TY - JOUR
T1 - Cognitive Complexity in the Remote Association Test - Chinese Version
AU - Hung, Su Pin
AU - Huang, Po Sheng
AU - Chen, Hsueh Chih
PY - 2016/10/1
Y1 - 2016/10/1
N2 - The remote association test (RAT) has been applied in various fields; however, evidence of construct validity for the original version and subsequent extensions of the RAT remains limited. This study aimed to elucidate the dimensionality and the relationship between item features and item difficulties for the RAT—Chinese Version (RAT-C) using the Rasch model and the linear logistic test model (LLTM). The revised 30-item RAT-C was administered to 475 undergraduates (263 women and 212 men) in 8 universities in Taiwan. Item features (including types of associations among stimulus words, and frequency and concreteness of target words) were recoded. The analysis found that the RAT-C measured a single latent construct, with all 30 items conforming to the Rasch model’s expectation. Furthermore, according to the LLTM analysis, most item features predicted Rasch item difficulty, suggesting that these features can explain why some items were more difficult than others and can be used to create new items with known item difficulty to tailor the difficulty level for different groups of participants in the future.
AB - The remote association test (RAT) has been applied in various fields; however, evidence of construct validity for the original version and subsequent extensions of the RAT remains limited. This study aimed to elucidate the dimensionality and the relationship between item features and item difficulties for the RAT—Chinese Version (RAT-C) using the Rasch model and the linear logistic test model (LLTM). The revised 30-item RAT-C was administered to 475 undergraduates (263 women and 212 men) in 8 universities in Taiwan. Item features (including types of associations among stimulus words, and frequency and concreteness of target words) were recoded. The analysis found that the RAT-C measured a single latent construct, with all 30 items conforming to the Rasch model’s expectation. Furthermore, according to the LLTM analysis, most item features predicted Rasch item difficulty, suggesting that these features can explain why some items were more difficult than others and can be used to create new items with known item difficulty to tailor the difficulty level for different groups of participants in the future.
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U2 - 10.1080/10400419.2016.1229988
DO - 10.1080/10400419.2016.1229988
M3 - Article
AN - SCOPUS:84995467610
VL - 28
SP - 442
EP - 449
JO - Creativity Research Journal
JF - Creativity Research Journal
SN - 1040-0419
IS - 4
ER -