Social Science Data Research Articles

Discovering research data management trends from job advertisements using a text-mining approach

In today’s data-driven culture, research data management (RDM) is essential for the research community. The demand for reusing research datasets is a challenging and diverse process for the scientific community. Despite this, it is essential in RDM to discover trends and themes using text mining, which is scarce. The purpose of this study is to employ text mining to discover insights from job advertisements associated with RDM profiles, which collected 810 advertisements. We found RDM-related patterns using latent Dirichlet allocation (LDA) and identified three key contexts. The first is ‘research services in libraries’, with the topics of research services, research information, research universities, collection processes and library services. The second context is ‘research data’, which includes RDM, business data, university data, research data, health research, science research, social science research, data centres, data services, statistical software, digital scholarship and digital preservation. The third context is ‘workplace environment’, and the topics are leadership, work development and scientific position. Job title normalisation reveals names such as ‘data librarian’, ‘librarian’, ‘director’, ‘data curator’, ‘data manager’, ‘research data librarian’, ‘data specialist’ and ‘data officer’ are frequently employed. Focusing on titles with a single or double occurrence is new and interesting for developing nations. Reputable institutions such as Harvard, Stanford and the Massachusetts Institute of Technology, as well as countries such as the United States, the United Kingdom, Canada and Germany, are the major participants in RDM practises and services. This discovery will assist higher education institutions, RDM stakeholders, which aid in the formulation of curriculum, and job seekers to familiarise themselves with the themes.

Linked Open Research Data for Social Science

The re-use of research data is an integral part of research practice in the social and economic sciences. To find relevant data, researchers need adequate search facilities. However, a comprehensive, thematic search for research data is made more difficult by inconsistent or missing semantic indexing of data at the level of social science concepts (e.g., representing the theory language). Either the data is not documented at a granular level, or primary investigators use their ad-hoc terminology to describe their data. Consequently, researchers have to make great efforts to find relevant or comparable data. From the user's perspective, the lack of theory language in data documentation impedes effective data searches and thus significantly limits the research potential of existing data collections. Because there is currently no semantic model for indexing the data content, the specific challenge for improving data search lies in establishing concept-based indexing of research data. Research infrastructures need technology for the harmonized semantic indexing of their research data. The LORD concept registry aims at closing this gap by developing a registry of sociological and economic concepts and, following the FAIR principles, making this concept registry generally available to the scientific community. As a first step, we developed a basic data model for the Concept Registry using United Modeling Language (UML). All links between are created and managed in the form of so-called RDF triples. An annotation application allows for linking specific questions/variables to concepts. The application also includes the two SKOS-compliant thesauri, "Thesaurus Social Sciences" (TheSoz) and "Standard Thesaurus Economics" (STW) but could be extended to other resources like ELSST.
 We illustrate the application of the LORD concept registry with examples from three large-scale survey programmes (German Socio-Economic Panel, German General Social Survey, National Academics Panel Study). The initial focus is on variables and questions with overlapping content in the three survey programmes, as they form a sound basis for cross-linking with concepts.

ChatGPT-3 Sorguları ile Sosyal Bilimlerde Veri Analizi için MATLAB İstatistik Araç Kutusu Kullanımı

This paper explores the potential usage of ChatGPT-3, a powerful language model developed by OpenAI, within the MATLAB Statistical Toolbox (ST) for social science research. ChatGPT-3 is a highly advanced model that has shown remarkable performance in a wide range of natural language processing tasks. However, its usage in social science research is still relatively new and has not been widely explored. The main advantage of using ChatGPT-3 in social science research is its ability to process large amounts of unstructured text data, which is becoming increasingly prevalent in social science research. However, there are also some potential disadvantages to using ChatGPT-3, such as its complexity, lack of interpretability, and proprietary nature. This paper aims to provide an overview of the current state of ChatGPT-3 usage in social science research and to discuss the potential advantages and disadvantages of using this model within MATLAB ST. This paper, it is aimed to show how ChatGPT-3 can assist social science researchers in MATLAB ST in the processing of their datasets. Because data analysis can be challenging for social science researchers for several reasons as social science data can often be complex, with multiple variables and multiple levels of analysis. This can make it difficult to analyze and interpret the data in a meaningful way. Therefore, some sample hints, where ChatGPT-3 prompts are used to handle such statistical operations in MATLAB ST, are provided. The comments that ChatGPT-3 gives out are analyzed. It is believed that ChatGPT-3 will be a good assistant for social science researchers in MATLAB ST.

