Don't Sell Social Science Short

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.

Addressing the policy challenges and opportunities of “Big data”

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.

Application of configurational approach and fuzzy set theory on social and behavioral science variable models needs more attention. Few social science researches have employed configurational methods of research to empirically investigate a research question. On the front of exploring unique and innovative ways of investigation, this paper discusses the application of fuzzy set theory in social sciences. The study primarily takes a model of emotional labor and employee well‑being as a case example to depict how configurational models are developed. The study examines emotional labor and well‑being, not as predictor or criterion variables, but rather as conditions and outcome. The study also distinguishes between correlational and configurational method of research and adopts a descriptive approach by taking five research papers that examine social and behavioral science constructs from a configurational approach. Scarcity of literature in the crossover between the mathematical models and social sciences, particularly for the respective variables, call for a study that can clarify the initial queries surrounding fuzzy set models and pave the way for future empirical studies. The research concludes that utilizing mathematical models like fuzzy set theory, configuration and qualitative comparative analysis in social sciences can break monotony and bring newness in approaching as well as addressing a research query. This paper draws attention towards exploring new avenues in research methodologies. Application of newer methods would not only increase the rigor and precision of research findings but also assist in approaching a research area from multiple angles thereby enhancing the research quality.

This review seeks to understand the implications of empiricism, interpretivism, phenomenology, hermeneutics, pluralism, and solipsism in social science inquiry within social reality, using scoping review method. An in-depth review of the literature was conducted to understand the various philosophical alignments or paradigms applied in social sciences research. The study is aimed at understanding and applying these paradigms with the view of having a deep comprehension of the turbulences plaguing society and proffering solutions to them. From the review, it was discovered that the various philosophical alignments in social science inquiry provide the scientist or researcher with a worldview of the different perspectives and multiple levels of analysis of the social world. Thus, creating a systematic lens from which individuals (at several levels), groups, and organizations are examined to know the; “when”, “how”, and “why” they behave the way and manner that they do. It was concluded that an adequate understanding of these paradigms would better shape the methodology to be adopted in conducting research studies within the social and/or behavioural sciences. This will help determine its objectivity, rigor, or the extent to which scientific methodology is applied within social science researches. Lastly, a combination of these paradigms creates a mixed-method, which demonstrates knowledge validity and objectivity in investigator triangulation (which involves; observation, questionnaire administration, and interview), data triangulation (which encompasses collecting data at different times from different people in different places as a cross-check for validity and to check the interpretation and conclusion arrived at), methodological triangulation (which includes within-method triangulation and between method triangulation: the former entails the application of various techniques within the same method, while the latter applies a combination of research methods), generalization, verification, explanation, and deductions. This will allow for data gathering and/or fact-finding, in search of new knowledge, and subsuming new valid knowledge, enhance research results and findings efficacy.

With growing attention to societal issues and implications of synthetic biology, we investigate sources of social science publication knowledge in synthetic biology and probe what might be learned by comparison with earlier rounds of social science research in nanotechnology. “Social science” research is broadly defined to include publications in conventional social science as well as humanities, law, ethics, business, and policy fields. We examine the knowledge clusters underpinning social science publications in nanotechnology and synthetic biology using a methodology based on the analysis of cited references. Our analysis finds that social science research in synthetic biology already has traction and direction, rooted in an ethical, legal, and social implications framework. However, compared with nanotechnology, social science research in synthetic biology could further explore opportunities and openings for engagement, anticipatory, and downstream application perspectives that will help to build a wider platform for insights into current and future societal impacts.

PurposeThe purpose of this paper is to describe and discuss a few principal and crucial steps of “antecedents” and “postcedents” in relation to structural equation modeling (SEM) in social science research.Design/methodology/approachThis paper is based upon a conceptual and reflective discussion of SEM in social sciences research.FindingsSEM is not science per se, but just a tool that is used to extract meaning from the previous steps of research processes in terms of a research phenomenon in focus.Research limitations/implicationsBased upon the steps of validity and reliability discussed in relation to predetermined measurement and structural models in SEM, application implications are provided.Practical implicationsLike many statistical tools, SEM can be misused to manipulate findings and fit of measurement and structural models. Researchers may become more interested in the tool than in the subject matter.Originality/valueUsing SEM does not elevate research to be science or make the researcher a scientist! An area of concern for further debate is whether the widespread and variable applications of SEM in social science (in particular, marketing) research are all science.

