Models In Social Sciences Research Articles

Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making.

Application of Mathematical Modeling in Social Science

Abstract. With the promotion and application of big data, mathematical models are also more widely referenced in the research and development of different professions. In this paper, the relationship between mathematical modeling and social sciences will be explored by examining the application of mathematical modeling in social sciences through quantitative analysis. In addition, the author is willing to learn about different models related to economics, psychology, and sociology. Based on the analysis, this paper provides relevant suggestions for solving potential problems in the application of models, including, that attention should be paid to the description of contextual details, that the results of the study should take into account motivational factors, and that the data involved must be valid and not too superficial.

Teacher Performance in the 21st Century: Examining the Roles of Leadership, Self-Efficacy, Compensation, and Job Satisfaction

The main purpose of this study is to examine the effect of leadership on teacher performance via self-efficacy and job satisfaction, a mediating variable, as well as compensation. Based upon an Indonesia study, the research engages a meta-analysis of these relationships for illuminating insights on optimizing teacher performance in educational environments. We collected data from a sample of 680 teachers 87% response rate and analyzed them using SmartPLS, the evaluation comparable to complex social science models. The results suggest that both self-efficacy and compensation affect teacher performance directly, respectively, through job satisfaction; principal leadership only has a significant direct effect on the dependent variable without being mediated by job satisfaction. These results may have implications for how intrinsic and extrinsic factors, motivation, rewards, and leadership styles influence faculty outcomes. The practical implications include a call for targeted teacher professional development and compensation policies that are more equitable and effective educational leadership practices to improve teaching performances, which will in turn favorably impact the quality of education generally.

Fostering Nurses’ Support in the Workplace: Addressing Job Burnout Among Malaysian Medical Doctors Working in Public Hospitals

Job burnout in employees is caused by individual factors, including demographic characteristics and nurses’ support. This study analyzed the relationship between nurses’ support and job burnout among medical doctors in Malaysian public hospitals. This research was a correlational study with a cross-sectional design. The research sample was 471 employees, using the non-probability sampling method with the purposive and quota sampling techniques. Data collection used the Abbreviated Maslach Burnout Inventory (aMBI) and nurses’ support items. The data in this study were analyzed using the Statistical Package Social Science (SPSS) and Partial Least Squares Structural Equation Modelling (PLS-SEM) software. Findings suggest that 77 medical doctors who participated in the study were from Hospital Pulau Pinang, from the Emergency Department, mostly female, ranging from 24-56 years old, single, and have an MBBS education level. The study assessed the measurement model’s loading, average variance extracted (AVE), and composite reliability (CR) where the loadings were above 0.60. AVEs were greater than 0.50 and CRs were greater than 0.708. Additionally, the bootstrap BCI LL (-0.079) and UL (-0.002) for nurses’ support and job burnout were not straddled a 0 in between. Job burnout had a negative association with nurses’ support. In particular, nurses’ support towards medical doctors lowers their risk of job burnout, which has a substantial impact on medical doctors’ overall well-being. The findings indicated that medical doctors who had complete support from their nurses performed their jobs more efficiently, displayed good task performance, and experienced less job burnout.

The received view and Revelation: A social-scientific reading of Revelation 2–3

This article aims to present a culturally plausible reading of Revelation 2–3. This will be done through the use of a social-scientific model focussing on the core values of honour and shame in the ancient Mediterranean world. Before describing this model, the article will present a cursory discussion on the currently received view of Revelation’s genre and Revelation 2–3. It is argued that while the received view provides valuable historical descriptions of the ancient Mediterranean world, this approach is inadequate to bring to the fore the underlying norms and values found in Revelation 2–3. Using the model of honour and shame as a lens through which to read Revelation 2–3, it becomes apparent that these seven letters are filled with honour claims that are either confirmed, challenged or denied. In addition, honour is also ascribed to specific communities, and in some cases, honour is redefined.Intradisciplinary and/or interdisciplinary implications: Reading Revelation 2–3 through social-scientific models engages critically with the traditional approach to these texts, and provides a culturally sensitive and responsible reading thereof. This reading further promotes a constructive engagement with cross-cultural anthropology.

