Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32

Search results for: bootstrapping

32 Handling Missing Data by Using Expectation-Maximization and Expectation-Maximization with Bootstrapping for Linear Functional Relationship Model

Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, A. H. M. R. Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in two types of LFRM namely the full model of LFRM and in LFRM when the slope is estimated using a nonparametric method. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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31 Modern Imputation Technique for Missing Data in Linear Functional Relationship Model

Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, Rahmatullah Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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30 Identifying Psychosocial, Autonomic, and Pain Sensitivity Risk Factors of Chronic Temporomandibular Disorder by Using Ridge Logistic Regression and Bootstrapping

Authors: Haolin Li, Eric Bair, Jane Monaco, Quefeng Li

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The temporomandibular disorder (TMD) is a series of musculoskeletal disorders ranging from jaw pain to chronic debilitating pain, and the risk factors for the onset and maintenance of TMD are still unclear. Prior researches have shown that the potential risk factors for chronic TMD are related to psychosocial factors, autonomic functions, and pain sensitivity. Using data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study’s baseline case-control study, we examine whether the risk factors identified by prior researches are still statistically significant after taking all of the risk measures into account in one single model, and we also compare the relative influences of the risk factors in three different perspectives (psychosocial factors, autonomic functions, and pain sensitivity) on the chronic TMD. The statistical analysis is conducted by using ridge logistic regression and bootstrapping, in which the performance of the algorithms has been assessed using extensive simulation studies. The results support most of the findings of prior researches that there are many psychosocial and pain sensitivity measures that have significant associations with chronic TMD. However, it is surprising that most of the risk factors of autonomic functions have not presented significant associations with chronic TMD, as described by a prior research.

Keywords: autonomic function, OPPERA study, pain sensitivity, psychosocial measures, temporomandibular disorder

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29 Air Pollution and Respiratory-Related Restricted Activity Days in Tunisia

Authors: Mokhtar Kouki Inès Rekik

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This paper focuses on the assessment of the air pollution and morbidity relationship in Tunisia. Air pollution is measured by ozone air concentration and the morbidity is measured by the number of respiratory-related restricted activity days during the 2-week period prior to the interview. Socioeconomic data are also collected in order to adjust for any confounding covariates. Our sample is composed by 407 Tunisian respondents; 44.7% are women, the average age is 35.2, near 69% are living in a house built after the 1980, and 27.8% have reported at least one day of respiratory-related restricted activity. The model consists on the regression of the number of respiratory-related restricted activity days on the air quality measure and the socioeconomic covariates. In order to correct for zero-inflation and heterogeneity, we estimate several models (Poisson, Negative binomial, Zero inflated Poisson, Poisson hurdle, Negative binomial hurdle and finite mixture Poisson models). Bootstrapping and post-stratification techniques are used in order to correct for any sample bias. According to the Akaike information criteria, the hurdle negative binomial model has the greatest goodness of fit. The main result indicates that, after adjusting for socioeconomic data, the ozone concentration increases the probability of positive number of restricted activity days.

Keywords: bootstrapping, hurdle negbin model, overdispersion, ozone concentration, respiratory-related restricted activity days

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28 Asymmetrical Informative Estimation for Macroeconomic Model: Special Case in the Tourism Sector of Thailand

Authors: Chukiat Chaiboonsri, Satawat Wannapan

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This paper used an asymmetric informative concept to apply in the macroeconomic model estimation of the tourism sector in Thailand. The variables used to statistically analyze are Thailand international and domestic tourism revenues, the expenditures of foreign and domestic tourists, service investments by private sectors, service investments by the government of Thailand, Thailand service imports and exports, and net service income transfers. All of data is a time-series index which was observed between 2002 and 2015. Empirically, the tourism multiplier and accelerator were estimated by two statistical approaches. The first was the result of the Generalized Method of Moments model (GMM) based on the assumption which the tourism market in Thailand had perfect information (Symmetrical data). The second was the result of the Maximum Entropy Bootstrapping approach (MEboot) based on the process that attempted to deal with imperfect information and reduced uncertainty in data observations (Asymmetrical data). In addition, the tourism leakages were investigated by a simple model based on the injections and leakages concept. The empirical findings represented the parameters computed from the MEboot approach which is different from the GMM method. However, both of the MEboot estimation and GMM model suggests that Thailand’s tourism sectors are in a period capable of stimulating the economy.

