Su-Pin Hung
National Cheng Kung University
10 Papers
45 Citations
Su-Pin Hung is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Rasch model & Item response theory. The author has an hindex of 3, co-authored 7 publications. Previous affiliations of Su-Pin Hung include National Taiwan Normal University.
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Papers
Improving Creativity Performance Assessment: A Rater Effect Examination with Many Facet Rasch Model
TL;DR: The authors used the many-facet Rasch model to investigate possible sources of rater bias, including the leniency/severity effect, central tendency effect, halo effect and randomness effect.
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Validating the creative self-efficacy student scale with a Taiwanese sample: An item response theory-based investigation
TL;DR: In this article, an item response model was used to analyze the dimensionality and gender differential item functioning (DIF) of the CSE Student Scale, and the consistency of the item difficulty hierarchy of the responses provided by university students and non-university students was compared.
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A Sequential Process Model for Cognitive Diagnostic Assessment With Repeated Attempts.
Su-Pin Hung,Hung-Yu Huang +1 more
TL;DR: Two formulations of the sequential generalized deterministic-input noisy-“and”-gate (G-DINA) model were developed and the effectiveness of the developed models was demonstrated by fitting real data from a longitudinal mathematical test to the developed model and the longitudinal G-Dina model using the LTA approach.
7
Cognitive Component Analysis Comparing Three Chinese Remote Associates Tests: Linear Logistic Latent Trait Model Approach.
Su-Pin Hung,Ching Lin Wu +1 more
TL;DR: The Remote Associates Test, generally used in creativity research, has Chinese versions for the three levels of radical-word-vocabulary as discussed by the authors, but research has not been conducted on the influence of these levels on creativity research.
7
Cognitive Complexity in the Remote Association Test--Chinese Version.
TL;DR: In this paper, the authors investigated the dimensionality and the relationship between item features and item difficulties for the remote association test (RAT-C) using the Rasch model and the linear logistic test model (LLTM).
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