Health care-seeking behavior for childhood illnesses in western Kenya: Qualitative findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) Study

Background Child mortality in Kenya is 41 per 1,000 live births, despite extensive investment in maternal, newborn, and child health interventions. Caregivers’ health-seeking for childhood illness is an important determinant of child survival, and delayed healthcare is associated with high child mortality. We explore determinants of health-seeking decisions for childhood illnesses among caregivers in western Kenya. Methods We conducted a qualitative study of 88 community members between April 2017 and February 2018 using purposive sampling in an informal urban settlement in Kisumu County, and in rural Siaya County. Key informant interviews, semi-structured interviews and focus group discussions were performed. We adopted the Partners for Applied Social Sciences model focusing on factors that influence the decision-making process to seek healthcare for sick infants and children. The discussions were audio-recorded and transcribed. Data management was completed on Nvivo® software. Iterative analysis process was utilized and themes were identified and collated. Results Our findings reveal four thematic areas: Illness interpretation, the role of social relationship on illness recognition and response, medical pluralism and healthcare access. Participants reported some illnesses are caused by supernatural powers and some by biological factors, and that the illness etiology would determine the health-seeking pathway. It was common to seek consensus from respected community members on the diagnosis and therefore presumed cause and necessary treatment for a child’s illness. Medical pluralism was commonly practiced and caregivers would alternate between biomedicine and traditional medicine. Accessibility of healthcare may determine the health seeking pathway. Caregivers unable to afford biomedical care may choose traditional medicine as a cheaper alternative. Conclusion Health seeking behavior was driven by illness interpretation, financial cost associated with healthcare and advice from extended family and community. These findings enrich the perspectives of health education programs to develop health messages that address factors that hinder prompt health care seeking.

The Application of Large Language Models in Social Sciences: Opportunities and Challenges for Measurement with Texts

The Application of Large Language Models in Social Sciences: Opportunities and Challenges for Measurement with Texts

Implementation of Project-Based Learning Model in Social Studies Subjects at Islamic Junior High School

This study aims to explore the implementation of the Project Based Learning (PjBL) learning model in learning Social Sciences (IPS) at MTs Zainul Hasan 4 Boto Lumbang, Probolinggo Regency. The research method used is qualitative with a case study approach. Data were collected through direct observation, in-depth interviews with teachers and students, and document analysis related to the implementation of PjBL. The results showed that the implementation of PjBL at MTs Zainul Hasan 4 had a positive impact on student learning engagement and motivation. Students are more actively participating in the learning process through projects that are relevant to real life, which allows them to develop critical thinking, collaboration and problem-solving skills. In addition, PjBL also helps students connect theory with practice, thus deepening their understanding of social studies concepts

Structure learning for continuous time Bayesian networks via penalized likelihood

AbstractContinuous time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions occurring in living processes, social science models or medicine. The literature on this topic is usually focused on a case when a dependence structure of a system is known and we are to determine conditional transition intensities (parameters of a network). In the paper, we study a structure learning problem, which is a more challenging task and the existing research on this topic is limited. The approach, which we propose, is based on a penalized likelihood method. We prove that our algorithm, under mild regularity conditions, recognizes a dependence structure of a graph with high probability. We also investigate properties of the procedure in numerical studies.

Health care-seeking behavior for childhood illnesses in western Kenya: Qualitative findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) Study.

Child mortality in Kenya is 41 per 1,000 live births, despite extensive investment in maternal, newborn, and child health interventions. Caregivers' health-seeking for childhood illness is an important determinant of child survival, and delayed healthcare is associated with high child mortality. We explore determinants of health-seeking decisions for childhood illnesses among caregivers in western Kenya. We conducted a qualitative study of 88 community members between April 2017 and February 2018 using purposive sampling in an informal urban settlement in Kisumu County, and in rural Siaya County. Key informant interviews, semi-structured interviews and focus group discussions were performed. We adopted the Partners for Applied Social Sciences model focusing on factors that influence the decision-making process to seek healthcare for sick infants and children. The discussions were audio-recorded and transcribed. Data management was completed on Nvivo® software. Iterative analysis process was utilized and themes were identified and collated. Our findings reveal four thematic areas: Illness interpretation, the role of social relationship on illness recognition and response, medical pluralism and healthcare access. Participants reported some illnesses are caused by supernatural powers and some by biological factors, and that the illness etiology would determine the health-seeking pathway. It was common to seek consensus from respected community members on the diagnosis and therefore presumed cause and necessary treatment for a child's illness. Medical pluralism was commonly practiced and caregivers would alternate between biomedicine and traditional medicine. Accessibility of healthcare may determine the health seeking pathway. Caregivers unable to afford biomedical care may choose traditional medicine as a cheaper alternative. Health seeking behavior was driven by illness interpretation, financial cost associated with healthcare and advice from extended family and community. These findings enrich the perspectives of health education programs to develop health messages that address factors that hinder prompt health care seeking.

