Erkan Oktay

A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey

A retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police between January 2008 and December 2013 which are classified into three injury severity categories: fatal, injury, and no injury. Based on this classification, a multinomial logit analysis is performed to determine the risk factors affecting the severity of traffic injuries. The estimation results reveal that the following factors increase the probability of fatal injuries: drivers over the age of 65; primary-educated drivers; single-vehicle accidents; accidents occurring on state routes, highways or provincial roads; and the presence of pedestrian crosswalks. The results also indicate that accidents involving cars or private vehicles or those occurring during the evening peak, under clear weather conditions, on local city streets or in the presence of traffic lights decrease the probability of fatal injuries. This study comprises the most comprehensive database ever created for a Turkish sample. This study is also the first attempt to use an unordered response model to determine risk factors influencing the severity of traffic injuries in Turkey.

Determinants of housing demand in the Erzurum province, Turkey

Purpose - – The main aim of this study is to determine the factors that influence the housing demand of households in Erzurum, northeastern Turkey. Housing demand is generally affected by several factors including housing prices, individuals’ income, expectations and choices and so on, as a means of its demographic and socio-psychological contexts. Design/methodology/approach - – A questionnaire-based cross-sectional survey was carried out, in which the outcome variable had binary responses such as whether to invest in housing or not. A binary logistic regression analysis was performed to estimate the underlying data. Findings - – The questionnaire was conducted in 2,927 households living in Erzurum city center, and 47 per cent of the respondents claimed that they would consider investing in housing in the future. The estimation results reveal that demographic or socio-economic factors that may possibly influence housing demand of the respondents are as follows: household head’s and spouse’s occupation, monthly income, the number of individuals in the family and car ownership. Originality/value - – This paper involves the most comprehensive survey addressing the housing demand in the East Anatolian Region, Turkey. Additionally, this paper aims to contribute to the existing housing literature through establishing the statistical analysis of housing demand in an unstudied territory of the world.

Analysing workplace violence towards health care staff in public hospitals using alternative ordered response models: the case of north-eastern Turkey

The main objective of this article is to determine key factors that may have a significant effect on the verbal abuse, emotional abuse and physical assault of health care workers in north-eastern Turkey. A self-administered survey was completed by 450 health care workers in three well-established hospitals in Erzurum, Turkey. Because of the discrete and ordered nature of the dependent variable of the survey, the data were analysed using four distinctive ordered response models. Results revealed that several key variables were found to be a significant determinant of workplace violence, such as the type of health institution, occupational position, weekly working hours, weekly shift hours, number of daily patient contacts, age group of the respondents, experience in the health sector, training against workplace violence and current policies of the hospitals and the Turkish Ministry of Health.

Research of Internal Migration by Compartment Models: The Case of Erzurum-Bursa

Migration, which consists of quite complex components, is a concept subjected in many scientific research areas. Internal migration refers a migration occurred in the boundaries of a country. Migration in Turkey usually tends to be directed towards larger and more industrialized provinces. Nevertheless, there exists a density on the direction of certain provinces such as migration from Erzurum to Bursa. Nowadays, the proceeding of the migration flows between Erzurum and Bursa since 1980’s is another point which necessitates to be examined. The purpose of this study is to investigate the migration between Erzurum and Bursa by using compartment models in the 1980-2015 period. The paper performs compartment models to explain the changes of migration flows in terms of gross domestic product, unemployment rate, and time. According to the results, there is a significant increase in migration from Erzurum to Bursa depending on the time of migration and unemployment rate. Nonetheless, there is no significant change related with the gross domestic product. In addition, the study has shown that migration increases as unemployment rate decreases; but there is no significant change in migration depending on time and gross domestic product from Bursa to Erzurum.

An investigation of export–import ratios in Turkey using spline regression models

Abstract This paper examines the use of spline functions in linear, squared, and cubic spline regression models and exhibits the estimation of spline parameters from data by ordinary least squares. Determination of the number and the location of knots is central to spline regression. In this paper, we initially propose a method based on the coefficient of determination R2 related to the estimation of knots in spline regression. This proposed method as applied to export–import ratio distributions in Turkey for the years 1923–2010 determines the knots, and linear, quadratic, and cubic spline regression models are established accordingly. Results reveal that spline regression models offer better results than polynomial regression models, and that the quadratic spline regression model is the best explanatory model for export–import ratio distributions in the smoothest spline regression models.