Quantitative Social Science Research Research Articles

Use and Abuse of Personal Information, Part I: Design of a Scalable OSINT Collection Engine

In most open-source intelligence (OSINT) research efforts, the collection of information is performed in an entirely passive manner as an observer to third-party communication streams. This paper describes ongoing work that seeks to insert itself into that communication loop, fusing openly available data with requested content that is representative of what is sent to second parties. The mechanism for performing this is based on the sharing of falsified personal information through one-time online transactions that facilitate signup for newsletters, establish online accounts, or otherwise interact with resources on the Internet. The work has resulted in the real-time Use and Abuse of Personal Information OSINT collection engine that can ingest email, SMS text, and voicemail content at an enterprise scale. Foundations of this OSINT collection infrastructure are also laid to incorporate an artificial intelligence (AI)-driven interaction engine that shifts collection from a passive process to one that can effectively engage with different classes of content for improved real-world privacy experimentation and quantitative social science research.

A different causal perspective with Necessary Condition Analysis

This article delves deeper into the causal perspective of Necessary Condition Analysis (NCA). In contrast to traditional probabilistic sufficiency approaches in quantitative social science research about what will happen on average in a group of cases, NCA is interested in what will not occur in almost every case if a necessary condition is absent. Rooted in David Hume's theory of causation NCA explores factors that act as ‘must-haves’ or bottlenecks for the outcome. Operating from this necessity perspective, NCA functions deterministically (without exceptions) or non-deterministically (allowing exceptions). NCA can enrich theories and models in social science research by identifying essential factors. The article contributes to the literature by precisely describing the necessity causal perspective that is used in NCA, and by explaining how this is different from causal perspectives that are commonly used in social science research.

A Review of the Literature About Broadband Internet Connections and Rural Development (1995-2022)

This article provides a systematic review of the quantitative social science research on broadband Internet technology in rural areas. Specifically, we summarize the literature on 1) rural broadband availability and adoption behaviors and 2) the implications of these behaviors for rural businesses, public policy initiatives, and regional development. In the studies reviewed, rural areas suffer from both lower availability and lower adoption rates compared to their urban counterparts. However, researchers disagree on whether supply frictions inherent to rural areas or heterogeneous demand and adoption behaviors are the major cause of this outcome. Researchers widely agree that high-speed Internet improves economic outcomes of rural areas, whether it is through increases in business activity or in more general economic development measures (e.g. productivity, jobs, income). Impact evaluations of rural broadband policy initiatives produced mixed results, highlighting the need for a better understanding of agents’ incentives. This review points to four areas for future research. One, assessments of the long-term impact of broadband on rural economies. Two, analyses of the net effect of broadband on economic development outcomes (e.g. migrants, job creation). Three, assessments of economic development impacts associated with broadband speed. Four, assessments of the efficacy of broadband policies.

Analysis of College Students' Misconceptions of Quantitative Research in Social Sciences in China: Implications for Teaching

Research design is essential in regard to obtaining objective reliability for social science research. However, some social sciences students may be prone to many misconceptions regarding the understanding of quantitative research and application of statistics, which threatens the credibility of the research. To understand and potentially alleviate this problem, we examined 25 research projects and interviewed 10 undergraduate students who participated in quantitative methodology courses in social work and social welfare in a university in mainland China. This study aimed to explore students’ misconceptions about quantitative research methods and statistical analysis. The results show that common misconception include but not limited to the following aspects: (1) taking research topics as research questions; (2) no clarification of sampling procedure in data collection and no random assignment in experimental research project; (3) no explanation of the validity and reliability of the construct measures; (4) violations of the assumptions of statistical tests; (5) misuses of statistical methods; and (6) misinterpretation of statistical findings; and (7) little consideration of research ethics. The findings indicate that institutes, educators, and students should make consorted efforts to improve the understanding of quantitative research methods and enhance statistical literacy of undergraduates in social sciences in China.

The soft tyranny of p-values in quantitative research: New considerations for leadership researchers.

