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Background: As researchers in primary care, we want to drive change in practice and conduct research that sparks meaningful transformation. These changes can only happen if our research work resonates in a meaningful way with the people who they are designed for, i.e. the healthcare professionals and the patients., Viewpoint: This viewpoint stems from first-hand insights gained as a social scientist engaged in trials and primary care research amidst epidemics and pandemics. Some examples stemming from the EU Funded GRACE INTRO, RECOVER and Prudence trial illustrate these experiences. I outline how primary care can effectively address the pressing challenges it encounters, whether as researchers, members of the public, or healthcare professionals, and how to integrate successfully social sciences within clinical primary care research., Conclusion: As interdisciplinary researchers, social scientists and medical researchers can work together under certain conditions, i.e. equal status, adequate resources, and seamless integration within trial structures.

Social science research has generated extensive knowledge on xenotransplantation, encompassing the perspectives of actual and potential patients, other stakeholders, public opinion and debate, human-animal relationships, animal production and husbandry, bioeconomy, as well as biotechnology governance and regulation. We therefore convened social science researchers to discuss the latest developments in xenotransplantation research and practice in late 2023. Based on a brief workshop report, we aim to highlight the various ethical implications of this debate. After outlining the role of social science research in the ethical evaluation of xenotransplantation, we elaborate three critical points that may become pivotal in the future evolution of xenotransplantation: the framing of xenotransplantation in the clinical setting and in the public, the potential impact of religious beliefs on patients' transplant choices, and the consequences for the allotransplantation system if xenotransplantation becomes clinically established, including the allocation of allo- and xeno-organs., (© 2024 The Author(s). Xenotransplantation published by Wiley Periodicals LLC.)

A thorough literature search is a key feature of scoping reviews. We investigated the search practices used by social science researchers as reported in their scoping reviews. We collected scoping reviews published between 2015 and 2021 from Social Science Citation Index. In the 2484 included studies, we observed a 58% average annual increase in published reviews, primarily from clinical and applied social science disciplines. Bibliographic databases comprised most of the information sources in the primary search strategy (n = 9565, 75%), although reporting practices varied. Most scoping reviews (n = 1805, 73%) included at least one supplementary search strategy. A minority of studies (n = 713, 29%) acknowledged an LIS professional and few listed one as a co-author (n = 194, 8%). We conclude that to improve reporting and strengthen the impact of the scoping review method in the social sciences, researchers should consider (1) adhering to PRISMA-S reporting guidelines, (2) employing more supplementary search strategies, and (3) collaborating with LIS professionals., (© 2024 The Author(s). Research Synthesis Methods published by John Wiley & Sons Ltd.)

How does scientific knowledge grow? This question has occupied a central place in the philosophy of science, stimulating heated debates but yielding no clear consensus. Many explanations can be understood in terms of whether and how they view the expansion of knowledge as proceeding through the accretion of scientific concepts into larger conceptual structures. Here we examine these views empirically by analysing 2,605,224 papers spanning five decades from both the social sciences (Web of Science) and the physical sciences (American Physical Society). Using natural language processing techniques, we create semantic networks of concepts, wherein noun phrases become linked when used in the same paper abstract. We then detect the core/periphery structures of these networks, wherein core concepts are densely connected sets of highly central nodes and periphery concepts are sparsely connected nodes that are highly connected to the core. For both the social and physical sciences, we observe increasingly rigid conceptual cores accompanied by the proliferation of periphery concepts. Subsequently, we examine the relationship between conceptual structure and the growth of scientific knowledge, finding that scientific works are more innovative in fields with cores that have higher conceptual churn and with larger cores. Furthermore, scientific consensus is associated with reduced conceptual churn and fewer conceptual cores. Overall, our findings suggest that while the organization of scientific concepts is important for the growth of knowledge, the mechanisms vary across time., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

In their commentaries, Berger (2024) and Carrillo et al. (2024) raise several thoughtful questions regarding machine-assisted hypothesis generation in the social sciences. We discuss their ideas and build upon them. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This paper explores what we can learn from the humanities and social sciences about how standards operate in and around science, in order to understand more about how 'the gold standard' can be shifted away from the use of animals in research and testing, and toward New Approach Methodologies (NAMs). These fields allow us to consider potential futures of NAMs as alternatives, replacements, or complements to animal use in testing and research. As we demonstrate, the questions that we pose and how they are framed are as important as the answers that result. Rather than asking how to 'redefine the gold standard', norms and expectations for NAMs must be actively debated and transparently defined. These considerations would be based, in part, on what has been learned in the past from non-human animal models and systems, but also use the norms within the fields from which the NAMs derive in light of the rich broader contexts within which they are being developed. As we argue, notions such as 'a gold standard' are limited and must be replaced by contextualised standards that depend on the scientific, sociocultural and other factors that contribute to our understanding of a particular method (new or otherwise) as 'good' for a particular purpose., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results.

