Data Analysis Methods Research Articles

ПРИМЕНЕНИЕ ЭТНОГРАФИЧЕСКОЙ ПАРАДИГМЫ ПРИ РАЗРАБОТКЕ МАТЕРИАЛОВ КОЛИЧЕСТВЕННЫХ ИССЛЕДОВАНИЙ

In this work, the author made an attempt to adapt the methods of collecting, storing and processing information when conducting a qualitative research (ethnography) to conduct and formulate a quantitative research. The relevance of the work is due to the need for continuous improvement of research methods, taking into account the possibility of convergence of research methods from various scientific fields. The aim of the work is to pose questions to the content of reporting materials that are deeply worked out in the course of qualitative research and the presentation of the results of such, but at the same time receive insufficient attention (in the author's opinion) of researchers when working in the framework of quantitative research. The main task was to determine common points in the development of ethnography (as a finished work) and the development of reporting material on abstract quantitative research. The model proposed by the authors R. M. Emerson, R. I. Fretz and L. L. Shaw is considered as the basic initial qualitative model. This model covers the entire process of ethnographic research: both field and desk components. The author of this work has studied a large array of materials in the field of qualitative research, in particular, qualitative methods of data analysis. The result of the work can be considered recommendations for filling the reporting materials of quantitative research, which are given in the text of the article with explanations on their meaning and the benefits that they can bring to authors and readers.

Exploring the Complexities of Dating Violence in Indonesia: Understanding Dynamics, Norms, and Strategies for Prevention

Dating violence is still being a pervasive issue globally, affecting the health and rights of millions young individuals. Despite its prevalence, dating violence often receives less attention compared to domestic violence. This study delves into the complexities of dating violence in Indonesia, employing a mixed methods design to propose strategies for prevention and intervention. The research utilized qualitative and quantitative data collection and analysis methods to comprehensively understand the phenomena under study. Using a thorough analysis, the research underscores the entrenched power dynamics and cultural norms that perpetuate gender-based violence. It also examined the role of gender norms in shaping attitudes towards intimate partner abuse, highlighting the influence of patriarchal beliefs, and hegemonic masculinity. Furthermore, the study evaluates the effectiveness of existing legal frameworks in combating dating violence, noting challenges related to implementation, and gender biases. In proposing solutions, the research emphasized the importance of empowerment initiatives for women and community development efforts to challenge harmful stereotypes and foster societal change. By addressing these multifaceted issues, this study contributes to the broader discourse on dating violence prevention and underscores the urgency of comprehensive interventions in Indonesia and beyond.

Application of Linear Mixed-Effects Model, Principal Component Analysis, and Clustering to Direct Energy Deposition Fabricated Parts Using FEM Simulation Data.

The purpose of this study is to investigate the effects of toolpath patterns, geometry types, and layering effects on the mechanical properties of parts manufactured by direct energy deposition (DED) additive manufacturing using data analysis and machine learning methods. A total of twelve case studies were conducted, involving four distinct geometries, each paired with three different toolpath patterns based on finite element method (FEM) simulations. These simulations focused on residual stresses, strains, and maximum principal stresses at various nodes. A comprehensive analysis was performed using a linear mixed-effects (LME) model, principal component analysis (PCA), and self-organizing map (SOM) clustering. The LME model quantified the contributions of geometry, toolpath, and layer number to mechanical properties, while PCA identified key variables with high variance. SOM clustering was used to classify the data, revealing patterns related to stress and strain distributions across different geometries and toolpaths. In conclusion, LME, PCA, and SOM offer valuable insights into the final mechanical properties of DED-fabricated parts.