In this article, findings from a study of a forestry curriculum are used to identify current gaps and potential avenues of pedagogical contributions pertaining to social science content. In setting a context for this analysis a brief review of the potential contributions of social science to forestry, and a description of current social science research on forestry in B.C. and the surrounding region is provided. Survey results from: 1) stakeholders in forestry, 2) forestry undergraduate students, and 3) forestry faculty all point to the need for incorporating more social science content into forestry curricula. These survey results dovetail with observations about the need for more social science research on forestry topics. Key words: social science, forestry curricula, intergroup differences, social survey research

Social science research has been attacked by neoliberal thinkers who allege that such research lacks economic objectives. In the face of neoliberal and positivist criteria for evaluating the social impact of social science inquiry, social science researchers are developing qualitative evaluation methodologies through which we can have direct contact with citizens. These qualitative methodologies declare our social responsibility as social researchers in addressing relevant problems, especially those affecting the most vulnerable people. From these qualitative methodologies, the most vulnerable groups are included in the assessment of the social impacts of social research. Some examples of people who have participated in this qualitative evaluation include women, youth, immigrants, and Roma organizations. Participants perceived social science researchers as being far from their social reality, but in this research, they began to overcome their skepticism that social science research can help to solve those problems affecting their everyday lives.

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

In this poster we describe a pilot study of searching social science literature for legacy corpora to evaluate text mining algorithms. The new emerging field of computational social science demands large amount of social science data to train and evaluate computational models. We argue that the legacy corpora that were annotated by social science researchers through traditional Qualitative Data Analysis (QDA) are ideal data sets to evaluate text mining methods, such as text categorization and clustering. As a pilot study, we searched articles that involve content analysis and discourse analysis in leading communication journals, and then contacted the authors regarding the availability of the annotated texts. Regretfully, nearly all of the corpora that we found were not adequately maintained, and many were no longer available, even though they were less than ten years old. This situation calls for more effort to better maintain and use legacy social science data for future computational social science research purpose.

Action research is the philosophy and methodology of research generally applied in social science. As people are the central concern in social sciences and it becomes the duty of the social scientist to involve oneself with contemporary problems of people for improving their situation. Action research is a type of social science research that that aimsat creating a desirable change in the situation as a result of planned action intervention. It is carried out to identify areas of concern, develop and test alternatives, and experiment with new approaches.By conducting action research, researchers are able to make changes in order to improve the settings in which they conducted research, rather than just gathering data and formulating theories, as in conventional research. Considering the significance of action research in social science, it becomes important to have a comprehensive knowledge of it. Thus, the present paper has been prepared with the objective to review and explain the concept of action research in social sciences.

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research.

Health and human behavior: areas of interest common to the social and medical sciences.

Increasing blood donor recruitment and retention is of key importance to transfusion services. Research within the social and behavioral science traditions has adopted separate but complementary approaches to addressing these issues. This article aims to review both of these types of literature, examine theoretical developments, identify commonalities, and offer a means to integrate these within a single intervention approach. The social and behavioral science literature on blood donor recruitment and retention focusing on theory, interventions, and integration is reviewed. The role of emotional regulation (anticipated anxiety and vasovagal reactions) is central to both the behavioral and the social science approaches to enhancing donor motivation, yet although intentions are the best predictor of donor behavior, interventions targeting enactment of intentions have not been used to increase donation. Implementation intentions (that is, if-then plans formed in advance of acting) provide a useful technique to integrate findings from social and behavioral sciences to increase donor recruitment and retention. After reviewing the literature, implementation intention formation is proposed as a technique to integrate the key findings and theories from the behavioral and social science literature on blood donor recruitment and retention.