Whilst many lessons were learned from the COVID-19 pandemic, ongoing reflection is needed to develop and maintain preparedness for future outbreaks. Within the field of infectious disease and public health there remain silos and hierarchies in interdisciplinary work, with the risk that humanities and social sciences remain on the epistemological peripheries. However, these disciplines offer insights, expertise and tools that contribute to understanding responses to disease and uptake of interventions for prevention and treatment. In this Perspective, using examples from our own cross-disciplinary research and engagement programme on vaccine hesitancy in South Africa and the United Kingdom (UK), we propose closer integration of expertise, research and methods from humanities and social sciences into pandemic preparedness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Frampton, Orievulu, Matthews, Giubilini, Hordern, Burns, Elias, Friederich, Majozi, Martin, Stevenson, Vanderslott and Seeley.)

While the biological hallmarks of aging are widely recognized as fundamental mechanisms of biological aging, more recently, there have been calls within geroscience to understand the aging process more comprehensively by adding a perspective of the social hallmarks of aging. Social and behavioral factors, such as socioeconomic status, life events or behavior and beliefs can alter the aging process per se and act in complex interactions with biological pathways. In addition, underlying biological pathways have been proposed for various psychosocial concepts, such as views on age and relationship quality. The aim of the present article is to provide narrative insights into theoretical and empirical developments between social and behavioral gerontology and geroscience or biogerontology. This article focuses on the potential of an interdisciplinary aging research but it also sets out the critical perspective that social gerontology provides., (© 2024. The Author(s).)

Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents and consequences of algorithmic bias. Through the lens of the 3-D Dependable AI Framework, this article explores how social science disciplines, such as psychology, can contribute to identifying and mitigating bias at the Design, Develop, and Deploy stages of the AI life cycle. Finally, we propose future research directions to further address the complexities of algorithmic bias and its societal implications., Competing Interests: Declaration of competing interest The author declares no conflict of interest, (Copyright © 2024 The Author. Published by Elsevier Ltd.. All rights reserved.)

Between the years 1925 and 1934, the Social Science Research Council (SSRC) awarded 198 postdoctoral research fellowships to early-career social scientists, among which 29 were awarded to women. This article, which is based on the SSRC directory and Rockefeller institutions' records, examines the professional paths of these female fellows to shed light on the presence of women in the social sciences and to probe the peculiarities of their professional trajectories. The SSRC fellowships represented a significant professional prospect for brilliant young female graduates who were often denied similar opportunities in other fields. Nonetheless, they did not eradicate all gender discrimination that remained prevalent, not only in the vertical sense by preventing women from progressing in the academic hierarchy, but also in the horizontal sense by retaining them in designated spaces (specific disciplines or institutions) that were underrecognized. Ultimately, the analysis of women's professional paths underscores the importance of examining the private or intimate lives of scientists to gain a more in-depth understanding of the social structure of science and its impact on its protagonists., (© 2024 The Author(s). Journal of The History of the Behavioral Sciences published by Wiley Periodicals LLC.)

For the past four decades, biomedical science has transformed clinical outcomes for HIV and AIDS. However, the social, economic and gendered determinants of HIV remain largely intact. The social science and humanities offer concepts and methods for articulating why these remain intractable. I used poetic inquiry - an arts-based, qualitative approach - as I reviewed literature on the "end of AIDS, and post-AIDS". As I did so, I considered what contribution the social sciences and humanities could make in moving us closer to these ideals. Several themes and found poems emerged in this reading: (1) how language oversimplifies complex social realities; (2) the voices of people living with HIV and AIDS must be included; (3) HIV and AIDS intersects with social inequalities; (4) social and structural issues are no barrier to HIV prevention and (5) the need for radical interdisciplinarity. The paper concludes that the end of AIDS requires responses that are integrated, holistic and that radically challenge our silo'd disciplinary boundaries and frames. The social sciences and humanities are key to this charge.

