Preservice teachers’ Internet addiction in terms of gender, Internet access, loneliness, and life satisfaction

Abstract The aim of this study is to investigate pre-service teachers' Internet addiction in terms of gender, Internet accessibility, loneliness and life satisfaction. Statistical analyses were completed on the data by the 247 preservice teachers that filled the surveys completely. According to findings, pre-service teachers' level of loneliness and Internet addiction scores is low while their life satisfaction scores are high. Male participants are more addicted to the Internet than female participants. Also those, males and females, who had Internet access, had more tendencies to be addicted to the Internet. There is evidence to indicate a positive correlation between loneliness and Internet addiction, and negative correlation between life satisfaction and Internet addiction. Based on the results several different suggestions are made for continuing research in this field

U.S. monetary policy and herding: Evidence from commodity markets

This paper investigates the presence of herding behavior across a spectrum of commodities (i.e., agricultural, energy, precious metals, and metals) futures prices obtained from Datastream. The main novelty of this study is, for the first time in the literature, the explicit investigation of the role of deviations of U.S. monetary policy decisions from a standard Taylor-type monetary rule, in driving herding behavior with respect to commodity futures prices, spanning the period 1990-2017. The results document that the commodity markets are characterized by herding, while such herding behavior is not only driven by U.S. monetary policy decisions, but also such decisions exert asymmetric effects this behavior. An additional novelty of the results is that they document that herding is stronger in discretionary monetary policy regimes.N/

Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold

This paper introduces a new methodology to estimate time-varying alphas and betas in conditional factor models, which allows substantial flexibility in a time-varying framework. To circumvent problems associated with the previous approaches, we introduce a Bayesian time-varying parameter model where innovations of the state equation have a spike-and-slab mixture distribution. The mixture distribution specifies two states with a specific probability. In the first state, the innovation variance is set close to zero with a certain probability and parameters stay relatively constant. In the second state, the innovation variance is large and the change in parameters is normally distributed with mean zero and a given variance. The latent state is specified with a threshold that governs the state change. We allow a separate threshold for each parameter; thus, the parameters may shift in an unsynchronized manner such that the model moves from one state to another when the change in the parameter exceeds the threshold and vice versa. This approach offers great flexibility and nests a plethora of other time-varying model specifications, allowing us to assess whether the betas of conditional factor models evolve gradually over time or display infrequent, but large, shifts. We apply the proposed methodology to industry portfolios within a five-factor model setting and show that the threshold Capital Asset Pricing Model (CAPM) provides robust beta estimates coupled with smaller pricing errors compared to the alternative approaches. The results have significant implications for the implementation of smart beta strategies that rely heavily on the accuracy and stability of factor betas and yields

Periodontal Health Knowledge and Smoking are Associated with Periodontal Treatment Need according to Tooth Brushing Levels

Gursoy, Ulvi Kahraman/0000-0002-1225-5751WOS: 000310401400016PubMed: 23155969Objective: The aim of this study is to determine whether periodontal health knowledge is associated with frequency of tooth brushing and periodontal treatment need. Methods: Four hundred and two subjects participated in the study. Data on sociodemographic variables (age, gender, marital status, income, and education), general health, smoking behaviour, tooth cleaning habits and knowledge on periodontal health/disease were collected with a questionnaire. Periodontal treatment need was examined using the Community Periodontal Index of Treatment Needs (CPITN). According to the CPITN scores, the treatment needs were grouped as minimum (CPITN = 0), low-level (CPITN = 1-2), or high-level (CPITN = 3 4). Results: Statistical differences were found between the frequency of tooth brushing and smoking status, marital status, periodontal health knowledge and periodontal treatment needs. Gender (females), place of residence (urban areas), education and periodontal health knowledge had positive relationship with tooth brushing frequency, while smoking and periodontal treatment need had negative relationship. When multivariate logistic regression analysis was applied, age, marriage and poor periodontal knowledge were associated with increased low-level periodontal treatment needs, and age, marriage and smoking were associated with increased high-level periodontal treatment need. Conclusion: In the limits of this study we suggest that gender, smoking habits, marital status, place of residence, education and periodontal health knowledge are determining factors related to tooth brushing frequency. Periodontal knowledge and smoking are associated with periodontal treatment needs

Double/Debiased/Neyman Machine Learning of Treatment Effects

Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data

Oil speculation and herding behavior in emerging stock markets

This paper explores the relationship between stock and commodity markets from a novel perspective by examining the relationship between speculation in the oil market and investor herding in stock markets. Using firm level data from three energy importing and exporting nations, namely Russia, Brazil, and Turkey, we show that these markets often switch between herding and anti-herding states, while herding is more prevalent in the case of Russia. We also find that speculative activities in the global oil market significantly affect investors behavior in Russia and Brazil with greater oil speculation associated with herding in these markets. Our findings suggest that policy makers should watch measures of speculative activities in the commodity markets for possible signals in order to model and monitor investor behavior in their local markets.https://link.springer.com/journal/121972020-01-01hj2018Economic

Local products and Geographical Indications research network in Turkey: a synthesis of the Antalya international Geographical Indications seminars

International audienceThis volume is a synthesis of the Antalya International Geographical Indications Seminars that were held in 2008, 2010 and 2012 respectively. The volume seeks to contribute to the growing body of research on GIs in Turkey and around the Mediterranean through a review ans summary of the presentations and discussions from the three Seminars hosted by the Akdeniz University, Center for Economics Research on Mediterranean Countries