Connecting physical and social science datasets: challenges and pathways forward

The integration of physical and social science data can enable novel frameworks, methodologies, and innovative solutions important for addressing complex socio-environmental problems. Unfortunately, many technical, procedural, and institutional challenges hamper effective data integration—detracting from interdisciplinary socio-environmental research and broader public impact. This paper reports on the experiences and challenges of social and physical data integration, as experienced by diverse Early Career Researchers (ECRs), and offers strategies for coping with and addressing these challenges. Through a workshop convened by the National Center for Atmospheric Research (NCAR) Innovator Program, 33 participants from different disciplines, career stages, and institutions across the United States identified four thematic data integration challenges related to complexity and uncertainty, communication, scale, and institutional barriers. They further recommended individual, departmental, and institutional scale responses to cope with and address these integration challenges. These recommendations seek to inform faculty and department support for ECRs, who are often encouraged—and even expected—to engage in integrative, problem-focused, and solutions-oriented research.

Performing Qualitative Content Analysis of Video Data in Social Sciences and Medicine: The Visual-Verbal Video Analysis Method

Videos are ubiquitous and have significantly impacted our communication and information consumption. The video, as data, has helped researchers understand how human interactions and relationships develop and change, and how patterns emerge in various circumstances and interpretations. Given the expanding relevance of video data in social science and medical research and the constant introduction of new formats and sources, it is critical to be able to conduct a thorough analysis of this multimodal data. However, the few methodologies (e.g., Actor Network Theory, Picture Theory) appropriate to video data analysis lack detailed guidelines on how to select, organize, and examine the multimodality of video data. This article aims to overcome this practice or methodological gap by proposing and demonstrating the Visual-Verbal Video Analysis (VVVA) method, a six-step framework adapted from Multimodal Theory and Visual Grounded Theory for organizing and evaluating video material according to the following dimensions: general characteristics of the video; multimodal characteristics; visual characteristics; characteristics of primary and secondary characters; and content and compositional characteristics including the transmission of messages, emotions, and discourses. This article also looks at the theories underlying video data analysis, focusing on Grounded Theory and Multimodality Theory, and provides multiple examples of coding and interpretive processes to deepen understanding and comprehension. The VVVA data extraction matrices provide a systematic coding approach for verbal, visual, and textual content, allowing for structured, coherent extraction that supports the discovery of patterns and links among disparate types of information. The VVVA method may be applied to a wide range of video data in social and medical sciences that vary in length and originate from different sources (e.g., open access web sources, pre-recorded organizational videos and recordings created for research purposes). The VVVA method effectively tracks the ongoing research process, and can manage data sets of various sizes.

Survey of Unreported Psychiatric Morbidity Induced by Job Dissatisfaction among Teachers: Inferential Point of View

Elegant statistical methods for categorical data analysis are rapidly evolving and being adopted, particularly for biomedical and social sciences data analysis. This study presents a case study for the application of the discrete Johnson systems of distribution approach for the analysis of secondary school teachers’ job satisfaction (JS). This new approach accommodates relative frequency behavioural patterns in the analysis of categorical data using the entropy measure of discrete Johnson systems of distribution (DJSD). The approach offers a better alternative to the existing chi-square and likelihood ratio tests because it captures more shared information compared to known measures of association. A focus on the JS of about 393 teachers, showed that above 60% of the teacher’s eventually developed job dissatisfaction induced psychiatric disorders before the end of their career. Further examples were used to illustrate the applicability of the approach and enhance its reproducibility.

Solving the many-variables problem in MICE with principal component regression.

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the many-variables problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy through two Monte Carlo simulation studies and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures.