Effect Of BOPPPS Model In Social Science With Digital Quotient On Secondary School Students

Effect Of BOPPPS Model In Social Science With Digital Quotient On Secondary School Students

Advancing Collective Intelligence in Human–AI Collaboration: Foundations for the COHUMAIN Framework

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capabilities in many ways, how can we ensure that the sociotechnical system as a whole—comprising a complex web of hundreds of human–machine interactions—is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Integrating these diverse perspectives and methods is crucial at this juncture. To truly advance our understanding of this important and rapidly evolving area, we need frameworks to facilitate research that bridges disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. To illustrate the approach we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, which articulates the critical processes underlying the emergence and functioning of collective intelligence in human–AI collaborations.

Health care-seeking behavior for childhood illnesses in western Kenya: Qualitative findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) Study

Background Child mortality in Kenya is 41 per 1,000 live births, despite extensive investment in maternal, newborn, and child health interventions. Caregivers’ health-seeking for childhood illness is an important determinant of child survival, and delayed healthcare is associated with high child mortality. We explore determinants of health-seeking decisions for childhood illnesses among caregivers in western Kenya. Methods We conducted a qualitative study of 88 community members between April 2017 and February 2018 using purposive sampling in an informal urban settlement in Kisumu County, and in rural Siaya County. Key informant interviews, semi-structured interviews and focus group discussions were performed. We adopted the Partners for Applied Social Sciences model focusing on factors that influence the decision-making process to seek healthcare for sick infants and children. The discussions were audio-recorded and transcribed. Data management was completed on Nvivo® software. Iterative analysis process was utilized and themes were identified and collated. Results Our findings reveal four thematic areas: Illness interpretation, the role of social relationship on illness recognition and response, medical pluralism and healthcare access. Participants reported some illnesses are caused by supernatural powers and some by biological factors, and that the illness etiology would determine the health-seeking pathway. It was common to seek consensus from respected community members on the diagnosis and therefore presumed cause and necessary treatment for a child’s illness. Medical pluralism was commonly practiced and caregivers would alternate between biomedicine and traditional medicine. Accessibility of healthcare may determine the health seeking pathway. Caregivers unable to afford biomedical care may choose traditional medicine as a cheaper alternative. Conclusion Health seeking behavior was driven by illness interpretation, financial cost associated with healthcare and advice from extended family and community. These findings enrich the perspectives of health education programs to develop health messages that address factors that hinder prompt health care seeking.

Effect of Affective Commitment on the Relationship Between Job Satisfaction and Job Performance of Public School Teachers in Yobe State, Nigeria

The main objective of this study is to investigate the mediating effect of affective commitment on the relationship between job satisfaction and teachers’ job performance in Yobe State. Two hypotheses were formulated for testing. The study design used in this research is a survey design. Sample size of 412 was arrived at, after taking into consideration the extent of variability in the population. Data analysis was done using Statistical Package for Social Science (SPSS) and Partial Least Square-Structural Equation Model (PLS-SEM) i.e. Pls3. The questionnaire items were all reported to have some degree of dependability and legitimacy. The results failed to support both the direct and the indirect null hypotheses revealing the empirical evidence that job satisfaction has significant effect on teachers’ job performance. Affective commitment was found to mediate the relationships between job satisfaction, and teachers’ job performance. Furthermore, the study recommend that Government, through it ministry of education should identify various ways that would increase teachers’ affective commitment such as shared value, fairness and trust with the view to improving their job performance.

Non-institutional model of social sciences and humanities: to the question of import of pedagogical technologies

In postmodern reality the methods of production and transmission of scientific knowledge are being transformed: along with the academic model of the social sciences and humanities, there are also non-institutional channels of humanities, which are characterized by their own ways of intellectual dialogue, pedagogical techniques and technologies. In the period of sociocultural transformation and complex challenges, it is important for academic humanists to import technologies and methods accumulated in the field of non-institutional humanities.

Designing a bias-rating news recommendation system

Media bias can significantly influence public perception, often subconsciously shaping opinions. To understand and measure this bias, diverse methodologies have emerged. While models from social sciences offer in-depth evaluations, they involve intensive manual analysis. In contrast, computerized models provide speed but often lack depth. This research explores the synergy between these disciplines, aiming to create a robust bias detection tool that combines the meticulousness of social science models with the automation of computer science. Using this interdisciplinary approach, a system was developed to evaluate articles and instantly present a 'bias score' on the user interface. This score offers readers an immediate indication of potential news slant. The research also integrated web crawling techniques into the system, allowing it to identify and recommend alternative articles on analogous subjects. This innovative feature enriches readers' choices, equipping them with multiple narratives for an enriched understanding. In conclusion, this work bridges the gap between depth and speed in media bias detection, offering a novel tool that promotes informed readership. The contribution of this study lies in its interdisciplinary approach and the development of a system that fosters holistic media consumption.

