Chia-Wen is a Psychometrician at the Psychometrics Centre, Judge Business School, Executive Education. Originally from Taiwan, he completed his master's degree in Psychology at the National Chung Cheng University. In 2012, Chia-Wen relocated to Hong Kong, where he served as a senior research assistant at the Education University of Hong Kong. From 2015 to 2018, he pursued his PhD under the supervision of Prof. Wen-Chung Wang at the Assessment Research Centre, Education University of Hong Kong. In 2019, Chia-Wen moved to Norway, joining the Centre for Educational Measurement at the University of Oslo as a postdoctoral researcher, a position he held until 2023.
Chia-Wen's academic interests primarily revolve around Item Response Theory (IRT) models and their various extensions. He has conducted studies on IRT models for forced-choice items, such as ranking items and Most-Least items. His research also encompasses the application of IRT, including computerized adaptive testing, differential item functioning, and multilevel modeling. His doctoral thesis focused on developing two new IRT models for compositional items, which pertain to the forced-choice format but yield continuous data.
Prior to his current roles, Chia-Wen began his career as a psychometrician assistant at the National Taiwan Normal University in Taiwan, where he contributed to the development of career interest, personality, values, and attitude tests for Taiwanese secondary students' career exploration. During his tenure in Hong Kong, he served as a senior research assistant involved in establishing the reliability and validity of the Principal Instructional Management Rating Scale (PIMRS). In Norway, Chia-Wen's work revolved around developing item selection algorithms in computerized adaptive testing (CAT) for ipsative tests with forced-choice items, as well as online estimation algorithms in CAT for student evaluation of teaching inventory. He also applied item response theory models (IRT) to analyze data in educational large-scale assessment datasets, such as the PISA data.