In the social and behavioral sciences, surveys are frequently used to collect data. During the COVID-19 pandemic, surveys provided political actors and public health professionals with timely insights on the attitudes and behaviors of the general population. These insights were key in guiding actions to fight the pandemic. However, the data quality of these surveys remains unclear because systematic knowledge about how the survey data were collected during the COVID-19 pandemic is lacking. This is unfortunate, since decades of survey research have shown that survey design impacts data. Our Survey Data Collection and the COVID-19 Pandemic (SDCCP) project deals with this research gap. We collected rich metadata on survey design for 717 social and behavioral science surveys carried out in Germany during the first two years of the COVID-19 pandemic. In this data descriptor, we present a unique resource for a systematic assessment of the survey data collection practices and quality of surveys conducted in Germany during the COVID-19 pandemic., (© 2024. The Author(s).)

How do parenthood and publishing contribute to gender gaps in academic career advancement? While extensive research examines the causes of gender disparities in science, technology, engineering, and mathematics (STEM) careers, we know much less about the factors that constrain women's advancement in the social sciences. Combining detailed career- and administrative register data on 976 Danish social scientists in Business and Management, Economics, Political Science, Psychology, and Sociology (5703 person-years) that obtained a PhD degree between 2000 and 2015, we estimate gender differences in attainment of senior research positions and parse out how publication outputs, parenthood and parental leave contribute to these differences. Our approach is advantageous over previous longitudinal studies in that we track the careers and publication outputs of graduates from the outset of their PhD education and match this data with time-sensitive information on each individual's publication activities and family situation. In discrete time-event history models, we observe a ∼24 per cent female disadvantage in advancement likelihoods within the first 7 years after PhD graduation, with gender differences increasing over the observation period. A decomposition indicates that variations in publishing, parenthood and parental leave account for ∼ 40 per cent of the gender gap in career advancement, suggesting that other factors, including recruitment disparities, asymmetries in social capital and experiences of unequal treatment at work, may also constrain women's careers., (© 2024 The Authors. The British Journal of Sociology published by John Wiley & Sons Ltd on behalf of London School of Economics and Political Science.)

A substantial number of social science studies have shown a lack of conceptual clarity, inadequate understanding of the nature of the empirical research approaches, and undue preference for deduction, which have caused much confusion, created paradigmatic incommensurability, and impeded scientific advancement. This study, through conceptual review and analysis of canonical discussions of concepts and the reasoning approaches of deduction and induction and their applications in social science theorization by philosophers and social scientists, is purported to unveil the logical nature of empirical research and examine the legitimacy of the preference of deduction among social scientists. The findings note that conceptual clarity as the foundation of social science research, exchange, and replication can be achieved through interdisciplinary stress of conceptual analyses to establish universal measurements and that the primacy of deduction in social sciences needs to concede to or be balanced with induction for new knowledge, more discoveries, and scientific advancement. The study recommends that institutions and researchers of social sciences invest more in conceptual analysis and inductive research through collaboration and separate efforts., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research-as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior., Competing Interests: Competing interests statement:The author declares no competing interest.

At its core, One Health promotes multidisciplinary cooperation amongst researchers and practitioners to improve the effectiveness and management of complex problems raised by the interplay of human, animal and environment interactions. Contemporary One Health literature has identified reducing disciplinary barriers as key to progress in the field, along with addressing the notable absence of social sciences from One Health frameworks, among other priorities. Efforts to position social scientists as experts on behaviour change and health decision-making has helped to articulate a concrete role for progressing One Health collaborations. Yet, there are other equally valuable functions the social scientist has in understanding complex systems, like One Health. We make explicit the multiple and diverse knowledge contributions the social sciences and humanities can make to progressing the One Health agenda. Articulating these more clearly invites a broader set of interdisciplinary perspectives to One Health discussions, allowing for stronger connections between sectors, actors, disciplines, and sub-systems. This perspective piece identifies a range of entry points for researchers and practitioners to better utilize the potential contributions social sciences and humanities scholars can make to One Health goals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Carter, Mankad and Okello.)

