Correlation Analysis Techniques Research Articles

The Effect of Digital Literacy, Technological Adoption, and Collaborative Learning on Student Engagement in Business Education

This study explores the influence of digital literacy, technological adoption, and collaborative learning on student engagement in business education. Utilizing a quantitative research design, data were collected from 300 undergraduate students enrolled in business programs through a structured questionnaire. The analysis employed descriptive statistics, correlation analysis, and multiple regression techniques to investigate the relationships between the variables. The results revealed that digital literacy, technological adoption, and collaborative learning significantly contribute to student engagement, with collaborative learning emerging as the strongest predictor. The findings underscore the importance of enhancing digital literacy and promoting technology use in educational settings, alongside fostering collaborative learning experiences, to improve student engagement in business education. These insights have implications for educators and curriculum designers seeking to create more effective and engaging learning environments.

Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities

Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.

Pengaruh Kohesivitas Kelompok dan Motivasi Kerja terhadap Kinerja Biro Sumber Daya Manusia, Organisasi, dan Tata Laksana, Sekretariat Kabinet

The Bureau of Human Resources, Organization, and Administration (SDMOT) of the Cabinet Secretariat experienced a performance decline in 2018, with personnel services decreasing by 6.98%, training utilization decreasing by 7.77%, and satisfaction with personnel service output decreasing by 7.71%. This decline is suspected to be due to a lack of group cohesiveness and work motivation among SDMOT employees. The purpose of this research is to determine the impact of group cohesiveness and work motivation on the performance of the SDMOT Bureau. This study uses a quantitative approach. Validity testing was conducted using expert judgment, discrimination power using the item-total correlation analysis technique, and reliability using Cronbach's alpha variance analysis, while data analysis used multiple regression techniques. The results show a very strong relationship between group cohesiveness and performance. The adjusted R2 value of group cohesiveness on performance is 0.644, which means that group cohesiveness contributes 64.4% to performance. Work motivation has a significantly positive effect on performance. The adjusted R2 value is 0.678, indicating that work motivation contributes 67.8% to performance. Together, group cohesiveness and work motivation on performance are seen from the correlation coefficient (R) of 0.861, which shows a very strong relationship between the independent variables of group cohesiveness and work motivation with the dependent variable performance. The combined contribution of group cohesiveness and work motivation to performance is 72.7%, while 27.3% is influenced by other variables outside the study.

The Effect of Graduate Unemployment on Career Development in Ghana

This study examines the effect of unemployed graduates in Ghana on their career development. The study adopts a mixed research methodology through a structured questionnaire and interview guide. Purposive sampling was employed for a sample of 250 respondents, including both private and public universities and Human Resource professionals in the Greater Accra region of Ghana. Based on content analysis, correlation and regression analysis techniques, the study found that the unemployment situation in Ghana can also be attributed to the programmes pursued in the universities. The study discovered that one key demographic characteristic that might influence graduate unemployment would be the age of the graduates. The study found that graduates who offered psychology, sociology, physical sciences, Mathematics, engineering, government, political studies, Health (Nursing, Doctors, etc.), arts and history normally find it difficult to be employed in Ghana. The findings of the study further demonstrated that unemployment status has a negative and significant impact on the career development of graduates. The study recommended that the majority of the teaching that goes on in our universities should be reviewed in terms of projects and skills that are relevant to the current economy and workplace demands.

Extraordinarily Time- and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep.

