Typically a longitudinal growth modeling based on item response theory (IRT) requires repeated measures data from a single group with the same test design. If operational or item exposure problems are present, the same test may not be employed to collect data for longitudinal analyses and tests at multiple time points are constructed with unique item sets, as well as a set of common items (i.e., anchor test) for a study of examinee growth. In this study, three IRT approaches to examinee growth modeling were applied to a single-group anchor test design and their examinee growth estimates were compared. In terms of tracking individual growth, growth patterns in the examinee population distribution, and the overall model-data fit, results show the importance of modeling the serial correlation over multiple time points and other additional dependence coming from the use of the unique item sets, as well as the anchor test.

No abstract

This study compared the perceptions of Korean-national university students studying in Korea and in the US regarding global citizenship. The study sample consisted of two distinct groups: students who had received their education exclusively in Korea and students who were born in Korea but had studied in the US since secondary school. By applying latent mean analysis, the study found that the US-educated Koreans had higher levels of trust and national identity, whereas the Koreans had higher levels of social responsibility and participation. The results suggest that university students of the same nationality who experience different social environments may perceive global citizenship in dissimilar ways. For future investigations, we suggest a longitudinally designed experimental study, which may reveal a causal relationship between the social experiences of university students who study abroad and their perceptions of global citizenship. In addition, future investigations should consider how these students are assimilated into and adapt to diverse interactions on multicultural campuses.

No abstract

No abstract