College students’ mobile phone addiction is negatively associated with physical and mental health and academic performance. Many self-made questionnaires are currently being administered to Chinese college students to evaluate the mobile phone addiction tendency. Using the univariate generalizability theory and multivariate generalizability theory, this study investigated the psychometric properties and the internal structure of the Mobile Phone Addiction Tendency Scale (MPATS), the most widely used survey questionnaire assessing the status of Chinese college students’ mobile addiction. Data were a sample of 1,253 college students from the southwest of China. Primary analytic approaches included the generalizability design of univariate random measurement mode p × ( i : h ) and multivariate random measurement mode p ˙ × i° . Results showed that the variance component of the participants and the variation related to the participants explained most of the variation of the scale, while the variance component of the items was small, and the generalizability coefficient and dependability index of the scale were 0.88 and 0.85. In the multivariate generalizability analysis, the variance component of the participants and the variation related to the participants accounted for most of the variation of the scale and the variance component of the items was small. The generalizability coefficients of withdrawal symptoms, salience, social comfort, and mood changes were 0.64–0.80, and the dependability indexes were 0.63–0.77. However, the generalizability coefficient and reliability index of universe score were 0.91 and 0.90. In addition, the contribution ratio of the four dimensions to the universe score variance was different from the assignment intention of the initial scale. Recommendations were discussed on the improvement of the test reliability for each dimension.
Faults along the boundaries of active tectonic blocks are the main structures that are responsible for major earthquakes in mainland China. Investigating the geometric distribution, rupture behavior, and paleoseismic history of these faults is the prerequisite for constraining geodynamic models and regional seismic hazard analyses. The Nanhe Fault, located at the eastern boundary of the Sichuan–Yunnan Block near Mianning County, has been paid less attention so far due to insufficient historical records of major earthquakes. In this paper, we focused on the Nanhe Fault and conducted satellite imagery interpretation, field investigations, and trench excavations. Our findings indicate that the Nanhe Fault initiates north of Mianning County; the north segment of the fault is connected with the Anninghe Fault; and it extends for about 70 km south-westward and terminates southwest of Ermaga Village. The fault has been faulting in the late Late Pleistocene with a left-lateral strike-slip rate of 2.40–2.56 mm/yr, while in the late Holocene, the left-lateral strike-slip and vertical slip rates are 2.50–2.60 mm/yr and about 0.60 mm/yr, respectively. Three paleoseismic events (5373–4525 BC, AD 1193–1576, and AD 1496–1843) were identified by excavating trenches at the Nanhe Fault. A comparative analysis of paleoseismic events between the Nanhe and the Anninghe fault indicates that both faults may have induced cascade rupture or triggered earthquakes—such related events may have occurred in 1496–1627. Additionally, by comparing the kinematic relationship of the faults at the eastern boundary of the Sichuan–Yunnan Block, we propose that the Nanhe Fault takes part in strain partitioning along the boundary. This interpretation reasonably explains the loss of the sliding rate between the Anninghe and Zemuhe faults, which also supports the GPS inversion results, and the discontinuous deformation model for the eastern margin of the Tibetan Plateau.
In the fields of education and psychology, nested data with small samples and imbalances are very common. Bauer et al. (2008) first proposed adjusting the traditional multilevel model to analyze the small sample imbalanced nested data (SSIND). In terms of parameter estimation, the Bayesian method shows the possibility of providing unbiased estimation when the sample size is small. This study proposes that the Bayesian method should be used to analyze the SSIND. This study explores the performance of different treatment effects and nesting effects estimation methods in the multilevel model based on the Bayesian method that performs well in the case of small samples, to provide an appropriate and scientific method reference for the subsequent analysis of the model. Two prior setting methods are compared for multilevel model effect estimation based on a small sample of imbalanced nested data in the Bayesian framework. Two prior setting methods are gamma prior setting method and uniform prior setting method. The research results show that when the treatment condition ICC is small (0.05), the bias and RMSE values of the parameter estimation by the gamma prior setting method are larger and the performance is unstable, while the bias and RMSE values of the parameter estimation by the uniform prior setting method are smaller and the performance is relatively stable, so the uniform prior setting method is recommended; when the treatment condition ICC is large (0.15), the bias and RMSE values of the parameter estimation by the uniform prior setting method are larger and the performance is unstable, while the bias and RMSE values of the parameter estimation by the gamma prior setting method are smaller and the performance is relatively stable, so the gamma prior setting method is recommended; when the treatment condition ICC is between 0.05 and 0.15, both prior setting methods have similar effects. Furthermore, when the number of treatment groups is small (8), the gamma prior setting method is recommended; when the number of treatment groups is large (16), the uniform prior setting method is recommended; when the number of treatment groups is between 8 and 16, both prior setting methods have similar effects. Summarily, when we choose which prior setting method to use for the SSIND, we must consider the interaction between the ICC and the number of treatment groups.
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