Canonical correlation analysis (CCA) is a correlation analysis technique that is widely used in statistics and the machine-learning community. However, the high complexity involved in the training process lays a heavy burden on the processing units and memory system, making CCA nearly impractical in large-scale data. To overcome this issue, a novel CCA method that tries to carry out analysis on the dataset in the Fourier domain is developed in this article. Appling Fourier transform on the data, we can convert the traditional eigenvector computation of CCA into finding some predefined discriminative Fourier bases that can be learned with only element-wise dot product and sum operations, without complex time-consuming calculations. As the eigenvalues come from the sum of individual sample products, they can be estimated in parallel. Besides, thanks to the data characteristic of pattern repeatability, the eigenvalues can be well estimated with partial samples. Accordingly, a progressive estimate scheme is proposed, in which the eigenvalues are estimated through feeding data batch by batch until the eigenvalues sequence is stable in order. As a result, the proposed method shows its characteristics of extraordinarily fast and memory efficiencies. Furthermore, we extend this idea to the nonlinear kernel and deep models and obtained satisfactory accuracy and extremely fast training time consumption as expected. An extensive discussion on the fast Fourier transform (FFT)-CCA is made in terms of time and memory efficiencies. Experimental results on several large-scale correlation datasets, such as MNIST8M, X-RAY MICROBEAM SPEECH, and Twitter Users Data, demonstrate the superiority of the proposed algorithm over state-of-the-art (SOTA) large-scale CCA methods, as our proposed method achieves almost same accuracy with the training time of our proposed method being 1000 times faster. This makes our proposed models best practice models for dealing with large-scale correlation datasets. The source code is available at https://github.com/Mrxuzhao/FFTCCA.

Interrelation of Spiritualism and Materialism of Madrasah Teachers

While materialism is strongly spreading in society, one of which is in the educational environment, madrasah teachers are supposed to have high spiritualism to be able to control it. This research aims to analyze the interrelation of spiritualism and materialism of madrasah teachers in East Kalimantan. The research used a quantitative-correlative approach with 1604 madrasah teachers in East Kalimantan as the research population. The selected sample, 241 State Madrasah Aliyah teachers, was taken using a purposive sampling technique. Data was collected using a questionnaire instrument, which was then analyzed using linear regression correlation analysis techniques. The research results show that there is a very significant relationship between spiritualism and materialism of madrasah teachers. The higher the teacher's spiritualism, the lower the materialism, conversely the lower the spiritualism, the higher the teacher's materialism. This research has implications for the importance of building the spiritualism of madrasah teachers so that they are not easily influenced by the strong materialism in the current era.

Improving IIoT security: Unveiling threats through advanced side-channel analysis

The widespread deployment of IIoT edge devices makes them attractive victims for malicious activities. Consequently, how to implement trustworthy operations becomes a realistic topic in embedded systems. While most current physical systems for detecting malicious activities primarily focus on identifying known intrusion codes at the block level, they ignore that even an unnoticeable injected function can result in system-wide loss of security. In this paper, we propose a framework called CNDSW built on deep-learning side-channel analysis for function-level industrial control flow integrity monitoring. By collaboratively utilizing correlation analysis and deep-learning techniques, the dual window sliding monitoring mechanism in the proposed CNDSW framework demonstrates a real-time code intrusion tracking capacity on embedded controllers with a 99% detection accuracy on average. Instead of focusing on known block-level intrusions, we experimentally show that our model is feasible to detect function-level code intrusions without knowing the potential threat type. Besides, we further explore how different configurations of the CNDSW framework can help the monitoring process with different emphases and to which extent the model can concurrently detect multiple code intrusion activities. All our experiments are conducted on 32-bit ARM Cortex-M4 and 8-bit RISC MCUs across five different control flow programs, providing a comprehensive evaluation of the framework’s capabilities.

Impact of elevated IOP on lamina cribrosa oxygenation; A combined experimental-computational study on monkeys.