Income level as a differentiating factor in assessing the impact of migration on economic growth

Aim. To study the nature of the impact of migration on economic growth depending on the level of gross domestic product (GDP) per capita and the direction of migration flows.Objectives. Study of theoretical approaches to the research of the impact of labor migration on economic growth; analysis of factors that differentiate the assessment of the impact of migration flows on economic growth indicators; collection and analysis of statistical information for the development of econometric models for assessing the impact of migration on economic growth.Methods. The authors used general scientific methods of research, such as deduction, analysis, synthesis. Statistical methods of data analysis were applied, linear regression models were built.Results. The research shows that the impact of migration on economic growth in countries with different levels of GDP per capita is due to different factors. For developed economies, the positive factors of the impact of immigration on economic growth are replenishment of scarce labor resources; improvement of labor force quality due to structural changes caused by the transition of the permanent working-age population to new professional fields. For countries with a low level of GDP per capita we can identify a number of positive factors of the impact of emigration (with a negative balance of migration growth) on economic growth. In particular, the outflow of excess labor force creates conditions for reducing unemployment; remittances of migrants support effective demand in the consumer market, contribute to the development of the banking system and investment in the country of origin; the expansion of social ties leads to the development of human capital, increasing the potential for innovation.Conclusions. The value of correlation coefficients in the constructed regression models indicates that there is a weak correlation between the variable characterizing the volume and nature of migration in the country (the ratio of migration balance to population) and the variable characterizing economic growth (GDP per capita at purchasing power parity) only for the group of high-income countries. There is little statistical dependence of these variables for other groups of countries. With regard to econometric modeling of the impact of migration on economic growth, this means that it is necessary to develop separate models for each group of countries depending on the level of GDP per capita and the direction of migration flows.

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Spectral Characteristics and Identification of Degraded Alpine Meadow in Qinghai–Tibetan Plateau Based on Hyperspectral Data

Grassland degradation poses a significant challenge to achieving the Sustainable Development Goals (SDGs) on the Qinghai–Tibetan Plateau (QTP). Effective monitoring of grassland degradation is essential for ecological restoration. Hyperspectral technology offers efficient and accurate identification of degradation. However, the influence of observation time, data analysis methods and classification techniques on the accuracy of identifying alpine grasslands remains unclear. In this study, the spectral reflectance of degraded alpine meadow, alpine meadow, alpine shrub and Tibetan barley was measured from May to September 2023 using a ground spectrometer in the northeastern QTP. First-order derivatives (FDR) and continuum removal were applied to the spectra, and characteristic parameters and vegetation indices were calculated. Support vector machine (SVM), random forest (RF), artificial neural network (ANN) and decision tree (DT) were then used to compare the classification accuracy between different months, transformation methods and characteristic parameters. The results showed that the spectral reflectance peaked in July, with significant differences in the near infrared (NIR) bands between alpine meadow and degraded alpine meadow. Alpine shrub and Tibetan barley showed greater differences in reflectance compared to other vegetation types, especially in the NIR bands. Data transformations improved reflectance and absorption characteristics in the NIR and visible bands. Indices such as DVI, RVI and NDGI effectively differentiated vegetation types. Optimal accuracy for the identification of degraded alpine meadow in July was achieved using FDR transformations and ANN or SVM for classification. This study provides methodological insights for monitoring grassland degradation on the QTP.

Psychological resilience and performance under pressure: a study of elite badminton players - a systematic review

Psychological resilience is a critical factor influencing athlete performance in many sports, including badminton. This study aims to systematically review the existing literature to understand the relationship between psychological resilience and elite badminton athlete performance. The research background details the importance of psychological factors in the context of sport, particularly under competitive pressure. Focusing on elite badminton players, the study aimed to identify consistency and variation in previous research findings and detail the practical implications for athlete training and coaching. Research materials and methods included a systematic design with explicit inclusion and exclusion criteria. A thorough literature search was conducted to identify relevant studies that met the inclusion criteria. A careful study selection process was completed, and data were extracted for systematic analysis. Data analysis methods used included synthesizing findings from multiple studies, potentially using meta-analysis where appropriate. The results present the main findings regarding the relationship between psychological resilience and the performance of elite badminton athletes. The analysis of findings from multiple studies highlights the consistency in this relationship while acknowledging the variation that may occur due to differences in competitive context and athlete characteristics. The practical implications of these findings are explained in the context of training and coaching badminton athletes to enhance their psychological resilience under competitive pressure.The research conclusions highlight the vital contribution of this study to understanding psychological factors in badminton. This research provides a foundation for developing more effective training strategies and coaching of athletes that can enhance their psychological resilience, thereby improving their performance at the elite level. As such, this study provides scientific insights and practical guidance for the potential development of elite badminton athletes under competitive pressure.