In this article, the author will discuss processes used by quantitative researchers to render judgments and decisions about the results of their statistical analyses, highlighting what "p-values" represent and how p-values became ubiquitous in quantitative social science research. Suggestions for alternative ways to measure the significance of quantitative outcomes in leadership research will also be offered.

Embracing Complexity: Variation in Faculty’s Attitudes toward Inclusive Measures of Gender and Sexuality in Social and Health Sciences Research

ABSTRACT In the absence of adequate measurement efforts, expansive gender and sexual identities will remain underexplored in quantitative social science and health research. We use primary survey data (N = 309) to identify factors associated with U.S.based social and health science faculty’s attitudes toward inclusive gender and sexuality measures in participant-based research. Results suggest that political science faculty rated expansive gender and sexuality measures as less important to their own research, relative to psychology, sociology, and health sciences faculty. In addition, cisgender/heterosexual women and LGBTQ+ identifying faculty rate and apply these measures more positively compared to faculy who identify as cisgender/heterosexual men. Finally, faculty engaging in predominantly quantitative research, or in teaching-focused positions, had lower ratings of the importance of gender expansive measurement. Results suggest that while individual characteristics shape faculty’s attitudes toward and use of inclusive gender and sexuality measures, disciplinary and academic contexts also matter.

Machine Learning Modeling: A New Way to do Quantitative Research in Social Sciences in the Era of AI

Improvements in big data and machine learning algorithms have helped AI technologies reach a new breakthrough and have provided a new opportunity for quantitative research in the social sciences. Traditional quantitative models rely heavily on theoretical hypotheses and statistics but fail to acknowledge the problem of overfitting, causing the research results to be less generalizable, and further leading to societal predictions in the social sciences being ignored when they should have been meaningful. Machine learning models that use cross validation and regularization can effectively solve the problem of overfitting, providing support for the societal predictions based on these models. This paper first discusses the sources and internal mechanisms of overfitting, and then introduces machine learning modeling by discussing its high-level ideas, goals, and concrete methods. Finally, we discuss the shortcomings and limiting factors of machine learning models. We believe that using machine learning in social sciences research is an opportunity and not a threat. Researchers should adopt an objective attitude and make sure that they know how to combine traditional methods with new methods in their research based on their needs.

Exploring key indicators of social identity in the #MeToo era: Using discourse analysis in UGC

Recent years have been characterized by the ubiquitous use of social networks as a mean of self and social identity, which offers new opportunities for qualitative and quantitative research in social sciences. The dynamics of interactions on social platforms such as Twitter promote the development of social movements around hashtags, such as #MeToo. According to previous research, this movement has set the beginning of an era. The present study aims to determine the key indicators of social identity in the #MeToo movement in Twitter using textual analysis and sentiment analysis of user-generated content. To this end, we use a cognitive pragmatics point of view to study a corpus of 31.305 tweets. Using the methodological approaches of corpus linguistics (CL) and discourse analysis (DA), we identify keywords, topics, frequency, and n-grams or collocations to understand the social identity of the #MeToo movement. The key indicators of the social identity in the #MeToo Era are validated using association statistical measures of Log-Likelihood and Mutual Information (MI). Our results reveal the polarization of sentiments where UGC is associated with both negative and positive topics. The social identity is particularly strongly correlated with women and the workplace. Finally, regardless the industry or area, these results present a holistic approach to the social identity of #MeToo.

First Nations Children Count: Enveloping Quantitative Research in an Indigenous Envelope

Indigenous peoples repeatedly call for disaggregated data describing their experience to inform socio-economic and political policy and practice change (United Nations Permanent Forum on Indigenous Issues, 2003; UNICEF, 2003; Rae & the Sub Group on Indigenous Children and Youth, 2006). Although there has been significant discourse on the destructive historical role of western research with Indigenous communities (RCAP, 1996; Smith, 1999; Schnarch, 2004) and more recently on cultural adaptation of qualitative research methods (Smith, 1999; Bennet, 2004; Kovach, 2007), there has been very little discussion on how to envelope western quantitative social science research within Indigenous ways of knowing and being. This paper begins by outlining the broad goals of Indigenous research before focusing on how quantitative research is used, and represented, in the translation of Indigenous realities in child health and child welfare. Given the rich diversity of Indigenous peoples and their knowledges, this paper is only capable of what respected Indigenous academic Margo Greenwood (2007) would term “touching the mountaintops’ of complex and sacred ideas.