Keywords: TThailand tourism, Maximum Entropy Bootstrapping approach, macroeconomic model, asymmetric information

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27 Two-Stage Hospital Efficiency Analysis Including Qualitative Evidence: A Greek Case

Authors: Panos Xenos, Milton Nektarios, John Yfantopoulos

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Background: Policy makers, professional organizations and payers have introduced a variety of initiatives and reforms for the health systems worldwide, aimed at improving hospital efficiency. Their efforts are concentrated in two main categories: to constrain increasing healthcare costs and to enhance quality of services provided. Research Objectives: This study examines the efficiency of 112 Greek public hospitals for the year 2009, evaluates the importance of bootstrapping techniques and investigates the effect of contextual factors on hospital efficiency. Furthermore, the effect of qualitative evidence, on hospital efficiency is explored using data from 28 large hospitals. Methods: We applied Data Envelopment Analysis, augmented by bootstrapping techniques, to estimate efficiency scores. In order to measure the effect of environmental factors on hospital efficiency we used Tobit regression analysis. The significance of our models is evaluated using statistical tests to compare distributions. Results: The Kolmogorov-Smirnov test between the original and the bootstrap-corrected efficiency indicates that their distributions are significantly different (p-value<0.01). The environmental factors, that seem to influence efficiency, are Occupancy Rating and the ratio between Outpatient Visits and Inpatient Days. Results indicate that the inclusion of the quality variable in DEA modelling generates statistically significant variations in efficiency scores (p-value<0.05). Conclusions: The inclusion of quality variables and the use of bootstrap resampling in efficiency analysis impose a statistically significant effect on the distribution of efficiency scores. As a policy conclusion we highlight the importance of these methods on hospital efficiency analysis and, by implication, on healthcare resource allocation.

Keywords: hospitals, efficiency, quality, data envelopment analysis, Greek public hospital sector

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26 Using the Bootstrap for Problems Statistics

Authors: Brahim Boukabcha, Amar Rebbouh

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The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

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25 The Use of Degradation Measures to Design Reliability Test Plans

Authors: Stephen V. Crowder, Jonathan W. Lane

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With short production development times, there is an increased need to demonstrate product reliability relatively quickly with minimal testing. In such cases there may be few if any observed failures. Thus it may be difficult to assess reliability using the traditional reliability test plans that measure only time (or cycles) to failure. For many components, degradation measures will contain important information about performance and reliability. These measures can be used to design a minimal test plan, in terms of number of units placed on test and duration of the test, necessary to demonstrate a reliability goal. In this work we present a case study involving an electronic component subject to degradation. The data, consisting of 42 degradation paths of cycles to failure, are first used to estimate a reliability function. Bootstrapping techniques are then used to perform power studies and develop a minimal reliability test plan for future production of this component.

Keywords: degradation measure, time to failure distribution, bootstrap, computational science

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24 Big Data Analytics and Data Security in the Cloud via Fully Homomorphic Encryption

Authors: Waziri Victor Onomza, John K. Alhassan, Idris Ismaila, Noel Dogonyaro Moses

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This paper describes the problem of building secure computational services for encrypted information in the Cloud Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy, confidentiality, availability of the users. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute theoretical presentations in high-level computational processes that are based on number theory and algebra that can easily be integrated and leveraged in the Cloud computing with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, homomorphic, homomorphic encryption scheme

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23 Big Data Analytics and Data Security in the Cloud via Fully Homomorphic Encyption Scheme

Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

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22 Reed: An Approach Towards Quickly Bootstrapping Multilingual Acoustic Models

Authors: Bipasha Sen, Aditya Agarwal

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Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4x and 7.4x respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.

Keywords: convolutional neural networks, language compatibility, low resource languages, multilingual automatic speech recognition

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21 Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops: Statistical Evaluation of the Potential Herbicide Savings

Authors: Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Henrik Skov Midtiby, Anders Krogh Mortensen, Sanmohan Baby

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This work contributes a statistical model and simulation framework yielding the best estimate possible for the potential herbicide reduction when using the MoDiCoVi algorithm all the while requiring a efficacy comparable to conventional spraying. In June 2013 a maize field located in Denmark were seeded. The field was divided into parcels which was assigned to one of two main groups: 1) Control, consisting of subgroups of no spray and full dose spraty; 2) MoDiCoVi algorithm subdivided into five different leaf cover thresholds for spray activation. In addition approximately 25% of the parcels were seeded with additional weeds perpendicular to the maize rows. In total 299 parcels were randomly assigned with the 28 different treatment combinations. In the statistical analysis, bootstrapping was used for balancing the number of replicates. The achieved potential herbicide savings was found to be 70% to 95% depending on the initial weed coverage. However additional field trials covering more seasons and locations are needed to verify the generalisation of these results. There is a potential for further herbicide savings as the time interval between the first and second spraying session was not long enough for the weeds to turn yellow, instead they only stagnated in growth.

