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Cross-national models of fertility, family planning, and development commonly assume that there are no reciprocal effect between fertility and other variables in the model and that when path models are used, there are no reciprocal or nonrecursive effects among any set of variables in the model. The present study tests for nonrecursiveness using 2-wave panel data, and finds that nonrecursive effects are present among variables commonly used in model of fertility, family planning, and development. In addition, the pattern of relationships found has implications for the explanation of the relationship between mortality and fertility in demographic transition theory.

This research aims to shed light on the appropriate measures countries should take when battling a pandemic. It explores the effectiveness of various non-pharmaceutical interventions (also known as quarantine restrictions) on the reduction of the spread of COVID-19 using Kazakhstan and Kyrgyzstan in a natural experiment. All types of non-pharmaceutical interventions are effective at reducing the spread of the virus. Comparing the results for the two countries, it appears that in the summer 2020, the partial lockdown in Kyrgyzstan was just as effective as the full-lockdown in Kazakhstan at reducing the growth rate of new cases. Further, and interesting enough, the decrease was not immediate but gradual as it reduced the growth rate at roughly .3 percentage points per day for each country. Therefore, policymakers should not expect immediate results in the reduction of the number of new cases after imposing restrictions. It is only when the growth rate of new cases turns negative that new cases begin to fall. This might take a few weeks.

Background and Objectives: The nation's urban sector has experienced significant changes since industrialization, both in terms of growth and improvement in terms of creating jobs and the depletion of natural resources. The development and destruction are commendable and need mention and attention. These days, issues such as environmental degradation, the wealth gap, and unequal access to opportunities and resources are increasing. These concerns have increased the need for urban management through sustainable and planned development. The development cannot be sustained unless the depletion is controlled and taken care of. The current study focused on how urban regions have changed and how sustainable development helps cope with the changes. Furthermore, the study focused on enhancing the well-being of urban communities and promoting inclusivity to develop sustainable businesses that are economically sustainable in the long run. The novelty of the study is that explores how sustainable development can mitigate the impacts of urban change and enhance community well-being and inclusivity.Methods: Quantitative methodologies, such as surveys and statistical analyses, may have shed light on the broader impact of sustainable business practices on urban development. To complement these findings, qualitative case studies and interviews could have fostered deeper insights into specific initiatives and the perspectives of entrepreneurs navigating this landscape. A mixed-methods approach, weaving together quantitative trends with rich qualitative narratives, would have further enriched the understanding of motivations and challenges encountered. Ultimately, regardless of the specific methodologies employed, this research likely aimed to achieve a holistic understanding of the intricate connections between urban management, sustainable business practices, and the innovative spirit of entrepreneurs driving change in India. In this study, the researcher used regression analysis and factor analysis to achieve the research objectives.Findings:The entrepreneurs examined in the study exhibited a proactive stance towards critical aspects of sustainable business, notably waste management, energy efficiency, and resource optimization. The study identifies significant associations between key variables and sustainable urban development. Urban management demonstrates a positive impact (B = 1.286, SE = 0.621, Beta = 0.116, T = 2.071, P = 0.0039), highlighting its crucial role in shaping sustainable practices. Sustainable practices, in turn, exhibit a strong positive correlation (B = 1.088, SE = 0.257, Beta = 0.238, T = 4.242, P = 0.0001), indicating their pivotal role in driving urban development towards sustainability. These findings underscore the importance of integrating effective urban management strategies and sustainable practices to foster inclusive and resilient urban communities.Conclusion: The findings provide the government with all the criteria essential for a valid approach to the sustainable development of the urban regions of the country and provide the appropriate balance between growth and development. The researcher recommended that creating supportive policies and incentives, investing in green infrastructure, supporting local entrepreneurship ecosystems, collecting and sharing data, and promoting community engagement are requirements for urban management. In addition, recommended that focusing on triple bottom line impact, collaborating with other stakeholders, Innovating and adapting, communicating transparently, and advocating for change are required for Sustainable Businesses by Entrepreneurs.

It is widely known that in the exchange rate market there are three factors influential to determine the currency exchange rate between the related countries in theory; i.e., the interest rate, inflation, and expectation on the exchange rate. This paper aims to find how the exchange rate between U.S. dollar and Japanese Yen, both of which are widely recognized as major currencies in the international financial market, are affected by the four key variables selected here in this paper related to these three factors under the recent inflationary economic environment; i.e., interest rate yield of the two year treasury bond, inflation rate, and the two moving average of the exchange rate to be considered as rational expectation on the movement of the exchange rate. As result it is our discovery that the selected predicator variables have been influential to the daily volatility and monthly movement of the exchange rate of the Japanese Yen /US $ with evidence the two different models of the regression analysis are able to show the exchange rate are significantly correlated with these four key variables and strongly affected by them. Also, over 30-month study period, the interest rate parity or the purchasing power parity did not hold because of the strong US dollar and the continuous increase in interest rate yield during the rapid inflation in the US economy.