PENGARUH KUALITAS PELAYANAN DAN HARGA TERHADAP KEPUASAN KONSUMEN PADA TOKO MONTE SWALAYAN CAFÉ SALON DI KOTA PALOPO

This study aims to analyze the effect of service quality and price on customer satisfaction at monte self-service café salon shops (self-service case study) in Palopo City. Sampling method with incidental sampling (incidental sampling). The number of samples in this study were 33 respondents from an unknown population. Data collection techniques using observation, questionnaire and documentation. Data processing techniques with a regression system with the help of SPSS software (Statistical Package for the Social Sciences) data analysis techniques used are validity test, reliability test, multiple linear regression test, determinant coefficient test, t test (partial) and F test (simultaneous) . The results of this study concluded that service quality has a positive and significant impact on consumer satisfaction at Monte Self-Service Stores in Palopo City. There is an effect of price on consumer satisfaction, the better the quality of service and the price provided, the more satisfied consumers will feel, the more positive and significant effect this will have on consumer satisfaction at monte supermarkets in the city of Palopo.

Conducting Social Science Research During Epidemics and Pandemics: Lessons Learnt.

The COVID-19 pandemic has had a significant impact on how field-based research is being conducted globally. Given the challenges of undertaking fieldwork during epidemics and the need for mixed methods research to address the social, political, and economic issues related to epidemics, there is a small but growing body of evidence in this area. To contribute to the logistical and ethical considerations for conducting research during a pandemic, we draw on the challenges and lessons learnt from adapting methods for two research studies conducted in 2021 during the COVID-19 pandemic in low- and middle-income country (LMIC) settings: (1) in-person research in Uganda and (2) combined remote and in-person research in South and Southeast Asia. Our case studies focus on data collection and demonstrate the feasibility of conducting mixed methods research, even with many logistical and operational constraints. Social science research is often used to identify the context of specific issues, to provide a needs assessment, or inform longer-term planning; however, these case studies have shown the need to integrate social science research from the start of a health emergency and in a systematic way. Social science research during future health emergencies can also inform public health responses during the emergency. It is also crucial to collect social science data after health emergencies to inform future pandemic preparedness. Finally, researchers need to continue research on other public health issues that are ongoing even during a public health emergency.

Analyzing the Usefulness of Demonstration Plots as Perceived by the Farmers in District Faisalabad, Pakistan

Agricultural extension is the driver of motivation of farmers towards the adoption of agricultural innovations. Therefore, different extension methods are employed to enhance the effectiveness of extension services and bring change in farmers' behaviour. In this study, an attempt is made to explore the use of demonstration plots as perceived by the farmers in district Faisalabad. In this study, a total of 130 randomly selected farmers, five Agriculture Officers (AOs) and five Field Assistants (FAs) participated. From the farmers, data were collected through face-to-face interview techniques on a structured and validated interview schedule whereas from the extension field staff, the data were collected on an interview guide. Quantitative data were analysed using Statistical Package for Social Sciences (SPSS) and qualitative data were analysed using a content analysis approach. Qualitative results indicated that the demonstration plots helped farmers to change their behaviour from a traditional farming approach to a modern approach of farming for more production and profits. Group discussion, method and result demonstration methods were focused areas while demonstrating technologies at the demonstration plot. Farmers found demonstration plots useful for the reasons of being active, cooperation from the extension staff, equal treatment to the farmers, participation of farmers in group meetings, enhanced learning and motivating farmers to adopt the latest technologies. Considering the usefulness, there is a need to enhance the number of demonstration plots. More research studies are also recommended to be conducted in other districts of the Punjab s well to investigate the further room for improvement and strengthen the extension services for farmers.