Keywords: herbicide reduction, macrosprayer, weed crop discrimination, site-specific, sprayer boom

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20 Early Warning System of Financial Distress Based On Credit Cycle Index

Authors: Bi-Huei Tsai

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Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightly-distressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models, are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the two-stage model incorporating financial ratios, corporate governance and market factors has the lowest misclassification error rate. The two-stage model is more accurate than the one-stage model as its distressed cut-off indicators are adjusted according to the macroeconomic-based credit cycle index.

Keywords: Multinomial logit model, corporate governance, company failure, reorganization, bankruptcy

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19 The Link between Childhood Maltreatment and Psychological Distress: The Mediation and Moderation Roles of Cognitive Distortion, Alexithymia, and Eudemonic Well-Being

Authors: Siqi Fang, Man Cheung Chung

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This study examined the inter-relationship between childhood maltreatment, cognitive distortion, alexithymia, eudemonic well-being, and psychological distress. One hundred and eighty-two university students participated in the study and completed an online survey comprising the Childhood Trauma Questionnaire, Cognitive Distortion Scale, Toronto Alexithymia Scale, Psychological Well-Being Scale, and General Health Questionnaire-28. Hierarchical multiple regression analysis showed that child maltreatment, perceptions of hopelessness and helplessness, preoccupation with danger, personal growth, and purpose in life predicted psychological distress. However, alexithymia was not a significant predictor. Further analysis using the regression models with bootstrapping procedure showed that feeling hopeless, helpless and preoccupation with danger mediated the path between child maltreatment and psychological distress. Meanwhile, coping with beliefs in personal growth and life purpose moderated the mediation effects of distorted cognition on psychological distress. To conclude, childhood maltreatment is associated with psychological distress. This relationship is influenced by people’s perceptions of life being hopeless, helpless or dangerous. At the same time, the effect of hopelessness, helplessness, and feelings of danger also depends on the degree of using coping strategies of positive psychological functioning.

Keywords: alexithymia, childhood maltreatment, cognitive distortion, eudemonic well-being, psychological distress

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18 Internet Use, Social Networks, Loneliness and Quality of Life among Adults Aged 50 and Older: Mediating and Moderating Effects

Authors: Rabia Khaliala, Adi Vitman-Schorr

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Background: The increase in longevity of people on one hand, and on the other hand the fact that the social networks in later life become increasingly narrower, highlight the importance of Internet use to enhance quality of life (QoL). However, whether Internet use increases or decreases social networks, loneliness and quality of life is not clear-cut. Purposes: To explore the direct and/or indirect effects of Internet use on QoL, and to examine whether ethnicity and time the elderly spent with family moderate the mediation effect of Internet use on quality of life throughout loneliness. Methods: This descriptive-correlational study was carried out in 2016 by structured interviews with a convenience sample of 502 respondents aged 50 and older, living in northern Israel. Bootstrapping with resampling strategies was used for testing mediation a model. Results: Use of the Internet was found to be positively associated with QoL. However, this relationship was mediated by loneliness, and moderated by the time the elderly spent with family members. In addition, respondents' ethnicity significantly moderated the mediation effect between Internet use and loneliness. Conclusions: Internet use can enhance QoL of older adults directly or indirectly by reducing loneliness. However, these effects are conditional on other variables. The indirect effect moderated by ethnicity, and the direct effect moderated by the time the elderly spend with their families. Researchers and practitioners should be aware of these interactions which can impact loneliness and quality of life of older persons differently.