In this paper, we use The World Happiness Report to illustrate the use of linear regression. We perform some linear regression using the dataset values to pinpoint the effectiveness of linear regression in data analysis. The results show that linear regression can be used to precisely define trends in the data between the output variable and the input variables. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Making society resilient is considered a critical and challenging issue in mental health planning or stress-relieving urban planning. However; although in recent years, many studies have separately addressed stress and urban resilience, no study has explained the relationship between the two. Therefore, the present study aims to explain the relationship between urban stress and urban resilience in the Region 8 of the District 1 of Tehran Municipality. METHODS: This study is applied descriptive-analytical research. The required data are collected using both quantitative and qualitative methods and analyzed by regression analysis. First, using the library method, the indicators of urban stress and urban resilience were identified, then the study sample was selected using these indicators and the Delphi method. After; that, the information related to the indicators was collected using questionnaire and library methods, and finally, the relationship between urban stress and urban resilience was investigated through multivariate regression.FINDINGS: The research findings indicate a high correlation between the two abovementioned concepts (correlation coefficient= 0.925). Moreover; the adjusted R2 shows that resilience indicators explain 83% of the total variations in urban stress. Also; the most critical indicators affecting the citizens' stress include the status of open space, gender structure of the population, permeability, average building density, education level, resident participation, spatial diversity, citizen's sense of belonging, social diversity, violence, crime, the safeness of the environment, social capital, social solidarity, the strength of buildings, vulnerability to natural hazards, income status, employment status, environmental pollution, access to services, the age structure of the population, and safety, respectively.CONCLUSION: it is possible to significantly reduce urban stress by enhancing urban resilience and paying attention to its effective indicators.

Şirketler faaliyetlerini gerçekleştirirken ortaya çıkan en önemli sera gazlarından birisi de karbondur. Karbon emisyonları küresel ısınma ve iklim değişikliğine olan etkisi nedeni ile emisyonunun azaltımına ilişkin pek çok düzenlemenin yapıldığı bir sera gazıdır. Gerek bu düzenlemeler gerekse çevresel sürdürülebilirlik nedeni ile günümüz şirketleri karbon emisyonlarını ölçmekte, açıklamakta ve emisyonların azaltımına ilişkin birçok faaliyet gerçekleştirmektedir. Bu çalışmada Borsa İstanbul’da faaliyet gösteren şirketlerin karbon emisyonu ve temerrüt riski arasında bir ilişkinin olup olmadığı eğer bir ilişki var ise bu ilişkinin şirketin temerrüt riski üzerindeki etkisinin tespit edilmesi amaçlanmıştır. Bu amaçla şirketlerin toplam karbon emisyonları bağımlı değişken olarak belirlenmiş ve Merton modeli ile hesaplanan temerrüde olan uzaklık (DD) değişkeni ile regresyon analizine tabi tutulmuştur. Analizde varlıkların getirisi, kaldıraç ve şirket büyüklüğü kontrol değişkenleri olarak belirlenmiştir. Analiz sonuçlarında karbon emisyonlarının temerrüt riski üzerinde anlamlı bir etkisi olduğu görülmüştür. Şirketlerin regresyon katsayılarına bakıldığında karbon emisyonu arttıkça temerrüt riski artmaktadır. Kontrol değişkenleri ile temerrüt riski arasında da anlamlı ilişkiler bulunmuştur. Karbon emisyonlarının temerrüt üzerindeki anlamlı etkisi nedeni ile işletme kararlarında karbon yönetiminin ve azaltımının da dikkate alınması gerektiği görülmektedir.

Cataract surgery helps to enhance visual function and improve the quality of life of cataract patients. The present study assessed visual outcomes and explored health-related quality of life (HRQoL) and factors associated with it following cataract surgery in Armenia. An interviewer-administered survey along with the ophthalmologic examination was conducted among 248 patients. It explored socio-demographic characteristics, use of eye care services, smoking status, comorbidities, and receiving and giving social support. A Short Form Health Survey (SF-36) was used to measure HRQoL. Simple and multivariable linear regression was used for the analysis. About 72.8% of examined eyes had good visual outcomes, while 17.7% had borderline outcomes. Poor visual outcomes were detected in 9.5% of the eyes. The mean composite SF-36 score for physical health was 50.8, while the mean composite score for mental health was 49.9. Gender, socioeconomic status, having a non-communicable disease, and receiving and giving tangible social support were significantly associated with SF-36 physical component in the adjusted analysis, while the variables which demonstrated significant association with the mental component included socioeconomic status, having a non-communicable disease, and giving tangible support. The visual outcome after cataract surgery in Armenian patients is below WHO-recommended standards. The quality of ophthalmological surgical care should be monitored to maximize the visual outcome in Armenian patients, with a focus on women, patients with poor socioeconomic status, and those with non-communicable diseases. [ABSTRACT FROM AUTHOR]

The current study explored the level to which socio-economic status predicts mathematics self-concept. A correlational and regression research designs were used. The study was carried out in OR Tambo Inland District in the Eastern Cape Province. The sample size comprised of 351 Grade 9 mathematics students, of which 171 were girls and 180 were boys. For generalizability purposes and fair distribution and representation of the sample across the population, both rural and urban geographical locations were represented by three schools each. The level of socio-economic status and mathematics self-concept was measured by use of a standardized questionnaire. The data collection tool was tested valid and reliable. Statistical Package for the Social Sciences (SPSS) was used to perform regression and correlation analysis. The research findings depict that mathematics self-concept was heavily influenced by socio-economic status of a parent. Furthermore, mathematics self-concept varied according to the socio-economic status and the variation was statistically significant. Recommendations were made to all implicated stakeholders to improvise strategies of improving mathematics self-concept despite the family's economic hardships. [ABSTRACT FROM AUTHOR]