Is Evolutionary Psychology a Scientific Revolution? A Bibliometric Analysis

ObjectiveThe emergence and growth of Evolutionary Psychology (EP) in the behavioral sciences has been characterized as a “scientific revolution” (e.g. Buss, 2020). According to Kuhn's framework, a scientific revolution in a discipline is marked by the emergence of a new, dominant school of thought, which eclipses all the other theories. The aim of this study was to assess quantitatively if EP may be regarded as a "scientific revolution" sensu Kuhn.Method I performed a bibliometric analysis of the prevalence of EP (broadly defined) in Psychology, and contrasted it with the prevalence of the socio-cultural approach, known as the Standard Social Science Model (SSSM) (Tooby & Cosmides, 1992).ResultsMy analysis reveals that the SSSM enjoys significantly greater prominence than EP and is growing at a swifter pace. My analysis also suggests that a “cultural evolutionary” approach, which integrates evolutionary and cross-cultural perspectives, is still underdeveloped.ConclusionsDespite being sympathetic to the claim that EP can potentially lead to a paradigm shift in the behavioral sciences, I argue that a prudent approach may involve recognizing the current state of affairs, envisioning realistic change, and building a more conceptually and methodologically heterogeneous research community in EP.

Pengaruh Model Project Based Learning Terhadap Hasil Belajar IPAS Siswa Sekolah Dasar

The purpose of this study was to explore the interrelationship between the project-based learning model and student learning outcomes in Natural and Social Sciences (IPAS) subjects. The experiment was conducted on fourth grade students of SDN Baru 03 Pagi East Jakarta. Quantitative and quasi experimental methodologies were used as research methods. In addition, the sampling method is purposive sampling technique. Samples were taken from the control and experimental groups. 40 multiple choice questions were given for validity testing, with 30 questions considered valid and 10 questions considered failed. This study obtained the results in the IPAS lesson Ha accepted Ho rejected means that the project-based learning model has a significant effect on students of SDN Baru 03 Pagi East Jakarta is considered capable of improving student learning outcomes resulting in differences in the average post-test class of 75.2 in the experimental class and 89.8 in the control class.

Psychologically-Valid Generative Agents: A Novel Approach to Agent-Based Modeling in Social Sciences

Incorporating dynamic realistic human behaviors in population-scale computational models has been challenging. While some efforts have leveraged behavioral theories from social science, validated theories specifically applicable to Agent-based modeling remain limited. Existing approaches lack a comprehensive framework to model the situated, adaptive nature of human cognition and choice. To address these challenges, this paper proposes a novel framework, Psychologically-Valid Generative Agents. These agents consist of a Cognitive Architecture that provides data-driven and cognitively-constrained decision-making functionality, and a Large Language Model that generates human-like linguistic data. In addition, our framework benefits from Stance Detection, a Natural Language Processing technique, that allows highly personalize initialization of the agents, based on real-world data, within Agent-based modeling simulations. This combination provides a flexible yet structured approach to endogenously represent how people perceive, deliberate, and respond to social or other types of complex decision-making dynamics. Previous work has demonstrated promising results by using a subset of the components of our proposed architecture. Our approach has the potential to exhibit highly-realistic human behavior and can be used across a variety of domains (e.g., public health, group dynamics, social and psychological sciences, and financial markets).

Development and validation of student worker worksheets based on the RADEC learning model for Natural and Social Sciences (IPAS) subjects in elementary schools

This research aims to develop and test the validity of student worksheet based on the RADEC Learning Model (Read, Answer, Discuss, Explain and Create) in Natural and Social Sciences (IPAS) subjects at elementary school level. The RADEC model is used as a basis for designing worksheets that support an active, participatory learning process and stimulate an in-depth understanding of science concepts. This research uses a development research approach. The development stage includes needs analysis, worksheet design, and validation by experts. It is hoped that the results of this research will contribute to improving worksheets as a learning aid that can improve students' understanding of science material in elementary schools. Apart from that, this research can also provide guidance to educators and students in implementing the RADEC Learning Model to improve the quality of learning in the classroom.