Keywords: internet use, loneliness, quality of life, social contacts

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17 The Impact of Life Satisfaction on Substance Abuse: Delinquency as a Mediator

Authors: Mahadzirah Mohamad, Morliyati Mohammad, Nor Azman Mat Ali, Zainudin Awang

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Globally, youth substance abuse has been identified as the problem that causes substantial damage not only to individuals, but also to families and communities. In addition, substance abuse youths have become unproductive resources that would play lesser roles in the nation’s development. The increasing trend of substance abuse among youths has raised a lot of concern among various quarters in Malaysia. It has also been reported that Malay youths are the majority group involved in substance abuse. However, it was noted that life satisfaction had been found to be an important mitigating factor that addressed substance abuse. The objectives of the study were twofold: firstly, to ascertain the effect of life satisfaction on substance abuse among Malay youth. Secondly, to identify the role of delinquency on the relationship between life satisfaction and substance abuse. This study adopted a cross-sectional research design. Self-administered questionnaires were distributed to 500 Malay youths at the youth programmes using a two-step sampling technique: area sampling and systematic sampling. The research hypotheses were tested using Structural Equation Modelling. The findings of the study revealed that there is no significance relationship between life satisfaction and substance abuse. There is a significant inverse relationship between life satisfaction and delinquency. Moreover, delinquency has a positive significant influence on substance abuse. The use of Bootstrapping analysis proved that delinquency plays a full mediating role in the relationship between life satisfaction and substance abuse. This study suggested that life satisfaction has no effect on youth substance abuse. In order to reduce substance abuse, efforts should be undertaken to reduce delinquency behaviour by increasing youth life satisfaction.

Keywords: delinquency, life satisfaction, substance abuse, youth

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16 Modelling Conceptual Quantities Using Support Vector Machines

Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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15 Factors Influencing the Enjoyment and Performance of Students in Statistics Service Courses: A Mixed-Method Study

Authors: Wilma Coetzee

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Statistics lecturers experience that many students who are taking a service course in statistics do not like statistics. Students in these courses tend to struggle and do not perform well. This research takes a look at the student’s perspective, with the aim to determine how to change the teaching of statistics so that students will enjoy it more and perform better. Questionnaires were used to determine the perspectives of first year service statistics students at a South African university. Factors addressed included motivation to study, attitude toward statistics, statistical anxiety, mathematical abilities and tendency to procrastinate. Logistic regression was used to determine what contributes to students performing badly in statistics. The results show that the factors that contribute the most to students performing badly are: statistical anxiety, not being motivated and having had mathematical literacy instead of mathematics in secondary school. Two open ended questions were included in the questionnaire: 'I will enjoy statistics more if…' and 'I will perform better in statistics if…'. The answers to these questions were analyzed using qualitative methods. Frequent themes were identified for each of the questions. A simulation study incorporating bootstrapping was done to determine the saturation of the themes. The majority of the students indicated that they would perform better in statistics if they studied more, managed their time better, had a flare for mathematics and if the lecturer was able to explain difficult concepts better. They also want more active learning. To ensure that students enjoy statistics more, they want an active learning experience. They want fun activities, more interaction with the lecturer and with one another, more computer based problems, and more challenges. They want a better understanding of the subject, want to understand the relevance of statistics to their future career and want excellent lecturers. These findings can be used to direct the improvement of the tuition of statistics.

Keywords: active learning, performance in statistics, statistical anxiety, statistics education

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14 Association among Trait Mindfulness, Leukocyte Telomere Length, and Psychological Symptoms in Singaporean Han Chinese

Authors: Shian-Ling Keng, Onn Siong Yim, Poh San Lai, Soo Chong Chew, Anne Chong, Richard Ebstein

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Research has demonstrated a positive association between mindfulness meditation and physical health. Little work, however, has examined the association between trait mindfulness and leukocyte telomere length (LTL), an emerging marker of cellular aging. The present study aimed to examine whether facets of trait mindfulness are correlated with longer LTL in a Singaporean Han Chinese sample and whether these facets may mediate the association between psychological symptoms and LTL. 158 adults (mean age = 27.24 years) completed measures assessing trait mindfulness and psychological symptoms (i.e., depression and stress) and provided blood samples for analyses of LTL using qPCR. Multiple regression analyses were conducted to assess the association between facets of trait mindfulness and LTL. Bootstrapping-based mediational analyses were run to examine the role of trait mindfulness as a mediator of the association between psychological symptoms and LTL. Of five facets of trait mindfulness (describe, act with awareness, observe, nonreactivity, and nonjudging), nonreactivity was significantly associated with LTL, after controlling for the effects of age, gender, and education, β = .21, p = .006. Further, there was a trend for overall trait mindfulness, β = .15, p = .06, and nonjudging, β = .13, p = .095, to each predict longer LTL. Nonreactivity significantly mediated the association between depression and LTL, BCa 95% CI [-.004, -.0004], p=.03, as well as the association between stress and LTL, BCa 95% CI [-.004, -.0004], p=.04. The results provide preliminary evidence for a positive association between selected facets of trait mindfulness and slower cellular aging, indexed by LTL. The findings suggest that individuals who are high on equanimity may experience slower aging at the cellular level, presumably through engaging in more effective coping mechanisms and modulation of stress. The findings also highlight the role of nonreactivity as a potential mechanism that underlies the association between LTL and psychological symptoms.