Purpose: The relationship between the macroeconomic environment and banking risks is one of the basic conditions for the stability of the state financial system. The purpose of this paper is to show the interdependence between the macroeconomic environment and the financial system, i.e., to highlight the importance of various macroeconomic factors and their impact on the banking system in Croatia. Methodology: Statistical data for the period from 2010 to 2022, obtained from publicly available databases, are analysed. Correlation analysis was performed to determine the intensity and direction of the relationship between macroeconomic and banking variables. Regression analysis was conducted to determine if macroeconomic variables could explain some of the changes in banking variables. Results: The results of the analysis show a significant correlation between the macroeconomic variable unemployment rate and all banking variables, as well as between the macroeconomic variable inflation rate and the banking variables: non-performing loans (NPL) and bank loans. Gross domestic product (GDP) is not correlated with any of the observed variables, which is a very interesting result. The unemployment rate is the predictor that is significant for all banking variables, while the inflation rate is the predictor that is significant only for non-performing loans. Conclusion: The conducted analyses show that economic development plays a role in the business policies of banks. These results can be particularly useful for bank management and the government, as they provide all stakeholders with important information about the role of macroeconomic policies of financial institutions in Croatia and, consequently, the Croatian economy.

CEO incentives have been the subject of great interest for human resource scholars. We explore the institutional context within which the CEO makes sense of their incentives. Our theory suggests that CEO equity incentives interact with institutional norms to influence foreign market entry choices. Specifically, we argue that CEOs will weigh the risk bearing created by equity incentives, along with the consequences of legitimacy loss, when deciding whether to deviate from institutional norms when internationalizing. In doing so, we advance human resource literature by demonstrating that CEO responses to incentives are influenced by institutional norms and that CEOs' decisions to deviate from institutional norms are shaped by their incentives. We find support for our framework in the analysis of the stake taken by acquirers in 4,184 cross‐border acquisitions. [ABSTRACT FROM AUTHOR]

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This paper presents the importance of the visual expression of factor decomposition in regression analysis, which is particularly worthwhile for undergraduate students whose majors are not mathematics but social science. The conventional purpose of regression analysis is to examine specific hypotheses empirically. In particular, the statistical significance of the explanatory variable was tested, which may have been difficult for many students to understand mathematically. To remedy this, factor decomposition is introduced in the same way that human body composition is broken down into water, fat, and muscle. As an illustrative example, multiple regression was applied to the determinants of housing rents in Japan. The explanatory variables were the living area, building age, and walking time from the nearest station. The findings suggest that, with the help of visual expression, a student can easily appreciate which variable significantly affects housing rents. [ABSTRACT FROM AUTHOR]

In modern world, under the pressure of buyers on retailers, the main foundations of classical marketing collapse and businesses need to master new pricing tools. Armed with new technologies and techniques, you can effectively influence the economic performance of an enterprise. Therefore, the development of new pricing methods in digital economy is not only an urgent task, but also necessary in the daily activities of business entities. The article describes a price function development model using the main parameters of supply and demand and the generalized Hooke’s law, as the closest one to display the processes of its state. Formulas are given to determine the price and its constituent parameters, taking into account supply and demand indicators, at which it becomes unprofitable for a seller to increase the amount of profit, and to increase the volume of consumption for a consumer. The results obtained allow trading and manufacturing companies during the period of economy digitalization, together with the involvement of financiers and IT experts, to automate the process of calculation and construction of retail prices for their products and move away from manual modeling of these parameters. Purpose is to find, as a first approximation, a mathematical expression of the price, taking into account supply and demand indicators, at which it becomes unprofitable for a seller to increase the amount of profit, and it becomes unprofitable for a consumer to increase the volume of consumption. Methodology: the article describes a model for a price function development using the main parameters of supply and demand and the generalized Hooke’s law. Results: the results obtained allow us to predict profit taking into account the state of demand, while the price of a product may differ significantly from the price calculated by the classical method at the same costs. Practical implication: it is advisable to apply the obtained results for automated calculation and modeling of retail prices for products.

Globally, there has been increasing interest in the study of caregiving. Female caregiver predictors or mediators and a multidimensional female caregiver burden (FCG burden) model that incorporates environmental hygiene factors such as toilet hygiene and kitchen are lacking, particularly in low- and middle-income countries. This study evaluates caregiver burden predictors and provides a multidimensional model of unremunerated care burden for family practice and policy in two different population group communities in Cape Town, South Africa. A systematic random sampling (SRS) procedure was employed, and 100 black or African and 100 mixed race female caregivers in two different cultural communities were selected for a reliable cross-section. A questionnaire was used to solicit caregiver burden information. The average age of the female caregivers was 47.9 years (standard deviation [SD] = 11.7 years). About 49.0% of the selected participants were older than 50 years. There was a significant relationship between environmental health (kitchen hygiene and toilet hygiene). Social grant receipt and physical health status of care recipients were predictors of caregiver burden. The overall model explained the largest variation (43.4%) in caregiver burden. This study recommends an increase in the social grants given to caregivers. National health policies should reflect female caregivers’ circumstances. Transdisciplinarity Contribution: This article contributes to the improvement in community health.