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Late, Ochsner. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Multidisciplinary teams (MDTs) are common in colorectal cancer and have been deemed important when providing care. Yet they take place outside of the patient, often with little consideration of the patient's views, goals and desires. In this paper specific examples from a patient perspective are integrated with the social science literature to provide an overview of areas of disconnect between MDT recommendations and the individual patient. The reasons for these disconnects are explored, including how MDTs relate to dyadic patient-clinician relationships, weak incorporation of patient-oriented outcomes in MDTs, poor integration of nonmedical patient information and the patient perspective and the impact of team dynamics and cognitive decision biases. Consideration of these issues should facilitate higher-quality MDT recommendations that are also more acceptable to patients., (© 2024 Association of Coloproctology of Great Britain and Ireland.)

Conspiratorial thinking has been with humanity for a long time but has recently grown as a source of societal concern and as a subject of research in the cognitive and social sciences. We propose a three-tiered framework for the study of conspiracy theories: (1) cognitive processes, (2) the individual, and (3) social processes and communities of knowledge. At the level of cognitive processes, we identify explanatory coherence and faulty belief updating as critical ideas. At the level of the community of knowledge, we explore how conspiracy communities facilitate false belief by promoting a contagious sense of understanding, and how community norms catalyze the biased assimilation of evidence. We review recent research on conspiracy theories and explain how conspiratorial thinking emerges from the interaction of individual and group processes. As a case study, we describe observations the first author made while attending the Flat Earth International Conference, a meeting of conspiracy theorists who believe the Earth is flat. Rather than treating conspiracy belief as pathological, we take the perspective that is an extreme outcome of common cognitive processes., (© 2023 Cognitive Science Society LLC.)

Background: The term 'care-experienced' refers to anyone who is currently in care or has been in care at any stage in their life. A complex interplay of factors leads to care-experienced children and young people (CECYP) experiencing poorer oral health and access to dental care than their peers. A rapid review of the co-production of health and social care research with vulnerable children and young people (CYP) was carried out to inform the development of a co-produced research project exploring the oral health behaviours and access to dental services of CECYP. Here, 'co-production' refers to the involvement of CYP in the planning or conduct of research with explicit roles in which they generate ideas, evidence and research outputs., Aim: To learn how to meaningfully involve vulnerable CYP in the co-production of health and social science research., Objectives: To identify: Different approaches to facilitating the engagement of vulnerable CYP in co-production of health and social science research; different activities carried out in such approaches, challenges to engaging vulnerable CYP in co-production of health and social science research and ways to overcome them and areas of best practice in relation to research co-production with vulnerable CYP., Search Strategy: A rapid review of peer-reviewed articles was conducted in six databases (MEDLINE, Embase, SocINDEX, CINAHL, PsycINFO and Web of Science) and grey literature to identify studies that engaged vulnerable CYP in co-approaches to health and social research., Main Results: Of 1394 documents identified in the search, 40 were included and analysed. A number of different approaches to co-production were used in the studies. The CYP was involved in a range of activities, chiefly the development of data collection tools, data collection and dissemination. Individual challenges for CYP and researchers, practical and institutional factors and ethical considerations impacted the success of co-production., Discussion and Conclusion: Co-production of health and social science with vulnerable CYP presents challenges to researchers and CYP calling for all to demonstrate reflexivity and awareness of biases, strengths and limitations. Used appropriately and well, co-production offers benefits to researchers and CYP and can contribute to research that reflects the needs of vulnerable CYP. Adherence to the key principles of inclusion, safeguarding, respect and well-being facilitates this approach., Patient and Public Contribution: Members of our patient and public involvement and stakeholder groups contributed to the interpretation of the review findings. This manuscript was written together with a young care leaver, Skye Boswell, who is one of the authors. She contributed to the preparation of the manuscript, reviewing the findings and their interpretation., (© 2024 The Authors. Health Expectations published by John Wiley & Sons Ltd.)

Over the last 12,000 y, human populations have expanded and transformed critical earth systems. Yet, a key unresolved question in the environmental and social sciences remains: Why did human populations grow and, sometimes, decline in the first place? Our research builds on 20 y of archaeological research studying the deep time dynamics of human populations to propose an explanation for the long-term growth and stability of human populations. Innovations in the productive capacity of populations fuels exponential-like growth over thousands of years; however, innovations saturate over time and, often, may leave populations vulnerable to large recessions in their well-being and population density. Empirically, we find a trade-off between changes in land use that increase the production and consumption of carbohydrates, driving repeated waves of population growth over thousands of years, and the susceptibility of populations to large recessions due to a lag in the impact of humans on resources. These results shed light on the long-term drivers of human population growth and decline., Competing Interests: Competing interests statement:The authors declare no competing interest.