Causality in the Social Sciences: a structural modelling framework

There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus on a particular framework, called structural causal modelling (SCM), as one possible perspective in quantitative social science research. We explain how this methodology provides a fruitful basis for causal analysis in social research, for hypothesising, modelling, and testing explanatory mechanisms. This framework is not based on a system of equations, but on an analysis of multivariate distributions. In particular, the modelling stage is essentially distribution-free. Adopting an SCM approach means endorsing a particular view on modelling in general (the hypothetico-deductive methodology), and a specific stance on exogeneity (namely as a condition of separability of inference), on the one hand, and in interpreting marginal–conditional decompositions (namely as mechanisms), on the other hand.

Transformation of Energy Systems as Common Projects: An Integration of Different Scientific Approaches to Address Real-World Challenges

Changes in attitudes and behavior are just as important as technological innovations and policy decisions when it comes to the transformation of energy systems. However, social science still plays a minor role with respect to energy research. In addition, social-science-based energy research tends to focus on a single subject of interest with only one scientific method. However, energy system transformations are complex transformational processes that require holistic research approaches in order to succeed. The combination of qualitative and quantitative social science research generates, for instance, an extensive state of knowledge about citizens’ acceptance and support of energy system transformations. Participation research is a promising line of research for improving transformational processes. This paper shows that the integration of different knowledge types, perspectives, methods, and research disciplines is key to understanding energy system transformations and developing recommendations to address the challenges they pose.

Test-retest stability of self-reported violence against women measures: results from the stepping stones and creating futures pilot

ABSTRACTBackground: Stability of measures in quantitative social science research is crucial to understand. There is very little evidence on the stability of violence against women and girls measures in the global South.Objective: To assess the test-retest stability of violence against women and girls measures, amongst young (18–30) people in South Africa.Methods: Data were collected from 124 women and 112 men at zero weeks (time 1) and two weeks (time 2), who resided in urban informal settlements in South Africa. Prevalence of each construct was assessed using chi-square contingency tables. Stability of self-report over time was assessed using Cohen’s Kappa. Bivariate logistic regression assessed factors associated with changing responses between time 1 and time 2.Results: At group level prevalence of all measures showed no significant differences. Stability of self-report: kappas for past year physical IPV were both k0.20, for ever physical IPV (women k0.58; men k0.50). Sexual IPV in past 12m (women k0.44; men k0.18), and for ever sexual IPV (women k0.56; men k0.46). Kappas for men’s perpetration of non-partner sexual violence was k0.29 for past 12m and k0.38 ever. In bivariate regression, completion of secondary education was associated with a reduced odds of changing responses over the time-period for sexual IPV ever women (OR0.16, 0.02–1.04), sexual IPV past 12 months men (OR 0.09, 0.01–0.56), past 12 month non-partner sexual violence men (OR0.19, 0.02–1.41) and lifetime non-partner sexual violence (OR0.23, 0.04–1.19). Being male, compared to being female, was associated with an increased likelihood of changing responses for past 12 month sexual IPV (OR2.10, 1.08–4.09).Conclusions: Prevalence estimates of violence against women measures are stable at group level, but stability of self-reported measures remains a concern. Individual statistical analyses must be treated with caution. Future studies are required to develop further understandings of stability of measures over time.