Modern cities are full of complex and substantial engineering structures that differ by their geometry, sizes, operating conditions, and technologies used in their construction. During the engineering structures’ life cycle, they experience the effects of construction, environmental, and functional loads. Among those structures are bridges and road overpasses. The primary reason for these structures’ displacements is land subsidence. The paper addresses a particular case of geospatial monitoring of a road overpass that is affected by external loads invoked by the construction of a new subway line. The study examines the methods of machine learning data analysis and prediction for geospatial monitoring data. The monitoring data were gathered in automatic mode using a robotic total station with a frequency of 30 min, and were averaged daily. Regression analysis and neural network regression with machine learning have been tested on geospatial monitoring data. Apart from the determined spatial displacements, additional parameters were used. These parameters were the position of the tunnel boring machines, precipitation level, temperature variation, and subsidence coefficient. The primary output of the study is a set of prediction models for displacements of the overpass, and the developed recommendations for correctly choosing the prediction model and a set of parameters and hyperparameters. The suggested models and recommendations should be considered an indispensable part of geotechnical monitoring for modern cities.

The COVID-19 pandemic has had a compelling impact on various sectors in Indonesia, including the national economy. This study aims to examine differences in regional financial performance before the pandemic (2019) and during the pandemic (2020) and to examine the effect of the ranking and status of local government administration published by the Ministry of Home Affairs on regional financial performance in the period before the pandemic (2019) and during the pandemic (2020). Paired sample t-test and regression analysis were used to test the hypotheses on the financial performance of 118 local governments consisting of 16 provincial governments, 48 ​​district governments, and 54 city governments. The regional financial independence ratio (RKKD), the effectiveness and efficiency ratio of PAD, the activity ratio, and the growth ratio are financial performance indicators tested in this research. The research's results show the differences between before and during the COVID-19 pandemic in regional financial performance in the PAD effectiveness ratio, capital expenditure activity ratio, and growth ratio. However, there is no difference in regional financial performance before and during the COVID-19 pandemic as measured by the regional financial independence ratio, efficiency ratio, and operating expenditure activity ratio. The results also show that the ranking and status of local government administration have no effect on regional financial performance before and during the COVID-19 pandemic.

Interdisciplinary research is widely recognized as necessary to tackle some of the grand challenges facing humanity. It is generally believed that interdisciplinarity is becoming increasingly prevalent among Science, Technology, Engineering, and Mathematics (STEM) fields. However, little is known about the evolution of interdisciplinarity in the Social Sciences. Also, how interdisciplinarity and its various aspects evolve over time has seldom been closely quantified and delineated. This paper answers these questions by capturing the disciplinary diversity of the knowledge base of scientific publications in nine broad Social Sciences fields over 55 years. The analysis considers diversity as a whole and its three distinct aspects, namely variety, balance, and disparity. Ordinary least squares (OLS) regressions are also conducted to investigate whether such change, if any, can be found among research with similar characteristics. We find that learning widely and digging deeply have become one of the norms among researchers in Social Sciences. Fields acting as knowledge exporters or independent domains maintain a relatively stable homogeneity in their knowledge base while the knowledge base of importer disciplines evolves towards greater heterogeneity. However, the increase of interdisciplinarity is substantially smaller when controlling for several author and publication related variables. [ABSTRACT FROM AUTHOR]

Crisis, defined partly similar, partly differently in social sciences, is an inherent part of everyone's life. Having many faces, it causes many consequences (on an ad hoc basis and in the future). The analysis of the results of studies by other researchers and mine shows that crises are strongly correlated with women's alcohol problems. They can be diagnosed on the continuum of their life: from alcohol initiation, through progressive addiction and to the process of gradual recovery. Crises in the biographies of women addicted to alcohol can be associated in two ways: with regression or progress, with a sense of lower or higher quality of life, which are confirmed by their narratives. [ABSTRACT FROM AUTHOR]

The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for social sciences due to the various advantages it offers; however, it requires very intensive assumptions. MARS method, on the other hand, is a multivariate and adaptive nonparametric statistical regression method used for data classification and modeling. MARS does not need any assumptions such as normality, linearity, homogeneity. It allows variables that do not provide linearity to be included in the analysis. The present study examines whether it is possible to use the MARS method, which is a more flexible method compared to SEM, taking both methods into account. Regarding this goal, the SEM model was created with the program R using the affective data and the achievement variable picked from TIMMS 2019 data. Then, the MARS method was created using the SPM (Salford Predictive Modeler) program. The results of the study showed that at certain points the MARS model gave similar results to the SEM model and MARS model is more compatible with the literature. [ABSTRACT FROM AUTHOR]

While hierarchical linear modeling is often used in social science research, the assumption of normally distributed residuals at the individual and cluster levels can be violated in empirical data. Previous studies have focused on the effects of nonnormality at either lower or higher level(s) separately. However, the violation of the normality assumption simultaneously across all levels could bias parameter estimates in unforeseen ways. This article aims to raise awareness of the drawbacks associated with compounded nonnormality residuals across levels when the number of clusters range from small to large. The effects of the breach of the normality assumption at both individual and cluster levels were explored. A simulation study was conducted to evaluate the relative bias and the root mean square of the model parameter estimates by manipulating the normality of the data. The results indicate that nonnormal residuals have a larger impact on the random effects than fixed effects, especially when the number of clusters and cluster size are small. In addition, for a simple random-effects structure, the use of restricted maximum likelihood estimation is recommended to improve parameter estimates when compounded residuals across levels show moderate nonnormality, with a combination of small number of clusters and a large cluster size. [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate the contribution of the maturity of moral judgment on cannabis use among college students. Participants were 1572 college students who completed self-report questionnaires measuring cannabis use, maturity of moral judgment, and emotional and personality variables. Thirty five percent of men and 30% of women reported having used cannabis at least once during the last 6 months. Logistic regression analysis found that the maturity of moral judgment was a significant predictor of cannabis use versus non-use. This study suggests the importance of evaluating moral judgment in studies of risk factors of cannabis use and to develop effective and non-moralizing interventions targeting permissive beliefs and other socio-cognitive variables determining cannabis use and misuse. Keywords: cannabis use, moral judgment, college students, Cannabis use is a major health problem among college students. Cannabis is the most commonly used illicit substance among college students in Europe and the USA (e.g, Arria et al., [...]