What Makes Swing Voters Participate in Protests? The Effects of Voting Patterns and Election Cycles on Participation in Candlelight Protests in South Korea

This paper aims to analyze how the traits of candlelight protest participants have changed from 2004 to 2017 in South Korea. While previous studies have typically identified these traits of the candlelight protesters based on a single survey, this paper examines the change of protest participants using a multi-level analysis. This study argues that voter type and election cycles have a significant impact on an individual's decision to participate in candlelight protests. To be specific, swing voters are less likely to participate in a candlelight protest when the next election is scheduled either too soon or too far. Core liberal voters with a high degree of motivation are more likely to take part in a candlelight protest when the distance from the next election is close, while core conservative voters with a low degree of motivation are not affected by the distance from the next election. This study contributes on both sub-fields, voting behaviors and contentious politics, by combining individuals' voting patterns with political opportunity structures. Survey data (2004, 2010, 2017) conducted by the Korean Political Science Association (KPSA) and the Korean Social Science Data Center (KSSDC) provide empirical support for the arguments.

How Data Papers Present a Unique Contribution To Open Research In The Humanities And Social Sciences

The open research movement and initiatives like the FAIR principles have been critical in establishing the importance of data in research, particularly within the sciences. Alongside the sciences, attention to openly available data in Humanities and Social Sciences (HSS) research has gradually grown. This growth is largely attributed to the increased availability of digital collections, the development of new data-intensive methods, an increasingly solid infrastructure, increased pressure from funders, the requirement of data management plans for preservation purposes, and the involvement of research libraries in data curation. In this context, attention to how data is produced, how it is openly and transparently shared, and how it can be reused has generated great interest, accompanied by an inevitable need for reputable data sharing outlets. One such outlet is the data paper – a peer-reviewed publication that focuses on describing a curated dataset. Data papers can be shared in traditional research journals as one subtype of article publication, or, more recently, in data journals which are dedicated to the publication of data papers. This presentation focuses on the work done by the open access Journal of Open Humanities (JOHD) in promoting the practice of publishing data papers with their accompanying open access datasets. JOHD was established with Ubiquity Press in 2015 to promote awareness, use, and reuse of humanities data. JOHD data papers promote the comprehensive description of how a dataset was assembled, where it may be accessed, and any crucial context including the research questions that framed the data gathering, including limitations to the original methods or scope of sources included. JOHD data papers suggest potential future reuses of data, which recent analytics seem to suggest has helped increase the visibility of datasets, and therefore their research impact (Marongiu et al., forthcoming; McGillivray et al., 2022). In addition, an overview of the three key elements (the “golden triangle”) that assess the impact of open research efforts as represented by different research outputs (datasets, data papers and research papers) will be presented, along with proposed initiatives for linking these. In doing so, we aim to (a) find a programmatic way to identify these links by extracting information from available metadata of datasets and verifying their accuracy, and (b) create a “ground truth” in a manual and/or machine-assisted way which would enable the training of more sophisticated NLP-based methods as a next step. We hope to illustrate the importance of including data papers into the research conversation given that they present a unique contribution to addressing global challenges within the open research arena.

A critical assessment of a protected area conflict analysis based on secondary data in the age of datafication

Recently, a global trend towards a broader use of secondary data in social sciences has been reinforced by the COVID-19 pandemic. This evoked doubts about the validity of the results unless restrictive assessment procedures are implemented. To address this need in the field of protected area (PA) conflict analysis, we propose a three-fold approach (theory-, method-, and cross-scale simulation-driven) to assess the usefulness of the utilized state register dataset and the indicator analysis methodology for the multi-level recognition of PA conflict determinants. With the ultimate aim to inform case study selection, we processed 187 relevant indicators from the official Statistics Poland register for a Lesser Poland region. We distinguished five types of PA conflict determinants in Lesser Poland (‘urbanity’, ‘agriculture’, ‘tourism’, ‘small-scale entrepreneurship’, and ‘sprawl’) and respective groups of 15 clusters comprising local-level units. For one cluster, we juxtaposed the obtained results with secondary data from another source (Internet content) and for a specific PA (Tatra National Park). Although the reported conflict issues corresponded to the indicator-derived descriptors of the cluster, in the theory-driven phase of the assessment, the state register failed to address the key prerequisites of PA conflicts. We have demonstrated that, in crisis conditions such as COVID-19, the proposed method can serve as a proxy for a multi-level recognition of PA conflict potentials, provided that it synthesises the results of different methodological approaches, followed by in-person interviews in the selected case studies.