Understanding Perceptions of the Bioeconomy in Austria—An Explorative Case Study

The bioeconomy provides new approaches to deal with environmental challenges by substituting fossil fuels for sustainable, renewable resources and fuels. In Europe, this process and discourse has mainly been driven from a strategic top-down level. This leads to a lack of inclusion of societal actors, which can consequently lead to reduced acceptance and engagement. Henceforth, in this study, we focus on exploring how the bioeconomy is perceived, understood and evaluated by a wider audience. Through convenience sampling, 456 interviews conducted with students, employees, farmers and pensioners living in Austria provide the database for the study. Due to the novelty of the study’s objective and the consequentially explorative research approach, qualitative and quantitative social science research methods are applied. The results indicate that the bioeconomy concept is associated with various themes and visions. These associated topics also have negative or positive implications. Furthermore, a division between two visions of the bioeconomy, a technology- and industry-driven vision and a vision defined by regional environmentalism, can be observed. The feasibility of a future bioeconomy identifies as the most critical aspect. Sustainable consumption was mentioned as an important topic of the bioeconomy by the participants, a result that could be of particular interest when creating an inclusive bioeconomy, since it calls for active involvement of consumers. The study also shows that responding farmers tend to believe that the bioeconomy will lead to more inequity.

Can\u2019t bibliometric analysts do better? How quality assessment without field expertise does not work

The article is an invited comment on Guy Madison and Therese Söderlund (M&S): Comparisons of content and scientific quality indicators across peer-reviewed journal articles with more or less gender perspective: Gender studies can do better. Scientometrics 115(3):1161–1183. The article pinpoints a series of serious problems in M&S’s quantitative quality assessment and analysis of the field of gender studies, pertaining to their overall conceptual framework, their general approach and their specific analysis. It is argued that the over-arching problem in M&S’s study is their lack of expert knowledge of the field of gender studies, their lack of respect for differences between qualitative and quantitative research, and their research design, which is biased towards quantitative social and natural science research. Firstly, it is demonstrated that a key concept, ‘gender perspective’, is used in an incoherent and confusing way in M&S’s analysis. Secondly, it is argued that the confusion is not an isolated definitional problem, but related to a series of slippages between M&S’s source of inspiration (Ganetz in Genusvetenskapliga projektansökningar inom humaniora-samhällsvetenskap – en uppföljning av Vetenskapsrådets beredning och utfall år 2004. Vetenskapsrådets rapportserie, Stockholm 15/2005, 2005) and their own adoption of the category. Thirdly, differences between qualitative and quantitative research, and between hermeneutic and explanatory knowledge production, are discussed more broadly to sustain the argument that the mentioned slippages occur, because M&S transfer analytical tools from Ganetz’ qualitative study, based on a peer review methodology, to a quantitative quality assessment, carried out without field specific expert knowledge. It is argued that, to be adequate and relevant, a quality assessment would need to respect these differences, and develop tools and research designs accordingly. Fourthly, the validity of M&S’s content analysis—the core of their study—is questioned in detail because of its use of inadequate analytical categories, and because of its exclusion of central elements from the analysis. Finally, it is argued that the bias in M&S’s research design is reproduced in their results.

"What else are you worried about?" - Integrating textual responses into quantitative social science research.

Open-ended questions have routinely been included in large-scale survey and panel studies, yet there is some perplexity about how to actually incorporate the answers to such questions into quantitative social science research. Tools developed recently in the domain of natural language processing offer a wide range of options for the automated analysis of such textual data, but their implementation has lagged behind. In this study, we demonstrate straightforward procedures that can be applied to process and analyze textual data for the purposes of quantitative social science research. Using more than 35,000 textual answers to the question “What else are you worried about?” from participants of the German Socio-economic Panel Study (SOEP), we (1) analyzed characteristics of respondents that determined whether they answered the open-ended question, (2) used the textual data to detect relevant topics that were reported by the respondents, and (3) linked the features of the respondents to the worries they reported in their textual data. The potential uses as well as the limitations of the automated analysis of textual data are discussed.

The limitations of quantitative social science for informing public policy

Quantitative social science (QSS) has the potential to make an important contribution to public policy. However it also has a number of limitations. The aim of this paper is to explain these limitations to a non-specialist audience and to identify a number of ways in which QSS research could be improved to better inform public policy.

