Outgrowing physics envy: Reconceptualizing social research

Although the representatives of different social sciences are trying to highlight and raise the methodological and methodical peculiarity of social sciences with respect to natural sciences, it should be noted that a big part of social sciences research is based on the positivist research paradigm. The positivist tradition allows a researcher to choose certain research methods that originated from natural sciences and to apply them in the research of social sciences. Regarding the possibilities to determine causality, an experiment is considered to be the best empirical research method. However, the emphasis is placed on the complicated application of this method to social sciences due to certain practical, political and ethical aspects. When developing ideas about an appropriate and effective use of the experimental research method in social sciences, the use of natural experiment is recommended. Although in recent years, the research based on the methodology of natural experiment is gaining popularity, there is still a lack of literature on how and when this methodology can be applied, what problems it can help to solve, how data can be collected and analysed. Thus, on the basis of methodological literature analysis, this article is an attempt to present natural experiment as a method for social researches, to provide guidelines and recommendations for the application of the methodology of natural experiment and to inspire scientific discussion about using the strategy of natural experiment in social sciences.

Click to increase image sizeClick to decrease image size Notes 1. We invited scholars from sport sociology, sport and exercise physiology, biomechanics, and sport, exercise and health psychology to share their views on qualitative research with us. We are grateful to all those who replied to our request. Unfortunately, for good reasons, some authors who replied could not commit to writing an article. 2. It must be remembered that the paradigms debate of the 1980s and 1990s, while significant in the academic world, never had a considerable impact on the political sphere from where public funding for social science research in many countries is decided upon. For example, in the UK context recent government research policies are restricting the development of qualitative inquiry by the way in which it categorises and privileges STEM subjects. The 'science' in STEM is, of course, a false dichotomy. In practice many forms of social science research straddle the divide between 'science' and 'social science'. However, the imposition of this dichotomy actively excludes most forms of social sciences on methodological grounds, especially the more hermeneutical, exploratory and critical fields of enquiry. The STEM science category thus reinforces an ideology at work in thinking about science; legitimate science is quantitative, positivist or post-positivist inquiry and this is the way we should all do publicly funded research in order to provide 'value for money' for the taxpayer. For a North American example please see Smith and Hodkinson (Citation2005). 3. We have certainly found that many of our more quantitative-orientated colleagues in our own institutions (Loughborough University, England and University of Wales Institute, Cardiff Wales) do support and value qualitative research. We know we are not alone here.

The News of the Week article that reports on Senator Kay Bailey Hutchison (R-TX) questioning the need to fund social science research at the National Science Foundation is alarming and shortsighted (“Senate panel chair asks why NSF funds social sciences,” 12 May, p. [829][1]). Social science research is at the fundamental core of basic research and has much to contribute to the economic viability of the United States. Twenty years of direct and jointly funded social and ecosystem science research at Colorado State University's Natural Resource Ecology Laboratory has produced deep insights into environmental and societal impacts of political upheaval, land use, and climate change in parts of Africa, Asia, and the Americas. Beyond greatly advancing our understanding of the coupled human-environmental system, the partnership of social and ecosystem science has brought scientists and decision-makers together to begin to develop solutions to difficult problems. Insights from the social sciences about the vulnerability, sustainability, and resilience of social and environmental systems have greatly increased our understanding of the complex interactions of the world on which we depend. The contributions of social and ecological science for data analyses associated with spatial relationships, transfer of goods and services, valuation, and decision-making processes have improved our understanding of how human activities are altering environmental systems worldwide. These findings have become the core of policy and development decisions used throughout the world. It is critical that the NSF support social science funding and that, as budget decisions are made, the social science allocation should move forward equally with increases as in other basic and applied research. Failure to do so will further hinder U.S. competitiveness in the future and will slow transfer of knowledge and usefulness to the public. [1]: /lookup/doi/10.1126/science.312.5775.829a

Social informatics: Beyond emergence

Interpretivism is a research paradigm that is associated with qualitative research.A tradition of Interpretivism can be linked to the ethnographies of non-western peoples in the 19th century and continues to be a popular way to explain human behaviour and social phenomenon today.The interpretivist view invites the researcher to investigate meaning behind the understanding of human behaviour, interactions and society.This involves the researcher attempting to develop an in-depth subjective understanding of people's lives.The difference between the natural sciences and social sciences is that the natural sciences requires the researcher to observe and interpret natural processes but the social sciences requires the researcher to observe a phenomenon and understand the interpretations that individuals have already made.Social work research aims to explain human behavior and social phenomenon and not to focus on isolating and objectively measuring variables or on developing generalizations.Social work research requires a focus on gaining an empathic understanding of an individual's feelings and the meaning that they give to everyday life with the goal to gaining greater understanding of the individual's behavior.For this reason, interpretive research is well suited to social work research.In this introductory paper, the authors present the validity of interpretive research and the value of using it in social work research.