Keywords: depression, mindfulness, stress, telomere length

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13 The Mediating Role of Early Maladaptive Schemas in the Relationship between Attachment and Trait Anger and Anger Expression

Authors: Ayperi̇ Haspolat Özcan, Meltem Anafarta Şendağ

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This study aimed to establish a model in the light of current approaches for understanding the mediating role of early maladaptive schemas in the relationship between attachment and anger. Accordingly, the proposed mediation model was tested by mediation with bootstrapping technique, considering gender and attachment figure differences. The university students (N= 444) with ages ranging from 17 to 28 participated in the study. Participants filled out Parental and Peer Attachment Scale Short Form, Young Schema Questionnaire - Short Form 3, Trait Anger and Anger Expression Scales. The mediating role of early maladaptive schemas (impaired autonomy, disconnection and rejection, unrelenting standards, other-directedness, and impaired limits) in the relationship between attachment (mother and father) and anger aspects (trait anger, anger in, anger out and anger control) were found to be significant for both male and female participants. Separate mediation analyses for both genders and different attachment figures have also drawn attention to noticeable differences in the results. Specifically, for females, various paths were discovered in predicting various aspects of anger (anger in, anger out, anger control, and trait anger). On the other hand, for males only anger directed inwards was found to be predicted by any source of attachment through disconnection and rejection schema only. These obvious gender differences in understanding the mechanism of anger are discussed in the light of cultural gender roles and the social acceptance of anger in males. In the area of application, the study of various aspects of anger with particular attention to attachment and early maladaptive schemas as well as the importance of distinguishing the gender differences are emphasized as important points.

Keywords: anger expression, attachment, early maladaptive schemas, trait anger

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12 The Role of Psychosis Proneness in the Association of Metacognition with Psychological Distress in Non-Clinical Population

Authors: Usha Barahmand, Ruhollah Heydari Sheikh Ahmad

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Distress refers to an unpleasant metal state or emotional suffering marked by negative affect such as depression (e.g., lost interest; sadness; hopelessness), anxiety (e.g., restlessness; feeling tense). These negative affect have been mostly suggested to be concomitant of metal disorders such as positive psychosis symptoms and also of proneness to psychotic features in non-clinical population. Psychotic features proneness including hallucination, delusion and schizotypal traits, have been found to be associated with metacognitive beliefs. Metacognition has been conceptualized as ‘thinking about thoughts, monitoring and controlling of cognitive processes’. The aim of the current study was to investigate the role of psychosis proneness in the association of metacognitions and distress. We predicted psychosis proneness would mediate the association of metacognitive beliefs and the distress. A sample of 420 university students was randomly recruited to endorse questionnaires of the study that consisted of DASS-21questionnaire for assessing levels of distress, Cartwright–Hatton & Wells, Meta-cognitions Questionnaire (MCQ-30) for assessing metacognitive beliefs, Launay-Slade Hallucination Scale-revised (LSHS-R), Peters et al. Delusions Inventory, Schizotypal Personality Questionnaire-Brief. Conducting a bootstrapping approach in order to investigate our hypothesis, the result showed that there was no a direct association between metacognitive dimensions and psychological distress and psychosis proneness significantly mediated the association. Finding suggested that individuals with dysfunctional metacognitive beliefs experience high levels of distress if they are prone to psychosis symptoms. In other words, psychosis proneness is a path through which individuals with dysfunctional metacognitions experience high levels of psychological distress.