Our goal is to evaluate how lamina cribrosa (LC) oxygenation is affected by the tissue distortions resulting from elevated IOP. Experimental study on monkeys. Four healthy monkey eyes with OCT scans with IOP of 10 to 50 mmHg, and then with histological sections of LC. Since in-vivo LC oxygenation measurement is not yet possible, we used 3D eye-specific numerical models of the LC vasculature which we subjected to experimentally-derived tissue deformations. We reconstructed 3D models of the LC vessel networks of 4 healthy monkey eyes from histological sections. We also obtained in-vivo IOP-induced tissue deformations from a healthy monkey using OCT images and digital volume correlation analysis techniques. The extent that LC vessels distort under a given OCT-derived tissue strain remains unknown. We biomechanics-based mapping techniques: cross-sectional and isotropic. The hemodynamics and oxygenations of the four vessel networks were simulated for deformations at several IOPs up to 60mmHg. The results were used to determine the effects of IOP on LC oxygen supply, assorting the extent of tissue mild and severe hypoxia. IOP-induced deformation, vasculature structure, blood supply, and oxygen supply for LC region. IOP-induced deformations reduced LC oxygenation significantly. More than 20% of LC tissue suffered from mild hypoxia when IOP reached 30 mmHg. Extreme IOP(>50mmHg) led to large severe hypoxia regions (>30%) in the isotropic mapping cases. Our models predicted that moderately elevated IOP can lead to mild hypoxia in a substantial part of the LC, which, if sustained chronically, may contribute to neural tissue damage. For extreme IOP elevations, severe hypoxia was predicted, which would potentially cause more immediate damage. Our findings suggest that despite the remarkable LC vascular robustness, IOP-induced distortions can potentially contribute to glaucomatous neuropathy.

EXAMINING SPORTSPERSONSHIP ORIENTATIONS AND ANGER EXPRESSION STYLES OF STUDENTS PARTICIPATING IN THE FACULTY OF SPORTS SCIENCES PHYSICAL APTITUDE TEST

Background/aim: This study aimed to examine the correlation between sportspersonship orientations and anger expression styles of candidates to enter sports sciences faculties via physical aptitude test. In addition, the study also examined the effect of gender, athlete license, monthly income, and sports experience of the participants on sportspersonship orientation and anger expression styles. Materials and methods: To collect data, the Personal Information Form, the Multidimensional Sportspersonship Orientation Scale, and the Trait Anger and Anger Expression Styles Scales were utilized. The study sample included 324 students, 178 (54.9%) female and 146 (45.1%) males, who were candidates to enter the faculty of sports sciences. Data were analyzed using frequency, arithmetic mean, standard deviation, Pearson correlation analysis, t-test, and ANOVA analysis techniques. Results: The study's findings showed statistically significant results in the variables of monthly income and sportspersonship orientation history and an opposite relationship between sportspersonship orientation and anger expression styles. Conclusion: These concepts, examined in this study, are crucial factors that can significantly impact the participants' sporting lives and athletic performance, either positively or negatively.

Unveiling the Link between Stress Levels and Job Performance among Women in Nepal Banking Sector

The banking sector in Nepal heavily relies on the contributions of female employees. However, concerns regarding the impact of stress levels on their job performance have emerged, given the industry's high-pressure nature and cultural dynamics. This study aims to explore the relationship between stress levels and productivity, efficiency, job satisfaction, and retention rates among female banking professionals in Nepal. A comprehensive investigation was conducted using a Likert scale questionnaire distributed among female employees at Nepal Bank I. The questionnaire assessed various dimensions of job performance, including interpersonal, job, and organizational performance. Spearman correlation analysis and regression techniques were employed to analyze the data and test hypotheses regarding the influence of stress factors on job performance. The study revealed moderate to strong positive correlations between stress factors (job burnout, occupational stress, and psychological stress) and different dimensions of job performance. All nine hypotheses proposed in the study were confirmed, indicating a significant impact of stress on job performance. Regression analysis further supported these findings, highlighting the positive influence of psychological and occupational stresses on organizational and interpersonal performance. The findings underscore the importance of addressing stress factors in the workplace to enhance the well-being and effectiveness of female banking professionals in Nepal. By implementing organizational support and gender-sensitive policies, institutions can create a conducive work environment, leading to increased job satisfaction, productivity, and retention rates among female employees. This study contributes to the existing literature by providing empirical evidence of the relationship between stress levels and job performance specifically among female employees in the banking sector of Nepal. The confirmation of hypotheses and the identification of positive correlations between stress factors and job performance dimensions offer valuable insights for practitioners and policymakers aiming to promote the well-being and effectiveness of female professionals in the industry.