Policymakers increasingly rely on behavioral science in response to global challenges, such as climate change or global health crises. But applications of behavioral science face an important problem: Interventions often exert substantially different effects across contexts and individuals. We examine this heterogeneity for different paradigms that underlie many behavioral interventions. We study the paradigms in a series of five preregistered studies across one in-person and 10 online panels, with over 11,000 respondents in total. We find substantial heterogeneity across settings and paradigms, apply techniques for modeling the heterogeneity, and introduce a framework that measures typically omitted moderators. The framework's factors (Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience) affect the effectiveness of many text-based interventions, producing different observed effect sizes and explaining variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences., Competing Interests: Competing interests statement:The authors declare no competing interest.

Cultural Psychology (CP) is a relatively young perspective, growing in the field of social sciences, nevertheless, its ideas are found throughout the fields and its ancestors' -Naturwissenschaften and Philosophy- history. CP has several central notions which are introduced in the following paper. Thru their introduction, the central goal of the revelation of the essence of the idea of CP is proffered. The core idea of CP is a general psychological concept that should be considered as a starting point from where new ideas, innovations and challenging theories can emerge. In other words, a new general psychological perspective we can use as a starting point., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Misophonia has gained attention in scientific circles that utilise brain imaging to validate diagnoses. The condition is promoted as not merely a symptom of other psychiatric diagnoses but as a discrete clinical entity. We illustrate the social construction of the diagnostic category of misophonia through examining prominent claims in research studies that use brain imaging to substantiate the diagnosis. We show that brain images are insufficient to establish the 'brain basis for misophonia' due to both technical and logical limitations of imaging data. Often misunderstood as providing direct access to the matter of the body, brain images are mediated and manipulated numerical data (Joyce, 2005, Social Studies of Science 35(3), p. 437). Interpretations of brain scans are further shaped by social expectations and attributes considered salient to the data. Causal inferences drawn from these studies are problematic because 'misophonics' are clinically pre-diagnosed before participating. We argue that imaging cannot replace the social process of diagnosis in the case of misophonia, nor validate diagnostic measures or otherwise substantiate the condition. More broadly, we highlight both the cultural authority and inherent limitations of brain imaging in the social construction of contested diagnoses while also illustrating its role in the disaggregation of symptoms into new diagnoses., (© 2023 Foundation for the Sociology of Health & Illness.)

This commentary analyzes the extent to which the incommensurability problem can be resolved through the proposed alternative method of integrative experiment design. We suggest that, although one aspect of incommensurability is successfully addressed ( dimensional incommensurability ), the proposed design space method does not yet alleviate another major source of discontinuity, which we call conceptual incommensurability .

The authors rightly critique existing social sciences approaches. However, they are too quick to dismiss the criticism that their proposed paradigm is atheoretical. Social and cognitive theories are indeed incommensurate, often due to the lack of a unifying framework. Without proper integration with theoretical frameworks, their proposal may merely produce a resource-intensive veneer of thoroughness without substantive improvements to understanding.

The target article ignores the crucial role of correlational methods in the behavioral and social sciences. Yet such methods are often mandated by the greater complexity of the phenomena investigated. This necessity is especially conspicuous in psychological research where its position in the hierarchy of the sciences implies the need for both experimental and correlational investigations, each featuring distinct assets.

Almaatouq et al.'s prescription for more integrative experimental designs is welcome but does not address an equally important problem: Lack of adequate theories. We highlight two features theories ought to satisfy: "Well-specified" and "grounded." We discuss the importance of these features, some positive exemplars, and the complementarity between the target article's prescriptions and improved theorizing.

We examine the opportunities and challenges of expert judgment in the social sciences, scrutinizing the way social scientists make predictions. While social scientists show above-chance accuracy in predicting laboratory-based phenomena, they often struggle to predict real-world societal changes. We argue that most causal models used in social sciences are oversimplified, confuse levels of analysis to which a model applies, misalign the nature of the model with the nature of the phenomena, and fail to consider factors beyond the scientist's pet theory. Taking cues from physical sciences and meteorology, we advocate an approach that integrates broad foundational models with context-specific time series data. We call for a shift in the social sciences towards more precise, daring predictions and greater intellectual humility., Competing Interests: Declaration of interests The authors declare no competing interests., (Crown Copyright © 2023. Published by Elsevier Ltd. All rights reserved.)

