing Three Chinese Remote Associates Tests: Linear Logistic Latent Trait Model Approach.
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            NameFull: Hung, Su-Pin
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            NameFull: Wu, Ching-Lin
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              Text: Jul-Sep2021
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              Y: 2021
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