Browsing by Author "Kayri, M."

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The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines
(Iranian Scientific Society Medical Entomology, 2010) Kayri, M.
Background: Determining real effects on Internet dependency is too crucial with unbiased and robust statistical method. MARS is a new non-parametric method in use in the literature for parameter estimations of cause and effect based research. MARS can both obtain legible model curves and make unbiased parametric predictions. Methods: In order to examine the performance of MARS, MARS findings will be compared to Classification and Regression Tree (C&RT) findings, which are considered in the literature to be efficient in revealing correlations between variables. The data set for the study is taken from "The Internet Addiction Scale" (IAS), which attempts to reveal addiction levels of individuals. The population of the study consists of 754 secondary school students (301 female, 443 male students with 10 missing data). MARS 2.0 trial version is used for analysis by MARS method and C&RT analysis was done by SPSS. Results: MARS obtained six base functions of the model. As a common result of these six functions, regression equation of the model was found. Over the predicted variable, MARS showed that the predictors of daily Internet-use time on average, the purpose of Internet- use, grade of students and occupations of mothers had a significant effect (P<0.05). In this comparative study, MARS obtained different findings from C&RT in dependency level prediction. Conclusion: The fact that MARS revealed extent to which the variable, which was considered significant, changes the character of the model was observed in this study.
Analyzing Factor Structure of the Scales by Hierarchical Clustering Analysis: an Alternative Approach
(2010) Çokluk, O.; Büyüköztürk, S.; Kayri, M.
In this study, factor structure obtained by exploratory factor analysis, where principal components factor extraction technique of the "Epistemological Belief Questionnaire" was used, was compared to cluster constructs obtained by the agglomerative hierarchical clustering analysis. By using "Pearson Correlation" similarity criteria, it was discussed which construct obtained by "Complete Linkage" and "Ward's Linkage" methods was more concordant with exploratory factor analysis results. Research group of this study consisted of total 243 students who attended Ankara University, Faculty of Educational Sciences, Department of Preschool Teaching in 2006-2007 academic year. In the research group, 1-4 grade level students were included on a voluntary basis. As the cluster memberships obtained by Complete Linkage method were examined, it was determined that two items, unlike exploratory factor analysis, were replaced from the second cluster into the third. As the cluster memberships obtained by Ward's Linkage method were examined, it was seen that the construct obtained by the exploratory factor analysis was attained identically. © 2010, INSInet Publication.
Augmented Hunger Games Search Algorithm Using Logarithmic Spiral Opposition-Based Learning for Function Optimization and Controller Design
(King Saud University, 2024) Izci, D.; Ekinci, S.; Eker, E.; Kayri, M.
This paper explains the construction of a novel augmented hunger games search algorithm using a logarithmic spiral opposition-based learning technique. The proposed algorithm (LsOBL-HGS) is used as an efficient tool for both function optimization and controller design. To assess the performance of the algorithm for function optimization, benchmark functions from the CEC2017 test suite were employed and comparisons were made with available and good performing algorithms. In terms of controller design, the proposed LsOBL-HGS algorithm was utilized to design a FOPID controlled magnetic ball suspension system. Comparative assessments were also performed for FOPID controller design, as well using other state-of-the-art methods reported for the magnetic ball suspension system. The results showed that the proposed LsOBL-HGS algorithm has good capability for FOPID controller design employed in a magnetic ball suspension system as it provided an improvement of more than 13% in terms of the transient response-related parameters and more than 34% in terms of bandwidth compared to the best-reported approach used for comparisons. © 2022 Karabuk University
Data Optimization With Multilayer Perceptron Neural Network and Using New Pattern in Decision Tree Comparatively
(2010) Kayri, M.; Cokluk, O.
Problem statement: The aim of the present study is to exemplify the use of Artificial Neural Networks (ANN) for parameter prediction. Missing value or unreal approach to some questions in scale is a problem for unbiased findings. To learn a real pattern with ANN provides robust and unbiased parameter estimation. Approach: To this end, data was collected from 906 students using "Scale of student views about the expected situations and the current expectations from their families during learning process" for the study entitled "Student views about the expected situations and the current expectations from their families during learning process". In the study, first the initial data set gathered using the measurement tool and the new data set produced by Multi-Layer Receptors algorithm, which was considered as the highest predictive level of ANN for the research were individually analyzed by Chaid analysis and the results of the two analyses were compared. Results: The findings showed that as a result of Chaid analysis with the initial data set the variable "education level of mother" had a considerable effect on total score dependent variable, while "education level of father" was the influential variable on the attitude level in the data set predicted by ANN, unlike the previous model. Conclusion/Recommendations: The findings of the research show Artificial Neural Networks could be used for parameter estimation in cause-effect based studies. It is also thought the research will contribute to extensive use of advanced statistical methods. © 2010 Science Publications.