Keywords: metacognition, non-clinical population, psychological distress, psychosis proneness

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11 Probability Sampling in Matched Case-Control Study in Drug Abuse

Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

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Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

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10 The Antecedents of Green Purchase Intention in Nigeria: Mediating Effect of Perceived Behavioral Control

Authors: Victoria Masi Haruna Karatu, Nik Kamariah Nikmat

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In recent times awareness about the environment and green purchase has been on the increase across nations due to global warming. Previous researchers have attempted to determine what actually influences the purchase intention of consumers in this environmentally conscious epoch. The consumers too have become conscious of what to buy and who to buy from in their purchasing decisions as this action will reflect their concern about the environment and their personal well-being. This trend is a widespread phenomenon in most developed countries of the world. On the contrary evidence revealed that only 5% of the populations of Nigeria involve in green purchase activities thus making the country lag behind its counterparts in green practices. This is not a surprise as Nigeria is facing problems of inadequate green knowledge, non-enforcement of environmental regulations, sensitivity to the price of green products when compared with the conventional ones and distrust towards green products which has been deduced from prior studies of other regions. The main objectives of this study is to examine the direct antecedents of green purchase intention (green availability, government regulations, perceived green knowledge, perceived value and green price sensitivity) in Nigeria and secondly to establish the mediating role of perceived behavioral control on the relationship between these antecedents and green purchase intention. The study adopts quantitative method whereby 700 questionnaires were administered to lecturers in three Nigerian universities. 502 datasets were collected which represents 72 percent response rate. After screening the data only 440 were usable and analyzed using structural equation modeling (SEM) and bootstrapping. From the findings, three antecedents have significant direct relationships with green purchase intention (perceived green knowledge, perceived behavioral control, and green availability) while two antecedents have positive and significant direct relationship with perceived behavioral control (perceived value and green price sensitivity). On the other hand, PBC does not mediate any of the paths from the predictors to criterion variable. This result is discussed in the Nigerian context.

Keywords: Green Availability, Green Price Sensitivity, Green Purchase Intention, Perceived Green Knowledge, Perceived Value

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9 Physical Activity and Academic Achievement: How Physical Activity Should Be Implemented to Enhance Mathematical Achievement and Mathematical Self-Concept

Authors: Laura C. Dapp, Claudia M. Roebers

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Being physically active has many benefits for children and adolescents. It is crucial for various aspects of physical and mental health, the development of a healthy self-concept, and also positively influences academic performance and school achievement. In addressing the still incomplete understanding of the link between physical activity (PA) and academic achievement, the current study scrutinized the open issue of how PA has to be implemented to positively affect mathematical outcomes in N = 138 fourth graders. Therefore, the current study distinguished between structured PA (formal, organized, adult-led exercise and deliberate sports practice) and unstructured PA (non-formal, playful, peer-led physically active play and sports activities). Results indicated that especially structured PA has the potential to contribute to mathematical outcomes. Although children spent almost twice as much time engaging in unstructured PA as compared to structured PA, only structured PA was significantly related to mathematical achievement as well as to mathematical self-concept. Furthermore, the pending issue concerning the quantity of PA needed to enhance children’s mathematical achievement was addressed. As to that, results indicated that the amount of time spent in structured PA constitutes a critical factor in accounting for mathematical outcomes, since children engaging in PA for two hours or more a week were shown to be both the ones with the highest mathematical self-concept as well as those attaining the highest mathematical achievement scores. Finally, the present study investigated the indirect effect of PA on mathematical achievement by controlling for the mathematical self-concept as a mediating variable. The results of a maximum likelihood mediation analysis with a 2’000 resampling bootstrapping procedure for the 95% confidence intervals revealed a full mediation, indicating that PA improves mathematical self-concept, which, in turn, positively affects mathematical achievement. Thus, engaging in high amounts of structured PA constitutes an advantageous leisure activity for children and adolescents, not only to enhance their physical health but also to foster their self-concept in a way that is favorable and encouraging for promoting their academic achievement. Note: The content of this abstract is partially based on a paper published elswhere by the authors.