<em>How and Why Do Researchers Reference Data</em>? A Study of Rhetorical Features and Functions of Data References in Academic Articles

Data reuse is a common practice in the social sciences. While published data play an essential role in the production of social science research, they are not consistently cited, which makes it difficult to assess their full scholarly impact and give credit to the original data producers. Furthermore, it can be challenging to understand researchers’ motivations for referencing data. Like references to academic literature, data references perform various rhetorical functions, such as paying homage, signaling disagreement, or drawing comparisons. This paper studies how and why researchers reference social science data in their academic writing. We develop a typology to model relationships between the entities that anchor data references, along with their features (access, actions, locations, styles, types) and functions (critique, describe, illustrate, interact, legitimize). We illustrate the use of the typology by coding multidisciplinary research articles (n = 30) referencing social science data archived at the Inter-university Consortium for Political and Social Research (ICPSR). We show how our typology captures researchers’ interactions with data and purposes for referencing data. Our typology provides a systematic way to document and analyze researchers’ narratives about data use, extending our ability to give credit to data that support research.

Behaviour and Thoughts: For a Pluralistic Model of Empirically Informed Political Philosophy

Is Political Philosophy Impossible? develops a distinctive and powerful vision of empirically informed political philosophy: one that gives a central role to data about what people do, rather than what they think or say. Here, I offer some critical reflections on this ‘normative behaviourist’ account of how, and why, we should integrate normative theorizing with empirical research. I suggest that normative behaviourism is at once too ambitious and too restrictive concerning the role of social scientific data in political philosophy. On the one hand, it implicates philosophy in complex and contested issues in criminology, and developing the approach to address more fine-grained normative problems would place unrealistic demands on the empirical data. On the other hand, the emphasis on crime and insurrection excludes alternative valuable forms of empirical evidence from normative theorizing. I conclude by defending a more modest and pluralistic picture of data-sensitive political philosophy.

Operational Capacity of County Governments to Plan on Performance of Devolved Health Sector in Kenya

In Kenya, devolution of health services has resulted in the decentralization of many health services from the national government to local governments in order to improve the health of citizens. Despite political support of such a system and initiatives to increase devolution, the performance of the devolved health sector in Kenya has not improved as expected. The was meant to determine how citizen participation influence organizational performance of devolved health care sector in Kenya. The current study utilized cross-sectional research design because it involved the collection of data in 47 counties across the country and focused on several selected officials who gave information regarding governance factors influencing devolved health care sector in Kenya. Since the study utilized census sampling, the 47 county referral hospitals were targeted. Further purposive sampling was used to arrive at the 188 selected respondents who became the sample size of the study. Descriptive statistics was used to outline the fundamental characteristics regarding the collected information in the research. The study combined the elements of both quantitative and qualitative methods of data collection. The primary data was collected using questionnaires whose content was appropriate to test the hypothesis and address the research questions. Statistical Package for Social Sciences (SPSS) data analysis software package was employed to examine the quantitative data using the descriptive statistics. Analysis of Variance (ANOVA) table techniques and beta coefficient tables were utilized in the presentation of regression analysis outcomes. The findings of the study revealed that operational capacity of county governments to plan and management transparency had positive significant relationship with organizational performance of devolved health care sector. The findings suggest that there is need for the health care sector function to be devolved to the counties to allow for more citizen inclusivity in decision making, timely response to health care needs and government planning.

Workshop on Comparative analysis of k populations with Non Parametric for Research in Social Sciences and Education

Data obtained from social science and education research is often in the form of categorical data, namely nominal or ordinal. This makes the parametric approach less appropriate for use in some social science and education data. One solution is to use a nonparametric approach. This underlies the holding of community service activities in a workshop on comparison analysis of k population with a nonparametric approach for social science research and education. Participants in this workshop consisted of academics and practitioners, and students from various study programs in Indonesia. The workshop was carried out by providing material and demonstrating using R software as an analytical tool, which was held online for two days. On the first day, the material presented was a nonparametric approach to the comparison of k independent populations along with a demonstration of using R software, while for k dependent populations along with a demonstration of using R software was given on the second day. Participants were given data on social sciences and education in providing materials and demos of the R software. Based on the results of questionnaires, observations, and questions and answers, participants seemed enthusiastic in participating in the R software's material and demo sessions. In addition, participants can perform various tests in a nonparametric approach for k independent and dependent populations using the R software. Participants can also provide an accurate interpretation of the output of the R software.

