This study examines whether the Internet-based questionnaire is psychometrically equivalent to the paper-based questionnaire. A random sample of 2,400 teachers in Taiwan was divided into experimental and control groups. The experimental group was invited to complete the electronic form of the Chinese version of Center for Epidemiologic Studies Depression Scale (CES-D) placed on the Internet, whereas the control group was invited to complete the paper-based CES-D, which they received by mail. The multisample invariance approach, derived from structural equation modeling (SEM), was applied to analyze the collected data. The analytical results show that the two groups have equivalent factor structures in the CES-D. That is, the items in CES-D function equivalently in the two groups. Then the equality of latent mean test was performed. The latent means of "depressed mood," "positive affect," and "interpersonal problems" in CES-D are not significantly different between these two groups. However, the difference in the "somatic symptoms" latent means between these two groups is statistically significant at alpha = 0.01. But the Cohen's d statistics indicates that such differences in latent means do not apparently lead to a meaningful effect size in practice. Both CES-D questionnaires exhibit equal validity, reliability, and factor structures and exhibit a little difference in latent means. Therefore, the Internet-based questionnaire represents a promising alternative to the paper-based questionnaire.

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The mental health of teachers had long been said to bring forth positive outcomes on students' schooling. In the current study, our purpose was to build up a mediation model that could explain how the depression caused by perceived work stress (PWS) would be reduced by lowering subjective well-being (SWB). A total random sample of 1214 teachers was recruited and measured by Subjective Well-Being Scale, Taiwan Depression Scale, and perceived work stress from students, parents, and peers. The whole data were analyzed by ANOVA, Pearson correlations, and structure equation modeling (SEM). Results showed that 1) PWS differed by positions of teachers. 2) PWS from students, parents, and peers had significantly caused Depression. 3) SWB successfully mediated the effects of the PWS and Depression. The current results further provided an empirical proof of how PWS lower teachers' well-being in psychology, social, and emotion of SWB. Our hypothesis of an existed mediation effect had been supported. The current study hoped that the findings would be able to help educational administrators, policy makers, and researchers in protecting teachers' mental health.

Flooding from torrential rain occurs in a short amount of time, while drought lasts for a longer period; the former may inflict huge losses in terms of both life and property. For these reasons, considerable research has been performed in the field of flood control system development. A physical model is mainly used for flood forecasting and warning. However, physical rainfall-runoff models for the conventional flood forecasting process require extensive information and data, and include uncertainties that can accumulate errors during the modeling process. On the other hand, ANFIS, which is a data-driven model combining the neural network and fuzzy techniques, can decrease the amount of physical data required for the construction of a conventional model and easily construct and evaluate a flood forecasting model using only rainfall and water level data. However, data-driven models have the disadvantage that they do not provide mathematical and physical logic, so that there are no logical correlations between the input and output data of the model. This study analyzes the characteristics of a data-driven model, ANFIS, according to its functional options and input data, such as changes in the clustering radius and the training data length. In addition, the suitability of ANFIS is evaluated through comparison with the results of HEC-HMS, which is widely used for rainfall-runoff models. In this study, the neuro-fuzzy technique is applied to the Cheongmicheon Basin using the observed precipitation and stream level data from 2008 to 2011.

In this study a new scaling method was proposed and validated, fuzzy partial credit scaling (FPCS), which combines fuzzy set theory (FST; Zadeh, 1965) with the partial credit model (PCM) for scoring the Beck Depression Inventory (BDI-II; Beck, Steer, & Brown, 1996). To achieve this, the Chinese version of the BDI-II (C-BDI-II) was administered to a clinical sample of outpatients suffering depression, and also to a nonclinical sample. Detailed FPCS procedures were illustrated and the raw score and FPCS were compared in terms of reliability and validity. The Cronbach alpha coefficient showed that the reliability of C-BDI-II was higher in FPCS than in raw score. Moreover, the analytical results showed that, via FPCS, the probability of correct classification of clinical and nonclinical was increased from 73.2% to 80.3%. That is, BDI scoring via FPCS achieves more accurate depression predictions than does raw score. Via FPCS, erroneous judgments regarding depression can be eliminated and medical costs associated with depression can be reduced. This study empirically showed that FST can be applied to psychological research as well as engineering. FST characterizes latent traits or human thinking more accurately than does crisp binary logic.