In the social sciences, measurement scales often consist of ordinal items and are commonly analyzed using factor analysis. Either data are treated as continuous, or a discretization framework is imposed in order to take the ordinal scale properly into account. Correlational analysis is central in both approaches, and we review recent theory on correlations obtained from ordinal data. To ensure appropriate estimation, the item distributions prior to discretization should be (approximately) known, or the thresholds should be known to be equally spaced. We refer to such knowledge as substantive because it may not be extracted from the data, but must be rooted in expert knowledge about the data-generating process. An illustrative case is presented where absence of substantive knowledge of the item distributions inevitably leads the analyst to conclude that a truly two-dimensional case is perfectly one-dimensional. Additional studies probe the extent to which violation of the standard assumption of underlying normality leads to bias in correlations and factor models. As a remedy, we propose an adjusted polychoric estimator for ordinal factor analysis that takes substantive knowledge into account. Also, we demonstrate how to use the adjusted estimator in sensitivity analysis when the continuous item distributions are known only approximately. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Data from the distant past are fertile ground for testing social science theories of education and social mobility. In this study, we construct a dataset from 3,640 tomb epitaphs of males in China's Tang Dynasty (618-907 CE), which contain granular and extensive information about the ancestral origins, family background, and career histories of the deceased elites. Our statistical analysis of the complete profiles yields evidence of the transition away from an aristocratic society in three key trends: 1) family pedigree (i.e., aristocracy) mattered less for career achievement over time, 2) passing the Imperial Examination ( Keju ) became an increasingly important predictor of one's career achievement, and 3) father's position always mattered throughout the Tang, especially for men who did not pass the Keju . The twilight of medieval Chinese aristocracy, according to the data, began in as early as the mid-seventh century CE., Competing Interests: Competing interests statement:The authors declare no competing interest.

This paper is the result of a scale validation process, applicable to social science research, which allows the constructs of otherness and coexistence and their relationship to be trasnformed into measurable, systematized variables. In turn, this scale is the product of a research project whose main objectives were (i) to demonstrate the relationship between coexistence as an independent variable and otherness as a dependent variable, and (ii) to create and statistically validate a scale to measure both variables, so to use it in applied research. The sample consisted of 600 participants. Three instruments were used: two semantic differentials and a 33-item questionnaire. The application was carried out virtually due to the SARS-CoV-2 virus. The method includes a mixed type of work, i.e., qualitative, and quantitative procedures. The results showed two factors, the other as strange or foreign and the other as equal. The final scale consisted of 10 items, with a Cronbach's alpha of 0.86 and variance explaining 58% of the otherness., (© 2024. The Author(s).)

The Likert item response format for items is almost ubiquitous in the social sciences and has particular virtues regarding the relative simplicity of item-generation and the efficiency for coding responses. However, in this article, we critique this very common item format, focusing on its affordance for interpretation in terms of internal structure validity evidence. We suggest an alternative, the Guttman response format, which we see as providing a better approach for gathering and interpreting internal structure validity evidence. Using a specific survey-based example, we illustrate how items in this alternative format can be developed, exemplify how such items operate, and explore some comparisons between the results from using the two formats. In conclusion, we recommend usage of the Guttman response format for improving the interpretability of the resulting outcomes. Finally, we also note how this approach may be used in tandem with items that use the Likert response format to help balance efficiency with interpretability. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The central limit theorem (CLT) is one of the most important theorems in statistics, and it is often introduced to social sciences researchers in an introductory statistics course. However, the recent replication crisis in the social sciences prompts us to investigate just how common certain misconceptions of statistical concepts are. The main purposes of this article are to investigate the misconceptions of the CLT among social sciences researchers and to address these misconceptions by clarifying the definition and properties of the CLT in a manner that is approachable to social science researchers. As part of our article, we conducted a survey to examine the misconceptions of the CLT among graduate students and researchers in the social sciences. We found that the most common misconception of the CLT is that researchers think the CLT is about the convergence of sample data to the normal distribution. We also found that most researchers did not realize that the CLT applies to both sample means and sample sums, and that the CLT has implications for many common statistical concepts and techniques. Our article addresses these misconceptions of the CLT by explaining the preliminaries needed to understand the CLT, introducing the formal definition of the CLT, and elaborating on the implications of the CLT. We hope that through this article, researchers can obtain a more accurate and nuanced understanding of how the CLT operates as well as its role in a variety of statistical concepts and techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The measurement of latent traits and investigation of relations between these and a potentially large set of explaining variables is typical in psychology, economics, and the social sciences. Corresponding analysis often relies on surveyed data from large-scale studies involving hierarchical structures and missing values in the set of considered covariates. This paper proposes a Bayesian estimation approach based on the device of data augmentation that addresses the handling of missing values in multilevel latent regression models. Population heterogeneity is modeled via multiple groups enriched with random intercepts. Bayesian estimation is implemented in terms of a Markov chain Monte Carlo sampling approach. To handle missing values, the sampling scheme is augmented to incorporate sampling from the full conditional distributions of missing values. We suggest to model the full conditional distributions of missing values in terms of non-parametric classification and regression trees. This offers the possibility to consider information from latent quantities functioning as sufficient statistics. A simulation study reveals that this Bayesian approach provides valid inference and outperforms complete cases analysis and multiple imputation in terms of statistical efficiency and computation time involved. An empirical illustration using data on mathematical competencies demonstrates the usefulness of the suggested approach., (© 2022. The Author(s).)