Müşterisi sosyal medya kullanıcıları olan işletmelerin bu kanallardan ürünleri hakkında yazılan iletileri toplayıp analiz etme, bunları satış rakamları ile karşılaştırma veya ilişkilendirme, gelecek için stratejiler geliştirme gibi imkanları vardır. Bu çalışmada uygulanan yöntem ile bir televizyon kanalına ait seyirci görüşlerinin sosyal medya üzerinden derlenerek kanal için faydalı bilgi elde edilmesi ve bu bilgilerin kanal için bir karar destek unsuru olması hedeflenmiştir. Bu amaçla bir TV kanalında yayınlanan programlar hakkında Kasım - Haziran 2017 tarihleri arasında Twitter’dan paylaşılan iletiler derlenmiş, duygu analizi tekniği ile her ileti pozitif, negatif ya da nötr olarak etiketlenmiştir. Bu periyoddaki yayın akışındaki tüm programlar için kaç pozitif, kaç negatif ve kaç nötr tweet atıldığı bilgilerine bağlı olarak reyting değeri incelenmiştir.

Etkin, verimli ve güçlü bir sosyo-ekonomik yapı için mevcut sistemin yeniden yapılandırılması olarak ifade edilebilecek yapısal reformlar günümüzde en çok tartışılan konulardan biridir. Ülkeden ülkeye farklı nedenlere göre çeşitlilik arz eden yapısal reformların başta ekonomik olmak üzere siyasi, sosyal ve mali boyutları bulunmakta ve makroekonomik değişkenler üzerinde belirleyici olmaktadır. Bu çalışmada, Türkiye’de yapısal mali reform çabalarının ekonomik büyüme üzerinde bir etki yaratıp yaratmadığı, reforma konu olan mali araçlar baz alınarak analiz edilmiştir. Belirlenen mali değişkenlerin büyüme üzerindeki etkisi regresyon analizi ile test edilmiştir. Veri seti 2006 Ocak ve 2017 Aralık dönemini kapsamaktadır. Analiz sonuçları, Türkiye’de giderlere yönelik yapılacak reformların aksine gelirlere yönelik yapılacak reformların büyümeyi hızlandıracağını göstermektedir.

Abstract How have China's princelings benefitted from their family backgrounds in their careers? This study seeks to answer the question and, in so doing, to add to the existing factionalist [...]

Research was conducted to determine if social capital influenced the norms of drinking and alcohol-related behavior amongst college students. A random sample survey was distributed to college students at a medium-sized regional university (N = 2600). A mixed-method design was used to examine the results, including correlation and regression analysis and content analysis. Social capital was operationalized in three forms. An inverse association indicates students who report heavy/binge drinking are less influenced by factors of social capital, whereas students who report light drinking are influenced by factors of social capital. Continued mixed-methods research at multiple universities is recommended. Keywords: social capital, alcohol, college, student, drinking, health, community, culture, INTRODUCTION The frequency, rate, and duration of alcohol consumption amongst college students has ramifications not just for the individual, but for the broader community. Sexual assault, traffic accidents, neighborhood crime, [...]

The Phillips curve was supposed to mean an expansion of the doctrine based on the original regulatory ideas of J. M. Keynes. At the time of its inception (1950s), it gave governments theoretical hope, coming from the possibility of choosing a negative correlation between the price level (P) and the product (Y). Her early denial (at least in the short term) by Milton Friedman, on the other hand, has not changed anything about other applications that are still relevant until present time. In the fact, advantage of Phillips curve is her ability based on broad-spectrum use for any type of national economy. The aim of this work is to determine the shape of the Phillips curve for the Czech Republic in the period from 2000 to the present and to compare its shape with the shape of the original Phillips curve. The method of regression analysis is used here, comparison and prediction are performed using time series. In this paper, we find out what the short-term Phillips curve looks like for the Czech Republic, that it does not coincide with the original Phillips curve, and that in the future we can count on a growing correlation between inflation and unemployment.

We investigate the state of multilingualism across the social sciences and humanities (SSH) using a comprehensive data set of research outputs from seven European countries (Czech Republic, Denmark, Finland, Flanders [Belgium], Norway, Poland, and Slovenia). Although English tends to be the dominant language of science, SSH researchers often produce culturally and societally relevant work in their local languages. We collected and analyzed a set of 164,218 peer‐reviewed journal articles (produced by 51,063 researchers from 2013 to 2015) and found that multilingualism is prevalent despite geographical location and field. Among the researchers who published at least three journal articles during this time period, over one‐third from the various countries had written their work in at least two languages. The highest share of researchers who published in only one language were from Flanders (80.9%), whereas the lowest shares were from Slovenia (57.2%) and Poland (59.3%). Our findings show that multilingual publishing is an ongoing practice in many SSH research fields regardless of geographical location, political situation, and/or historical heritage. Here we argue that research is international, but multilingual publishing keeps locally relevant research alive with the added potential for creating impact. [ABSTRACT FROM AUTHOR]

Abstract. This paper extends the dynamically formulated hidden Markov models to a high-order hidden Markov model (HO-HMM) formulation. In the HO-HMM, the Markovian assumption that the future states (interpreted as [...]