Using a Phenomenological and Fanon Analytic: A Methodological Framework and Method for Research with Impoverished Black Male Youth

While there is a good amount of general literature on the experiences of Black men in Canada, there is a paucity of empirical data regarding the specific lived experiences of first- and second-generation, Canadian-born, impoverished Black young men (ages 15–29). Black male youth are often impoverished due to anti-Black racism and classism, which together further social and economic barriers. While many methods of data collection and analysis provide a useful snapshot of the experiences of young Black men, many traditional methods are unable to highlight the nuance that lies in complex intersections of identity for Black young men experiencing poverty and un(der)employment. This article demonstrates the success of practices rooted in phenomenology to understand and articulate the unique, heterogeneous lived experiences of first- and second-generation impoverished Black young men who utilize youth employment training programs in three Canadian cities. Further, this article demonstrates how using Frantz Fanon’s scholarly work and applied phenomenology can inform future research on Black and other underserved youth who face complex marginalization.

Stakeholder Perspectives on the School Food Environment: Insights from South African Learners-A Pilot Study.

Leveraging learner-driven insights to enhance the SFE can significantly influence food choices by decreasing the consumption of unhealthy foods and increasing access to healthier options. Using learners' voices as important stakeholders in the school food environment (SFE), this qualitative research study aimed to gain a deeper understanding of their perspectives, identify barriers and enablers to fostering a healthy SFE in South African public schools, and explore the link between the SFE and learners' food choices. Six focus group discussions (FGDs) with 4-6 participants were held in six schools, including three primary and three secondary schools in the KwaZulu-Natal Department of Education's iLembe district, SA, with learners aged 12-14 and 15-18 years, respectively. Learners were asked how schools could help them eat healthier and how their SFE benefitted them. The data were analysed using thematic data analysis methods. The FGDs generated 14 themes and subthemes. Emerging information suggests a lack of control over the sale of unhealthy food despite the existence of guidelines. This is further exacerbated by the marketing of unhealthy foods, which prevents learners from selecting healthier options. Furthermore, they stated that promoting the school food programme (SFP) and changing attitudes towards healthy food intake through comprehensive nutrition education are useful ways to enhance the SFE. The findings of this study underscore the critical role of learner insights in shaping effective strategies to improve the SFE.

Market concentration of the Brazilian hospital medical supplementary health system

BackgroundThe Brazilian supplementary health market has undergone transformations in recent years due to constant mergers and acquisitions of by large corporations, contributing to the increase in market concentration, especially in the poorest and least developed regions of the country. Thus, given the care it provides and its economic relevance, understanding the fundamentals of these movements, the likely consequences and trends for the health market are relevant, important, and strategic.ObjectiveTo understand the general and specific context of Brazilian supplementary health, its scenarios, and trends, with emphasis on the analysis of market concentration and recent mergers and acquisitions.MethodologyThe research is applied, descriptive and exploratory and uses secondary data from various sources, submitted to quantitative data analysis methods. The data are organized into three groups: historical and regulatory documents; industry data; and market.ResultsThe results show the growing concentration of the market promoted by large publicly traded corporations, the growing relevance of tech startups on the healthcare landscape, the predominant use of the relative valuation model, with implicit multiples for asset pricing and the prevalence of corporate health plans.ConclusionThe growing concentration of the system projects a market with fewer options and less competitiveness, since the growth of large operators is evident, in addition to the relevant increase in the number of complaints from users of the system, which signals the growing gap between the expectations of users and the levels of quality care offered. The study also highlights the predominance of corporate health plans, revealing the direct relationship between access to supplementary health services and employability rates. The analysis of M&A operations, in addition to the increase in market concentration, reveals the prevalence of the use of the relative valuation model and implicit multiples for the pricing of traded assets. This denotes the future expectation of wealth generation, at least equivalent to the historical series of the sector, on the part of investors, whose frustration may signal the decreasing attractiveness of resources and M&A operations in the sector in the coming years.