Smartphone Dependency, Happiness, and Social Class Awareness Among Middle School Students in South Korea

Smartphone dependency (SD) has been linked to decreased happiness; however, research has not yet explored potential mediating mechanisms such as social class awareness (SCA) in this relationship. This study examined the association between SD and happiness among middle school students and the mediating effect of SCA in this relationship. Data from the 11th Korea-Child Happiness Index 2019 Survey were used, which is a cross-sectional survey administered to children and adolescents residing in South Korea collected by the Korea Social Science Data Archive (KOSSDA). A total of 2,296 Korean middle school students were selected for analysis. Findings suggest that higher levels of SD were associated with lower levels of happiness. SCA mediated the relationship between SD and happiness among middle school students. SD prevention and intervention approaches should consider the extent to which SCA impacts happiness of middle school students.

STATE OF THYROID GLAND FUNCTION IN PATIENTS ADMITTED TO CORONARY CARE UNIT

Background 
 A large number of patients are admitted to coronary care units daily. Thyroid gland abnormalities are one of the reasons which cause people to have cardiac events. Therefore, detecting thyroid gland abnormalities among cardiac care unit patients and early management leads to less morbidity/mortality and a better long-term prognosis for those patients.
 Objectives 
 The study was done to detect the function of thyroid gland abnormalities and their effect on different cardiac conditions among patients admitted to CCU.
 Patients Methods
 This descriptive cross-sectional study includes a consecutive non-random sampling of 201 patients. All the patients enrolled in this study were admitted to the CCU unit of Sulaimanya Teaching Hospital and Shar Hospital in Sulaimanya city/Iraq between August 2021 and January 2022. Thyroid function test and lipid profile were checked for the participants. The pediatric age group, pregnant women, patients with malignancies/ severe systemic illness /on amiodarone/ congenital heart disease were excluded. Statistical Package analysed the collected data for Social Science (SPSS) (version 22.0).
 Results
 One hundred fifteen (57.2%) participants were male, and 86(42.8%) were female. The most common age group was between 45-64 years. HTN(%44.3), diabetes (29.9%) and hyperlipidemia (17.9%) were common comorbid conditions. The most common cause of admissions to CCU was due to ACS (63.2%) and then arrhythmia (22.9%), decompensated heart failure (12.9%) and pericardial diseases (1%). In addition, 82.59% (166) of the participants were euthyroid, and 17.41% (35) had thyroid gland function abnormalities.
 Conclusion 
 The effect of thyroid hormone problems on cardiac conditions is one of the important causes of CCU admissions. Patients with ACS and heart failure are presented with hypothyroidism. However, arrhythmia is present more in cases of hyperthyroidism.

Lasso-based variable selection methods in text regression: the case of short texts

Communication through websites is often characterised by short texts, made of few words, such as image captions or tweets. This paper explores the class of supervised learning methods for the analysis of short texts, as an alternative to unsupervised methods, widely employed to infer topics from structured texts. The aim is to assess the effectiveness of text data in social sciences, when they are used as explanatory variables in regression models. To this purpose, we compare different variable selection procedures when text regression models are fitted to real, short, text data. We discuss the results obtained by several variants of lasso, screening-based methods and randomisation-based models, such as sure independence screening and stability selection, in terms of number and importance of selected variables, assessed through goodness-of-fit measures, inclusion frequency and model class reliance. Latent Dirichlet allocation results are also considered as a term of comparison. Our perspective is primarily empirical and our starting point is the analysis of two real case studies, though bootstrap replications of each dataset are considered. The first case study aims at explaining price variations based on the information contained in the description of items on sale on e-commerce platforms. The second regards open questions in surveys on satisfaction ratings. The case studies are different in nature and representative of different kinds of short texts, as, in one case, a concise descriptive text is considered, whereas, in the other case, the text expresses an opinion.