Impact of Bilingual Education on Students Linguistic Proficiency and Cognitive Development at University Level

This study investigates the impact of bilingual education on university students' linguistic proficiency and cognitive development. The research aims to explore the relationship between bilingual education and students' linguistic abilities while assessing its influence on cognitive functions such as memory, critical thinking, and problem-solving skills. The research hypothesizes that students enrolled in bilingual education programs were demonstrate higher linguistic proficiency and enhanced cognitive development compared to those in monolingual programs. The population consists of university students enrolled in bilingual education programs across various disciplines, with a sample of 200 students selected through stratified random sampling. The study employs a cross-sectional design, utilizing a quantitative research approach. Data collection is conducted through standardized language proficiency tests and cognitive assessment tools, supplemented by structured questionnaires to gather demographic and educational background information. The data were analyzed using descriptive statistics, correlation analysis, and multiple regression techniques to identify significant relationships and impacts. The findings of this study are expected to contribute valuable insights into the effectiveness of bilingual education at the university level, offering implications for curriculum development and educational policy in multilingual contexts.

CORPORATE GOVERNANCE AND REAL EARNINGS MANAGEMENT IN NIGERIAN LISTED FINANCIAL FIRMS

The focus of the study is the examination of the relationship that exists between corporate governance and real earnings management in Nigerian listed financial firms, with attention on three corporate governance variables (board tenure, board expertise and CEO ownership). The study adopts a longitudinal research design. Descriptive statistics, correlation analysis and panel regression analysis technique, with emphasis on the random effect model, were used for data analysis. A purposive sample of thirty seven (37) firms, out of the Forty nine (49) financial firms listed on the Nigerian Exchange Group, at year end 31st December, 2022 was studied for a period of seven years, 2016 to 2022. Findings from data analysis revealed that board expertise and CEO ownership have insignificant but positive relationships with real earnings management. In addition, board tenure was found to be significantly but negatively related to real earnings management. The correlation analysis revealed that there is a negative correlation between the variables board expertise, CEO ownership and real earnings management. Also, board tenure has a positive correlation with REM. The study recommends that directors of companies should be encouraged to spend longer time on the board as this will serve as incentive to avoid unfavourable earnings management.

Pengaruh Motivasi Belajar Mahasiswa PAI UNP melalui Pergaulan

Sociability is a continuation of the process of social interaction that exists between individuals and their social environment, then motivation is one of the things that affects the success of learning activities, without the learning process will not run well. This study aims to determine the effect of association on the learning motivation of PAI students class of 2020 UNP. This research includes quantitative research with the type of correlation and descriptive statistical analysis techniques. The data collection method uses primary data. In this study using validity test and reliability test. There were 60 PAI 2020 UNP students who were sampled in this study. Data data were collected through instrument questionnaires from socialization and learning motivation variables. The results of the data data are processed through statistical calculations and average correlations obtained through the use of SPSS 16.0. The data shows that there is a positive and significant influence between socialization on learning motivation by 59.3%. The results showed that association partially had a positive and significant effect on learning motivation, which means that learning motivation is influenced by association. Researchers recommend that be smart in choosing associations.

Evolution Characteristics of PM2.5 and O3 and Their Synergistic Effects on Atmospheric Compound Pollution in Tangshan

The concentrations of atmospheric pollutants PM2.5, O3, SO2, NO2, and CO together with the meteorological factors of temperature （T）, relative humidity （RH）, wind speed, and other relevant data in Tangshan from 2015 to 2021 were collected to study the variation characteristics of PM2.5 and O3 at different periods in Tangshan City in the past seven years and their influencing factors, to discuss the contributions of air mass transport to PM2.5 and O3 pollution, and to reveal the synergistic influence mechanism of PM2.5 and O3 on atmospheric compound pollution by using correlation analysis and backward trajectory cluster analysis techniques. The results showed that PM2.5 concentrations in Tangshan decreased year by year from 2015 to 2021, whereas O3 concentration showed a unimodal trend, with the peak appearing in 2017. Both PM2.5 and O3 concentrations showed obvious seasonal variation trends； PM2.5 was characterized by the highest concentration in winter and the lowest concentration in summer, whereas O3 was characterized by the highest concentration in summer and the lowest concentration in winter. In addition, the diurnal variation in PM2.5 showed a bimodal distribution, with the peak occurring during the morning and evening on weekdays, and O3 showed a unimodal distribution, with the peak value appearing during the period with strong ultraviolet radiation in the afternoon. PM2.5 had a significant positive correlation with SO2, NO2, and CO, whereas O3 had a significant positive correlation with radiation and temperature. Under the different pollution conditions, PM2.5 and O3 were affected by air mass transports from different directions. Being impacted by various factors, the synergistic effect of PM2.5 and O3 on atmospheric compound pollution showed an obvious negative effect in winter, whereas there was an obvious positive effect in spring, summer, and autumn. Under the backgrounds of different pollutions, when the concentration of PM2.5 exceeded 150 μg·m-3, the synergistic effect of PM2.5 and O3 showed an obvious negative effect.