Exploring Digital Mobility in China’s Tourism Metaverse

ABSTRACT This study aims to explore digital mobility in the tourism metaverse through dual factors of information entropy and media functionality moderated by structural differentiation, including China’s institutional governance of information systems. The research model was constructed through influential factors to determine the digital mobility of metaverse systems using quantitative data analysis methods. Results showed that information entropy negatively influenced digital mobility. However, media functionality could be harnessed to reduce information entropy and thus enhance digital mobility in China’s tourism metaverse systems. Structural differentiation had significant moderation and mediation effects in the research model that influenced the digital mobility of Chinese tourists.

Paradox-Free Data Analysis in Numerical Comparisons of Optimization Algorithms

Numerical comparison is essential for evaluating an optimization algorithm. Unfortunately, recent research has shown that two paradoxes may occur, namely the cycle ranking paradox and survival of the nonfittest paradox. Further exploitation reveals that these paradoxes stem from the method of data analysis, especially its comparison strategy. Therefore, the design and widespread use of paradox-free data analysis methods has become urgent. This paper is dedicated to reviewing the recent progress in paradox in numerical comparisons of optimization algorithms, especially the reasons for paradoxes and the ways to eliminate them. Specifically, significant progress has been made in eliminating paradoxes from two popular data analysis methods, including hypothesis testing and methods based on the cumulative distribution function. Furthermore, in this paper the authors provide case studies, aiming to show that paradoxes are common and how to eliminate them with paradox-free data analysis methods.

Factors Associated with Student Feedback to Teachers in Quality Assurance in Iraqi Kurdistan

Over the past few decades, the quality of education has emerged as a critical strategic concern in tertiary education institutions worldwide. University managers’ daily tasks now include comparing educational outcomes and rankings, for which quality assurance is significant. Quality assurance incorporates three separate but interdependent elements including student feedback to teachers, teacher portfolios, and continuous academic development. Student feedback concerns the quality and effectiveness of teaching and teacher performance. University instructors often complain about the grades and feedback they gain from students and reckon that the grades are based on students’ rate of success and teachers’ intimacy with students, rather than on teachers’ performances. Therefore, this study is devoted to investigating student feedback and the factors that determine the quality of feedback provided to instructors. To this intent, the study follows the principles of mixed methods in data collection and analysis; using self-reported questionnaires, instructor feedback scores in quality assurance, and student rates of success for each module as well as using IBM SPSS 25 and thematic analysis. The participants include university students and instructors selected from a public university in the Kurdistan Region of Iraq. The study assumes that three factors play a significant role in determining the level of feedback including the rate of success, instructors’ intimacy and rapport with students, and the quantity of material provided to students. The study concludes with far-reaching implications for higher education institutions in the Kurdistan Region of Iraq.

The Relationship between Global Competitiveness, Corruption, Democratic Systems on Economic Growth ASEAN countries

Corruption is a detrimental act for many people, especially related to the economy. Various groups widely discuss corruption and economic growth from academia, government, and the private sector. This is even more complex when coupled with competitiveness between countries and democratic systems. So this study aims to analyze the influence of global competitiveness, corruption and democracy on economic growth in ASEAN countries. This study used panel data analysis methods in 7 ASEAN countries from 2014-2019. The seven countries are Indonesia, Malaysia, Thailand, the Philippines, Vietnam, Laos, and Cambodia. It is related to economic growth, corruption, and competitiveness in ASEAN countries. The results of this study show that corruption brings mudhorot but has yet to be significant to economic growth. Another interesting finding is that democracy negatively affects global competitiveness and increases economic growth. This research can be one of the government’s policy recommendations by increasing economic growth through strict enforcement against corruption and increasing global competitiveness. To realize economic growth that prospers society requires the role of the government through increasing human and institutional resources to support other competitiveness factors that focus on technological, environmental, and innovation aspects.