Examining the Influence of Narcissism and Some Demographic Variables on Online Shopping Addiction Via the Exhaustive Chaid Method
(Springer, 2025) Emin, C.; Kayri, M.; Doğan, E.
The literature posits that narcissism may theoretically influence online shopping addiction, with materialism being regarded as a consequence of narcissistic tendencies that subsequently exacerbate shopping addiction. Additionally, it is stated in the literature that age and gender are also associated with online shopping addiction. So, the present study aims to examine the effect of narcissism, age, and gender on online shopping addiction using the exhaustive CHAID analysis. In the research, which was designed as a survey and correlational study, data were collected from 1010 adults using the online shopping addiction scale and narcissism scale. The data obtained were analyzed with descriptive statistics and the exhaustive CHAID method. According to the analysis results, the participant group’s level of online shopping addiction was low, and their level of narcissism was moderate. In line with the results of the exhaustive CHAID analysis, the variables that most related to online shopping addiction were age, gender, and narcissism level. According to the analysis results, online shopping addiction decreases with advancing age. Women in younger age groups are more addicted to online shopping than men. Moreover, the increase in the level of narcissism is a condition that elevates the online shopping addiction of women in younger groups. It is reported in the literature that narcissism is more common in men and younger individuals. However, one of the most important study results is that, in this study, individuals with online shopping addiction are mostly relatively young female participants with high levels of narcissism. © The Author(s) 2024.
Investigation of Coronavirus Pandemic Indicators of the Countries With Hierarchical Clustering and Multidimensional Scaling
(Yuzuncu Yil Universitesi Tip Fakultesi, 2021) Güre, Ö.B.; Kayri, M.; Şevgin, H.
In this study it is aimed to analyze the similarities of 50 countries where coronavirus pandemic, which has been profoundly affecting the whole world socially, psychologically and economically, was mostly seen. The similarities of the countries were investigated with Hierarchical Cluster Analysis and Multi-dimensional Scaling Analysis, which are among multivariate statistical analysis techniques in terms of coronavirus pandemic indicators. The variables used in the analysis are death rat e, recovery rate, active rate, serious case rate, case rate per 1 million, death rate per 1 million, and test rate per 1 millio n. As a result of Hierarchical Cluster Analysis, the countries were divided into seven clusters. In the two-dimensional projections of Multidimensional Scaling, Kruskal stress statistics was found as 0,00001. According to this, a complete compatibility was found between data distances and configuration distances. Also, the fact that R2 is 1,00000 shows that the model is quite powerful. As a result of the study, the results of both methods were found to be very close to each other. In the same subgroup, Turkey; Peru, Poland, Panama, Romania, Netherlands and Kazakhstan take place. In the study; both developed and underdeveloped countries were found to be in the same cluster. This is a surprising situation. While developed countries are expected to be more effective in combating the epidemic, it was observed that they showed similarities with underdeveloped countries. © 2021, Yuzuncu Yil Universitesi Tip Fakultesi. All rights reserved.
A Novel Modified Opposition-Based Hunger Games Search Algorithm To Design Fractional Order Proportional-Integral Controller for Magnetic Ball Suspension System
(John Wiley and Sons Inc, 2022) Izci, D.; Ekinci, S.; Eker, E.; Kayri, M.
This study focuses on construction of novel enhanced metaheuristic algorithm using a modified opposition-based learning technique and the hunger games search algorithm. The proposed modified opposition-based hunger games search (mOBL-HGS) algorithm is aimed to be used as an efficient tool to tune a fractional order proportional-integral-derivative (FOPID) controller in order to control a magnetic ball suspension system with greater flexibility. The challenging benchmark functions from CEC2017 test suite are used to confirm the greater performance of the proposed mOBL-HGS algorithm. The proposed mOBL-HGS algorithm is also utilized to reach the optimum values of a FOPID controller employed in a magnetic ball suspension system in order to demonstrate its capability in terms of a complex real-world engineering problem. The latter case is confirmed through comparative evaluations of statistical analysis, convergence profile, transient response, frequency response, disturbance rejection, and robustness. The demonstrated results confirm the greater ability of the proposed approach to control a magnetic ball suspension system. © 2022 John Wiley & Sons Ltd.