2 results
Search Results
Now showing 1 - 2 of 2
Article Citation - WoS: 39Citation - Scopus: 49Individual Flipped Learning and Cooperative Flipped Learning: Their Effects on Students' Performance, Social, and Computer Anxiety(Routledge Journals, Taylor & Francis Ltd, 2019) Eryilmaz, Meltem; Cigdemoglu, CeyhanThe purpose of this study is to differentiate the effect of cooperative learning strategy integrated with a flipped learning (FL) model from sole FL implementation in promoting students' performances while decreasing their social and computer anxiety in an undergraduate course. As a method, a classical experimental design is used. The participants were from the department of English Language and Literature, and Translation and Interpretation. Students were randomly assigned to individual FL (the control group) class; and FL with cooperative activities (experimental group) class. The groups were randomly assigned as experimental and control by tossing a coin. The implementation took 10 weeks. Students' performances (grades), social anxiety, and computer anxiety were dependent variables of the study and they were compared through multivariate analysis of variance. The results indicated that there is no significant mean difference between groups' performances; however; the group of FL with cooperative activities had less social anxiety, but no significant change occurred at their computer anxiety level.Article Citation - Scopus: 2The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: an Extensive Simulation Study(Mdpi, 2024) Omay, Tolga; Akdi, Yilmaz; Emirmahmutoglu, Furkan; Eryilmaz, MeltemThe Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.