Early diagnosis of stroke risk factors in high school students in Makassar, South Sulawesi, Indonesia

The number of stroke patients in Indonesia is ranked as the first country to experience the most strokes in all of Asia. The prevalence of stroke in Indonesia reaches 8.3 out of 1000 population. This prevalence rate increases with increasing age. Indonesian national data shows that stroke is the highest cause of death, at 15.4%, with approximately 750,000 strokes per year in Indonesia and 200,000 recurrent strokes. People who are physically inactive (those who exercise less than three times per week, each for 30 minutes) have an almost 50% increased risk of stroke compared to those who are active. Physical inactivity can lead to weight problems and increased blood pressure and is associated with diabetes, all of which are important risk factors for stroke. Inactivity also contributes to the onset of early atherosclerosis and other cardiovascular diseases, such as heart attacks. This study is an analytical observational study with a cross sectional design to see and observe the risk factors for stroke that exist in school children in Makassar city. Data processing was carried out using computer application programs and SPSS version 22.0 with data analysis methods using the Chi Square Test with the Yate's Correlation formula in SPSS called Continuity Correction and Risk. The number of respondents studied was 896 people. Hypertension is a major risk factor for health problems in society, especially in socioeconomic transition. Hypertension is a primary risk factor for the onset of heart disease and stroke. Doing physical activity will prevent us from various diseases, by doing adequate physical activity we can suppress the increase in blood sugar levels, cholesterol levels, and avoid obesity and strengthen the heart. All of which are risk factors for stroke. So thus by doing adequate physical activity we can avoid stroke. There is a relationship between gender, obesity, hypertension, family history of disease, stress, physical activity, risky diet and smoking on stroke risk factors and there is no relationship between age and knowledge with stroke risk factors.

Use of large language models as a scalable approach to understanding public health discourse.

Online public health discourse is becoming more and more important in shaping public health dynamics. Large Language Models (LLMs) offer a scalable solution for analysing the vast amounts of unstructured text found on online platforms. Here, we explore the effectiveness of Large Language Models (LLMs), including GPT models and open-source alternatives, for extracting public stances towards vaccination from social media posts. Using an expert-annotated dataset of social media posts related to vaccination, we applied various LLMs and a rule-based sentiment analysis tool to classify the stance towards vaccination. We assessed the accuracy of these methods through comparisons with expert annotations and annotations obtained through crowdsourcing. Our results demonstrate that few-shot prompting of best-in-class LLMs are the best performing methods, and that all alternatives have significant risks of substantial misclassification. The study highlights the potential of LLMs as a scalable tool for public health professionals to quickly gauge public opinion on health policies and interventions, offering an efficient alternative to traditional data analysis methods. With the continuous advancement in LLM development, the integration of these models into public health surveillance systems could substantially improve our ability to monitor and respond to changing public health attitudes.

The Investigation on the Application of Digital Technologies for Logistics Business Competitiveness

The logistics sector's performance is integral to nations' economic and industrial progress. The advent of digital technologies marks a monumental historical shift, impacting worldwide transformations in structural organization, business strategies, economic principles, and regulatory frameworks. The landscape of logistics management provides a fertile ground for integrating these digital advancements, as exemplified by the dynamic emergence of logistics 4.0. This study evaluates how implementing digital technology profoundly influences logistics management's competitiveness in the business context. The study investigates applying digital technologies within the logistics sector using multi-stage methodology, which integrated different methods: the formation of hierarchy of quantitative methods and the application of statistical data analysis methods. Statistical data analysis helps to collect knowledge about logistics business, the application of digital technologies and its performance within European Union (EU) countries. The application of hierarchy of quantitative methods allows to compare the applications of various technologies with digital technologies and the competitiveness of various sectors with logistics sector competitiveness. These findings accentuate the intricate interplay between technological advancement and competitiveness within the logistics sector. The study contributes to a deeper comprehension of how digital technologies reshape competitive dynamics and lays the foundation for refined logistics practices in an increasingly digital-driven world.