Keywords: Academic Achievement, Mathematical Performance, Physical Activity, Self-Concept

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8 Phenotype Prediction of DNA Sequence Data: A Machine and Statistical Learning Approach

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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7 Phenotype Prediction of DNA Sequence Data: A Machine and Statistical Learning Approach

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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6 Information Technology Capabilities and Organizational Performance: Mediating Role of Strategic Benefits of It: A Comparison between China and Pakistan

Authors: Rehan Ullah

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The primary purpose of the study is to observe the relationship that exists between the organizational information technology (IT) capabilities and the organizational performance in China and Pakistan. Nations like China and Pakistan utilize modern techno-how to enhance their production endeavors. Therefore, making a wide-ranging comparison of the manufacturing services between China and Pakistan was chosen due to numerous reasons. One reason for carrying out this comparison is to determine how IT of the two countries enhances organizational competency on small and medium-sized manufacturing enterprises (SMEs). The study hypothesized that organizational IT capabilities (IT infrastructure, IT competence) have a positive influence on organizational performance and the strategic benefits of IT have a mediating effect on the relationship between IT capability and organizational performance. To investigate the relationship between IT capabilities and organizational performance, surveys were sent to managers of small, medium-sized manufacturing organizations located in the southwestern region, Sichuan province of China, and Pakistani companies, which are located in Islamabad, Lahore, and Karachi. These cities were selected as typical representatives of each country. Organizational performance has been measured in terms of profitability, organizational success, growth, market share, and innovativeness. Out of 400 surveys distributed to different manufacturing organizations, 303 usable and valid responses were received that are analyzed in this research. The data were examined using SPSS and Smart PLS computer software. The results of the study, including the descriptive statistics of each variable, are used. The outer model has been measured with considerations to content validity, discriminant validity, and convergent validity. The path coefficients among the constructs were also computed when analyzing the structural model using the bootstrapping technique. The analysis of data from both China and Pakistan yields an identical but unique result. The results show that IT infrastructure, IT competence, strategic benefits of IT are all correlated to the performance of the organizations. Moreover, strategic benefits of IT have been proved to mediate the relationship between IT capabilities and organization performance. The author, concerning the role of IT on the performance of an organization, highlights the different aspects as well as its benefits in an organization. The overall study concludes several implications for both managers and academicians. It also provides the limitations of the study and offers recommendations for future studies and practice.

Keywords: organizational performance, IT capabilities, IT infrastructure, IT competence, strategic benefits of IT, China, Pakistan

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5 A Study on the Effect of the Work-Family Conflict on Work Engagement: A Mediated Moderation Model of Emotional Exhaustion and Positive Psychology Capital

Authors: Sungeun Hyun, Sooin Lee, Gyewan Moon

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Work-Family Conflict has been an active research area for the past decades. Work-Family Conflict harms individuals and organizations, it is ultimately expected to bring the cost of losses to the company in the long run. WFC has mainly focused on effects of organizational effectiveness and job attitude such as Job Satisfaction, Organizational Commitment, and Turnover Intention variables. This study is different from consequence variable with previous research. For this purpose, we selected the positive job attitude 'Work Engagement' as a consequence of WFC. This research has its primary research purpose in identifying the negative effects of the Work-Family Conflict, and started out from the recognition of the problem that the research on the direct relationship on the influence of the WFC on Work Engagement is lacking. Based on the COR(Conservation of resource theory) and JD-R(Job Demand- Resource model), the empirical study model to examine the negative effects of WFC with Emotional Exhaustion as the link between WFC and Work Engagement was suggested and validated. Also, it was analyzed how much Positive Psychological Capital may buffer the negative effects arising from WFC within this relationship, and the Mediated Moderation model controlling the indirect effect influencing the Work Engagement by the Positive Psychological Capital mediated by the WFC and Emotional Exhaustion was verified. Data was collected by using questionnaires distributed to 500 employees engaged manufacturing, services, finance, IT industry, education services, and other sectors, of which 389 were used in the statistical analysis. The data are analyzed by statistical package, SPSS 21.0, SPSS macro and AMOS 21.0. The hierarchical regression analysis, SPSS PROCESS macro and Bootstrapping method for hypothesis testing were conducted. Results showed that all hypotheses are supported. First, WFC showed a negative effect on Work Engagement. Specifically, WIF appeared to be on more negative effects than FIW. Second, Emotional exhaustion found to mediate the relationship between WFC and Work Engagement. Third, Positive Psychological Capital showed to moderate the relationship between WFC and Emotional Exhaustion. Fourth, the effect of mediated moderation through the integration verification, Positive Psychological Capital demonstrated to buffer the relationship among WFC, Emotional Exhastion, and Work Engagement. Also, WIF showed a more negative effects than FIW through verification of all hypotheses. Finally, we discussed the theoretical and practical implications on research and management of the WFC, and proposed limitations and future research directions of research.