Diffractive Possibilities: Cultural Studies and Quantification

AbstractCultural studies have not shared the widespread belief in quantitative methods. On one hand, the dominant orientation of quantitative social science research continues to hold on to positivist assumptions of objectivity and the privileged access to the “truths” of natural phenomena via the logics of mathematics. On other hand, cultural studies have maintained a hermeneutics of suspicion toward the methods of quantification. But, to what extent does this suspicion toward quantitative inquiry compromise the deconstructive project of cultural studies by falling into the trap of the quantitative/qualitative and, related, nature/culture binaries? Building on new materialist ideas, the author develops an ontological reconfiguring of measurement and statistics that is informed by Barad's () post‐humanist performativity and diffractive method. The post‐humanist deconstructive interventions on the nature/culture binary enable us to rethink the critical possibilities of quantification for the materialist analysis of power relations in cultural studies.

Considering Quantitative and Qualitative Issues Together

Quantitative and qualitative researchers use different methods and have different goals. At the level of methods, quantitative researchers criticize qualitative researchers for not performing null hypothesis significance tests. However, I review literature showing that these are invalid, and so it is not particularly meaningful to criticize a lack of performance of something that should not be performed anyhow. More generally, I suggest that there are strengths and limitations to quantitative and qualitative methods. The more interesting question pertains to goals, and quantitative and qualitative researchers differ there, too. I briefly mention some limitations of the usual quantitative goal, which is to find causal mechanisms. But a typical qualitative goal of describing personal or subjective experience also has limitations. Finally, I compare both quantitative and qualitative social science research to physics and show that each has similarities and differences. There is much for quantitative and qualitative social science researchers to gain, not only by considering each other's methods and goals carefully but also by going outside social science and considering the accomplishments in nonsocial sciences.

Video Games as a Multifaceted Medium: A Review of Quantitative Social Science Research on Video Games and a Typology of Video Game Research Approaches

Although there is a vast and useful body of quantitative social science research dealing with the social role and impact of video games, it is difficult to compare studies dealing with various dimensions of video games because they are informed by different perspectives and assumptions, employ different methodologies, and address different problems. Studies focusing on different social dimensions of video games can produce varied findings about games’ social function that are often difficult to reconcile— or even contradictory. Research is also often categorized by topic area, rendering a comprehensive view of video games’ social role across topic areas difficult. This interpretive review presents a novel typology of four identified approaches that categorize much of the quantitative social science video game research conducted to date: “video games as stimulus,” “video games as avocation,” “video games as skill,” and “video games as social environment.” This typology is useful because it provides an organizational structure within which the large and growing number of studies on video games can be categorized, guiding comparisons between studies on different research topics and aiding a more comprehensive understanding of video games’ social role. Categorizing the different approaches to video game research provides a useful heuristic for those critiquing and expanding that research, as well as an understandable entry point for scholars new to video game research. Further, and perhaps more importantly, the typology indicates when topics should be explored using different approaches than usual to shed new light on the topic areas. Lastly, the typology exposes the conceptual disconnects between the different approaches to video game research, allowing researchers to consider new ways to bridge gaps between the different approaches’ strengths and limitations with novel methods.

Qualitative Research in Early Childhood Education and Care Implementation

Abstract Governments around the world have boosted their early childhood education and care (ECEC) engagement and investment on the basis of evidence from neurological studies and quantitative social science research. The role of qualitative research is less understood and under-valued. At the same time the hard evidence is only of limited use in helping public servants and governments design policies that work on the ground. The paper argues that some of the key challenges in ECEC today require a focus on implementation. For this a range of qualitative research is required, including knowledge of organisational and parent behaviour, and strategies for generating support for change. This is particularly true of policies and programs aimed at ethnic minority children. It concludes that there is a need for a more systematic approach to analysing and reporting ECEC implementation, along the lines of “implementation science” developed in the health area.