Addressing the policy challenges and opportunities of “Big data”

Social identity is now considered one of the most popular and controversial concepts in social science. Probably no other social phenomenon has received such intensive and rapt scrutiny from philosophers, psychologists, sociologists, political scientists, and anthropologists. Social identity has become a prism through which the most important aspects of social life are explored and assessed. At the same time, it is one of the most diffuse and loose concepts in social research. As Brubaker and Cooper (2000) recently argued, the term “tends to mean too much (then understood in a strong sense), too little (then understood in a weak sense), or nothing at all (because of its sheer ambiguity)” (1). In this book, I will analyze social identity as a feeling of belonging to a social group, as a strong connection with social category, and as an important part of our mind that affects our social perceptions and behavior.

Over the years the research endeavours in social sciences have been directed towards producing profound discoveries about humankind and the society they live in. Now-a-days qualitative research has been flourishing in many fields of the social sciences. Rapid social change and the resulting diversification of life scenarios are increasingly confronting social researchers with new social contexts and perspectives. There is a specific relevance for qualitative research to the study of social contexts and life situations. This has lead to the development of several social theories and social research methods which have still continued to develop in order to understand the diverse social problems. The challenge for social scientists and researchers worldwide is to continue in the search for a unified consensus on what methodology is to be adopted for research for meeting the emerging diverse needs of humankind and the society. This thought has led to the proliferation of pluralism or multi-method research in which the researcher synthesizes multiple research methodologies in order to study social problems in a holistic way. This concept of pluralism in research has to a greater extend subverted methodological tribalism which had existed till the recent past. This paper focuses on the philosophical assumptions as guidelines to research paradigms have influenced the development of social research theory and social research methods towards pluralism. This paper intends to explore pluralism in qualitative Social Science researches as it may be applied methodologically and philosophically. In this context, the paper heeds a thought on refocusing pluralistic qualitative research in terms of its needs and demands based on the level and depth of the study undertaken. Key Terms: Qualitative research, methodological pluralism, research paradigms, philosophical assumptions

The Society of Behavioral Medicine (SBM) Leadership Institute provides leadership training for mid-career behavioral medicine scientists. As an institute fellow, I explored the experiences of social scientists working in healthcare settings. Exploratory research to describe the experiences, barriers, and facilitators social scientists face working in a healthcare delivery and research system. Our research questions were exploratory, focused on understanding (a) the experiences of social scientists working in a healthcare delivery and research system, (b) barriers that social scientists face working in a healthcare delivery and research system, (c) perceived value of sociological and anthropologic methods to clinicians and other health researchers, and (4) to understand the landscape of organizational models that feature social scientists as part of a larger scientific enterprise. We developed a plan for needs assessment activities. We conducted a design workshop of social scientists (n = 11). Using human-centered design methods, we elicited experiences and asked participants to develop a prototype model where social scientists could find career fulfillment with minimal structural barriers. We found that social scientists feel a strong sense of pride in their social science training and passion for the work they do. They reported occupying multiple disciplines at once, including multiple promotion and reputational expectations. Participants commonly reported that they themselves, as well as collaborators, viewed social science work, particularly qualitative research, as supplemental to quantitative work. Participants described perceptions that social science research is less rigorous or requires less training compared with other disciplines. Participants identified several contextual factors that influenced the perceived value of social science research in healthcare, including the focus of external funding priorities on larger clinical questions, organizational structure, and tangible leadership support of social science. The SBM Leadership Institute created a space for preliminary research into an area that could improve the experiences and research impact of social and behavioral scientists.

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.

The availability of user-generated spatial data (user spatial content, USC) has transformed social science research, enabling the real-time, large-scale exploration of socio-spatial dynamics. This article traces the evolution from volunteered geographic information (VGI) to USC, highlighting their multidimensional nature and epistemological significance. Brief examples underscore USC’s potential for capturing the interplay between territorial factors, digital activity, and social phenomena, ranging from mapping urban vitality to tracking large-scale crises. However, the recent tightening of data access in the post-API era demands a rethinking of research approaches. Alternatives such as data donation, dedicated applications, and geoparsing can maintain the viability of USC-driven analyses. Overall, this article underlines the need for diversified, ethical, and methodologically sound strategies to harness USC’s value in understanding the digitally intertwined realities of contemporary society.