Keywords: emotional exhaustion, positive psychological capital, work engagement, work-family conflict

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4 Analysis of Eco-Efficiency and the Determinants of Family Agriculture in Southeast Spain

Authors: Emilio Galdeano-Gómez, Ángeles Godoy-Durán, Juan C. Pérez-Mesa, Laura Piedra-Muñoz

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Eco-efficiency is receiving ever-increasing interest as an indicator of sustainability, as it links environmental and economic performances in productive activities. In agriculture, these indicators and their determinants prove relevant due to the close relationships in this activity between the use of natural resources, which is generally limited, and the provision of basic goods to society. In this context, various analyses have focused on eco-efficiency by considering individual family farms as the basic production unit. However, not only must the measure of efficiency be taken into account, but also the existence of a series of factors which constitute socio-economic, political-institutional, and environmental determinants. Said factors have been studied to a lesser extent in the literature. The present work analyzes eco-efficiency at a micro level, focusing on small-scale family farms as the main decision-making units in horticulture in southeast Spain, a sector which represents about 30% of the fresh vegetables produced in the country and about 20% of those consumed in Europe. The objectives of this study are a) to obtain a series of eco-efficiency indicators by estimating several pressure ratios and economic value added in farming, b) to analyze the influence of specific social, economic and environmental variables on the aforementioned eco-efficiency indicators. The present work applies the method of Data Envelopment Analysis (DEA), which calculates different combinations of environmental pressures (water usage, phytosanitary contamination, waste management, etc.) and aggregate economic value. In a second stage, an analysis is conducted on the influence of the socio-economic and environmental characteristics of family farms on the eco-efficiency indicators, as endogeneous variables, through the use of truncated regression and bootstrapping techniques, following Simar-Wilson methodology. The results reveal considerable inefficiency in aspects such as waste management, while there is relatively little inefficiency in water usage and nitrogen balance. On the other hand, characteristics, such as product specialization, the adoption of quality certifications and belonging to a cooperative do have a positive impact on eco-efficiency. These results are deemed to be of interest to agri-food systems structured on small-scale producers, and they may prove useful to policy-makers as regards managing public environmental programs in agriculture.

Keywords: data envelopment analysis, eco-efficiency, family farms, horticulture, socioeconomic features

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3 Examining Moderating Mechanisms of Alignment Practice and Community Response through the Self-Construal Perspective

Authors: Chyong-Ru Liu, Wen-Shiung Huang, Wan-Ching Tang, Shan-Pei Chen

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Two of the biggest challenges companies involved in sports and exercise information services face are how to strengthen participation in virtual sports/exercise communities and how to increase the ongoing participatoriness of those communities. In the past, relatively little research has explored mechanisms for strengthening alignment practice and community response from the perspective of self-construal, and as such this study seeks to explore the self-construal of virtual sports/exercise communities, the role it plays in the emotional commitment of forming communities, and the factor that can strengthen alignment practice. Moreover, which factor of the emotional commitment of forming virtual communities have the effect of strengthening interference in the process of transforming customer citizenship behaviors? This study collected 625 responses from the two leading websites in terms of fan numbers in the provision of information on road race and marathon events in Taiwan, with model testing conducted through linear structural equation modelling and the bootstrapping technique to test the proposed hypotheses. The results proved independent construal had a stronger positive direct effect on affective commitment to fellow customers than did interdependent construal, and the influences of affective commitment to fellow customers in enhancing customer citizenship behavior. Public self-consciousness moderates the relationships among independent self-construal and interdependent self-construal on effective commitment to fellow customers. Perceived playfulness moderates the relationships between effective commitment to fellow customers and customer citizenship behavior. The findings of this study provide significant insights for the researchers and related organizations. From the theoretical perspective, this is empirical research that investigated the self-construal theory and responses (i.e., affective commitment to fellow customers, customer citizenship behavior) in virtual sports/exercise communities. We further explore how to govern virtual sports/exercise community participants’ heterogeneity through public self-consciousness mechanism to align participants’ affective commitment. Moreover, perceived playfulness has the effect of strengthening effective commitment to fellow customers with customer citizenship behaviors. The results of this study can provide a foundation for the construction of future theories and can be provided to related organizations for reference in their planning of virtual communities.

Keywords: self-construal theory, public self-consciousness, affective commitment, customer citizenship behavior

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