The author of this article attempts to explain the evolution and analysis of multiple regression. The author will also describe the history, the present, and the future of multi-variate analysis. The future will be described in more general terms as reality is so chaotic in what appears at a distance to be orderly. At the time of this writing, it appears that multiple regression is lessening in prestige although it may guarantee publication. Simpler measures should be included and monitored replication may be necessary. Without making significant changes, the social science may find that there is even less trust in the fields of social science or behavioral methodologies. Multiple Regression is a limited tool and must be monitored. [ABSTRACT FROM AUTHOR]

Background. A partograph is a graphic representation of labor which is used by health professionals for monitoring labor progress and fetal and maternal wellbeing. However, its utilization and associated factors have not been studied yet in Hadiya Zone, Southern Ethiopia. Hence, the aim of this study was to determine partograph utilization and associated factors among obstetric care providers at public health facilities in Hadiya Zone, Southern Ethiopia. Methods. A facility-based cross-sectional study was conducted on 436 health professionals. The study was conducted from March 04 to April 07, 2019. A simple random sampling method was carried out to select 19 health facilities and study participants from selected facilities. Data was collected using a pretested structured questionnaire, entered into EPI-data version 3.1 and exported to Statistical Package for Social Sciences (SPSS) version 20. Descriptive statistics and binary and multivariable logistic regression analyses were done. P values less than 0.05 were used to declare significant association between dependent and independent variables. Results. The overall magnitude of partograph utilization was found to be 54.4%, and finding from data abstraction from a document revealed that out of 18 parameters, only 10 parameters were recorded completely. Type of health facility (hospital as compared to HC) (AOR = 2.96 ; CI = 1.71 , 5.12), received on-the-job training on partograph (AOR = 7.06 ; CI = 4.3 , 11.37), knowledgeable about partograph (AOR = 2.12 ; CI = 1.3 , 3.9), and favorable attitude toward partograph use (AOR = 1.8 ; CI = 1.12 − 2.97) were significantly associated with partograph use. Conclusion. Overall partograph utilization was low, and incomplete recording of required parameters on partograph was observed in this study. Participants who received on-the-job training on partograph, who are working in a hospital, who are knowledgeable about partograph, and who have favorable attitude toward partograph use were factors affecting partograph use positively. [ABSTRACT FROM AUTHOR]

When the sample size is not too small, M-estimators of regression coefficients are approximately normal and unbiased. This leads to the familiar frequentist inference in terms of normality-based confidence intervals and p-values. From a Bayesian perspective, use of the (improper) uniform prior yields matching results in the sense that posterior quantiles agree with one-sided confidence bounds. For this, and various other reasons, the uniform prior is often considered objective or non-informative. In spite of this, we argue that the uniform prior is not suitable as a default prior for inference about a regression coefficient in the context of the bio-medical and social sciences. We propose that a more suitable default choice is the normal distribution with mean zero and standard deviation equal to the standard error of the M-estimator. We base this recommendation on two arguments. First, we show that this prior is non-informative for inference about the sign of the regression coefficient. Second, we show that this prior agrees well with a meta-analysis of 50 articles from the MEDLINE database. [ABSTRACT FROM AUTHOR]

Fertility in Ghana has declined steadily since 1980, however, a slight increase was observed between 2008 and 2014. While several factors may account for this pattern, research on the contribution of type of union is limited. This study examined differentials in the fertility of women in different types of union. Secondary data from 6,285 (weighted) ever-married women aged 15–49 years were analysed using compare means, t-test, analysis of variance, Poisson and binary logistic regression analyses. The findings indicate that, independent of other factors, fertility among remarried women is higher compared to first-time married women but this does not hold true when other factors are controlled for. Additionally, there was no significant difference in the fertility of remarried women who were in union and women who were in union in a first-time marriage. However, compared to remarried women who were currently in a union, fertility was significantly lower among remarried women who were not currently in union (β = -0.121, p<0.01) and women who have been married only once but were not currently in union (β = -0.212, p<0.001). Further analysis revealed that remarried women were significantly more likely to desire more children and less likely to use any method of contraception compared to first-time married women. There is the need for further research to better understand the fertility needs of remarried women. [ABSTRACT FROM AUTHOR]

The problem of handling non-ignorable non-response has been typically addressed under the design-based approach using the well-known sub-sampling technique introduced by Hansen and Hurwitz [1946, Journal of the American Statistical Association, Vol 41(236), Page 517- 529]. Alternatively, the model-based paradigm emphasizes on utilizing the underlying model relationship between the outcome variable and one or more covariate(s) whose population values are known prior to the survey. This article utilizes the model relationship between the study variable and covariate(s) for handling non-ignorable non-response and obtaining an unbiased estimator for the population total under the sub-sampling technique. The main idea is to combine the estimates obtained from the sample on first call and the sub-sample from second call using separate model relationships. The contribution of this paper helps us in providing unbiased estimates with an improved efficiency under model-based paradigm in presence of non-ignorable non-response. The provided method is more economical than the available estimators under callback methods as we are working sub-sampling and also increase response rate as a stronger mode of interview is employed for data collection. A numerical study using Monte Carlo is presented to illustrate the behavior of the proposed and the efficiency comparison. [ABSTRACT FROM AUTHOR]

Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error. [ABSTRACT FROM AUTHOR]

We analyse and compare NBA and Euroleague basketball through box-score statistics in the period from 2000 to 2017. Overall, the quantitative differences between the NBA and Euroleague have decreased and are still decreasing. Differences are even smaller after we adjust for game length and when playoff NBA basketball is considered instead of regular season basketball. The differences in factors that contribute to success are also very small—(Oliver’s) four factors derived from box-score statistics explain most of the variability in team success even if the coefficients are determined for both competitions simultaneously instead of each competition separately. The largest difference is game pace—in the NBA there are more possessions per game. The number of blocks, the defensive rebounding rate and the number of free throws per foul committed are also higher in the NBA, while the number of fouls committed is lower. Most of the differences that persist can be reasonably explained by the contrasts between the better athleticism of NBA players and more emphasis on tactical aspects of basketball in the Euroleague. [ABSTRACT FROM AUTHOR]

Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression. We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically-selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates. [ABSTRACT FROM AUTHOR]

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails). [ABSTRACT FROM AUTHOR]

In order to support decision making on how to most effectively improve broiler welfare an innovative expert survey was conducted based on principles derived from semantic modelling. Twenty-seven experts, mainly broiler welfare scientists (n = 20; and 7 veterinarians), responded (response rate 38%) by giving welfare scores (GWS, scale 0–10) to 14 benchmarking housing systems (HSs), and explaining these overall scores by selecting, weighing and scoring main welfare parameters, including both input and output measures. Data exploration followed by REML (Linear Mixed Model) and ALM (Automatic Linear Modelling) analyses revealed 6 clusters of HSs, sorted from high to low welfare, i.e. mean GWS (with superscripts indicating significant differences): 1. (semi-natural backyard) Flock (8.8a); 2. Nature (7.7ab), Label Rouge II (7.4ab), Free range EU (7.2ab), Better Life (7.2ab); 3. Organic EU (7.0bc), Freedom Food (6.2bc); 4. Organic US (5.8bcd), Concepts NL (5.6abcdef), GAP 2 (4.9bcd); 5. Conventional EU (3.7de), Conventional US (2.9ef), Modern cage (2.9abcdef); 6. Battery cage (1.3f). Mean weighting factors (WF, scale 0–10) of frequently (n> = 15) scored parameters were: Lameness (8.8), Health status (8.6), Litter (8.3), Density (8.2), Air quality (8.1), Breed (8.0), Enrichment (7.0) and Outdoor (6.6). These did not differ significantly, and did not have much added value in explaining GWS. Effects of Role (Scientist/Vet), Gender (M/F) and Region (EU/non-EU) did not significantly affect GWS or WF, except that women provided higher WF than men (7.2 vs 6.4, p<0.001). The contribution of welfare components to overall welfare has been quantified in two ways: a) using the beta-coefficients of statistical regression (ALM) analyses, and b) using a semantic-modelling type (weighted average) calculation of overall scores (CalcWS) from parameter level scores (PLS) and WF. GWS and CalcWS were highly correlated (R = ~0.85). CalcWS identified Lameness, Health status, Density, Breed, Air quality and Litter as main parameters contributing to welfare. ALM showed that the main parameters which significantly explained the variance in GWS based on all PLS, were the output parameter Health status (with a beta-coefficient of 0.38), and the input parameters (stocking) Density (0.42), Litter (0.14) and Enrichment (0.27). The beta-coefficients indicated how much GWS would improve from 1 unit improvement in PLS for each parameter, thus the potential impact on GWS ranged from 1.4 welfare points for Litter to 4.2 points for Density. When all parameters were included, 81% of the variance in GWS was explained (77% for inputs alone; 39% for outputs alone). From this, it appears that experts use both input and output parameters to explain overall welfare, and that both are important. The major conventional systems and modern cages for broilers received low welfare scores (2.9–3.7), well below scores that may be considered acceptable (5.5). Also, several alternatives like GAP 2 (4.9), Concepts NL (5.6), Organic US (5.8) and Freedom Food (6.2) are unacceptable, or at risk of being unacceptable due to individual variation between experts and farms. Thus, this expert survey provides a preliminary semi-quantified decision-support tool to help determine how to most effectively improve broiler welfare in a wide range of HSs. [ABSTRACT FROM AUTHOR]

This study is based on the approach that not only the quality of a course is significant for the learning output but also individual aspects of dealing with the requirements and of mastering them. It examines a situated and problem based learning arrangement of a course. Students have to acquire knowledge, analyse authentic situations at school, create a learning situation themselves and evaluate this lessons as an own case study, focused on the learning of the students and the fit of the support of the teachers to their learning. Based on a goal focused self-assessment, we investigate the effect of student teachers’ evaluation of the quality of the course (1), their use of different learning settings and different learning strategies (2), their work motivation and their volition to reach the goals (3) on their learning output of the course. Results of regression analyses show that each of these approaches contribute to the explained variance of the learning output; the quality of the course explains 16% (1), the use of the learning opportunities 33% (2) and motivational factors 31% (3). A conclusion for teacher education is that good qualified courses are essential, but student teachers’ motivation to deal with the requirements of a demanding learning arrangement is significant for their professionalization.