The author considers the role and place of theory in criminal justice studies. The argument is that the operation and interrogation of fundamental categories is integral to social scientific enquiry and if criminal justice studies is to resist a technocratic “protective service” orientation it must promote theorising and thinking conceptually via the texts which represent the discipline to undergraduates. Although theory is situated at the core of social science curricula, there is little or no agreement on its role or place in research and pedagogy. The dominant understanding of theory within criminal justice studies (including its sociological and criminological incarnation) is that it is something to be referred to. What is seldom emphasised in theory or methods texts is the practice of theorising. Texts that are designed to be the student’s first contact with the field of criminal justice studies, and which reflect broader attitudes toward social enquiry, seldom consider the methodological and pedagogical issues related to the production and role of analytic concepts and do not present social science as an imaginative or reflexive practice. Drawing on critical realist metatheory, this paper advances a distinction between social and sociological problems and social science and protective service toward illustrating that a social science approach to the study of criminal justice demands the operation and interrogation of analytic categories and explicit consideration of issues of epistemology and ontology. Works which seek to avoid this serve only to foster a passive rather than active engagement with their subject matter.

The view that social sciences are very different from other forms of science is common, hence the need to develop special methods. In section 1.1 it is argued that, although there are some important differences, modern developments both in social sciences and in what are called here natural sciences tend to reduce them, pointing towards a common methodological framework. This idea serves as an introduction to further sections where the scientific method for the social sciences is outlined, and then is shown the role that models play in it. Natural and social sciences: the hypothesis of convergency Traditionally, social sciences have been considered a very special form of science, different from biology, physics or natural sciences in general. Many arguments are put forward to support this distinction. By contrast, social phenomena are considered to be ever:changing. It may well be that in natural sciences theories themselves change and old ones are replaced by new ones, but once adopted, they are considered as permanent. If social phenomena are ever:changing, social theories may be valid only for a short period of time; that is, they have a particular historical reference. Because it is generally assumed that natural phenomena are permanent, theories that successfully explain their past behaviour have strong predictive power. In social sciences, even if a theory has been very successful in explaining a phenomenon that occurred in the past, it can only provide predictions as long as the historical conditions prevail. Another difference that is commonly pointed out is that natural phenomena can be reproduced in the controlled conditions of a laboratory, which isolates them from unwanted externalities. The use of experiments has become a key element in the creation of knowledge in natural sciences.

Drugs, sex and social science: Social science research and health policy in Australia

Social science has an important role in aviation biofuels research, yet social science methods and approaches tend to be underdeveloped and under-utilized in the broader aviation biofuels literature and biofuels overall. Over the last 5 years, social science approaches in aviation biofuels research, particularly site-selection, have made several advances. Where early site-selection models either entirely excluded social science concepts or included only a few measurements using poor proxies, current models more accurately, and more comprehensively capture key social science concepts to better examine and predict project implementation success and long-term sustainability. Despite several studies published within the last 20 years noting the need for more empirical studies of social sustainability and improvement in incorporation of social criteria, progress has remained rather stagnant in several areas. To help move the field forward, we conduct a review of the current state of social science research in aviation biofuels with a focus on sustainability, site-selection, and public acceptance research, identifying key approaches, important developments, and research gaps and weaknesses of current approaches. While several review studies already exist, they tend to focus on a single area of biofuels such as public acceptance. By broadening our review to several areas, we are able to identify several common limitations across these areas that contribute to the continued underutilization of social science approaches in aviation biofuels. This includes the preference for practical and reliable indicators for social criteria that prioritize quantitative methods over other approaches. Based on these limitations, we make several recommendations to improve social science research in aviation biofuels, including ensuring that social scientists are key members of the research team, the adoption of a mixed-methods research designs that combines quantitative and qualitative approaches that better measure some criteria and local-level impacts, and adequate resources for social science research throughout biofuel development projects as these methods are often more time-consuming and costly to implement. We argue that implementing these recommendations in future aviation biofuel development projects will improve social science approaches utilized in aviation biofuels research and address a long-acknowledged gap in the field.