The evolution and long-term sustenance of cooperation has consistently piqued scholarly interest across the disciplines of evolutionary biology and social sciences. Previous theoretical and experimental studies on collective risk social dilemma games have revealed that the risk of collective failure will affect the evolution of cooperation. In the real world, individuals usually adjust their decisions based on environmental factors such as risk intensity and cooperation level. However, it is still not well understood how such conditional behaviours affect the evolution of cooperation in repeated group interactions scenario from a theoretical perspective. Here, we construct an evolutionary game model with repeated interactions, in which defectors decide whether to cooperate in subsequent rounds of the game based on whether the risk exceeds their tolerance threshold and whether the number of cooperators exceeds the collective goal in the early rounds of the game. We find that the introduction of conditional cooperation strategy can effectively promote the emergence of cooperation, especially when the risk is low. In addition, the risk threshold significantly affects the evolutionary outcomes, with a high risk promoting the emergence of cooperation. Importantly, when the risk of failure to reach collective goals exceeds a certain threshold, the timely transition from a defective strategy to a cooperative strategy by conditional cooperators is beneficial for maintaining high-level cooperation.

Burt's argument relies on a motte-and-bailey fallacy. Burt aims to argue against the value of genetics for social science; instead she argues against certain interpretations of a specific kind of genetics tool, polygenic scores (PGSs). The limitations, previously identified by behavioural geneticists including ourselves, do not negate the value of PGSs, let alone genetics in general, for social science.

It is a hotly contested issue whether polygenic scores should play a major role in the social sciences. Here, we defend a methodologically pluralist stance in which sociogenomics should abandon its hype and recognize that it suffers from all the methodological difficulties of the social sciences, yet nevertheless maintain an optimistic stance toward a more cautious use.

Genetic studies in the social sciences could be augmented through the additional consideration of functional (transcriptome, methylome, metabolome) and/or multimodal genetic data when attempting to understand the genetics of social phenomena. Understanding the biological pathways linking genetics and the environment will allow scientists to better evaluate the functional importance of polygenic scores.

Burt formulates her critique at a general level of abstraction that highlights the methodological deficiencies of sociogenomics without also calling attention to precisely the same deficiencies in the social science model she seeks to defend against its encroachments. What might have been a methodological bulwark against the excesses of sociogenomics is instead a one-sided critique that merely renews its charter.

Burt's critique of using polygenic scores in social science conflates the "scientific costs" of sociogenomics with "sociopolitical and ethical" concerns. Furthermore, she paradoxically enlists recent advances in controlling for environmental confounding to argue such confounding is scientifically "intractable." Disinterested social scientists should support ongoing efforts to improve this technology rather than obstructing progress and excusing genetically confounded research.

We sympathize with many of the points Burt makes in challenging the value of genetics to advance our understanding of social science. Here, we discuss how recent reflections on epistemic validity in the behavioral sciences can further contribute to a reappraisal of the role of sociogenomics to explain and predict human traits, aptitudes, and achievement.