The article is devoted to the analysis and assessment of the impact of dividends on the market value of the company. The study is based on data from company reports and statistics from the Moscow Exchange. This paper presents a regression analysis of a choice consisting of 1) 20 systems of Russian companies and 2) 5 largest companies in the oil and gas sector for the period 2013-2019. The regression results show that dividends have a positive effect on capitalization only if the policy is based on the residual principle. The conclusions made in the work, which provide recommendations and a statistical assessment of dependence, can find practical application in determining the dividend policy of an organization and predicting changes in the capitalization of companies.

The purpose of this study was to analyze the current status and needs of infrastructure for basic life in Gwangjin district in Seoul, South Korea. In this study, we examined whether the national minimum standard was satisfied in terms of the infrastructure for basic life in the district. Specifically, we employed and compared the empirical utilities of two types of geographic datasets, 100-square-meter grids and 500-square-meter grids. The study compares the prediction accuracy between two types of geographic datasets by employing multivariate linear estimation using influential factors. The evaluation methods for prediction accuracy were to compare the root mean of squared error (RMSE) and mean of absolute error (MAE) from each dataset. The results were as follows: (a) the dataset with 100-square-meter grids showed more significant associations among influential factors and the infrastructure than the dataset with 500-square-meter grids; (b) the 100-square-meter grids showed better prediction accuracy compared with the 500-square-meter grids; and (c) in terms of basic level local government, it was more powerful to use the datasets with 100-square-meter grids for finding blind sides of infrastructure than the datasets with 500-square-meter grids. The results imply that it is necessary to adjust urban policy by using appropriate datasets, such as 100-square-meter grids.

Research background: Commodities are attractive investment opportunities and are objects of many researches. Traditional and most stable seem to be precious metals gold and silver. In the global world where anyone can access information in seconds it is crucial to know the background of price development in order to make the right investment decision. Purpose of the article: This paper focuses on international commodity exchanges and analyzes factors affecting prices of selected commodities. It offers a summary of recent studies and continues with an analysis of most relevant factors affecting price of gold and silver. Methods: After collecting data and testing their consistency we construct two hypothesis models. Using linear regression model we test the appropriateness of models which are constructed for each commodity. Subsequently, we test statistical significance of individual explanatory variables, such as dollar exchange rate, VIX, CPI, DJIA, DAX or oil price. Additionally, we take into consideration not traditional factors e.g. Bitcoin. Findings & Value added: Finally an evaluation of results are carried out and the influence of variables are quantified. For instance, negative impact of VIX and dollar exchange rate in observed period of time was confirmed. Given the fact that there can be various other factors influencing price development of gold and silver this paper serves as basis for further analysis in this direction.

Background: Multi-substance use is accompanied by increased morbidity and mortality and responsible for a large number of emergency department (ED) consultations. To improve the treatment for this vulnerable group of patients, it is important to quantify and break down in detail the ED resources used during the ED treatment of multi-substance users. Methods: This retrospective single centre case-control study included all ED consultations of multi-substance users over a three-year study period at a university hospital in Switzerland. Resource consumption of these patients was compared to an age-matched control group of non-multi-substance users. Results: The analysis includes 867 ED consultations of multi-substance users compared to 4,335 age-matched controls (5:1). Multi-substance users needed more total resources (median tax points [medical currency] (IQR): 762 (459–1226) vs. 462 (196–833), p<0.001), especially physician, radiology, and laboratory resources. This difference persisted in multivariable analysis (geometric mean ratio (GMR) 1.2, 95% CI: 1.1–1.3, p = 0.001) adjusted for sociodemographic parameters, consultation characteristics, and patient comorbidity; the GMR was highest in ED laboratory and radiology resource consumption. Among multi-substance user, indirect and non-drug-related consultations had higher ED resource consumption compared to drug-related consultations. Furthermore, leading discipline as well as urgency were predictors of ED resource consumption. Moreover, multi-substance users had more revisits (55.2% vs. 24.9%, p<0.001) as well as longer ED and in-hospital stays (both: GMR 1.2, 95% CI: 1.1–1.3, p<0.001). Conclusion: ED consultations of multi-substance users are expensive and resource intensive. Multi-substance users visited the ED more often and stayed longer at the ED and in-hospital. The findings of our study underline the importance of this patient group. Additional efforts should be made to improve their ED care. Special interventions should target this patient group in order to decrease the high frequency and costs of emergency consultations caused by multi-substance users. [ABSTRACT FROM AUTHOR]

This study examines the relationship between female representation in different organisational positions and performance using a sample of Spanish secondary schools. These organizations have been usually depicted as ‘feminised environments’ although women in managerial positions are still underrepresented. Based on different theoretical approaches, we separately investigate the relationship between a greater female presence and school performance in three positions: a) as principal; b) on the management team; and c) as teachers. We also investigate if having a female leader exerts a significant mediator role on the relationship between greater female representation on the management team and teachers, respectively, and school performance. Our results reveal a positive and significant relationship between having a woman principal or a greater proportion of women teachers and school performance. However, the relationship between a high proportion of women on the management team and school performance is negative. We also find that a female principal does not play a significant role as mediator in the relationship between having a greater proportion of women on the management team and as teachers and school performance. [ABSTRACT FROM AUTHOR]