Pengaruh Gaya Belajar Terhadap Kemampuan Penalaran Matematis Siswa SD 5 Mejobo

This research aims to examine the influence of learning styles on students' mathematical reasoning abilities at SDN 5 Mejobo. The learning styles studied include three main categories, namely auditory, kinesthetic and visual. Mathematical reasoning abilities are measured through tests that cover various aspects of reasoning such as identifying patterns, making generalizations, and solving mathematical problems logistically. This research uses quantitative methods with a survey approach. The research population was all 6th grade students at SDN 5 Mejobo, totaling 12 students, and the entire population was used as a sample population using saturated sampling techniques. Data was collected through a learning style questionnaire adapted from the VARK model and a mathematical reasoning test that refers to NCTM standards. Data analysis was carried out using regression and correlation analysis techniques with the help of SPSS software. The research results show that learning style does not have a significant influence on students' mathematical reasoning abilities (value 0.030 > 0.05). These findings indicate that individuals' learning style preferences do not determine their mathematical reasoning abilities. Therefore, it is recommended that other factors such as teaching methods, learning motivation, and learning environment be considered in an effort to strengthen students' mathematical reasoning abilities. It is hoped that this research will provide insight into the development of learning strategies that are more efficient and meet student needs.

Indonesian Primary School Teachers’ Work-Related Stress, Emotional Exhaustion, and Job Performance: A Survey Study in Southern Papua

This study aimed to explore the relationship between work-related stress, emotional exhaustion, and job performance among elementary school teachers working at Catholic schools in Southern Papua, Indonesia. The research employed a quantitative approach and utilized a survey research design. A total of 95 elementary school teachers from Southern Papua participated by completing three survey questionnaires. The collected data were analyzed using correlation analysis techniques with SPSS version 21 software. The results revealed a significant negative relationship between teacher work-related stress, emotional exhaustion, and job performance in the elementary schools of Southern Papua, Indonesia. These findings offer valuable insights into the intricate dynamics among work-related stress, emotional exhaustion, and job performance within the surveyed district. The theoretical implications of these findings enrich the current understanding of this subject matter, while also providing practical guidance for educators, policymakers, and educational institutions to formulate focused interventions and cultivate supportive work environments.

Associations between lyric and musical depth in Chinese songs: Evidence from computational modeling.

Musical depth, which encompasses the intellectual and emotional complexity of music, is a robust dimension that influences music preference. However, there remains a dearth of research exploring the relationship between lyrics and musical depth. This study addressed this gap by analyzing linguistic inquiry and word count-based lyric features extracted from a comprehensive dataset of 2372 Chinese songs. Correlation analysis and machine learning techniques revealed compelling connections between musical depth and various lyric features, such as the usage frequency of emotion words, time words, and insight words. To further investigate these relationships, prediction models for musical depth were constructed using a combination of audio and lyric features as inputs. The results demonstrated that the random forest regressions (RFR) that integrated both audio and lyric features yielded superior prediction performance compared to those relying solely on lyric inputs. Notably, when assessing the feature importance to interpret the RFR models, it became evident that audio features played a decisive role in predicting musical depth. This finding highlights the paramount significance of melody over lyrics in effectively conveying the intricacies of musical depth.