Analysis Brand Image, Product Quality, And Brand Ambassador On Customer Purchase Decision For Scarlett Whitening Product In Sidoarjo

The tight business competition between companies today means that all companies want to be the best to attract the attention of consumers. Scarlett Whitening is a company that reached a sales record of 1 billion in 4 hours when collaborating with Exo. EXO is used as a strategy to attract consumers to buy this product. However, Scarlett Whitening consumers do not only look at the type of product or quality, consumers also look at the advantages in terms of the brands that are most popular. This research aims to examine the influence of Brand Image, Product Quality, and Brand Ambassador on Consumer Purchasing Decisions in Sidoarjo. This type of research is quantitative using descriptive statistical data analysis methods and testing methods including: Validity Test, Reliability Test, Classical Assumption Test, and Hypothesis Test. Data collection took the form of distributing questionnaires which produced valid data for 100 samples. The results show that the brand image variable has an insignificant positive influence. Meanwhile, the product quality and brand ambassador variables have a positive and significant effect.

STOPS and multivariate hierarchical aglomerative clustering: segmentation of the public regarding children’s vaccinaton communication in Slovenia

PurposeFrom a public health perspective, vaccination programmes significantly add to long-term, safe co-existence. However, because there is no social consensus about their benefits and risks, the promotion of vaccinations is difficult. Based on Kim and Grunig’s situational theory of problem solving (STOPS), including communicative action in problem solving (CAPS), this paper both proposes a model for identifying the involvement of mothers of young children in communication regarding vaccination and advocates for a novel approach to STOPS and CAPS data analyses.Design/methodology/approachThe methodological design develops empirical analyses of the data yielded by the STOPS model. Two approaches to determining associations between situational-motivational variables and communicative-action variables in random-sample survey data obtained in Slovenia in 2016 (N = 1704) are implemented – i.e. visual methods and multivariate agglomerative clustering algorithm.FindingsThe STOPS model has been confirmed and both data-analyses approaches have shown potential by clearly demonstrating associations and patterns in the data. Based on these findings, we conclude that they have the potential to be the norm in analysing STOPS models.Research limitations/implicationsLimitations of the study, which are still to be overcome, involve drawing on one sample in one country and testing only one set of indicators.Practical implicationsFrom an academic point of view, confirmation of both the model and the analytical power of the pragmatic data-analyses methods significantly add to communication studies. From practical and social points of view, relationships among attitudes and communication behaviour, as outlined in the exposed segments of the public, enable the improvement of every step in strategic-communication planning and implementation.Originality/valueThis paper fulfils an identified need to establish a theoretical framework and methodology of segmentation in vaccination-communication studies.

Upaya Peningkatan Kepuasan Kerja Guru SMK Islam Miftahul Huda Berbasis Lingkungan Kerja Fisik dan Non Fisik

An upsurge in the rate of teacher absences at Miftahul Huda Islamic Vocational School occurred during the academic year 2022-2023, necessitating an evaluation and enhancement of the school's infrastructure. Enhancing teacher job satisfaction yields numerous advantages for both teachers and students, particularly in the context of teaching and learning activities. The objective of this study is to examine endeavours to enhance teacher job happiness by concentrating on several elements of the work environment, encompassing both tangible and intangible factors. This research is characterised as quantitative and involved a total of 34 respondents. The sampling technique used was saturation sampling. The data gathering process involves the utilisation of library research and field research methodologies, along with data analysis methods such as descriptive and associative approaches. Data analysis involves the interpretation of the output table generated by the SPSS version 26 programme. This table is derived from previously processed data. The findings of this study indicate that both the tangible and intangible aspects of the work environment have a simultaneous impact on job satisfaction at Miftahul Huda Islamic Vocational School.