Pengaruh Pembelajaran Kitab Amśīlatu At-Taşrīfiyyah Terhadap Kemampuan Memahami Perubahan Kosakata Bahasa Arab Santri di Pondok Pesantren Nas'atul Muta'allimin Blumbungan Pamekasan

Learning is the process of student interaction with educators and learning resources in a learning environment. The learning in this research focuses on studying the book Amśīlatu At-Taşrīfiyyah in understanding word changes based on wazan. So that it can provide understanding to students when processing and changing Arabic vocabulary. The outline of the study of this book that is taught is taşrīf işṭilāḥī and taşrīf lugawī. This research aims to find out the influence of studying the book Amśīlatu At-Taşrīfiyyah on the ability to change Arabic vocabulary and to describe how much influence learning the book Amśīlatu At-Taşrīfiyyah has on the ability of students to change Arabic vocabulary at the Nas'atul Muta'allimin Islamic Boarding School in Blumbungan Pamekasan. This article used a field research methodology that uses a quantitative approach using correlational data analysis techniques to describe two or more facts and properties of the object under study. The results of this study show that: First, there is no strong and significant correlation between learning the book Amśīlatu At-Taşrīfiyyah and the ability to change Arabic vocabulary. Second, the Pearson product-moment correlation test results using the SPSS application and manual calculation show the Pearson correlation value is (0.025) with a Sig level. (0.897). So, based on the correlational table, the r value (0.025) shows a very weak correlation level. The findings of this research indicate that there is no influence from studying the Amśīlatu At-Taşrīfiyyah book on the students' ability to change Arabic vocabulary at the Nas'atul Muta'allimin Blumbungan Pamekasan Islamic boarding school.

Parameter learning of multi‐input multi‐output Hammerstein system with measurement noises utilizing combined signals

SummaryIn this article, the parameter learning scheme for the multi‐input multi‐output (MIMO) Hammerstein nonlinear systems under measurement noises is studied, which is derived by exploiting the correlation analysis and data filtering technique. The coupled MIMO Hammerstein system presented involves a static nonlinear subsystem modeled by neural fuzzy model (NFM), and a dynamic linear subsystem established by autoregressive moving average with extra input (ARMAX) model. To learn the unknown parameter of the MIMO Hammerstein system, the combined signals are designed to realize that identification of the nonlinear subsystem is separated from that of linear subsystem. First, the correlation properties of separable signals in a nonlinear system are analyzed, then the parameters of the linear subsystem are estimated utilizing correlation analysis, which can deal with the issue of unmeasured intermediate variable in the Hammerstein system. Second, the data filtering technique is introduced to derive the data filtering‐based recursive least squares technique for learning the nonlinear subsystem parameter, which can reduce the impact of the moving average noise and improve the precision of parameter estimation. Finally, the effectiveness and feasibility of the proposed identification scheme is proved by numerical simulation and nonlinear pH process.

The Effect of Career Anxiety on High School Students' Career Decision Making

This study aimed to examine the effect of career anxiety on high school students' career decision-making. The study group of the research consists of a total of 449 students, 146 boys (32.5%) and 303 girls (67%), who continue their education in different high schools in Konya center and districts. Research data was obtained using the "Personal Information Form" prepared by the researchers, the "Career Anxiety Scale" developed by Çetin-Gündüz and Nalbantoğlu-Yılmaz (2016) and the "Vocational Decision Making Inventory" developed by Çakır (2004). A relational screening model based on a descriptive method was used in the research. In the analysis of the data, parametric statistical techniques such as t test, one-way analysis of variance (ANOVA), Tukey test, correlation and regression analysis techniques were used according to the normality test. According to the research findings, there is a significant relationship between both career anxiety regarding family influence and career anxiety regarding career choice and all sub-dimensions of career decision-making. In addition, there is a low level positive relationship between students' career concerns related to family influence and the sub-dimensions of internal conflicts, not knowing oneself well enough, lack of profession and field knowledge, and irrational beliefs about career choice, and a moderate positive relationship between the students' career concerns and the external conflicts sub-dimension. It was found that there was a relationship.